,._1'1

,,.

�

._;� �

'I' 90% High / Fast < 1.0 80% Medium 1.0 – 2.0 9080% % < Low / Slow > 2.0

Figure 3 Daugman’s Rubber Sheet Model

3.

2.4 Fractal Feature Extraction In this stage, differential box counting (DBC) algorithm [4] is applied to compute the fractal dimension (FD) of normalized iris image. A kernel of size r × r is created with function (3): 𝑎𝑎

𝑏𝑏

𝑤𝑤(𝑠𝑠, 𝑡𝑡) = ∑ ∑ 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 (

where,

𝑠𝑠=−𝑎𝑎 𝑠𝑠=−𝑏𝑏

𝑔𝑔𝑚𝑚𝑚𝑚𝑚𝑚 − 𝑔𝑔𝑚𝑚𝑚𝑚𝑚𝑚 )+1 𝑟𝑟

The analysis of the performance of iris recognition system is shown in Table 2. By refer to the parameter in Table 1, the proposed iris recognition system is concluded as high accuracy and fast recognition speed. Table 1 Analysis of performance Number Recognition FA FR Accuracy of Test Time (s) Image 50 0 1 98% 0.2943 *FA = False accept, FR = False reject

(3)

𝑟𝑟 = 2,3, … , 𝑗𝑗 𝑟𝑟 − 1 ) 𝑎𝑎 = 𝑏𝑏 = 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 ( 2

4.

Then, the kernel is applied on every pixel of normalized iris image as equation (4) to get the number of boxes necessary to cover the image. 𝑎𝑎

CONCLUSION

In this project, the accurate and fast iris recognition system is proposed to recognize the person through iris. The proposed iris recognition starts with image acquisition. Next, the acquired iris image is undergone edge detection to obtain the parameters of iris such as centre of iris, radius of inner and outer boundaries. By using these parameters, the iris is segmented from iris image. The annular iris image is then normalized into rectangular form to standardize the segmented iris image into a standard size.

𝑏𝑏

𝑗𝑗 2 𝑁𝑁𝑑𝑑 (𝑥𝑥, 𝑦𝑦, 𝑟𝑟 − 1) = ∑ ∑ 𝑤𝑤(𝑠𝑠, 𝑡𝑡)𝐼𝐼(𝑥𝑥 + 𝑠𝑠, 𝑦𝑦 + 𝑡𝑡) ( ) (4) 𝑟𝑟 𝑠𝑠=−𝑎𝑎 𝑠𝑠=−𝑏𝑏

Next, log (𝑟𝑟) and log(𝑁𝑁𝑁𝑁) for each scaling factor 𝑟𝑟 is computed and least squares linear regression is applied to get the slope which is the FD of the image. Lastly, the lacunarity of the FD which represent as fractal feature is computed as equation (5): 2 𝑠𝑠𝑠𝑠𝑠𝑠(𝑥𝑥, 𝑦𝑦) (5) ) 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝑛𝑛𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎(𝑥𝑥, 𝑦𝑦) = ( 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚(𝑥𝑥, 𝑦𝑦)

ACKNOWLEDGEMENT The authors would like to thank Universiti Teknikal Malaysia Melaka for the financial support.

2.5 Deep Learning Neural Network Convolutional Neural Network (CNN) is used as the classification algorithm of iris recognition system. The designed CNN consists of 10 layers. First is input layer. Next, the first 2D convolution layer with 3×3 filter and max-pooling layer with pool size of 2×2 and stride of 2 is specified. Next, the second 2D convolution layer with 4×4 filter and the second max-pooling layer with the same argument as first max pooling layer is specified. After that, the fully connected layer combines all features into 10 classes since iris images of 10 persons are used. Finally, the output layer which contains softmax layer and classification layer are specified.

REFERENCES [1] [2]

[3]

2.6 Simulation After training with 50 train images, the iris recognition system is test with the 50 test images. The performance of proposed iris recognition system is analyzed as Table 1. The CRR is the measurement of accuracy while for recognition time, the time is start counting from image acquisition until the classification © Faculty of Electronic and Computer Engineering, FKEKK

RESULT AND DISCUSSION

[4]

64

66

Jain, L. Hong and S. Pankanti, "Biometric Identification," Communications of the ACM, vol. 43, no. 2, pp. 90-98, 2000. M. M. M. Wai , N. P. Aung and L. L. Htay, "Software Implementation of Iris Recognition System using MATLAB," International Journal of Trend in Scientific Research and Development (IJTSRD) Volume 3 Issue 5, pp. 290-295, 2019. "Center for Biometrics and Security Research," CBSR, 2005. [Online]. Available: http://www.cbsr.ia.ac.cn/china/Iris%20Databas es%20CH.asp. [Accessed 11 November 2020]. S. Nirupam and C. B. B., "An Efficient Approach to Estimate Fractal Dimension of Textural Images," Pattern Recognition, vol. 25, no. 9, pp. 1035 - 1041, 1992.

GREEN TECHNOLOGY

Proceedings of Innovation and Technology Competition (INOTEK) 2021, Proceedings of Innovation and Malaysia, Technologypp. Competition Melaka, 65-66, (INOTEK) 2021

Analysis of The Circuit Model for Photovoltaic Energy Conversion System M. S. F. M. Hanafi1 and Z. A. F. M. Napiah1 1

Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia Corresponding author’s email: [email protected]

*

ABSTRACT: Photovoltaics (PV) is one of the cheapest and easiest resources among the other source of electrical power by convert of the light into electric by using semiconducting materials. Photovoltaic system solar modules are a system that have comprising the number of solar cells. It also does not cause any type of pollution and greenhouse effect. This project is going develop better PV power plant. This circuit model will use system power block set in the MATLAB/Simulink. DC-DC boost converter and closed-loop control of maximum power point tracking (MPPT) algorithm will be integrated by the developed model. Simulation results for this assignment are validated with the experimental setup in MATLAB. Keywords: Photovoltaic Energy, Circuit Model, MATLAB

simulation are validated with the experimental setup. In this experiment also to know function of PV Array as a component in MATLAB and the to see how mathematical block using MATLAB function. This project is going develop better PV power plant. This circuit model will use system power block set Simulink in MATLAB. The advance model will be integrated with DC-DC boost converter and with closed-loop control of maximum power point tracking (MPPT) algorithm is experiment is to develop. METHODOLOGY

INTRODUCTION Renewable energy such as solar, wind and other energy is very easy to obtain. but this energy is a limited energy because it can only be possessed in certain circumstances. This energy is environmentally friendly energy because it does not cause pollution. Solar energy is the easiest energy to find because every place on earth experiences daylight where sunlight shines. Now the increasing worry about fossil fuel deficit, oil prices is increasing, greenhouse effect, and environment damaging that have polluted with other pollution and the unstable ecosystem, the promising in develop another alternative energy resources is important to give high efficiency and low emission incentives. Photovoltaic (PV) also consequence can be considered the most essential, no pollution, no harm effect and sustainable resource because of the benefits, abundance, and durability of solar radiant energy than other renewable energy resources that can be obtain. By using semiconducting materials. Photovoltaics (PV) is the changing of solar into electricity by exhibit the photovoltaic effect using semiconducting materials. Photovoltaic (PV) as a cheapest source of electrical energy by covert from solar. The photovoltaic outcome also is commercially utilized for electricity generation and as photosensors. The best way to use this method as much as possible is to directly deliver energy that have been produced by PV array will directly the AC mains, no need to use battery banks. Solar insolation is depended by the output of Photovoltaic model, the temperature and PV module of output voltage. DC-DC boost converter and closedloop control of maximum power point tracking (MPPT) algorithm will be integrated by the developed model. The results of the © Faculty of Electronic and Computer Engineering, FKEKK

Figure 1 Flowchart of Methodology

Figure 2 Photovoltaic Equivalent circuit of solar cell

65 69

The figure shows the ideal circuit of photovoltaic. It has been used for research to develop a better solar panel. Electrical formula of PV 𝐼𝐼 = 𝐼𝐼𝑃𝑃𝑃𝑃 − 𝑃𝑃𝑑𝑑 − 𝐼𝐼𝑠𝑠𝑠 𝑈𝑈𝑠𝑠𝑠 = 𝑈𝑈 + 𝐼𝐼𝐼𝐼𝑆𝑆 𝑈𝑈𝑠𝑠𝑠 𝑈𝑈 + 𝐼𝐼𝐼𝐼𝑠𝑠 𝐼𝐼𝑠𝑠𝑠 = = 𝑅𝑅𝑠𝑠𝑠 𝑅𝑅𝑠𝑠𝑠 𝑈𝑈𝑠𝑠𝑠 𝐼𝐼𝑑𝑑 = 𝐼𝐼𝑂𝑂 [ 𝑛𝑛𝑛𝑛𝑛𝑛 ] 𝑒𝑒 𝑘𝑘𝑘𝑘 𝑉𝑉𝑇𝑇 = 𝑞𝑞 𝑉𝑉 + 𝐼𝐼𝐼𝐼𝑆𝑆 𝑈𝑈 + 𝑅𝑅𝑆𝑆 𝐼𝐼 = 𝐼𝐼𝑃𝑃𝑃𝑃 − 𝐼𝐼𝑂𝑂 [ 𝑛𝑛𝑛𝑛𝑛𝑛 − 1] − 𝑒𝑒 𝑅𝑅𝑠𝑠𝑠 𝑈𝑈𝑜𝑜𝑜𝑜 𝑉𝑉𝑜𝑜𝑜𝑜 0 = 𝐼𝐼𝑃𝑃𝑃𝑃 − 𝐼𝐼𝑂𝑂 [ 𝑛𝑛𝑛𝑛𝑛𝑛 ]− 𝑒𝑒 −1 𝑅𝑅𝑠𝑠𝑠

Proceedings of Innovation and Technology Competition (INOTEK) 2021

Hanafi & Napiah, 2021

𝐼𝐼𝑆𝑆𝑆𝑆

𝐼𝐼𝑃𝑃𝑃𝑃 𝑈𝑈𝑜𝑜𝑜𝑜 ≈ 𝑛𝑛𝑛𝑛𝑇𝑇 𝑙𝑙𝑛𝑛 [ + 1] 𝐼𝐼𝑜𝑜 𝐼𝐼𝑆𝑆𝑆𝑆 𝑅𝑅𝑆𝑆 𝐼𝐼𝑆𝑆𝑆𝑆 𝑅𝑅𝑆𝑆 = 𝐼𝐼𝑃𝑃𝑃𝑃 − 𝐼𝐼𝑂𝑂 [ 𝑛𝑛𝑛𝑛𝑛𝑛 ]− 𝑒𝑒 −1 𝑅𝑅𝑠𝑠ℎ 𝐼𝐼𝑆𝑆𝑆𝑆 ≈ 𝐼𝐼𝑃𝑃𝑃𝑃

a)

b)

Figure 8 a) DC Boost Converter with Closed Maximum Power Point (MPPT) and b) Photovoltaic with DC-DC Boost Converter

CONCLUSION Photovoltaic (PV) circuit model that been use have two. Using PV array that has been provided from MATLAB is more efficient that design one by one. Mathematical circuit that has use block diagram a little bit complex that PV Array. The mathematical block and circuit cannot put in the same place. But using PV Array must have a knowledge about the component that we use such as value of voltage, temperature and so on. Using PV Array can combine with circuit diagram without need change to block. DC-DC Boost converter with closed loop MPPT is also bring a huge change of the outcome

Figure 3 DC -DC boost converter close loop

It is complete circuit of DC-DC Boost Converter with closed loop Mppt RESULT AND DISCUSSION

ACKNOWLEDGEMENT I would like to thank Universiti Teknikal Malaysia Melaka for the financial support through PJP/2017/FKEKK/HI06/S10483

Figure 4 PV Array Simulink Block

This is PV Array that know have been use many people to develop more high technology of sonar panel. The Ir is 80, Temp = 25. And the voltage is 324.8.

REFERENCES 1.

2.

Figure 5 PV Array with DC-DC Boost Converter Closed Loop

3.

For the both figures are DC-DC Boost Converter that first with voltage source and the other one is connect with PV Array. PV + DC-DC Boost Converter with

4.

Figure 6 Closed Maximum Power Point (MPPT).

This figure is the final circuit with full Photovoltaic Circuit. DC-DC Boost converter Closed Loop.

5.

Figure 7 PV Array with DC-DC Boost Converter Closed Loop

© Faculty of Electronic and Computer Engineering, FKEKK

66 70

Jay Patel and Gaurag Sharma, “Modeling and Simulation of Solar Photovoltaic Module Using Matlab / Simulink”, International Journal of Research in Engineering and Technology vol.2(3), 2013. Bibek Mishra Bibhu Prasanna Kar, “Matlab Based Modeling of Photovoltaic Array Characteristics”, B.Eng. dissertation, National Institute of Technology, Rourkela, India. 2012. N. Pandiarajan and Ranganath Muthu, “Mathematical Modeling of Photovoltaic Module with Simulink”, International Conference on Electrical Energy Systems (ICEES 2011), 2011. Huan-Liang Tsai, Ci-Siang Tu and Yi-Jie Su, Member, IAENG, “Development of Generalized Photovoltaic Model Using MATLAB/SIMULINK”, Proceedings of the World Congress on Engineering and Computer Science (WCECS) USA, 2008. S. Sheik Mohammed, ‘Modeling and Simulation of Photovoltaic module using MATLAB/Simulink’, International Journal of Chemical and Environmental Engineering, vol. 2(5) 2011.

Proceedings of Innovation and Technology Competition (INOTEK) 2021, Proceedings of Innovation andMalaysia, Technologypp. Competition Melaka, 67-68, (INOTEK) 2021

Smart Street Light with Lora Technology L. S. Lian1 and A. M. Darsono1 Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia

1

[email protected] ABSTRACT: The problem of the current street lighting system of Malaysia is high energy consumption and depending on timer controlling system, this has made the street light has the poor capability to the changes of the surrounding light. Therefore, this project is proposed to manage the on/off and output brightness of street lights using Internet of Things (IoT) and a feedback control system based on LoRa technology. Light Dependent Resistor (LDR) and motion sensor implemented to detect the intensity of surrounding light and the movement on the street, and hence to control the output brightness of LED light. Next, Artificial Intelligence (AI) approach fuzzy logic will be used as a feedback control system to drive a suitable output signal. Moreover, LoRa technology will be used to monitoring the situation of street lights because it has a larger coverage area with a license-free ISM band. Finally, the Internet of Things (IoT), The Things Network (TTN), and TagoIO application will be used for the transmission of data and sensors to detect real-time changes. In conclusion, this smart LED street light could help to reduce power wastage and better monitoring system to improve the roadway lighting standard of Malaysia in the future.

LED street light through the implementation of a closedloop control system with the fuzzy logic controller to control the output brightness and to analyze the sensor’s data and simulate the program to monitoring the situation of the street light using LoRa technology. 2.

METHODOLOGY

Keywords: Street light monitoring system; Fuzzy logic; LoRa 1.

INTRODUCTION

Street lights are important to light up roads at night and the conventional High-Pressure Sodium is the most common street light we can see. HPS street lights operate at higher internal pressure to light up the lamp [1]. However, this HPS street light is high energy consumption compared to LED light [2]. Moreover, the mercury element inside the sodium metal was a hazardous material that will lead the problems with trash disposal [3]. In the meantime, the HPS street light operates in a manual setup that operated from sunset to sunrise caused an energy wastage and lower device performance [4]. This system can only adjust the time switch of daily street lights and the operating condition of lighting facilities cannot be reflected in time, so this caused difficulty in maintenance work also [5]. Moreover, Global System for Mobile Communication (GSM) used in conventional street lights had charged up an expensive dedicated regional frequency and is said to have higher power consumption and cost operation [6]. Therefore, this project proposed to develop a smart © Faculty of Electronic and Computer Engineering, FKEKK

Figure 1 Project flow chart This project is divided into five experiments. The first and second experiment was to design a lux meter using LDR (GL5516) and a microwave motion sensor to collect surrounding light intensity and motion on the street. Next, a fuzzy logic controller was designed by using MATLAB to control the brightness level control in the third experiment. Afterward, LoRa gateway and node are set up in experiment 4 to enable the communication and transmission of data. Last but not least, the last part was to develop a TTN cloud server and TagoIO application for real-time monitoring. The node device is then placed outdoor for 3 consecutive days to collect consistent data for data analysis. 67 71

Leeand & Technology Darsono, 2021 Proceedings of Innovation Competition (INOTEK) 2021

3.

RESULTS AND DISCUSSION

At the end of this project, a smart LED street light had developed through the implementation of LDR and microwave motion sensors. LDR sensor is able to capture the level of illuminance that falls on it. As the result shown in experiment 1, it can be concluded that the illuminance is decreased inversely proportional to the average LDR resistance. Then, the best fit line was generated from the graph of Log (Lux) vs Log (Ω), and a linear equation was get as shown in Equation (1). Y=-1.5143x+7.944 (1) log10 (𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑙𝑙𝑙𝑙𝑙𝑙) = 𝑚𝑚 log10 (𝑅𝑅) + 𝑐𝑐 (2)

Figure 3 Overall output brightness percentage from 10 May 2021 (00:00am) to 13 May 2021 (00:00am) Before sunrise, the brightness percentage maintains at 20% when there is no motion detected. But when there is motion detected, the brightness percentage increase according to the time of motion detected. The higher the motion time, the higher the output brightness percentage. The LED turns off when sunrise at around 7:45 am. Any motion detected after sunrise will not turn it on, unless the LDR sensor detects a decrease in light intensity due to cloudy and rainy days. Figure 3 shows LED turn on in the afternoon time for all 3 days, this is because of the rainy day. Lastly, the LED turns on again when the LDR sensor detects a low light intensity.

The gradient and c-intercept are then substituted into Equation (2), and Equation (3) was the final summarized equation to find the approximate lux value. (3) approximate lux = 𝑅𝑅 −1.5143 × 107.944

Equation (3) was rewritten in coding and the final illuminance value will be print on the serial monitor of Arduino software. While motion sensor emits continuous waves of microwave radiation to detect motion on the street Continue to design a fuzzy logic controller for brightness level control using MATLAB software, a fuzzy logic rule evaluation was designed as showed in Table 1 below, and lastly, there is a total of 25 rules of membership function are set. The rules are then rewritten in coding and upload to the Arduino sketch board to simulate the result. Table 1 Fuzzy logic rule evaluation Illuminance VL L M1 H VLM D D D D LM B N N N Motion M2 B B N N HM VB B B N VHM VB VB B B

4.

In conclusion, a smart LED street light had successfully developed through the implementation of a closed-loop control system. The LDR and motion sensor manages to collect input variables which are illuminance and motion time. Next, the input variables are combined to drive suitable output brightness of COB LED by the fuzzy logic controller. The data is transmitted between gateway and node by LoRa technology. Lastly, the TagoIO application is used to monitoring the situation of the street light.

VH VD VD VD VD VD

5.

ACKNOWLEDGEMENT

I would like to thank Universiti Teknikal Malaysia Melaka for supporting this works. 6.

REFERENCES

[1] “Lighting Comparison: LED vs High Pressure Sodium/Low Pressure Sodium.” https://www.stouchlighting.com/blog/led-vs-hps-lps-highand-low-pressure-sodium [2] S. Yoomak, C. Jettanasen, A. Ngaopitakkul, S. Bunjongjit, and M. Leelajindakrairerk, “Comparative study of lighting quality and power quality for LED and HPS luminaires in a roadway lighting system,” Energy Build., vol. 159, pp. 542– 557, 2018. [3] V. J. S. Fathima Dheena P.P, Greema S Raj and Gopika Dutt, “IOT Based Smart Street Light Management System,” ICCS, pp. 76–81, 2017. [4] V. Gupta, K. Thakur, and S. Surnar, “IOT Based Smart Street Lights,” International Journal of Research, vol. 2, no. 10, pp. 270–272, 2015. [5] H. Deng, X. Xie, W. Ma, and Y. Han, “A LED Street Lamp Monitoring System Based on Bluetooth Wireless Network and LabVIEW,” 2016 2nd IEEE Int. Conf. Comput. Commun. ICCC 2016 - Proc., pp. 2286–2291, 2017 [6] P. Pandey, “Comparative Study of Long Range Communications Systems for IoT - Cellular, LoRA & Sigfox,” no. April, 2018.

Figure 2 LoRa node setup Lastly, The Things Network (TTN) and TagoIO application are developed to manage node device as shown in Figure 2 to store and display the output brightness of COB LED. The output brightness percentage will be upload to TTN cloud and TagoIO every 30 seconds by using LoRa technology. The node device is placed outdoor for 3 consecutive days, and the result was shown in Figure 3. © Faculty of Electronic and Computer Engineering, FKEKK

CONCLUSIONS

68 72

Proceedings of Innovation and Technology Competition (INOTEK) 2021, Melaka, 69-70, (INOTEK) 2021 Proceedings of Innovation and Malaysia, Technologypp. Competition

Automatic Color Recognition in Agriculture Application P. X. Law1 and A. M. Darsono1 Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya,

1

76100 Durian Tunggal, Melaka, Malaysia corresponding author : [email protected]

ABSTRACT: Ripeness of fruit can be determined by several parameters of size, weight, texture, fragrance of fruit and color features is one of it. Mango is chosen among tropical fruit in Malaysia to be tested in this project. Normally, the classification of maturity of mangoes is done manually by using the sense of sight to distinguish its maturity. After classification the stages of ripeness, sorting process will be done by using manpower before shipment. This method required to use a lot of manpower for the sorting process for mangoes according to its stage of ripeness. In this project, color recognition system that used for fruit ripeness in agriculture enable to save more time and reduce manpower in the process of sorting based on level of ripeness of mangoes. Every color has its own color code and RGB value, therefore RGB color sensor will be applied in this project as an input in order to determine the color frequency while LCD screen would display the level of ripeness of fruit whether is unripe, partially ripe, ripe or over ripe. Next, sorting process will be done based on the level of ripeness of mangoes by using Arduino as a controller module. Keywords: Color recognition; RGB value; Sorting

ripe tomato based on RGB value by insert the fruit one by one into sensor chamber. In color sensor testing on fruit, this research shown the percentage of color uniformity and average accuracy is 80% with no lighting condition effect, and the accuracy will be 78.3% if the alteration of lighting condition affects the color reading process. [3] Color recognition algorithm using neural network model can improve the ripeness recognition rate. From the experimental results in this article, the simulations show that the ripeness recognition rate is 96%. [4]. 2.

METHODOLOGY Color detection is designed using frequency of RGB value which obtained from the color sensor TCS 3200. Conveyor will move the item to the position of color sensor to detect the color and obtain frequency RGB value to identify stage of ripeness. With the code access in Arduino IDE, frequency of RGB value can be observed in serial monitor to determine the stage of ripeness whether is unripe, half ripe, ripe, over ripe or undetectable. The range for the frequency of each stage of ripeness will obtain the maximum and the minimum value and show the result on the second line on LCD with l2C module. In this project, color paper follows the color of ripeness on mango’s skin is replaced for the real mango due to the limited power of servo motor to sort them based on their different level of ripeness. When the item is detected unripe, it will be sort by using servo motor where servo motor move to certain degree.

1.

INTRODUCTION Normally, the classification of maturity of mangoes is done manually by using the sense of sight to distinguish its maturity. However, this requires a lot of manpower to classify the stage of ripeness of mangoes and sort according to their maturity before shipment. In recent year, color recognition is widely use in agriculture field for AI and robotics arm machine in harvest and determining the maturity of the fruit and vegetables. Some of the technology has included the color of fruit, size and ethylene to determine the ripeness of the fruit. Based on the article with title information system prototyping of strawberry maturity stage which using Arduino and color sensor has shown the RGB color space to determine the level of ripeness of the strawberries and the result is shown it had worked well. [1] There are also some examples of project which is similar to this which also using the color sensor and Arduino to implement fruit sorting device automatically. The speed of the sorting process also being consider as it need to speed up the sorting process. In this research, it has shown the result TCS 3200 color sensor can detect the orange fruit very well and the time consuming for one orange fruit is 220ms.[2] In journal of application of color sensor in the determination of tomato fruit ripeness and gravitation type fruit sorting tool, it detects the ripe tomato and half © Faculty of Electronic and Computer Engineering, FKEKK

3.

RESULT AND DISCUSSION Arduino uno is connect with color sensor TCS 3200, LCD with l2C module, servo motor as shown in Figure 1 below.

Figure 1: Setup of color recognition and sorting system 69 73

Law Law&&Darsono, Darsono,2021 2021

Proceedings of Innovation and Technology Competition (INOTEK) 2021

On Onthe thefirst firstline lineofofLCD LCDscreen screenisisset set“Stage “StageofofRipeness” Ripeness” while while the the second second line line isis the the outcome outcome ofof the the color color detection detectionwhether whetherititisisunripe, unripe,half halfripe, ripe,ripe ripeororover overripe. ripe. Whenthe thecolor colordetected detectedthe thefrequency frequencyfor forRGB RGBvalue valueisis When withinthe therange, range,stage stageofofripeness ripenessfor forunripe unripewill willshow show within LCDininFigure Figure3.3.When Whenthe theitem itemisisdetected detectedunripe, unripe,itit ononLCD willbebesort sortbybyusing usingservo servomotor motorwhere whereservo servomotor motor will moves3636degree. degree. moves

The Thevalue valuefor foreach eachstage stageofofripeness ripenesswill willbebeshown showninin serial serialmonitor monitorininFigure Figure2 2from frommaximum maximumtotominimum minimum can canbebeobtained obtainedtotoidentify identifythe thedifferent differentlevel levelofofripeness. ripeness.

CONCLUSION 4.4. CONCLUSION Colorchanges changesononthe theskin skinofofmango mangoenable enabletoto Color identifythe thelevel levelofofthe theripeness. ripeness.This Thisisisbecause becauseevery every identify colorhas hasitsitsown ownfrequency frequencyRGB RGBvalue valueininitsitscolor colorspace. space. color canbebedone donebybydetermine determinethe therange rangeofofthe thefrequency frequency ItItcan RGBvalue valuefor fordifferent differentlevel levelofofripeness ripenessininmangoes mangoes RGB whichare areunripe, unripe,half halfripe, ripe,ripe ripeand andover overripe. ripe.When Whenthe the which colorsensor sensordetects detectsfrequency frequencyRGB RGBvalue valuefor forunripe unripe color mangowhich whichisisininthe therange rangeofofitsitsfrequency frequencyRGB RGBvalue, value, mango theresult resultofofstage stageofofripeness ripenessdisplayed displayedononLCD LCDscreen. screen. the Afterthat thatsort sortofofthe themangoes mangoesaccording accordingtototheir theirstage stageofof After ripenessautomatically automaticallybybyrotating rotatingservo servomotor motortoto3535 ripeness degrees.The Thesimilarity similarityamong amongthe thecolors colorscan canbebeeasily easily degrees. identifybybydetermine determinethe therange rangeofoffrequency frequencyRGB RGBvalue. value. identify Thissystem systemisisnot notonly onlyreduced reducedmanpower manpowerininagriculture agriculture This whenharvest harvestand andsort sortfor forthe thefruit fruitaccording accordingtototheir theirlevel level when ripeness. ofofripeness.

Figure Figure2:2:Frequency FrequencyofofRGB RGBvalue valuefor foreach each step stepofofripeness ripeness Table Table1 1Range Rangeofoffrequency frequencyfor forRGB RGBvalue valuefor foreach each stage stageofofripeness ripeness RR GG BB Stage Stage ofof Ripeness Ripeness Max Max

Min Min Max Max

Min Min Max Max

Min Min

536 536

200 200 347 347

200 200 133 133

9090

Unripe Unripe

200 200

103 103 200 200

120 120 6363

3636

Half HalfRipe Ripe

130 130

5050

2929

8888

2525

Ripe Ripe

190 190

145 145 250 250

150 150 7575

5454

Over Over Ripe Ripe

200 200

ACKNOWLEDGEMENT ACKNOWLEDGEMENT would like like toto thank thank Universiti Universiti Teknikal Teknikal I I would MalaysiaMelaka Melakafor forsupporting supportingthis thisworks. works. Malaysia REFERENCES REFERENCES

InInserial serialmonitor, monitor,the therange rangefor forthe thefrequency frequencyofofRGB RGB value valuefrom frommaximum maximumtotominimum minimumcan canbebeobtained obtainedtoto identify identifythe thedifferent differentlevel levelofofripeness ripenessininTable Table4.1. 4.1.From From Table Table4.1, 4.1,ititcan canbebeobserved observedthat thatthe thefrequency frequencyofofRGB RGB value valuefor forstage stageofofripeness ripenesshalf halfripe ripeand andover overripe ripeisisquite quite close closetotoeach eachother, other,ititwould wouldeasily easilyconfuse confuseclassified classified between betweenthese thesetwo twostages. stages.InInthis thiscase, case,more moretesting testingfor for the thecolor colorneeds needstotobebeconducted conductedtotoobtain obtaina amore morestable stable range rangefor forfrequency frequencyRGB RGBvalue valuefor foryellow yellowgreen green(half (half ripe) ripe)and anddark darkyellow yellow(over (overripe). ripe).

Figure Figure3:3:Outcome Outcomewhen whenrecognize recognizeunripe. unripe. ©©Faculty FacultyofofElectronic Electronicand andComputer ComputerEngineering, Engineering,FKEKK FKEKK

7070 74

[1] [1]

Juliano,A.A.Hendri HendriHendrawan Hendrawanand andR.R.Ritzkal, Ritzkal, A.A.Juliano, “InformationSystem SystemPrototyping PrototypingofofStrawberry Strawberry “Information Maturity Stages Stages Using Using Arduino Arduino Uno Uno and and Maturity TCS3200”,Journal JournalofofRobotics Roboticsand andControl, Control,vol. vol. TCS3200”, 1(3),pp. pp.86-91, 86-91,2020. 2020. 1(3),

[2] [2]

Sihombing,F.F.Tommy Tommyand andS.S.Sembiring Sembiringand and P.P.Sihombing, Silitonga,“The “TheCitrus CitrusFruit FruitSorting SortingDevice Device N.N.Silitonga, AutomaticallyBased BasedononColor ColorMethod MethodBy ByUsing Using Automatically TCS3200 Color Color sensor sensor and and Arduino Arduino Uno Uno TCS3200 Microcontroller”, The The 3rd 3rd International International Microcontroller”, Conference onon Computing Computing and and Applied Applied Conference Informatics,2018. 2018. Informatics,

[3] [3]

Putraand andT.T.Rizaldi, Rizaldi,Application ApplicationofofColour Colour F.F.Putra Sensorininthe theDetermination DeterminationofofTomato TomatoFruit Fruit Sensor Ripeness (Solanum (Solanum Lycopersicum, Lycopersicum, L)L) inin Ripeness Gravitational Type Type Fruit Fruit Sorting Sorting Tool Tool Gravitational (Gravitational Type), Type), Indonesian Indonesian Journal Journal ofof (Gravitational AgricultureResearch, Research,vol. vol.1(1), 1(1),pp. pp.13-20, 13-20,2009. 2009. Agriculture

[4] [4]

M.P.P.Paulraj, Paulraj,C.C.R.R.Hema, Hema,R.R.P.P.Krishnan Krishnanand andS.S. M. M. Radzi, Radzi, “Color “Color Recognition RecognitionAlgorithm Algorithm S.S. M. usinga aNeural NeuralNetwork NetworkModel ModelininDetermining Determining using theRipeness Ripenessofofa aBanana”, Banana”,Proceedings Proceedingsofofthe the the International Conference Conference onon Man-Machine Man-Machine International Systems(ICoMMS) (ICoMMS)2009. 2009. Systems

Proceedings of Innovation and Technology Competition (INOTEK) 2021, Proceedings of Innovation Technology (INOTEK) 2021 Melaka,and Malaysia, pp.Competition 71-72,

Design and Development of a Self-Dimming Road Traffic Signals W.H.J. Leslie1, A. M. Khafe1, S.K. Subramaniam1, M. Esro1 Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya,

1

76100 Durian Tunggal, Melaka, Malaysia1 corresponding author: [email protected] collected sensor data is being utilized as input toward the microcontroller to optimise the LED traffic light brightness for the best use of energy consumption. Current level detection is applied to track the traffic light display’s fault status. Data collected is sent to the IoT platform for the user’s monitoring purpose.

ABSTRACT – The aim of the project is to design and develop a self-dimming traffic signals display based on illuminance and relative humidity. Besides, fault detection on traffic signals display is developed which focus on current value measurement that links to the IoT platform for tracking purpose. Moreover, analysis for power consumption to ensure that the project has sustainable energy to reduce operation cost. Keywords: Self-dimming, Fault Detection, IoT 1.

This project introduces self-dimming LEDs that help in reducing the usage of energy and hence reduce power consumption. This helps in saving electricity cost. Besides, LEDs dimmer help to increase the lifespan of the traffic light, hence it helps to reduce the maintenance cost.

INTRODUCTION

The power consumption usage is high because the traffic light operates at its full power for the whole day and night. A dimming schedule can be adjusted to traffic intensity [1]. The self-dimming LEDs embedded with a controller to detect the different level of light intensity, to process it and come out with desirable output. Besides, LEDs failure can be detected using a current sensor with a message sent to notify related parties for the restoration process. Light dependent resistor (LDR) sensor will be used for light intensity detection, to achieve an outcome of minimum brightness at night, maximum brightness during the daytime. A humidity sensor will be used for foggy and rainy environment detection.

2.

METHODOLOGY

The block diagram of project main work is shown in Figure 1.

The LED traffic light faulty needs more time to reach the maintenance team for a solution. The malfunction of LED can be detected by measuring the real current vs the nominal current for that particular light [2]. Manual reporting from road users toward related authority would result in the delay of time for the restoration process by the maintenance team. An IoT platform will be created to send a message which immediately notifies the authority when the display fault detection is detected. By reducing the intensity at these times such as night-time or fewer traffic conditions, energy can be saved and the data is uploaded to the cloud [3]. Fault detection, minimization of cost, reducing the loss of electricity and manpower are also possible [3].

Figure 1: Block diagram of project main work The flowchart of self-dimming mechanism is shown in Figure 2.

The scope of this project is to develop a selfdimming system and a traffic signals display fault detection system. This project will decrease the brightness of LED at night with only 60% of full brightness and increase the brightness of LED to full brightness when in rainy or foggy environments. The © Faculty of Electronic and Computer Engineering, FKEKK

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The lux measurement for full brightness and partial brightness is shown in table 1. Table 1 Lux measurement for self-dimming mechanism Brightness (%) Lux (Lx) 100

35

60

9

Display partial malfunction detection is measured based on current value, SI unit defined as Ampere (A). For current sensor ACS712 - 5A, has a sensitivity of 185mV/A. The current value measurement for full brightness and partial brightness is shown in table 2. Table 2 Current measurement for self-dimming mechanism Brightness (%) Current (mA) Figure 2: Flowchart of self-dimming mechanism The flowchart of fault detection notification is shown in figure 3. 4.

100

33

60

6

CONCLUSIONS

The self-dimming road traffic signals system is designed to reduce power consumption for energyefficient purpose. The self-dimming system will contribute to sustainable energy, which results in a reduction of electricity cost. The traffic signals display fault detection system enables online monitoring via IoT platform, which brings instant notification to the authority when there is traffic signals faulty display. 5.

ACKNOWLEDGEMENT

I would like to thank Universiti Teknikal Malaysia Melaka for supporting this work. REFERENCES [1]

Figure 3: Flowchart of fault detection notification 3.

[2]

RESULTS AND DISCUSSION

For the LDR sensor module, the light intensity measurement is based on a parameter of illuminance, the SI derived units are measured in lux (lx). Illuminance is the total luminous flux incident on a surface, per unit area, which is represented as lm/m2. The formula for calculating the value of lux is shown in equation 1. Lx = lm / m2

© Faculty of Electronic and Computer Engineering, FKEKK

[3]

(1)

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A. K. Jägerbrand, “LED (Light-Emitting Diode) road lighting in practice: An evaluation of compliance with regulations and improvements for further energy savings”, Energies, vol. 9, no. 5, pp. 357, 2016. M. Z. Amin Marzuki, A. Ahmad, S. Buyamin, K. H. Abas, and S. H. Mat Said, “Fault Monitoring System for Traffic Light,” J. Teknol., vol. 73, no. 6, pp. 59-64, 2015. A. Sravani, P. Malarvezhi, and R. Dayana, “Design and implementation of dimmer based smart street lighting system using raspberry Pi and IoT,” Int. J. Eng. Technol., vol. 7, no. 2.8, pp. 524, 2018.

Proceedings of Innovation and Technology Competition (INOTEK) 2021, Proceedings of Innovation and Malaysia, Technologypp. Competition Melaka, 73-74, (INOTEK) 2021

An Evaluation of Wireless Real Time Data of Solar Tracking System M. A. S. M. Shabri1 and A. M. Yusop1 Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya,

1

76100 Durian Tunggal, Melaka, Malaysia Corresponding author: [email protected] potential issues before they arise[3]. At this point, using digital technologies and more advanced computing facilities to leverage the power of IoT for monitoring solar power plants appears to be promising[4]. In this project, the prototype of dual axis solar tracker will be absorbing the energy form the sun to get the maximum power. Dual axis solar tracker consists of one Arduino uno to control the LDR light sensor and the servo motor to move the PV solar panel. Wireless module will control the data monitoring through IoT platform. Temperature and humidity sensor also being control by the microcontroller in this project.

ABSTRACT: Sunlight and heat are a natural source in our earth where can use a variety of continually changing techniques, including solar thermal and artificial photosynthesis. Solar energy from renewable sources is a major source of electricity. The trackers direct solar panels to the sun. These mechanisms shift their orientation during the day, as the sun maximises absorption of energy. In any solar system, the shift efficiency is increased by continuous adjustment of the tracking system at the best angle as sun goes through the sky. The project presents the development of the solar tracking system using Arduino UNO that allows the panel to move in any direction towards the high intensity of sunlight via four LDRs. The monitoring system is implemented in this tracking system to view in real time the data of solar energy parameter and factors affecting its deficiencies using Thing Speak platform interfacing with Wemos D1 R2. The result shows the tracking system has efficiencies of 55.38% higher than single-axis system. The monitoring system is practical to analyse the solar panel component environmental factor through real-time. Keywords: Wireless Real Time Data, Solar Tracking, LDR

1.

2.

To develop the good tracking system features with weather condition in Malaysia, the solar panel will design into a dual-axis tracking system consist of two servo motor as dual-axis. Four LDR module sensors is using to track the sun intensity to sure the solar panel absorb highest energy. Arduino Uno use to operate the servo motor and LDR sensor as a dual axis solar tracker. While for data monitoring using Wemos D1 R2 Wi-Fi module to collect the data by wireless through the cloud using ThingSpeak. The data collected will display in personal computer on ThingSpeak platform. Figure 3 shows the block diagram of the solar panel was develop into dualaxis solar tracker.

INTRODUCTION

Solar tracking system is the system to track the sun and generate the energy to store and there are two fundamental tracker categories which is are a single axis and a dual axis. Dual axis tracking system has two axis freedom, horizontal and vertical. Dual axis solar tracker is the solar panels moves according to the movement of the sun and get the radiation all day. There a variety of experiments have been performed to determine the optimal angle of tilt and orientation (azimuth) of PV system, solar collector, or any other application in some part of the world. Since the sun's direction varies from east to west and from north to west, several types of angles are required if the ideal angle of the sun is to be calculated[1][2]. The monitoring system through wireless is more efficient where can store the data in cloud and monitor from far. The Internet of Things (IoT) platform combines data from various solar panels and uses analytics to communicate the most useful information with applications tied to specific needs. These advanced IoT platforms, such as Thingspeak, cloud platform, can pinpoint exactly which data is useful and which can be safely ignored. This data can be used to identify flaws, make recommendations, and predict © Faculty of Electronic and Computer Engineering, FKEKK

METHODOLOGY

Figure 1: Hardware design for dual axis solar tracker Photovoltaic solar panel used for the project as a solar energy is 2 W 5 V solar panel where consists of monocrystalline cell material and has a power capacity is 2 W. The solar chosen has peak power maximum is 2.5 W and the voltage maximum capture is 5 V. While the

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current in this solar panel is 500 mA during the operation. Monitoring system for solar tracker system are used to help and improve the monitoring the data while to obtain energy efficiency from the solar panel and track the factor reduces the solar electricity and other problem with solar power. The solar power system is used to monitor the maximum performances produces by the solar panel. The project use INA219 component to get the bus voltage, current and power of the solar panel, while the DHT11 function to get the temperature and humidity surrounding the solar tracking system. The sensor uses which is INA219 and DHT11 will interfacing into WEMOS D1R2 ESP8266 Wi-Fi Module by sending the sensor data into the ThingSpeak platform.

Figure 4: Movement of Dual axis solar tracking system

Figure 5: Comparison output voltage between single axis and dual axis tracking system 4. CONCLUSION A solar tracker system that has been constructed and tested to track the sun, as well as a monitoring system that uses ThingSpeak to analyse the data and performance of the solar tracker system. A dual-axis solar tracker has been developed in this project along with single-axis solar tracker data uses to compare the result. Therefore, dual-axis solar tracking system shows more improvement and efficiency than single-axis by 55.38% since it more precious to track and detect sun intensity even been block by cloud still shows the best performance when there are two servo motor use. This system was created by combining all the hardware and software that were used.

Figure 2: Hardware design of the monitoring system 3.

RESULTS AND DISCUSSION

The solar tracking system is the system to move the solar panel perpendicular to the sun according to the light intensity from the LDR sensor. Luminous intensity is the quantity of visible light that is emitted in unit time per unit solid angle. The outputs of the LDR depends on how much light was falling on the surfaces. The four LDR is used in the solar tracking system to distinguish the light intensity to makes the panel move to the right or left and rotate in 180 degree according to LDR that has a higher light intensity. During the prototype been left under the sunlight to record the data, the values of each LDR light intensity has be read and recorded using a light meter where measure in lux. The output voltage from the solar panel been recorded and compare with the single axis data. Table 1 shows the light intensity and table 2 shows the output result.

5.

ACKNOWLEDGEMENT

I would like to thank Universiti Teknikal Malaysia Melaka for supporting this works. REFERENCES [1]

[2]

[3] [4]

Figure 3: LDR light intensity value in unit LUX

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A. Z. Hafez, A. Soliman, K. A. El-Metwally, and I. M. Ismail, “Tilt and Azimuth Angles in Solar Energy Applications – A review,” Renew. Sustain. Energy Rev., vol. 77, no. April, pp. 147– 168, 2017. Y. Zhang, Z. Z. Qiu, P. Li, W. Guo, Q. Li, and J. He, “Calculating the Optimum Tilt Angle for Parabolic Solar Trough Concentrator With The North-South Tilt Tracking Mode,” Proc. - 2013 4th Int. Conf. Digit. Manuf. Autom. ICDMA 2013, pp. 329–334, 2013. A. Ahrary, M. Inada, and Y. Yamashita, “Solar Power Monitoring System ‘SunMieru,’” Smart Innov. Syst. Technol., vol. 73, pp. 216–224, 2018. S. Adhya, D. Saha, A. Das, J. Jana, and H. Saha, “An IoT Based Smart Solar Photovoltaic Remote Monitoring And Control Unit,” 2016 2nd Int. Conf. Control. Instrumentation, Energy Commun. CIEC 2016.

Proceedings of Innovation and Technology Competition (INOTEK) 2021, Proceedings of Innovation and Malaysia, Technologypp. Competition Melaka, 75-76, (INOTEK) 2021

Solar Energy Harvester for Pet GPS Tracker S. A. A. Yusry1 and A. M Yusop1 Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya,

1

76100 Durian Tunggal, Melaka, Malaysia Corresponding author: [email protected]

ABSTRACT: With the advancement of nowadays technology, all device are smaller in size and wireless. The wireless technology are the most advance technology so far and one of the advantage of this kind of technology are the power source. The power source of this wireless technology depends on the device battery life and need to be plug in for recharge purpose in order for it to be functioning. This problem can be solved with harvesting renewable energy in our surrounding for example solar energy radiated from our sun. This system can be used to powered up the wireless device and charging the backup battery installed in the same time. For that reason, this project developed a solar energy harvesting pet collar that will give the GPS pet collar ability to harvest the solar energy as their power source and charging the battery installed. The GPS system using minimum of 2.2v up to 3.6V in maximum, and the result obtained within this paper showing the solar panel can give enough power to power up the GPS system as that energy harvester circuit can give output up to 4.3V in direct sunlight and as low as 3.25V. Keywords: Solar Power, GPS Pet Collar, Energy Harvesting 1.

wild life animal study [4].The GPS system widely used in every technology and it is much easier for us to access the location of desired object only with smartphone. The objective of this paper was to develop a solar energy harvester for a pet collar and analyse the performance of the project. 2. Methodology

INTRODUCTION

The Sun is an example of a renewable energy that can be harvested freely and anyone can use. The energy gives an excess for the whole world, and it will not run out at any point in the near future, in contrast to fossil fuels. Although, the energy harvest from the solar is very non-linear respected to nature [1]. This is by considering the environment, weather and angle of the base towards the sun surrounding the solar panel. One of it is the use of solar energy harvesting in wireless sensor network (WSN). The small size solar panels suitably connected to low-power energy harvester circuits and rechargeable batteries provide a room to make the WSN nodes or any wireless devices completely self-powered with an infinite life time [2]. The Global Positioning System (GPS) first developed by the U.S Department of Defence [3] and widely used ever since. GPS devices is included and widely used for many kind of purpose and most of them are very important involving tracking and locating important stuff or life. One of GPS application is to track and monitor wildlife, livestock animal or pets. The use of this GPS device in animal tracking give scientist or pet owner to study their animal movement and know exactly their pattern on where did they go during their presence and their habitat of life for © Faculty of Electronic and Computer Engineering, FKEKK

Figure 1 project flow chart From the figure above, the first move for every project was to understand literature and research by using every data source within our reach like the Internet, journal, books and other secondary and primary data source as many as possible in supporting this project work paper and theory. Throughout several reference and study on solar energy harvesting concept and theory, then the design of the solar energy harvesting for pet collar can be constructed as below. The circuit are using two solar panel, a charging module, 3.7V lithium ion rechargeable battery, and a NodeMCU ESP-32 and NEO-6 M-0-001 GPS module. For the software within this system, the location and the charging battery voltage was obtained through Blynk application and the coding was build using Arduino IDE software. Second step was by experimenting with the circuit on a breadboard to collect the data of the energy harvester circuit and testing the functionality of the designed circuit. Problem encountered the solved and start form first step to improve the circuit. Then, the prototype was designed for the pet collar and the last step was by documented all the collected result for analysis and report.

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3.

Results and Discussion

Figure 4 state of discharge of battery in a day 4.

Figure 2 Blynk interface application

CONCLUSION

For the conclusion of this project, this project was a success by achieving all three sated objective that solved all my problem statement. Although there are a lot more room of improvement especially on increasing the battery capacity for longer lifetime and also using much more accurate GPS module to increase the accuracy of the object location so this solar energy harvesting pet collar to be worked perfectly without any flaw. This project also promote the green energy usage from any kind of renewable energy source available on this planet. The solar energy harvested from the sun for this system has zero pollution effect on the environment and also can help improving our living cost today.

The figure 2 showing Blynk application interface before and after starting and connected the system. Figure 4 show after the system was connected, the location appeared as a pin location in a Google map. The black pin representing the GPS or the pet location and the green dot represent the location of the smartphone. The percentage of the current battery life also showed on the application together with latitude and longitude value of the pet current location. The location was a little bit inaccurate because of the inaccuracy of the used GPS module but still tolerable and able to track down the cat.

5.

ACKNOWLEDGEMENT

I would like to thank Universiti Teknikal Malaysia Melaka for supporting this works. REFERENCES [1] Figure 3 State of charge of battery in a day Then, figure 3 showing the rate of charge of the battery taken in a range of 24 hours cycle. The discharge was at night when there are no presence of sun light to supply the voltage for the circuit and charging the circuit. The graph show that the battery was at the peak of the voltage at 4.2V which is the maximum voltage charge of the 3.7V battery represented as 100% and reaching the average of 50% when the voltage of the battery reaching 3.79V. After 7 hours of running at night, the voltage dropped to 3.27V and the circuit stop operating as the battery was at 0% of usage. The state of discharge graph on figure 3 showing the state of charge of the battery starting from the start of sunrise. The battery rises to the range of 50% at voltage of 3.64 and at its peak of 100% at 4.2V. This showed that this circuit does have its flawed which is during nonpresence of sunlight, the battery cannot supply enough power for at a certain time and can only lasted for about 6 hours of usage from full charge. © Faculty of Electronic and Computer Engineering, FKEKK

[2]

[3]

[4]

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K. Jayaraman and A. Joseph, “Solar Tracking for Maximum and Economic Energy Harvesting”, International Journal of Engineering and Technology, vol. 5, pp. 50305037. 2013. H. Sharma, A. Haque and Z. Jaffery. “Solar Energy Harvesting Wireless Sensor Network Nodes: A Survey”. Journal of Renewable and Sustainable Energy. 10(023704) 2018. F. Abulude, A. Akinnusotu and A. Adeyemi, “Global Positioning System And It's Wide Applications”, Continental J. Information Technology, vol. 9(1), 2015. W. Bouten, E. Baaij, J. Shamoun-Baranes and K. Camphuysen, “A Flexible GPS Tracking System For Studying Bird Behaviour At Multiple Scales” Journal of Ornithology, vol. 154(2), pp. 571-580, 2013.

Proceedings Innovation and and Technology Technology Competition (INOTEK) 20212021, Proceedings ofofInnovation Competition (INOTEK) Melaka, Malaysia, pp. 77-78

Smart Aquaculture using Internet of Things (IoT) Nursyahira binti Halim1, Badrul Hisham Ahmad1 1

Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia Corresponding author’s email: [email protected]

*

ABSTRACT: In this report, I propose Smart Aquaculture using Internet of Things (IoT) to develop system using NODEMCU ESP32 with Cayenne platform as a dashboard for indoor farming fish. Basically, this project is to collect the data of pH level and water temperature in the small aquarium. Basically, the aquatic species live inside it will die if the water’s pH is too high or too low. Furthermore, this project is to analyze the sensing technique in aquaculture farm and develop an IoT system which can monitor indoor silver catfish farming using smart phone and web-based application. Extra food increases production costs and contaminates the water, feeding is another important issue for owners of fish farms. For this reason, there is a steady increase in the number of studies on fish feeding solutions.

temperature shown in Figure 1: The connection of pH level and water temperature. Users can monitor the IoT via smart phone and web-app application. The data will send to microcontroller and start to execute the process.

Keywords: pH level; water temperature; feeding

Besides that, MB-12 Timer Hobby Kit, gear motor, AD/DC adapter and mechanical programmable timer are used to feed the fish automatically. The Timer Hobby Kit connected to the adapter with 6V and 1A while plugged together with the mechanical programmable timer, gear motor and 10uF capacitor to start the process of feeding the fish as shown in the Figure 2: The connection of timer hobby kit and gear motor. A small plastic container is used to store the food. The fish are feed twice a day which is 9:00 am and 9:00 pm.

1.

Figure 1: The connection of pH level and water temperature.

INTRODUCTION

This project in-line with United Nation’s Sustainable Development Goals. There are a few goals that match to this proposed project that called Smart Aquaculture using IoT. First, end poverty (No. 1: No Poverty) in all its forms everywhere which helps B40’s group (fish farmer) can do multiple job while taking care of fish pond. Next goals is to end hunger (No. 2: End Hunger) to achieve food security and improved nutrition and promote sustainable agriculture and farming. This project helps fish farmer to make sure the fish is healthy and weighable fish so that the fish has a good market. Furthermore, conserve and sustainably use the oceans, seas and marine resources for sustainable development (No. 14: Live below Water) so that fish farmer can make sure the pH level and water temperature of fish is healthy so that the fish is healthy to eat through smart phone by using Cayenne IoT online dashboard. On top of the three goals, this project come out with a system that can monitor the data by collecting the pH level and water temperature. Next, this project is equipped with an automation system that can feed the fish. 2.

Figure 2: The connection of timer hobby kit and gear motor. For the set-up of the aquarium, the pH level and water temperature are placed on the left side of aquarium as shown in Figure 3: The set-up of pH level and water temperature. The set-up of the food timer as shown in the Figure 4 placed on top of the aquarium.

METHODOLOGY

This project is using the NodeMCU ESP32 as the microcontroller. The electronic component used in this project to check the pH level of water and water temperature in the aquarium are pH level sensor and water temperature sensor. First, establish the connection between the NodeMCU ESP32 and internet connection. Once the connection establishes the IP address is generate. The sensor will collect the data to the NodeMCU ESP32 and the value of pH value and water © Faculty of Electronic and Computer Engineering, FKEKK

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from everywhere. 4.

CONCLUSION

Smart Aquaculture is to develop an automated system for indoor farming fish which able to feed the fish using mechanical programmable timer. Then, develop an IoT system which can monitor indoor fish farming using application which able to monitor data of pH level and water temperature through Cayenne IoT dashboard platform. Next, develop an automated aquarium for indoor fish farming which able to collect data of pH level and water temperature. This system is built to help the farmer. B40’s fish farmer can afford to have the system. Thus, increasing their income. They can save time, money and energy. Besides that, the ecosystem of underwater lives are well taken care of. By applying the proposed project, few goals of Sustainable Development Goals by United Nation can be achieved..

Figure 3: The set-up of pH level and water temperature.

ACKNOWLEDGEMENT The authors would like to thank FKEE of Universiti Teknikal Malaysia Melaka for moral and financial throughout the project. Very special thanks to my family members and all friends for their efforts and motivations in order to complete the project. REFERENCES Figure 4: The set-up of the food timer to feed the fish. 3.

[1]

RESULTS AND DISCUSSION

The result of the pH value and water temperature can be shown in the Cayenne IoT platform as shown in Figure 5. During the day, the pH value of the water is constant with 7.24 and 7.25 while the temperature of water increase from 30.29 until 30.31 at 3:36 pm until 3:41 pm.

[2]

[3]

[4]

[5] Figure 5: The result of pH level and water temperature using Cayenne IoT platform. The Internet of Things (IoT) is a constantly developing technology that is already spreading its wings across all industries. With developments in computers such as the NodeMCU ESP32, innovation is reach the upper level with applications in aquaculture. A user can monitor the water condition using web-based application through Wi-Fi within Wi-Fi range and through Internet © Faculty of Electronic and Computer Engineering, FKEKK

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Balakrishnan, S., Rani, S. S., & Ramya, K. C. (2019). “Design and Development of IoT Based Smart Aquacultur System in a Cloud Environment’’. Internatinal Journal of Oceans and Oceanography, 13(1), 121-127. A. Ramya, R. Rohini, S. Ravi (2019). “IoT Based Smart Monitoring System for Fish Farming”. International Journal of Engineering and Advanced Technology (IJEAT), ISSN: 2249-8958, Volume-8 Issue-6S. Mohammed M. Alammar and Ali Al-Ataby (2018). “An Intelligent Approach of The Fish Feeding System”. Department of Electrical Engineering and Electronics, University of Liverpool, UK. L. Parra, S. Sendra, L. García, and J. Lloret (2018). “Design and Deployment of Low-Cost Sensors for Monitoring the Water Quality and Fish Behavior in Aquaculture Tanks during Feeding Process”. Journals Sensors MDPI. D. Manjunatha Reddy, R. Varun Kumar, Likith and Anupama Hongal (2019). "Sustainable Aquaculture and Smart Aqriculture using IoT". Department of ECE, Sambhram Institute of Technology, Bangalore, India.

Proceedings of Innovation and Technology Competition (INOTEK) 2021, Proceedings of Innovation and Technology Competition Melaka, Malaysia, pp. 79-80 (INOTEK) 2021

Solar Irradiance Forecasting Using Deep Neural Network N. J. Ikhwani1, H.Y Hwa1 Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia

1

Corresponding author's email: [email protected]

*

ABSTRACT: Predicting solar irradiance improves the planning and operation of photovoltaic systems and yields many economic advantages for electric utilities. There are many methods introduced that we can use to predict solar irradiance. Still, many ways need to be investigated because many of them either lack accuracy because they cannot capture long-term dependence or because of scalability, they cannot be used with big data. This project studies a technique using deep neural networks of the Nonlinear Autoregressive (NAR) model to predict solar irradiance. Actual solar irradiance measurement obtained from FKEKK, UTeM will be used for this project. The results of the NAR network are evaluated on the neural network algorithm basis of mean squared error. Root mean square error and regression.

simulate the output of this project. This project focuses on the prediction of solar irradiance using the time series prediction process. Therefore, this paper involves the project's objectives, which are designing the most accurate model with a technique using deep neural networks for predicting solar irradiance and enabling deep learning neural network algorithm for data analysis. 2.

The steps for designing a NAR model for solar radiation forecasting are described and explained in this section. The model will predict data using historical data. Real solar irradiance measurement for one year will be used as the target. 2.1 Training, Testing, and Validation sets Before activating the network, the time series was divided into three different sets: training, testing, and validation sets[4] 70% will be used for training. 15% will be used to check that the network is generalizing before overfitting and to stop training. And lastly, 15 % will be used as an entirely separate generalization test of the network. The model's performance has been carried out based on statistical analysis: regression, root mean squared error (RMSE), and mean squared error (MSE).

Keywords: Nonlinear Autoregressive; Solar Irradiance; Deep Learning Neural Network Algorithm. 1.

INTRODUCTION

In this era, the world desperately needs a lot of electricity. This is because it is now growing in the industrial sector and even in the global economy. But that energy is still not sufficient to support each country's economic development. Each country makes various alternatives to obtain enough power in the future. Therefore, predicting solar irradiance is very important in renewable energy generation. After all, prediction can improve the planning and enhance photovoltaic systems' operation and yield many economic advantages for electric utilities [1-2]. Many studies have been done on utilizing neural networks to predict solar irradiation. In this paper, however, time series neural networks were employed to make the prediction. Time series forecasting is a technique through a sequence of time for the prediction of events. By analyzing the past patterns, the techniques forecast future events on the premise that future trends would be close to historical trends. A nonlinear autoregressive neural network[3] determines a discrete, nonlinear, autoregressive model that can be used for time series forecasting. It is to be written as follows:

y(t) = h ( y (t-1), y(t-2), …, y(t-p))

2.2 Network Architecture To implement the model, MATLAB is used to construct a deep neural network. The neural network is configured with 10 hidden neurons and a delay of 2 timesteps. The hidden layer and neuron layer can be adjusted if the network training performance is poor. 2.3 Train the network After that, we can apply a training algorithm. There are several training algorithms to choose from: Levenberg-Marquardt: It normally takes more memory, but it is usually the fastest. Bayesian Regularization: It takes longer, but it may be better for more challenging problems. Scaled Conjugate Gradient: It takes up less memory. Suitable in low-memory situations. 3.

(1)

RESULTS AND DISCUSSION

The models have been tested with different training algorithms. The statistical analysis is used to compare the performance of the approaches. Computing the Mean Squared Error(MSE), Root Mean Square Error(RMSE), and regression(R) analysis are used in the statistical analysis. The square of the average deviation of the estimated values from the matching target data is represented by the Mean Squared Error(MSE). The

The formula shows how the NAR network is used to predict the value of the info series y at time t, y(t) using the p values of the past series. The function h is previously unknown, and therefore the training of the neural network aims to approximate the function by optimizing the network of weight and bias of the neuron. MATLAB software is being used in this project to © Faculty of Electronic and Computer Engineering, FKEKK

METHODOLOGY

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regression analysis is performed to figure out how close the actual and predicted results seem to be. From Table 1 and Table 2, the number of neurons and delays for the results are set to the values, which are 10 and 2, respectively.

the best training performance with having lower MSE and higher regression value based on Table 1 at epoch 1000, i.e. (MSE=3320.1735 and R=0.97895).

Table 1 Result of regression

Table 2 Result of MSE and RMSE

Figure 1 The performance curve of the NAR model using Bayesian Regularization algorithms 4.

An approach to estimate solar radiation using the NAR model has been employed, and also the objectives during this project are successfully achieved. Models were compared supported different training algorithms— comparative analysis between the estimated of statistical analysis. Therefore, the Bayesian Regularization algorithm is the best neural network because gives better result in comparison to other two algorithms.

It was observed that the best data distribution among training, validation, and testing of the neural network was 70%, 15%, and 15%, respectively. That was because these ratios provided the minimum MSE with the best regression values. The results show in Table 1 the significance of regression, R. The R-value indicates the relationship between the expected output and the target data. If R = 1, it means that the results and targets have a perfect linear relationship. If R is close to 0, it suggests that the outputs and targets do not have a linear relationship. From Table 1 and Table 2, the validation for the NAR model is for Levenberg- Marquardt algorithm is (MSE=3489.68757, R=0.97803) found at epoch 16. Similarly, for Scaled Conjugate Gradient is (MSE= 3566.37071, R=0.97754) at epoch 92. And Bayesian Regularization is (MSE=0, RMSE=0, R=0) found at a maximum number of epochs to train. The default value is 1000. The MSE and R values for the validation set are not present when using the Bayesian Regularization training procedure. This is because validation is typically used as a type of Regularization, whereas this algorithm has its built-in form of validation. This algorithm doesn't require a validation dataset because the purpose of checking validation is to determine if the error on the validation set gets better or worse as training goes on. All statistical results in all algorithms are the best neural network, but we must choose the best neural network algorithm with lower MSE and higher regression. Bayesian Regularization has been seen as the best algorithm compared to others because more accurate. Then, the proposed learning model can exhibit excellent predictive performance with a lower RMSE. Figure 1 shows the performance curve of the NAR model trained with the Bayesian Regularization algorithm gives © Faculty of Electronic and Computer Engineering, FKEKK

CONCLUSION

ACKNOWLEDGEMENT The authors would like to thank FKEKK of Universiti Teknikal Malaysia Melaka for moral and the financial support. REFERENCES [1]

[2]

[3]

[4]

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A. Alzahrani, J.W. Kimball, C. Dagli, Predicting Solar Irradiance Using Time Series Neural Networks, Procedia Computer Science, Volume 36, 2014, Pages 623-628. Ahmad Alzahrani, Pourya Shamsi, Cihan Dagli, Mehdi Ferdowsi, Solar Irradiance Forecasting Using Deep Neural Networks, Procedia Computer Science, Volume 114,2017, Pages 304-313. Ruiz, L.G.B.; Cuéllar, M.P.; Calvo-Flores, M.D.; Jiménez, M.D.C.P. An Application of Nonlinear Autoregressive Neural Networks to Predict Energy Consumption in Public Buildings. Energies 2016, 9, 684. Mohanty, Sthitapragyan & Patra, Prashanta & Sahoo, Sudhansu. (2015). Prediction of global solar radiation using nonlinear auto regressive network with exogenous inputs (narx).

Proceedings Innovation and and Technology Technology Competition (INOTEK) 20212021, Proceedings ofofInnovation Competition (INOTEK) Melaka, Malaysia, pp. 81-82

Solar Irradiance Prediction Using Weather Forecasts by Long Short Term Memory (LSTM) Viniyta Vijayan1, HY Hwa1 1

Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia Corresponding author’s email: [email protected]

*

ABSTRACT: Solar irradiance prediction is for maximizing energy saving costs and providing high power quality in electrical power with distributed solar photovoltaic generations. The irradiance data can not be obtained because of expensive solar irradiance meter and the irradiance forecasting data are often unavailable. There are many methods is introduced that we can use to predict solar irradiance for the hourly day ahead by using weather forecasting data. This project studies a technique using Long Short Term Memory (LSTM) to predict solar irradiance.The weather parameter from Faculty of Electronic and Computer Engineering (FKEKK) will be used for Neural Network (NN) modeling.Therefore, by giving predicted weather parameter as input, the model will give solar irradiance prediction.

point, humidity and wind speed. 2.1 Network architecture and parameters selection For Neural Network (NN) modeling and analysis, MATLAB programming (R2020b) was used. There are three representative training techniques are LevenbergMarquardt(LM), Bayesian Regularization(BR), and Scaled Conjugated Gradient (SCG). The topology of the Neural Network(NN) model consists of input, hidden layer and output layer. The number of neuron in the hidden layer is increased gradually (7,10,20 and 30 hidden neuron) to give the best performance for each training algorithm used. Each topology was repeated twelve times, and 20 neurons are selected for sunny day and 30 neurons selected for a rainy day in each hidden layer, which was determined to be the optimum topology due to the lowest Root Mean Square Error (RMSE) for training. The lower the RMSE, the more accurate is the prediction. The RMSE mathematically defined as:

Keywords:Neural Network, Solar irradiance prediction Weather forecasting 1.

INTRODUCTION

1

RMSE =� ∑𝑛𝑛𝑛𝑛𝑖𝑖𝑖𝑖=1(𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦 − 𝑦𝑦𝑦𝑦�)2

Energy is an important source for human’s lives and a key factor for the development of a country. In recent times, renewable energy installation has become a big solution to the present problem since it's minimal environmental issues. Distributed photovoltaic is that the outcome of the solar irradiance which is absorbed by the PV panels. There are various factors that influence solar irradiation, including weather conditions. The past values of solar irradiance and weather data are important to model an accurate solar forecasting model. In this study, machine learning algorithms will be used for solar irradiance prediction and Long Short Term Memory(LSTM) model used the deep learning toolbox provided by MATLAB and specified various parameters and algorithms that determine learning performance. 2.

𝑛𝑛𝑛𝑛

where yi is the predicted value and 𝑦𝑦𝑦𝑦� is observed value and n is the number of days in the testing datasets.

2.2 Training, Testing and Validation sets The input and target are divided into three subsets which are 70% will be used for training ,15% will be used for validation, and 15% will be used for testing set. When generalization stops increasing, as shown by an increase in the Mean Square Error (MSE) of the validation samples, the network automatically stops training. The MSE is the average squared difference between outputs and targets. Regression (R) analysis is performed to measure the correlation between outputs and targets. The Levenberg–Marquardt (LM) algorithm is the fastest learning function because of their very good convergence behavior and provides the best results for fitting problems (nonlinear regression). Bayesian regularization is a mathematical procedure that, similar to ridge regression, converts a nonlinear regression into a "wellposed" statistical issue. It is tough to overtrain them. The scaled conjugate gradient(SCG) algorithm is based on conjugate directions, but unlike other conjugate gradient methods, this approach does not require a line search at each iteration.

METHODOLOGY

The solar irradiance datasets collect for 1 year (Oct 2019 to Nov 2020) from the weather station in the Faculty of Engineering and Computer (FKEKK), Malacca. Two different datasets have been used which are for a sunny day and rainy day 12/14/2019 (7 AM to 7 PM) and 10/24/2019 (7 AM to 7 PM). The hourly GHI data are obtained by averaging the data collected one day ahead, between 7.00 AM and 7.00 PM are considered as the duration of sunshine and true solar time. The corresponding weather data such as temperature, dew © Faculty of Electronic and Computer Engineering, FKEKK

(1)

3.

RESULT AND DISCUSSION The Table 1 and Table 2 above shows comparison

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of three algorithm performance which are LevenbergMarquardt, Bayesian Regularization and Scaled Conjugate Gradient of training, validation and testing for Regression(R), Root Mean Square Error (RMSE) and Mean Square Error (MSE), it can be seen that LevenbergMarquardt (LM) perform much better for each of the datasets for sunny and rainy day. Table 1 Comparison for three algorithm for Sunny Day

Figure 1 Best Validation Performance of LevenbergMarquardt (Sunny day)

Table 2 Comparison for three algorithm for Rainy Day 4.

In this work, by do all the comparison the implementation of the machine learning approaches in the form of Long Short Term Memory( LSTM) has been one of the most essential concepts in improving the accuracy of the approach significantly and the RMSE achieved of the prediction of the solar radiance.From the results, the Neural Network Levenberg-Marquardt was the best model for predicting the solar irradiance weather forecasts. However, the NN Bayesian Regularization, Scaled Conjugate Gradient can also be applied with minimal error.

It is discovered that the value of Regression(R) is slightly close R=1 for Sunny day using 20 no of neuron, the training is 0.90342, validation is 0.850173 and testing 0.934780, indicating that the prediction is correct and the outputs and targets have a perfect linear relationship during sunny day, while for rainy day using 30 no of neuron, the training is 0.846070, validation 0.839284 and testing is 0.814656. Hence, during rainy day the value of Regression (R) indicating that the prediction is less accurate than sunny day, but yet still achieved the prediction correct. Hence for RMSE it can be seen that LevenbergMarquardt (LM), have the lowest value for training is 18.659, validation is 19.910 and testing is 15.546 for sunny, while rainy day the training is 145.806, validation is 164.62 and testing is 168.7629 compared to Bayesian Regularization (BR) and Scaled Conjugated Gradient (SCG) algorithms which mean the lower RMSE the more accurate is the prediction. However, the LevenbergMarquardt(LM) algorithm gives the best fitting results for each performance compared to Bayesian Regularization(BR) and Scaled Conjugated Gradient(SCG). While Figure 1 shows the performance of the proposed network when trained, it is observed 573.8918 the best validation performance for sunny day at epoch 33 is obtained. The error of the training data decreases with each epoch till the 25th epoch. From the 0th to 24th epoch, the network is in underfitting state. After 25th epoch, it is seen that the error on the training data continuous decrease but the error on the test and validation data starts to increase. For Bayesian Regularization and Scaled Conjugate Gradient the data drastically decrease. © Faculty of Electronic and Computer Engineering, FKEKK

CONCLUSION

ACKNOWLEDGEMENT The authors would like to thank FKEKK of Universiti Teknikal Malaysia Melaka for the moral and financial support throughout the project. REFERENCES [1] Lee, J.; Wang, W.; Harrou, F.; Sun, Y. Reliable solar irradiance prediction using ensemble learningbased models: A comparative study. Energy Convers. Manag. 2020, 208, 112582. [2] Qing, Xiangyun & Niu, Yugang.Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM, Energy, Elsevier, 2018. [3] Kayri, Murat. (2016). Predictive Abilities of Bayesian Regularization and Levenberg– Marquardt Algorithms in Artificial Neural Networks: A Comparative Empirical Study on Social Data. Mathematical and Computational Applications. 21. 1-11.

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Proceedings of Innovation and Technology Competition (INOTEK) 2021, Proceedings of Innovation and Malaysia, Technologypp. Competition Melaka, 83-84 (INOTEK) 2021

LPG Cooking Gas Warning System via the IOT System Sathia Thauthu1, K. Osman1 1

Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia *

Corresponding author’s email: [email protected]

ABSTRACT: The ignitable mixture of organic compound gases used as fuel in heating appliances, cookery equipment, and cars are known as liquefied rock oil gas (LPG or LP gas). LPG is ready by refining petroleum or wet gas, and it entirely derived from fuel sources. In households and industries, LPG or Liquid rock oil Gas cylinders used widely. This project sends an early indication of LPG cooking gas warning signs to the phone via the IoT system. There are two unfavourable situations or accidents that can occur in LPG cylinders. First, the LPG stops without any warning. It is so common that the LPG cooking gas finishes while cooking a meal. This results in an unfinished meal. The second unfavourable scenario happens when the LPG cylinder blasts, which causes fire accidents. This is very dangerous, and this is due to gas supply leakages. The explosion of the direct "blast overpressure" can cause severe injuries. LPG Gas (hydrocarbons propane, propane, butene, and butane), which appears to damage air-filled tissues, causes injuries. The scope provides LPG leakage reports or any other gaseous substance and LPG cooking gas cylinder load/pressure. Modern resuscitation helps to save several lives across the world.

can see 200 to 10,000 ppm of liquefied petroleum gas, smoke, alcohol, propane, hydrogen, methane and carbon monoxide [4]. The Internet of Things (IoT) is the most recent innovation that individuals utilize these days. This innovation brings things within the real world to life. The Internet of Things connects to the Internet through objects such as doors, cars, refrigerators, or houses. In this way, users can access and collect connected devices anytime and anywhere [5]. A wireless LPG leakage monitoring system proposed for family safety. This system detects LPG leaks and alerts the users about the leakage by a notification. Moreover, as an emergency measure, the system shuts off the gas supply. It uses a load cell to monitor the level of LPG in the cylinder and monitors when the gas level drops below the threshold so that the user can replace the old cylinder with a new one. The equipment can provide safety and prevent suffocation and explosion caused by gas leakages [6]. For various purposes, many research and electronic designs based on discrete components have proposed. Within the same point of discrete components plan, the work in this paper suggests a complete electronic plan with a simulation of discrete components based on an accurate analogue framework. The system will monitor, alert, and determine appropriate protective measures for LPG leaks in domestic and industrial applications [7].

Keywords: Gas Sensor, IoT, Weight, leakage 1.

INTRODUCTION

Approximately 30% of LPG users in the world, of which 40% are ordinary people. Several guidelines apply to gas contamination detection systems. The modern frame is discrete, mainly used to distinguish the overflowing gas in the living room and office space [1]. Liquefied petroleum gas (LPG), also known as propane or butane, is usually stored as a liquid in a pressure cylinder, and it evaporates at room temperature. This may lead to a Leakage which will ignite and cause an explosion. Therefore, gas leak detection has attracted more interest in recent years, especially in safety, industry, environment, and emission control. Traditional gas leak systems use alarms as warnings to indicate that the leak detection system. Triggering the alarms will be more effective when there is no one on-site [2]. Before the invention of home electronic gas detectors in the 1980s and 1990s, the chemically impregnated paper used to detect the presence of gas. The paper changes its colour when it exposed to the gas. Since then, many detections have been created, such as monitoring, alerting technologies and equipment to detect significant amounts of gas escapes [3]. In this design, an MQ-2 gas sensor used to detect gas leaks. It © Faculty of Electronic and Computer Engineering, FKEKK

2.

METHODOLOGY

The design system is composed of hardware and software modules. The hardware modules consist of components and devices. Meanwhile, the software modules designed in C language. If a gas leak occurs, the gas sensor will detect the gas leak, and it automatically activates the valve and buzzer. A buzzer warns the people near the gas cylinder, and the valve will avoid the gas flow. The function load sensor is to measure the weight of the LPG cylinder with and without the gas. It will then notify the user after completion. This document introduces how to use the MQ-2 gas sensor, solenoid valve and load cell with NodeMCU 8266 to detect and protect LPG cylinders. 3.

RESULT AND DISCUSSION

The test results performed on the device for concentration of gas in the air surrounding the sensor at various locations, as shown in Table 1. The open field tests were performed close to the stove cabinet. Depending on the specified distance of the gas sensor. The lighter was activated to release the surrounding LPG 83 87

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gas. The display read was captured on the serial monitor. The test was conducted three times in order to generate an average data. Based Figure 1, the device positioned within 5 cm, according to the data observed, with a threshold value of 100 as the maximum quantity of leakage.

Blynk from their smartphone at whatever point and wherever they need. ACKNOWLEDGEMENT The authors would like to thank FKEKK of Universiti Teknikal Malaysia Melaka for moral and financial support.

Table 1: Result of MQ 2 Gas Sensor according to distance

REFERENCES [1]

[2]

Analysis Of Gas Sensor [3]

400 200 0

1 cm

2 cm Outside

3 cm

4 cm

5 cm

[4]

Inside

Figure 1 Analysis of Gas Sensor [5]

The Load sensor tested with an LPG cylinder for 23 minutes. However, the differences in the weight of LPG cylinders could only be seen after a considerable period. This result can be seen below in Figure 2, where Blynk Super Chart extracts weight details.

[6]

[7]

Figure 2: Weight of LPG Cylinder Result on Blynk 4.

CONCLUSION

In a nutshell, a liquid gas (LPG) detection and monitoring system have successfully developed. The discovery unit has effectively observed the weight of the gas barrel and recognized the gas leak within the specific zone. Moreover, physical and non-physical caution, such as the alert, is being worked appropriately concurring to the required command. In expansion, the online observing framework using the IoT Blynk platform was also effectively created. Subsequently, the client can screen gas leakage and the weight of the gas barrel in © Faculty of Electronic and Computer Engineering, FKEKK

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A. MacKer, A. K. Shukla, S. Dey, and J. Agarwal, “ARDUINO Based LPG Gas Monitoring... Automatic Cylinder Booking with Alert System,” Proc. 2nd Int. Conf. Trends Electron. Informatics, ICOEI 2018, no. Icoei, pp. 1209– 1212, 2018, doi: 10.1109/ICOEI.2018.8553840. T. Arpitha, D. Kiran, V. S. N. S. Gupta, and P. Duraiswamy, “FPGA-GSM based gas leakage detection system,” 2016 IEEE Annu. India Conf. INDICON 2016, 2017, doi: 10.1109/INDICON.2016.7838952. P. Chen, “B. D. Jolhe, P. A. Potdukhe, N. S. Gawai Jawaharlal Darda Institute of Engineering and Technology Automatic LPG Booking, Leakage Detection And Real Time Gas Measurement Monitoring System,” vol. 2, no. 3, pp. 1–10, 2013. V. Suma, R. R. Shekar, and K. A. Akshay, “Gas Leakage Detection Based on IOT,” Proc. 3rd Int. Conf. Electron. Commun. Aerosp. Technol. ICECA 2019, pp. 1312–1315, 2019, doi: 10.1109/ICECA.2019.8822055. T. S. Sitan and A. S. Ab Ghafar, “Liquefied Petroleum Gas (LPG) Leakage Detection and Monitoring System,” J. Sci. Technol., vol. 10, no. 2018, doi: 3, pp. 46–53, 10.30880/jst.2018.10.03.007. G. Loshali, R. Basera, L. Darmwal, and S. Varma, “Design & Implementation of LPG Gas Detector using GSM Module,” Int. J. Emerg. Technol., vol. 8, no. 1, pp. 98–100, 2017. H. A. Attia and H. Y. Ali, “Electronic design of liquefied petroleum gas leakage monitoring, alarm, and protection system based on discrete components,” Int. J. Appl. Eng. Res., vol. 11, no. 19, pp. 9721–9726, 2016.

Proceedings of Innovation and Technology Competition (INOTEK) 2021, Proceedings of Innovation andMalaysia, Technologypp. Competition Melaka, 85-86 (INOTEK) 2021

Implementation of Fast Fourier Transform for Image Processing Ch’ng Dick Son1, CF Khoo1 Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia

1

*Corresponding author’s email: [email protected] ABSTRACT: The aim of this paper is to present the implementation of Fast Fourier Transform for Image Processing. The optimization technique of FFT algorithm is applied in image processing in this research. The Python language is used, and simulation was done by using command prompt in the Windows OS. The outcomes of this research will be the analysis and the comparison for the processed image with few FFT implementation.

𝒌𝒌𝒌𝒌𝒂𝒂𝒂𝒂 𝒍𝒍𝒍𝒍𝒃𝒃𝒃𝒃

−𝒊𝒊𝒊𝒊𝟐𝟐𝟐𝟐𝝅𝝅𝝅𝝅( + ) 𝑵𝑵𝑵𝑵−𝟏𝟏𝟏𝟏 𝑴𝑴𝑴𝑴 𝑵𝑵𝑵𝑵 𝑭𝑭𝑭𝑭(𝒌𝒌𝒌𝒌, 𝒍𝒍𝒍𝒍) = ∑𝑴𝑴𝑴𝑴−𝟏𝟏𝟏𝟏 𝒃𝒃𝒃𝒃=𝟎𝟎𝟎𝟎 ∑𝒂𝒂𝒂𝒂=𝟎𝟎𝟎𝟎 𝒇𝒇𝒇𝒇(𝒂𝒂𝒂𝒂, 𝒃𝒃𝒃𝒃)𝒆𝒆𝒆𝒆

We also can the 2D DFT as two 1D DFT in succession

= ∑𝑴𝑴𝑴𝑴−𝟏𝟏𝟏𝟏 𝒃𝒃𝒃𝒃=𝟎𝟎𝟎𝟎 𝑃𝑃𝑃𝑃(𝑘𝑘𝑘𝑘, 𝑎𝑎𝑎𝑎)𝑒𝑒𝑒𝑒

𝑘𝑘𝑘𝑘𝑘𝑘𝑘𝑘 −𝑖𝑖𝑖𝑖2𝜋𝜋𝜋𝜋 𝑀𝑀𝑀𝑀

�

(2)

𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙

−𝑖𝑖𝑖𝑖2𝜋𝜋𝜋𝜋 𝑁𝑁𝑁𝑁 Where 𝑃𝑃𝑃𝑃(𝑘𝑘𝑘𝑘, 𝑎𝑎𝑎𝑎) = ∑𝑁𝑁𝑁𝑁−1 𝑎𝑎𝑎𝑎=0 𝑒𝑒𝑒𝑒

B. COOLEY-TUKEY FFT The Cooley-Tukey FFT are depending on the divide and conquer approach to produce an O (n log n) implementation of the DFT. From the DFT we know that the 2D DFT can be as two 1D DFT in succession. For this Cooley-Tukey FFT, we will consider the 1D DFT and set Z as the array of data. It is started by dividing the Z into odd and even vectors indexed entries and it will show the equation as below.

1.

INTRODUCTION Nowadays, image Processing has been widely used in various fields in our daily life. And the most common technique that is used for image processing are the Discrete Fourier Transform (DFT) and Fast Fourier Transform (FFT). The discrete Fourier transform can be described as the most remarkable and useful tool in signal processing and data analysis. The Fourier transform's ability to compactly capture nuances in time signals or images has greatly aided scientific innovation. The Fourier transform has been particularly useful in biological settings, assisting scientists with edge detection in cellular images, the development of hearing aids to transform audio signals, and the analysis of echocardiograms to detect heart murmurs, among many other applications. James Cooley and John Tukey's Fast Fourier transform (FFT) algorithm propelled the Fourier transform to the forefront of computing. In this project, we show a few FFT implementations such as CooleyTukey FFT, Prime Factor, Rader FFT etc. in the context of image processing, with the goal of exploring variants other than the popular Cooley-Tukey that are helpful for image data.

𝑘𝑘𝑘𝑘𝑙𝑙𝑙𝑙

−𝑖𝑖𝑖𝑖2𝜋𝜋𝜋𝜋 𝑁𝑁𝑁𝑁 𝑍𝑍𝑍𝑍(𝑘𝑘𝑘𝑘) = ∑𝑁𝑁𝑁𝑁−1 𝑎𝑎𝑎𝑎=0 𝑒𝑒𝑒𝑒 𝑁𝑁𝑁𝑁

−1

2 = ∑𝑚𝑚𝑚𝑚=0 𝑥𝑥𝑥𝑥2𝑚𝑚𝑚𝑚 𝑒𝑒𝑒𝑒 −𝑖𝑖𝑖𝑖2𝜋𝜋𝜋𝜋

𝑘𝑘𝑘𝑘∗2𝑚𝑚𝑚𝑚 𝑁𝑁𝑁𝑁

+

𝑁𝑁𝑁𝑁 −1 2

𝑘𝑘𝑘𝑘∗(2𝑚𝑚𝑚𝑚+1) −𝑖𝑖𝑖𝑖2𝜋𝜋𝜋𝜋 𝑁𝑁𝑁𝑁

𝑁𝑁𝑁𝑁 −1 2

𝑘𝑘𝑘𝑘∗2𝑚𝑚𝑚𝑚 𝑘𝑘𝑘𝑘 −𝑖𝑖𝑖𝑖2𝜋𝜋𝜋𝜋 −𝑖𝑖𝑖𝑖2𝜋𝜋𝜋𝜋 𝑁𝑁𝑁𝑁 𝑁𝑁𝑁𝑁

∑𝑚𝑚𝑚𝑚=0 𝑥𝑥𝑥𝑥2𝑚𝑚𝑚𝑚+1 𝑒𝑒𝑒𝑒 𝑁𝑁𝑁𝑁

−1

2 = ∑𝑚𝑚𝑚𝑚=0 𝑥𝑥𝑥𝑥2𝑚𝑚𝑚𝑚 𝑒𝑒𝑒𝑒 −𝑖𝑖𝑖𝑖2𝜋𝜋𝜋𝜋

∑𝑚𝑚𝑚𝑚=0 𝑥𝑥𝑥𝑥2𝑚𝑚𝑚𝑚+1 𝑒𝑒𝑒𝑒 𝑁𝑁𝑁𝑁 −1 2

= ∑𝑚𝑚𝑚𝑚=0 𝑥𝑥𝑥𝑥2𝑚𝑚𝑚𝑚 𝑒𝑒𝑒𝑒 𝑁𝑁𝑁𝑁

−1

𝑘𝑘𝑘𝑘∗2𝑚𝑚𝑚𝑚 𝑁𝑁𝑁𝑁

𝑘𝑘𝑘𝑘𝑚𝑚𝑚𝑚 −𝑖𝑖𝑖𝑖2𝜋𝜋𝜋𝜋 𝑁𝑁𝑁𝑁/2

2 ∑𝑚𝑚𝑚𝑚=0 𝑥𝑥𝑥𝑥2𝑚𝑚𝑚𝑚+1 𝑒𝑒𝑒𝑒

−𝑖𝑖𝑖𝑖2𝜋𝜋𝜋𝜋

(3)

+

𝑒𝑒𝑒𝑒

𝑘𝑘𝑘𝑘

+ 𝑒𝑒𝑒𝑒 −𝑖𝑖𝑖𝑖2𝜋𝜋𝜋𝜋𝑁𝑁𝑁𝑁 ∗

𝑘𝑘𝑘𝑘𝑚𝑚𝑚𝑚 𝑁𝑁𝑁𝑁/2

C. Split Radix This Split Radix is the variation from the CooleyTukey algorithm. It takes the odd indices in the CooleyTukey method and divides them into two parts: one with indexes of 1 and the other with indexes of 3. Overall, we divided the DFT into three sums that we can solve recursively. The computation required to manage twiddle factors is kept to a minimum with this technique.

METHODOLOGY

A. DISCRETE FOURIER TRANSFORM The discrete Fourier Transform (DFT) is used to convert vector or matrix into a frequency domain. It can be represented the original data as multiple sinusoidal functions with various aspect such as amplitudes, phases and frequencies. The DFT in image processing is a 2D DFT since the image used are models as 2D matrix, whose the value represent pixel intensities. Where k and l are pixel coordinates in the processed image, and the picture has dimensions M by N, the 2D DFT is computed as follows. © Faculty of Electronic and Computer Engineering, FKEKK

𝒌𝒌𝒌𝒌𝒂𝒂𝒂𝒂 𝒍𝒍𝒍𝒍𝒃𝒃𝒃𝒃

−𝒊𝒊𝒊𝒊𝟐𝟐𝟐𝟐𝝅𝝅𝝅𝝅� + 𝑵𝑵𝑵𝑵−𝟏𝟏𝟏𝟏 𝑴𝑴𝑴𝑴 𝑵𝑵𝑵𝑵 𝑭𝑭𝑭𝑭(𝒌𝒌𝒌𝒌, 𝒍𝒍𝒍𝒍) = ∑𝑴𝑴𝑴𝑴−𝟏𝟏𝟏𝟏 𝒃𝒃𝒃𝒃=𝟎𝟎𝟎𝟎 ∑𝒂𝒂𝒂𝒂=𝟎𝟎𝟎𝟎 𝒇𝒇𝒇𝒇(𝒂𝒂𝒂𝒂, 𝒃𝒃𝒃𝒃)𝒆𝒆𝒆𝒆

Keywords: Fast Fourier Transform (FFT); Discrete Fourier Transform (DFT); Image Processing

2.

(1)

D. Vector Radix By using the row-column method, the 2D FFT can be compute by applying the 1D FFT algorithm over the row and then columns. Alternatively, Vector-radix employs a different approach, implementing the CooleyTukey decomposition to both axes at the same time.

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Ch’ng & Khoo, 2021

𝑁𝑁𝑁𝑁

𝑁𝑁𝑁𝑁

−1

−1

2 2 ∑𝑗𝑗𝑗𝑗=0 𝑋𝑋𝑋𝑋𝑘𝑘𝑘𝑘,𝑟𝑟𝑟𝑟 = ∑𝑖𝑖𝑖𝑖=1 𝑥𝑥𝑥𝑥2𝑖𝑖𝑖𝑖,2𝑗𝑗𝑗𝑗 𝑒𝑒𝑒𝑒

𝑒𝑒𝑒𝑒

𝑖𝑖𝑖𝑖2𝜋𝜋𝜋𝜋

𝑒𝑒𝑒𝑒

𝑖𝑖𝑖𝑖2𝜋𝜋𝜋𝜋

𝑒𝑒𝑒𝑒

𝑘𝑘𝑘𝑘 𝑛𝑛𝑛𝑛

𝑁𝑁𝑁𝑁 −1 2

𝑁𝑁𝑁𝑁 −1 2

𝑖𝑖𝑖𝑖2𝜋𝜋𝜋𝜋

𝑖𝑖𝑖𝑖𝑘𝑘𝑘𝑘+𝑗𝑗𝑗𝑗𝑟𝑟𝑟𝑟 𝑁𝑁𝑁𝑁/2

+

∑𝑖𝑖𝑖𝑖=1 ∑𝑗𝑗𝑗𝑗=0 𝑥𝑥𝑥𝑥2𝑖𝑖𝑖𝑖+1,2𝑗𝑗𝑗𝑗 𝑒𝑒𝑒𝑒

𝑟𝑟𝑟𝑟 𝑖𝑖𝑖𝑖2𝜋𝜋𝜋𝜋 𝑁𝑁𝑁𝑁/2

𝑘𝑘𝑘𝑘+𝑟𝑟𝑟𝑟 𝑁𝑁𝑁𝑁/2

𝑁𝑁𝑁𝑁 −1 2

𝑁𝑁𝑁𝑁 −1 2

𝑖𝑖𝑖𝑖2𝜋𝜋𝜋𝜋

∑𝑖𝑖𝑖𝑖=1 ∑𝑗𝑗𝑗𝑗=0 𝑥𝑥𝑥𝑥2𝑖𝑖𝑖𝑖,2𝑗𝑗𝑗𝑗+1 𝑒𝑒𝑒𝑒 𝑁𝑁𝑁𝑁 −1 2

𝑁𝑁𝑁𝑁 −1 2

𝑖𝑖𝑖𝑖𝑘𝑘𝑘𝑘+𝑗𝑗𝑗𝑗𝑟𝑟𝑟𝑟 𝑁𝑁𝑁𝑁/2

Trial

+

𝑖𝑖𝑖𝑖𝑘𝑘𝑘𝑘+𝑗𝑗𝑗𝑗𝑟𝑟𝑟𝑟 𝑖𝑖𝑖𝑖2𝜋𝜋𝜋𝜋 𝑁𝑁𝑁𝑁/2

∑𝑖𝑖𝑖𝑖=1 ∑𝑗𝑗𝑗𝑗=0 𝑥𝑥𝑥𝑥2𝑖𝑖𝑖𝑖+1,2𝑗𝑗𝑗𝑗+1 𝑒𝑒𝑒𝑒

𝑖𝑖𝑖𝑖2𝜋𝜋𝜋𝜋

(4)

+

𝑖𝑖𝑖𝑖𝑘𝑘𝑘𝑘+𝑗𝑗𝑗𝑗𝑟𝑟𝑟𝑟 𝑁𝑁𝑁𝑁/2

E. Prime Factor This prime factor is another useful property for Fourier Transform which lend itself to a FFT without twiddle factors. This implementation relies on the Chinese Remainder Theorem (CRT) to construct a bijective mapping between an index n as the input and two indexes i, j as well as k as the output and two indexes k1, k2. 𝑛𝑛𝑛𝑛 = 𝑖𝑖𝑖𝑖(𝑁𝑁𝑁𝑁2 𝑛𝑛𝑛𝑛2 ) + 𝑗𝑗𝑗𝑗(𝑁𝑁𝑁𝑁1 𝑛𝑛𝑛𝑛1 ) 𝑘𝑘𝑘𝑘1 = 𝑘𝑘𝑘𝑘𝑁𝑁𝑁𝑁2 (𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 𝑛𝑛𝑛𝑛1 ) (5) 𝑘𝑘𝑘𝑘2 = 𝑘𝑘𝑘𝑘𝑁𝑁𝑁𝑁1 (𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 𝑛𝑛𝑛𝑛2 ) 𝑘𝑘𝑘𝑘 = (𝑛𝑛𝑛𝑛2 𝑘𝑘𝑘𝑘1 + 𝑛𝑛𝑛𝑛1 𝑘𝑘𝑘𝑘2 ) (mod N)

𝑋𝑋𝑋𝑋(𝑘𝑘𝑘𝑘1 , 𝑘𝑘𝑘𝑘2 ) = � ( � 𝑋𝑋𝑋𝑋(𝑖𝑖𝑖𝑖, 𝑗𝑗𝑗𝑗)𝑒𝑒𝑒𝑒 𝑗𝑗𝑗𝑗=0

𝑒𝑒𝑒𝑒

𝑖𝑖𝑖𝑖=0 𝑗𝑗𝑗𝑗𝑘𝑘𝑘𝑘 −𝑖𝑖𝑖𝑖2𝜋𝜋𝜋𝜋 2

𝑖𝑖𝑖𝑖𝑘𝑘𝑘𝑘 −𝑖𝑖𝑖𝑖2𝜋𝜋𝜋𝜋 1 𝑁𝑁𝑁𝑁1

𝑁𝑁𝑁𝑁2

(6)

).

F. Rader FFT This implementation is specialized for prime numbers. Not likely for the Cooley-Tukey FFT which exploits symmetries in the sum representation of the DFT for composite N, this FFT uses a number-theoretic approach to exploit the structure of integers modulo prime N. This allows the us to rewrite the equation as below. 𝑋𝑋𝑋𝑋(0) = ∑𝑁𝑁𝑁𝑁 𝑛𝑛𝑛𝑛=0 𝑥𝑥𝑥𝑥𝑛𝑛𝑛𝑛

Vector

Trial

Tukey

Radix

Radix

1

1.209

1.434

0.982

0.00268

2

1.198

1.325

1.015

0.00196

3

1.199

1.333

0.956

0.00199

4

1.368

1.323

1.045

0.00199

© Faculty of Electronic and Computer Engineering, FKEKK

1

0.00268

0.02231

0.00100

2

0.00062

0.00299

0.00118

3

0.00099

0.00299

0.00100

4

0.00199

0.00508

0.00100

ACKNOWLEDGEMENT The authors would like to thank FKEKK of Universiti Teknikal Malaysia Melaka for moral and the financial support. REFERENCE [1] Understanding the FFT Algorithm. Jake VanderPlas. Python Perambulations. Aug 2013. [Online]. Available:https://jakevdp.github.io/blog/2013/08/2 8/understanding-the-fft/ [2] Rich Radke. U.K. The Cooley-Tukey and GoodThomas FFTs. (Oct. 16, 2014) Accessed: May. 14, 2021. [3] Shlomo Engelberg, “Elementary Number Theory and Rader’s FFT”, SIAM Review, Vol.59, No. 3, pp.671-678, 2017. [4] Dean P. Kolba, “A Prime Factor FFT Algorithm using high speed convolution” M.S. thesis, Dept. Science, Rice University, Houston, Texas, U.S., 1977.

Table 1 FFT Implementations on sample.gif Split

numpy

CONCLUSION In a nutshell, we gained much more instinctive understanding of the DFT and FFT and how to handle both of it mathematically. More specifically, we gained a greater acknowledgement for the various symmetries of the 2D DFT and how the 2D Cooley-Tukey FFT is a simple extension from the one-dimensional case.

3. RESULT AND DISCUSSION Cooley-

Rader

4.

(7)

2𝜋𝜋𝜋𝜋𝑖𝑖𝑖𝑖 −(𝑝𝑝𝑝𝑝−𝑞𝑞𝑞𝑞)

− 𝑔𝑔𝑔𝑔 𝑋𝑋𝑋𝑋(𝑔𝑔𝑔𝑔−𝑝𝑝𝑝𝑝 ) = 𝑥𝑥𝑥𝑥0 + ∑𝑁𝑁𝑁𝑁−2 𝑞𝑞𝑞𝑞=0 𝑥𝑥𝑥𝑥𝑔𝑔𝑔𝑔𝑞𝑞𝑞𝑞 𝑒𝑒𝑒𝑒 𝑁𝑁𝑁𝑁

Prime factor

We implemented each of the O((MN)2) DFT, Cooley-Tukey FFT, Prime Factor FFT, and Rader FFT for 2D images, along with 1D implementations of the prime-factor FFT and Rader FFT. The 2D matrix was provided by running the algorithms on the sample.gif image. We also compare the FFT module with the Python’s numpy package. From the Table 1, we can observe that the Vector Radix is the quickest and the Split Radix is the slowest among 3 of them. However, when these 3 implementations were comparing again to the numpy implementation, it can say to be totally different. This is because it is implemented in the pure Python for simplicity and undergoes optimization avoiding twiddle factor. For the 1D FFT implementation, the Rader FFT took 3-4 times longer. We were particularly concerned that the PFT outperformed numpy's implementation by using unique indexing to avoid solving for "twiddle factors," which are required in Cooley-Tukey-like implementations as shown in Table 2.

Where 𝑁𝑁𝑁𝑁 = 𝑛𝑛𝑛𝑛1 𝑛𝑛𝑛𝑛2 and 𝑛𝑛𝑛𝑛1 , 𝑛𝑛𝑛𝑛2 are relatively prime. And we will get the equation below. 𝑛𝑛𝑛𝑛2 −1 𝑛𝑛𝑛𝑛1 −1

Table 2 1D FFT Implementations

numpy

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Proceedings of Innovation and Technology Competition (INOTEK) 2021, Proceedings of Innovation andMalaysia, Technologypp. Competition Melaka, 87-88, (INOTEK) 2021

Design and Development of Triboelectric Nanogenerator for Powering Small Devices Mazratul Mazida Bt Mohamed Zahari1, SL Kok1 Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia

1

Corresponding author’s email: [email protected]

*

ABSTRACT: Practical applications od triboelectric nanogenerators (TENGs) are hard for power and circuit management strategies. TENG has two jobs for powering electronic devices which are converting alternating current (AC) to direct current (DC) and boost the output power. In this paper, TENG power management by fullwave rectification has been discussed. A full-wave rectifier is a method for AC to DC conversion subtly and supported the TENG unique characters. Negative component of the input voltage to a positive voltage rectifies using full-wave rectification and then converts AC to DC utilize a diode configuration. The charge then will be store in capacitor until it fully charges. TENG technology are easy to achieved and compatible with environment. This method can become a standard power management module and can extend the application of TENG to all fields.

the two triboelectric surfaces are separated, which brought about the invention of early electrostatic turbines which include the “friction machines” and Van de Graff generator [2]. 2.

METHODOLOGY

2.1 Circuit Design Based on Figure 1, the AC source from triboelectric materials will be input for the rectifier circuit. The source then will be rectified and smooth by full wave rectifier and capacitor before stored in storage circuit.

Keywords: Alternating current; Rectification; TENG 1.

Figure 1 Full wave rectifier circuit

INTRODUCTION

A triboelectric nanogenerator is one of energy harvesting device technologies. It can convert external energy from a pair of material with different conductivity into an electricity. The material will go through a conjunction of triboelectric effect and electrostatic induction. Prof. Zhong Lin Wang’s group at Georgia Institute of Technology was the first one demonstrated this new type of nanogenerator in the year of 2012 to successfully harness the ubiquitous that normally wasted in our regular life [1]. For internal circuit power generating units, the possibility is created by a triboelectric effect with charge transfer between two materials showing opposite tribo polarities. In the outer circuit, electrons moving and flow between two electrodes mounted on the back side of the material to balance the potential. it is also called an organic nanogenerator, because the first uses organic matter to harvest energy. This new energy harvesting technology also has other advantages, such as low cost in manufacturing and fabrication, excellent durability and reliability, environmentally friendly, etc. The triboelectric nanogenerator are often applied to reap all kind energy that's available but wasted in our lifestyle, like human motion, walking, vibration, mechanical triggering, rotating tire, wind, flowing water and more. From an energy power point of view, the ones electrostatic fees represent a capacitive power tool while © Faculty of Electronic and Computer Engineering, FKEKK

The LM2596 regulator is convenient design of a step−down switching regulator (buck converter). It is capable of driving a 3.0 A load with excellent line and load regulation, as shown in Figure 2.

Figure 2 LM 2596 circuit 2.2 Financial Consideration The financial assumptions of the project which including the price electronic components, the prototype design cost. The components are brought through the electronic components shop and internet. Prices of electronic components and material for prototype design are summarized in Table 1. Table 1

87 91

No.

Component

Quantity

1 2 3

Diode Capacitor LED

4 1 1

Price per unit (RM) 0.60 0.80 0.60

Price (RM) 2.40 0.80 0.60

Proceedings of Innovation and Technology Competition (INOTEK) 2021

Zahari & Kok, 2021

4 5 7 8 9

3.

Copper tape Aluminium tape Wire LM 2596 Acrylic Sheet TOTAL

1 1

7.90 7.00

7.90 7.00

REFERENCES

1 1 2

3.80 6.90 5.90

3.80 6.90 11.80

[1]

41.20

[3]

[2]

RESULTS AND DISCUSSION

TENG usually produce high voltage output and low current. A circuit to manage this problem is chosen and designed. In Figure 3, energy harvesting circuit consists of energy harvester, rectifier circuit and storage circuit. The available energy from environment depends on its conditions, which is extracted and stored in capacitor. Thus, the load can work even when the environment energy not available. The buck regulator is used to gather more energy from harvester to load.

[4]

[5]

Figure 3 Triboelectric Nanogenerator circuit In this paper, the aluminum tape and copper tape serve as the triboelectric contact pair. Electrification occurs when they are contacted, because the aluminum tape carries positive charges on the contacting surface while the copper tape carried negative charges. When the pair separates, a potential difference occurs. This would push charges transferring between electrodes. Once the aluminum tape gets close to the copper tape, an opposite potential difference emerged, and the inductive charges move reversely. The buck converter then helps to increase current in circuit to power up device. 4.

CONCLUSIONS

Triboelectric nanogenerators utilize the coupling effect between contact electrification and electrostatic induction. It provides a practical approach in harvesting energy in several forms. ACKNOWLEDGEMENT The authors would like to thank FKEKK of Universiti Teknikal Malaysia Melaka for the moral and financial support.

© Faculty of Electronic and Computer Engineering, FKEKK

92 88

Fan, F., Tian, Z., & Wang, Z. (2012, January 20). Flexible triboelectric generator. F.A.Furfari, A history of the Van de Graaff generator Wang, A., Wu, C., Pisignano, D., Wang, Z., & Persano, L. (2017, September 12). Polymer nanogenerators: Opportunities and challenges for large‐scale applications. Zhu, G., Zhou, Y., Bai, P., Meng, X., Jing, Q., Chen, J., & Wang, Z. (2014, April 01). A Shape‐Adaptive Thin‐Film‐Based Approach for 50% High‐Efficiency Energy Generation through Micro‐Grating Sliding Electrification. Wang, Z., Jiang, T., & Xu, L. (2017, June 22). Toward the blue energy dream by triboelectric nanogenerator networks.

Proceedings of Innovation and Technology Competition (INOTEK) 2021, Proceedings of Innovation and Malaysia, Technologypp. Competition Melaka, 89-90 (INOTEK) 2021

IoT based Real-Time Water Quality Index (WQI) Monitoring System Nurul Aida Nordin1, M. Esro1, SK Subramaniam1 1

Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia Corresponding author’s email: [email protected]

*

ABSTRACT: The Water Quality Index (WQI) is a valuable that may be used as a phrase to represent the general state of water quality, assisting in the selection of the best treatment technology to address the issues. WQI, on the other hand, summaries the combined impact of a variety of water quality parameters and disseminates water quality data to the general public and policymakers. Despite the lack of an internationally recognized comprehensive water quality index, certain countries have used and continue to employ comprehensive water quality data in the development of water sources. Furthermore, the project focused on the creation of a new streamlined, worldwide known “Water Quality Index” that draws people’s attention, can be utilized universally, and can accurately indicate a trustworthy water quality situation.

using the pH sensor. The controlled node controller accesses the data retrieved from these sensors and sends it to the Thinspeak IoT platform.

Figure 1: Setup for water quality monitoring

Keywords: Internet of Things; water quality; water sensors

THE WATER CLASSES AND USES Table 1 shows the water classes and uses.

1.

INTRODUCTION

Table 1: Water Classes and Uses

The water quality index can be used to determine if river water is suitable for a variety of uses, including agriculture, aquaculture and residential water. WQI is a measure that connects a series of parameters to a common measure and combines them into a single number. WQI is one of the most useful instruments for delivering water quality feedback to decision-makers and environmentalists, as well as determining overall water quality at a certain time and location. To determine the quality of river water, various water quality indicators have been created around the world. The Malaysian rivers have clean water [1]. A river’s quality can be determined by its physical, chemical, and biological characteristics. The Malaysian Ministry of Environment divides rivers into five categories: Class I, Class II, Class III, Calss IV, and Class V. These products are categorised using the water quality index [2]. 2.

The formulas used in the calculation of WQI are: WQI = 0.22SIDO + 0.19SIBOD + 0.16SICOD + 0.16SISS + 0.15SIAN + 0.12SIpH 3.

RESULTS AND DISCUSSION

The experimental setup comprises of an MCU with a sensor network that tests the water storage tank every 10s and displays the parameters on the Arduino IDE serial display. A Wi-Fi module was utilised for real-time monitoring, which would update the Thingspeak server every 20 seconds with different parameters. A water samples from a public water source, as well as various types of water and groundwater, were analysed, as shown as Figure 2 and Figure 3.

METHODOLOGY

The introduced set-up can be used to extract data from the input configuration, such as water sample data from the sensor via nodemcu, and analyse it using the code. Sensors coupled to the nodemcu to measure various physical characteristics, such as pH and total dissolved solids, are included in the block diagram in Figure 1. Alkalinity and acidity in water samples are measured © Faculty of Electronic and Computer Engineering, FKEKK

(1)

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Proceedings of Innovation and Technology Competition (INOTEK) 2021

Nordin et al., 2021

ICSSA 2018, pp. 105–110, 2018,

Figure 2: Serial Monitor pH value

Figure 3: Suppose chart get in IoT platform 4.

CONCLUSION

The major goal of this project is to monitor the quality of water samples by creating a smart water quality monitoring environment that can detect physical and chemical properties of water using an IoT platform. The sensor’s interface with the wireless connectivity system. For the monitoring process, the reliability and feasibility of the system can be achieved by checking the water parameters. ACKNOWLEDGEMENT The authors would like to thank FKEKK, Universiti Teknikal Malaysia Melaka for the moral, facilities and financial support. REFERENCES [1]

[2]

I. Naubi, N. H. Zardari, S. M. Shirazi, N. F. B. Ibrahim, and L. Baloo, “Effectiveness of water quality index for monitoring Malaysian river water quality,” vol. 25, no. 1, pp. 231–239, 2016, O. Elijah et al., “Application of UAV and Low Power Wide Area Communication Technology for Monitoring of River Water Quality,” 2018.

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Proceedings Innovationand and Technology Technology Competition (INOTEK) 20212021, Proceedings ofofInnovation Competition (INOTEK) Melaka, Malaysia, pp. 1-2,

Analysis of Chitosan binder-based ZnO Photoanode for Dye-Sensitized Solar Cell Khairul Hareeq Haiqal Khairul Azman, Mohamad Harris Misran, Muhammad Idzdihar Idris1

Faculty of Electronic and Computer Engineering (FKEKK), Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia

1

[email protected] 2.

ABSTRACT — Dye-Sensitized Solar Cells (DSSCs) have been attracting huge attraction because of their efficiency, simple and low-cost fabrication process. In comparison to a commercially available silicon solar cell, DSSC can be produced at a lower cost per generated power. However, the efficiency rate of DSSC still not as high as commercial solar cell efficiency rate. Here we show the changes chitosan can bring for DSSC improvement. In this work, chitosan was synthesis from shrimp shell to be used for photoanode to analyze the performance of dye-sensitized solar cell. Four different techniques have been used to produce the chitosan. Scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD) were used to characterize the quality of chitosan. Samples of white powder chitosan with degree of deacetylation of 78% were obtained, as determined by FTIR spectra. Chitosan with the highest percentage of degree of deacetylation was used to fabricate a dyesensitized solar cell and achieving voltage value of 0.2783V and current value of 0.2519μA. In the second part, a simulation of DSSC using GPVDM software were done by varying the thickness of chitosan. Highest efficiency of 2.7645% with 160nm thickness layer of chitosan were obtained from this analysis. Keywords:Chitosan binder; ZnO Photoanode; DyeSensitized; Solar Cell

METHODOLOGY

Figure 1: Project flowchart 3.

RESULTS AND DISCUSSION

XRD analysis:

1. INTRODUCTION Dye-Sensitized Solar Cell (DSSC) is advancing today as one of the reassuring third-generation solar cells [1]. The dye sensitizer will absorb the incident light, and the electron transfer reaction will happen due to the light energy [2]. DSSC's efficiency can be intensified by altering or adjusting the parameter used for the solar cell. DSSC can be regarded as a multi-component system by understanding its operating principles and enhance the cell's performance. The components of DSSC were thoroughly studied, both for individually and interaction with other components. When sandwiched with a pair of glass contacts, a working electrode, dye sensitizer, electrolyte, and counter electrode are composed of a DSSC. In TiO2-based DSSCs with big bandgap energy like TiO2 (Eg ≈ 3.37eV) and higher electron mobility compared to TiO2, ZnO can resolve higher electron recombination [3]. Chitosan is a natural polysaccharide with a definite structure that is linear. Chitosan molecular chains can interconnect with each other via hydrogen bonds. Chitosan can be considered the most environmentally friendly binder, and aqueous slurries based on its excellent viscosity. Furthermore, in 2013, efficiency of 4.16% have been achieved with chitosan binder-based titanium dioxide electrode with 2.0 wt.% of chitosan [4].

XRD Spectrum for Synthesis Chitosan Intensitiy [a.u.]

25000 20000 15000 10000 5000 0

2θ(degree) Sample 1

Sample 2

Sample 3

Sample 4

Figure 2: XRD spectrum for synthesis chitosan SEM analysis:

© Faculty of Electronic and Computer Engineering,95 FKEKK

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Proceedings of Innovation and Technology Competition (INOTEK) 2021

Azman et. al., 2021

Table 3: Performance of simulated organic solar cell Type of layer ZnO layer ZnO+100nm thickness Chitosan layer ZnO+120nm thickness Chitosan layer ZnO+140nm thickness Chitosan layer ZnO+160nm thickness Chitosan layer ZnO+180nm thickness Chitosan layer ZnO+200nm thickness Chitosan layer

Sample 2

Sample 1

Sample 3

Sample 4

Figure 3: Surface morphology of synthesis chitosan FTIR analysis:

4.

Voc (V)

Jsc (Am-2) -64.9106 -45.9030

Fill factor (a.u.) 0.7698 0.7698

Efficiency (%) 3.0947 2.2302

0.7056 0.6950

0.6956

-53.3587

0.7521

2.5456

0.6941

-57.8242

0.7426

2.7070

0.6917

-60.3839

0.7297

2.7645

0.6928

-58.0733

0.7367

2.7363

0.6950

-54.9131

0.7478

2.6436

CONCLUSION

As a conclusion, synthetization of chitosan were successful. It has been characterize using XRD, SEM, FTIR and degree of deacetylation (DOD) analysis. Sample 2 resembles the highest value of DOD with 78%. For future reference, a greater quality chitosan can be obtained with a more complex method. The fabrication of DSSC show a better value in voltage and current for 6 wt.% Chi-ZnO thin film than ZnO thin film. The simulation of organic solar cell shows that ZnO with 160nm thickness chitosan layer achieving the highest efficiency of 2.7645% for chitosan add-on layer but it’s still lower than ZnO layer with 3.0947% of efficiency.

Figure 4: FTIR spectra of synthesis chitosan Degree of deacetylation analysis: Table 1: Degree of deacetylation of synthesis chitosan Samples

Degree of Deacetylation (%)

Sample 1

73%

ACKNOWLEDGEMENT

Sample 2

78%

Sample 3

77%

Sample 4

70%

Authors are grateful to Faculty of Electronic and Computer Engineering (FKEKK), Universiti Teknikal Malaysia Melaka (UTeM) for the financial support through PJP/2017/FKEKK/HI06/S10483. REFERENCES

Fabrication of dye-sensitized solar cell analysis:

[1] M. Gupta, “Energy Harvesting From Space Based,” no. February, pp. 11–15, 2016. [2] D. Wei, "Dye sensitized solar cells," Int. J. Mol. Sci., vol. 11, no. 3, pp. 1103–1113, 2010, doi: 10.3390/ijms11031103. [3] K. Sharma, V. Sharma, and S. S. Sharma, "DyeSensitized Solar Cells: Fundamentals and Current Status," Nanoscale Research Letters. 2018, doi: 10.1186/s11671-018-2760-6. [4] E. M. Jin et al., "Preparation and characterization of chitosan binder-based TiO 2 electrode for dyesensitized solar cells," Int. J. Photoenergy, vol. 2013, pp. 1–8, 2013, doi: 10.1155/2013/296314.

Table 2: Measured voltage and current of experimental DSSC Thin films

Measured voltage (V) 0.2658 0.2783

Measured current (μA) 0.2352 0.2519

ZnO thin film 6 wt.% ChiZnO thin film Simulation of organic solar cell analysis:

© Faculty of Electronic and Computer Engineering, FKEKK 96

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Proceedings of Innovation and Technology Competition (INOTEK) 2021, Proceedings of Innovation and Technology Competition (INOTEK) 2021 Melaka, Malaysia, pp. 93-94

Analysis of leaf identification performance by using a machine learning approach Nur Sharehan Yusof , Norazlina Abd Razak*

Faculty of Electronic and Computer Engineering (FKEKK), Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia *Corresponding author’s email: [email protected] ABSTRACT — This method can be used to classify plants into various taxonomies such as edible parts and leaves. It can also identify the various features of plants that are edible. In this proposed system, a statisticalbased feature is a subset of color or texture analysis. Texture analysis involves quite a several statistical techniques and mathematical techniques Finally, these statistical-based features were trained using different classifiers techniques such as SVM, KNN, Naïve Bayes, Decision Tree, Ensemble Learning, and also Discriminant. As the result, the performance of accuracy and time processing was obtained. Keywords: Leaf identification, machine learning 1.

INTRODUCTION

Plants are essential to life on Earth because they provide oxygen, food, medicine, and fue. [1]. There are around 500,000 plant species in the world, and any botanist or researcher may only know a small percentage of the entire number of known species [2]. In-plant classification, leaf recognition technology plays an important role and its main problems are whether the selected features are reliable and have a strong ability to discriminate against various types of leaves [3]. There is a programming tool that can create a framework where an image can be identified and matched with the information in the dataset by simply providing an image [4]. Due to the complexity and the nature of the leaf identification task, the performance of various classification learning algorithms is very important [5]. This proposed paper aims to compare in terms of classification performance and processing time to identify the leaf. 2.

METHODOLOGY

This project has four steps which are image acquisition image pre-processing, image segmentation, and feature extraction. 2.1 Image Acquisition The acquisition of a leaf image is the initial stage in the identifying procedure. An image of the entire plant, a leaf, a flower, a stem, or even the fruits might be used [6]. Authors in [7] the images of various leaves acquired using a digital camera with a required resolution for better quality. The image database can be created in various ways depending on the application. It can be structured as a series of predefined categories, or it can © Faculty of Electronic and Computer Engineering, FKEKK 93 97

be separated into three groups: plant photos, scan photos, and pseudo-scan photos. These photos are taken in a lab under optimal illumination. The data for this study came from various sources such as the Kaggle dataset. This dataset contains 489 images of different plant leaves. 2.2 Image Pre-processing Image pre-processing helps in reducing the noise that occurs in the image. It also helps in handling the degraded data. For this system proposed, the RGB color leaf images were converted to grayscale images. 2.3 Image Segmentation Image segmentation refers to the process of separating the entire pixel count of an image. It is used to extract the region of interest (ROI) from the overall image [6]. The purpose of image segmentation is to remove the background area of an image. Instead of using RGB images, grayscale images were used to classify images. They require less time to analyze and are more efficient. 2.4 Feature Extraction After identifying the ROI for analysis, feature extraction is performed. This step helps minimize the number of resources that are required to represent a large amount of data [6]. Hence, the right feature extraction is important to describe the image. Feature extraction can involve statistical-based features. The statistical-based feature is a subset of color or texture analysis. Texture analysis involves quite a several statistical techniques and mathematical techniques. In this project, the color which includes the mean, standard deviation, skewness, and kurtosis, and texture features which involve contrast, energy, entropy, correlation, and homogeneity have been used to represent data of the images. 2.5 Classification Learning Classification is the process of using prior knowledge to determine the class label of a new input image [6]. In this project, supervised classification techniques such as Support Vector Machine (SVM), Naïve Bayes, Nearest Neighbor Classifiers, and Ensemble have been used. 3.

RESULTS AND DISCUSSION

The proposed method is based on the Kaggle data, which contains over 489 images of four different plant species. The data includes a single leaf image. Figure 1 shows the performance of accuracy while Figure 2 shows the performance of time processing for different classifiers. Based on Table 1, the SVM technique shows

Proceedings of Innovation Nur and Technology Competition (INOTEK) 2021 et. al., 2021

the highest accuracy which is 86.9% with 2.6392 seconds for processing time compared to previous results in the literature [6] which shows 93.26% with 1.2 seconds for the SVM technique. Other than that, authors in [7] have used the KNN classifiers which give the 94.37% by using shape and edge features extraction compared to this study which gives 81.6% accuracy by using color and texture as feature extraction. Table 1 Accuracy and Time Processing for Different Appr Tre Discri Naï SV KN Ense oach e minant ve M N mble Ba yes Accur acy

78. 1%

79.8%

63. 8%

86. 9%

81. 6%

85.1 %

Time Proce ssing

4.9 742 sec

1.4525 sec

3.9 029 sec

2.6 392 sec

1.0 887 sec

6.020 7 sec

Classifiers

the given leaf species. When using the individual classifiers, the accuracy was slightly different, namely 86.9% with 2.6392 seconds for SVM, 85.1% with 6.0207 seconds for Ensemble, 81.6% with 1.0887 seconds for KNN, 79.8% with 1.4525 for Discriminant, 78.1% with 4.9742 seconds for Decision Tree and 63.8% with 3.9029 seconds for Naïve Bayes. From this obtained result, the SVM shows the highest accuracy followed by the Ensemble technique. However, in terms of time processing, the KNN technique shows less processing time compared to other as Figure 6 above. Therefore, the SVM is a good technique for this identification of leaf because has the highest accuracy and less time processing. REFERENCES [1]

[2]

[3]

[4]

[5] Figure 1 Accuracy of Different Classifiers

[6]

[7]

Figure 2 Time Processing of Different Classifiers 4.

CONCLUSION

The performance of a machine learning approach to identify leaves has been studied. The study focused on the accuracy, processing time, and features extraction for © Faculty of Electronic and Computer Engineering, FKEKK98 94

M. Murat, S. W. Chang, A. Abu, H. J. Yap, and K. T. Yong, “Automated classification of tropical shrub species: A hybrid of leaf shape and machine learning approach,” PeerJ, vol. 2017, no. 9, 2017. P. Mittal, M. Kansal, and H. K. Jhajj, “Combined classifier for plant classification and identification from leaf image based on visual attributes,” Proc. - 2nd Int. Conf. Intell. Circuits Syst. ICICS 2018, pp. 188–194, 2018. D.N. Jaan., Caldito, E.B. Dagdagan., M.G. Estanislao., B.K. Leonard.,. Jutic, B.M. Regina., Apsay, M.G. Chua., J.F. Calim., F.S. Camata., “A Leaf Recognition of Vegetables using Matlab,” Int. J. Sci. Technol. Res., vol. 5, no. 2, pp. 38-45, 2015. K. B. Lee and K. S. Hong, “An implementation of leaf recognition system using leaf vein and shape,” Int. J. Bio-Science Bio-Technology, vol. 5, no. 2, pp. 57–65, 2013. M. A. F. Azlah, L. S. Chua, F. R. Rahmad, F. I. Abdullah, and S. R. W. Alwi, “Review on techniques for plant leaf classification and recognition,” Computers, vol. 8, no. 4, 2019. S. Kaur and P. Kaur, “Plant Species Identification based on Plant Leaf Using Computer Vision and Machine Learning Techniques,” J. Multimed. Inf. Syst., vol. 6, no. 2, pp. 49–60, Jun. 2019. M. K. R. Gavhale and P. U. Gawande, “An Overview of the Research on Plant Leaves Disease detection using Image Processing Techniques,” IOSR J. Comput. Eng., vol. 16, no. 1, pp. 10–16, 2014.

Proceedings of Innovation and Technology Competition (INOTEK) 2021, Proceedings of Innovation andMalaysia, Technologypp. Competition Melaka, 95-96 (INOTEK) 2021

An Analysis of Water Contamination Level Through IoT Ahmad Shahir Zamari, Norihan Abdul Hamid

Faculty of Electronic and Computer Engineering (FKEKK), Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia *Corresponding author’s email: [email protected] ABSTRACT — For last few years, numerous contaminations of water treatment plant occur around few state in Malaysia which leaving many domestic and industrial area lack of water supply for more than a day. Therefore, tool and device to give an early warning on water contamination level is necessary. Therefore, in this paper, an alarm system, as well as a real-time database that can store pH, turbidity and temperature data was develop and analyzed. Main purpose of the project is to analyse and monitoring the quality of water through Iot technology that will convert data from the reading sensor to the Internet for cloud computing and to identify data on the basis of the pH, turbidity and water temperature reading capabilities. At the end of the paper, its show that the water contamination level through IoT system is successfully develop, beside sort of water contamination is tested and measured to validate the effectiveness of the develop system. Keywords: Water contamination; IoT 1.

Basically Figure 1, shown the whole component use in this water contamination system. pH sensor is used to determine water quality whether it is acidic or alkaline. The water is considered as alkaline when the pH reading is > 8.5, and acidic when the pH is < 6.5. Another proposed in system is turbidity, which use to analyses water transparency and the level of dissolved solids. NTU stands for Nephelometric Turbidity unit, the unit used to measure the turbidity of a fluid or the presence of suspended particles in water. If the analog voltage reading show below 2.5 V, the NTU is set to 3000. Hence 3000 is the maximum NTU value of the project. Temperature is also implemented in the system as a significant parameter that directly influenced the water quality. The temperature range of water surface is usually between 0ºC and 30ºC [1]

INTRODUCTION

Water is an essential natural resource for all human beings. However, rapid development activities and human actions contributed to of water supplies contamination. Recently, the assess of water quality is perform by operator whereby they have to collect a sample from the water source few times a day, bring to laboratory and perform a testing. Obviously, this process consumes a lot of time and energy. The embarks of Internet of Things (IoT) technologies has offer many solutions to this issue. By using IoT, the water quality can be monitor at all times beside providing real time data in cloud that can be used for research purposes at any time. Nevertheless, the system can also give an early warning on water contamination occur. 2. METHODOLOGY

Figure 2 The relationship between turbidity and voltage Figure 2 illustrated the relationship between turbidity and voltage [2]. The relationship of graph is only applicable if the sensor shows the value of 4.2 V roughly at zero turbidity which also indicate a clear water. Reading range of clear water is from 2.5 V to 4.2 V which mean 3000 to 0 turbidity. 3.

RESULTS AND DISCUSSION Table 1 Sensors reading in various type of water Type

Figure 1 Hardware use in the system

© Faculty of Electronic and Computer Engineering, FKEKK 95 99

pH

Temp (°C)

Turbidity (ntu)

Voltage (V)

Tap water

6.28

28.19

0

4.400

Orange Juice Dirt Water Rain

1.79

30.57

36.581

4.190

7.96

34.50

3000.00

2.060

5.64

28.44

0

4.380

Lake

7.55

31.75

251.349

4.130

6.9 – 9.2

26 -33

0-100

>4.2

Standard

All the sensors reading will transmit the data using Wi-Fi connection and transfer to the Blynk app as the real time monitoring. Figure 3 shows the sensors reading when it tested in the lake. Blynk app also can generated graph of the sensors data shows in Figure 4. The prototype has Proceedings of Innovation and Technology Competition (INOTEK) 2021 tested and demonstrated at Tasik Ayer Keroh in Figure 5. Ahmad et. 2021 Theal., test has been demonstrated for 15 minutes. Ahmad et. 2021 4. al.,CONCLUSION through IoT is successfully develop. The system offers an efficient and inexpensive IoT solution for real-time As a conclusion, the water contamination levelwater quality monitoring. Due develop. to the The limitation of the through IoT is successfully system offers an Movement Control Order (MCO) 3.0.5 V to 4.2 V which efficient and inexpensive IoT solution for real-time water © Faculty of Electronic and Computer Engineering, FKEKK mean to 0 turbidity. 0.5 V 4.2 Vlimitation which mean quality3000 monitoring. Due to tothe of3000 the 96 to 0 turbidity., the focus on measuring of Movement Control Orderonly (MCO) 3.0.5 V tothe 4.2quality V which water at Tasik Ayer Keroh. This project can be extended mean 3000 to 0 turbidity. 0.5 V to 4.2 V which mean 3000 into an efficient management system a local to 0 turbidity., the water focus only on measuring theof quality of area. other parameters which wasn’t scope waterMoreover, at Tasik Ayer Keroh. This project can be the extended of as management total dissolved solid,ofchemical intothis an project efficientsuch water system a local oxygen demandother andparameters dissolved which oxygen can the also be area. Moreover, wasn’t scope quantified. So, such the additional budget is required for of this project as total dissolved solid, chemical further the overall system.can Lastly, oxygen improvement demand andofdissolved oxygen also this be project support Sustainable Development Goals (SDG6) quantified. So, the additional budget is required for which is ‘Ensure of availability and Lastly, sustainable further improvement the overall system. this Figure 3 Sensors reading on Blynk App management of water and sanitation for all’ [3]. project support Sustainable Development Goals (SDG6) tested on the lake which is ‘Ensure availability and sustainable Figure 3 Sensors reading on Blynk App ACKNOWLEDGEMENT management of water and sanitation for all’ [3]. tested on the lake Authors are grateful to Faculty of Electronic and ACKNOWLEDGEMENT Computer Engineering (FKEKK), Universiti Teknikal Malaysia Melaka (UTeM)to for the of financial support Authors are grateful Faculty Electronic and through PJP/2017/FKEKK/HI06/S10483. Computer Engineering (FKEKK), Universiti Teknikal Malaysia Melaka (UTeM) for the financial support REFERENCES through PJP/2017/FKEKK/HI06/S10483.

Figure 4 Real time monitoring system for 15 minutes tested on the lake Figure 4 Real time monitoring system for 15 minutes tested on the lake

Figure 5 The prototype tested on the lake This project been conducted by lake comparing Figure 5 Thehas prototype tested on the various type of liquids with the standard value according to theThis World Healthhas Organization (WHO).byAscomparing shown in project been conducted Table several liquids arethetested suchvalue as tap water, various1,type of liquids with standard according orange juice, Health dirt water, rain and (WHO). lake. TheAsparameters to the World Organization shown in measured were pH, temperature, voltage turbidity. Table 1, several liquids are tested such and as tap water, All the sensors reading will transmit the data Wi-Fi orange juice, dirt water, rain and lake. The using parameters connection and transfer to the Blynk app asand the turbidity. real time measured were pH, temperature, voltage monitoring. Figure 3 shows the sensors reading it All the sensors reading will transmit the data usingwhen Wi-Fi tested in the lake. Blynk app also can generated graph of connection and transfer to the Blynk app as the real time the sensors data shows in Figure 4. Thereading prototype monitoring. Figure 3 shows the sensors whenhas it tested and demonstrated Tasik Keroh in Figure 5. in the lake. Blynk at app alsoAyer can generated graph of The test has data been shows demonstrated for 4. 15The minutes. the sensors in Figure prototype has tested and demonstrated at Tasik Ayer Keroh in Figure 5. 4. CONCLUSION The test has been demonstrated for 15 minutes. 4.

As a conclusion, the water contamination level CONCLUSION

100 As aofconclusion, the Computer water contamination © Faculty Electronic and Engineering,level FKEKK 96 © Faculty of Electronic and Computer Engineering, FKEKK 96

[1] Water, Sanitation, Hygiene and Health. (2017, REFERENCES February 14). Water quality and health: Review of turbidity. Retrieved May 2021,(2017, from [1] Water, Sanitation, Hygiene and28, Health. Who.intwebsite:https://www.who.int/publicatio February 14). Water quality and health: Review ns/i/item/who-fwc-wsh-17.01. of turbidity. Retrieved May 28, 2021, from [2] Mulyana, Y., and Hakim, D. L., “Prototype of Who.intwebsite:https://www.who.int/publicatio Water Turbidity Monitoring System.”, IOP ns/i/item/who-fwc-wsh-17.01. Conference Materials Science and [2] Mulyana, Y., Series: and Hakim, D. L., “Prototype of Engineering, vol. 384, pp. 012052, 2018. IOP Water Turbidity Monitoring System.”, [3] SDG6: How disclosure drives business action Conference Series: Materials Science and on water - CDP. Retrieved Engineering, vol.(2021). 384, pp. 012052,May 2018.28, 2021, website: [3] SDG6:from Howcdp.net disclosure drives business action https://www.cdp.net/en/water/SDG6on water - CDP. (2021). Retrieved May 28, disclosure-drives-business-water-action. 2021, from cdp.net website: https://www.cdp.net/en/water/SDG6disclosure-drives-business-water-action.

Proceedings of Innovation and Technology Competition (INOTEK) 2021, Proceedings of Innovation and Technology Competition Melaka, Malaysia, pp. 97-98 (INOTEK) 2021

Smart Energy Meter Based on Internet of Things (IoT) Nur Atiqah Abd Razak, Ridza Azri Ramlee*

Faculty of Electronic and Computer Engineering (FKEKK), Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia *Corresponding author’s email: [email protected] ABSTRACT — Smart Energy Meter can help the consumers to monitor billing and manage their data electric consumption easily by using the great development in the field of internet and technologies. This project also can monitor the pattern of the data energy consumption on the types of lamps. In order to give awareness to consumers for saving energy. Therefore, this project able to read the data energy consumption and manage the data in the cloud by using the Internet of Things (IoT) platform, it also can collect and analysed real-time energy consumption. A microcontroller are used to control the system is NodeMCU ESP8266 (microcontroller), current sensor, and Wi-Fi module that built in the ESP8266. ESP8266 receives the measured data from the sensor and sends calculated data to the IoT. The data can be monitor in the platform called the Blynk application. Keywords: Smart Energy Meter; IoT 1.

INTRODUCTION

based on IoT work. 2.1 Flow Chart

Figure 1 Flow chart of smart energy meter

In 2020, the demands of electrical energy for transportation, industry, and also for national, rapidly rising in Malaysia. This is due to the large population and increased demand mainly from manufacturing, domestic industries, and commercial. While electricity is an essential source of national growth, the increased electric energy has resulted in high electricity flows, which harm Malaysia municipal growth [1]. Thus, the Malaysian Government has agreed to reduce greenhouse gas emissions by up to 40% by applying the concept of sustainable energy use and development [2]. This is because fossil fuels in general are causes of contributing to carbon (CO2) and greenhouse gas levels emissions [3]. Hence, the bad impact of global warming on the increases use of fossil fuel resources. Therefore, the efficient use of energy consumption can help to secure energy sustainability for the next generations. Researchers have found that the traditional metering system is undesirable [4]. The reasons which are inaccurate, slow, expensive, and lack of flexibility as well as reliability. Therefore, the goal of the smart energy meter based on IoT is used to monitor energy consumption in kWh (kilowatt-hours). Smart Meter Energy is invented based on an IoT system which is the data of energy consumption can be read by the sensor and transfer to cloud storage. Lastly, the data can be displayed on smartphone applications.

The flow chart of Smart Energy Meter have shown in Figure 1, from the power supply the electricity will flowing through Distribution Board (DB) and distribute to each Miniature Circuit Breaker (MCB) that connected to 13A sockets. Then, the current sensor will read the energy consumption from the MCB. The data will transmit to the NodeMCU as a microcontroller to process the data which is can calculate automatically according to the coding that program by using IDE Arduino software. Next, the data that has been processed will be send out to the Wi-Fi that already built in ESP8266 and the signal of data will receive and store in the cloud. Lastly, the data will be display on the Blynk Application.

2.

Based on Figure 2 shown the formula to calculate the RMS value and to measure the peak to peak voltage, then divide it by 2 to get the peak of voltage and multiply

METHODOLOGY

In this section will be discussed on methodology about the flow chart in Figure 1 of how the Smart Energy Meter © Faculty of Electronic and Computer Engineering, FKEKK 97 101

2.2 Electrical Measurement

Figure 2 Calculation RMS of current sensor

Proceedings of Innovation andand Technology Competition (INOTEK) 2021 Nur Ridza, 2021

the peak voltage by 0.0707.

Vrms = Peak Voltage X 0.707

(1)

Irms = Vrms X sensitivity

(2)

Power = current X voltage

(3)

Energy = power X time

(4)

Otherwise, to get Irms value the Vrms need to multiply with the sensitivity value. Sensitivity value for current sensor ACS712 30A module is 66MV/A.

The formula of Ohm’s Law is to calculate the power and the Equation (3) are used. The unit of power is kW.

more energy than fluorescent and LED. The maximum value of total energy for incandescent lamp is 0.06479kWh. While, the maximum value energy consumption for fluorescent lamp is 0.01912kWh. Other than that, LED shows less energy consumption than incandescent and fluorescent lamps with the total energy is 0.01615kWh. So, the LED has consumed energy about 15.53% - 75.07% less than fluorescent and incandescent lamps in 1 hour. 3.3 Software Design (Internet of Things)

In order to calculate energy consumption the power multiply by time in hours. The energy units is in kilowatt hours (kWh).

The tariff of energy usually was calculated in 1000 unit per 1kW. The Equation (5) are used to get the price.

Tarif/hour = units consumed / hour x tariff in ringgit (5) Malaysia (MYR)

3.

RESULT AND DISCUSSION

Figure 4 Blynk application on smartphone Blynk application are used to display the data from this project. Users can check and monitor the current, power, total energy and cost of billing data through their mobile devices using internet. 4.

3.1 Types of Lamps There are 3 types of lamps that have been analyzed to come out with result of total energy consumption. The types of lamps that has been used is incandescent, fluorescent, and Light Emitting Diode (LED). The incandescent lamp is a traditional lamp consists of an electric current that heats a filament to a high temperature. Next, the fluorescent lamps use a combination of mercury vapour and electricity to produce their filaments. On the other hand, LED lamps are light-emitting diodes that come in a variety of colours and typically last up to 10 times longer than conventional incandescent lighting, which can last about 6,000 hours. Additionally, LED bulbs use much less energy than traditional bulbs. 3.2 Analysis Graph Total Energy between Types of Lamps

In conclusion, the objective of this project was successfully achieved. The project gave great knowledge of processes, implementing and testing a system that involved several hardware and software components. The NodeMCU ESP8266 and current sensor ACS712 as a control system of this project were chosen due to their low cost and easy to configure. However, the future work is to improve the sensor accuracy that the energy reading become more precise. REFERENCES [1]

[2]

[3] .

[4]

Figure 3 Comparing total energy between types of lamps Based on Figure 3, shows the analysis 3 types of lamps. The graph shows the incandescent lamps consume © Faculty of Electronic and Computer Engineering, FKEKK 102 98

CONCLUSION

J. B. Shukla, M. Verma, and A. K. Misra, “Effect of global warming on sea level rise: A modeling study,” Ecological Complexity, vol. 32, pp. 99110, 2017. S. Shofirun, S. Ali, M. Rizal, and A. Awang, “Tren Penggunaan Tenaga Elektrik dan Pembebasan Gas Rumah Hijau di Malaysia,” vol. 3, pp. 39–45, 2019. T. Partridge, M. Thomas, B. Harthorn, N. Pidgeon, A. Hasell, L. Stevenson, C. Enders, “Seeing futures now: Emergent US and UK views on shale development, climate change and energy systems,” Global Environmental Change, vol. 42, pp. 1–12, 2017. P. Vadda and S. Murthy Seelam, “Smart Metering for Smart Electricity Consumption,” Master Thesis, pp. 1–62, 2013.

Proceedings of Innovation and Technology Competition (INOTEK) 2021, Proceedings of Innovation andMalaysia, Technologypp. Competition Melaka, 99-100 (INOTEK) 2021

Development of Optimal PID Controller for Wastewater Treatment Plants Sia Yok Kit, Sharatul Izah Samsudin*

Faculty of Electronic and Computer Engineering (FKEKK), Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia *Corresponding author’s email: [email protected] ABSTRACT — The aim of this paper is to present a development of optimal PID controller for a wastewater treatment plant. The optimization technique of PSO algorithm is applied in PID controller to find the best optimal parameters for wastewater treatment plant (WWTP) in this research. The MATLAB/SIMULINK software is used to perform the simulation. The outcomes of this research will be the analysis the system performances of the optimal nonlinear PID for the wastewater treatment plants and the output will be only observed and analyzed in the time domain. Keywords: PID Controller; Wastewater; Treatment plant 1. INTRODUCTION In Wastewater Treatment Plant (WWTP), the main treatments include secondary treatment which is the Activated Sludge Process (ASP). This stage process treatment is a biological process for maintaining the concentrations of substrate(S) and dissolve oxygen (DO) for treating the wastewater. Nowadays, demand of people on clean water have become increased, an optimal control design of WWTP become stricter and challenging. The standard form PID controller generates its control action according to the error by the given equation is u(t) = 𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾(𝑡𝑡𝑡𝑡) + 𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾 ∫ 𝐾𝐾𝐾𝐾(𝑡𝑡𝑡𝑡)𝑑𝑑𝑑𝑑𝑡𝑡𝑡𝑡 + 𝐾𝐾𝐾𝐾𝑑𝑑𝑑𝑑

𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑(𝑡𝑡𝑡𝑡) 𝑑𝑑𝑑𝑑𝑡𝑡𝑡𝑡

The transfer function of the PID controller is H(s) = 𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾 +

𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾 𝑠𝑠𝑠𝑠

+ 𝐾𝐾𝐾𝐾𝑑𝑑𝑑𝑑 𝑠𝑠𝑠𝑠

2. METHODOLOGY 2.1 Modeling of WWTP (Activated Sludge Process)

Figure 1 Plant Modeling of WWTP The modeling of plant consists of two inputs (dilution rate and air flow rate) and two outputs (substrate and dissolve oxygen). The model was derived based on component mass balance equation yields a set non linear differential equations include biomass, X(t); substrate, S(t); dissolve oxygen, C(t) and recycled biomass, X r(t) [1]:

(1)

𝑋𝑋𝑋𝑋𝑋𝑋(𝑡𝑡𝑡𝑡) = 𝜇𝜇𝜇𝜇(𝑡𝑡𝑡𝑡)𝑋𝑋𝑋𝑋(𝑡𝑡𝑡𝑡) − 𝐷𝐷𝐷𝐷(𝑡𝑡𝑡𝑡)(1 + 𝑟𝑟𝑟𝑟)𝑋𝑋𝑋𝑋(𝑡𝑡𝑡𝑡) + 𝑟𝑟𝑟𝑟𝐷𝐷𝐷𝐷(𝑡𝑡𝑡𝑡)𝑋𝑋𝑋𝑋𝑟𝑟𝑟𝑟(𝑡𝑡𝑡𝑡) μ(t) 𝑆𝑆𝑆𝑆𝑋𝑋(𝑡𝑡𝑡𝑡) = 𝑋𝑋𝑋𝑋(𝑡𝑡𝑡𝑡) − 𝐷𝐷𝐷𝐷(𝑡𝑡𝑡𝑡)(1 + 𝑟𝑟𝑟𝑟)𝑆𝑆𝑆𝑆(𝑡𝑡𝑡𝑡) + 𝐷𝐷𝐷𝐷(𝑡𝑡𝑡𝑡)𝑆𝑆𝑆𝑆𝐾𝐾𝐾𝐾𝑆𝑆𝑆𝑆

(2)

2.2 Non Linear Gain Function

Where Kp, Ki, Kd are the gains of PID respectively. The proportional is used to increase the system response, the integral reduce the steady-state error and the derivative part improves the system stability. The linear PID controller are useful for controlling a normal physical process and can achieve the desired operating condition. However, WWTP is a multivariable process and it is a strongly nonlinear system due to involves in control the numbers of parameters. The linear PID controller faces with unstable factors to control the nonlinear system. Therefore, the PSO algorithm will be applied to optimize the control parameter of the controller and it enhances the classical PID parameter tuning techniques such as Ziegler-Nichols. By tuning of nonlinear PID controller gain using the PSO algorithm, the tracking response of substrate level and dissolve oxygen is well improved hence result a better treated water. © Faculty of Electronic and Computer Engineering, FKEKK 99 103

Y

𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾(t)

𝑋𝑋𝑋𝑋(𝑡𝑡𝑡𝑡) − 𝐷𝐷𝐷𝐷(𝑡𝑡𝑡𝑡)(1 + 𝑟𝑟𝑟𝑟)𝐶𝐶𝐶𝐶(𝑡𝑡𝑡𝑡)+ 𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾 𝐶𝐶𝐶𝐶𝑋𝑋(𝑡𝑡𝑡𝑡𝑡 = − Y (𝐶𝐶𝐶𝐶𝑠𝑠𝑠𝑠 − 𝐶𝐶𝐶𝐶(𝑡𝑡𝑡𝑡)) + 𝐷𝐷𝐷𝐷(𝑡𝑡𝑡𝑡)𝐶𝐶𝐶𝐶𝐾𝐾𝐾𝐾n 𝑋𝑋𝑋𝑋𝑟𝑟𝑟𝑟𝑋𝑋 (𝑡𝑡𝑡𝑡) = 𝐷𝐷𝐷𝐷(𝑡𝑡𝑡𝑡)(1 + 𝑟𝑟𝑟𝑟)𝑋𝑋𝑋𝑋(𝑡𝑡𝑡𝑡) − 𝐷𝐷𝐷𝐷(𝑡𝑡𝑡𝑡)(𝛽𝛽𝛽𝛽 + 𝑟𝑟𝑟𝑟)𝑋𝑋𝑋𝑋𝑟𝑟𝑟𝑟(𝑡𝑡𝑡𝑡) (3)

The enhanced nonlinear PID (NPID) is proposed in this research paper. The design of the NPID has a linear Kp, Ki, Kd gains of the PID controller is cascaded to a bounded nonlinear gain function with a non linear gain (Kn). A non linear gain function will be used to the PID controller are obtain from the established paper which can be described as [2]: exp(𝑤𝑤𝑤𝑤𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾 )+exp (−𝑤𝑤𝑤𝑤𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾 )

𝐾𝐾𝐾𝐾𝑆𝑆𝑆𝑆𝐾𝐾𝐾𝐾(𝐾𝐾𝐾𝐾) = 𝑐𝑐𝑐𝑐𝑐(𝑤𝑤𝑤𝑤𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾)= 2 𝐾𝐾𝐾𝐾 |𝐾𝐾𝐾𝐾| ≤ 𝐾𝐾𝐾𝐾𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 𝐾𝐾𝐾𝐾 = � � 𝐾𝐾𝐾𝐾𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑠𝑠𝑠𝑠𝑒𝑒𝑒𝑒𝑆𝑆𝑆𝑆(𝐾𝐾𝐾𝐾) |𝐾𝐾𝐾𝐾| > 𝐾𝐾𝐾𝐾𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒

(4)

where 𝐾𝐾𝐾𝐾 = 1, 2, 3 and 𝑤𝑤𝑤𝑤𝐾𝐾𝐾𝐾 and 𝐾𝐾𝐾𝐾𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 are user-defined positive constants. The nonlinear gain 𝐾𝐾𝐾𝐾𝑆𝑆𝑆𝑆(𝐾𝐾𝐾𝐾) is lower bounded by 𝐾𝐾𝐾𝐾𝑆𝑆𝑆𝑆(𝐾𝐾𝐾𝐾)min = 1 when e = 0, and upperbounded by 𝐾𝐾𝐾𝐾𝑆𝑆𝑆𝑆(𝐾𝐾𝐾𝐾)max = ch (𝑤𝑤𝑤𝑤𝐾𝐾𝐾𝐾 𝐾𝐾𝐾𝐾𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒). Therefore,

Proceedings of Innovation and Technology Competition (INOTEK) 2021

Sia and Sharatul, 2021

𝐾𝐾𝐾𝐾𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 stand for the range of variation, and 𝑤𝑤𝑤𝑤𝐾𝐾𝐾𝐾 describes the rate of variation of 𝐾𝐾𝐾𝐾𝑆𝑆𝑆𝑆(𝐾𝐾𝐾𝐾).

3.

PSO ALGORITHM

The PSO optimization algorithm is developed into the NPID controller. The tuning of NPID gain is done by using PSO. All parameters include position, velocity and acceleration will be used in PSO algorithm will be initialized randomly at the beginning. During particles initialization, the fitness of each parameter is evaluated and the data will import to the MATLAB workspace. Next, the PSO will start its search in finding optimum NPID gain parameters. Then fitness function will be evaluated to find the particles best (P best) and global best (G best) by comparing the previous P best and G best. Next, the new velocity and position will be updated. The evaluation will repeat all the steps and will stop until the criteria met when there is no improvement observed over the number of iteration or when the number of maximum iteration is reached. 4.

PERFORMANCE INDEX

Rise

Overs

Setting

Time

hoot

Time

PID

0.113

0.01

PSO-PID 2

10.3e

PSO-PID 3

2.6e

(6)

RESULTS AND DISCUSSION

IAE

ISE

1.53

3.077

15.8

0.00

18.7e

0.017

2.59

0.00

4.7e

0.018

2.3

Table 2 Optimize Gains

PSO-PID 3

Kn1

K1

Kp

Ki

Kd

3.05

5.36

6.75

2.5

2.2665

Table 3 Transient Response of Output DO (e=×10−5 ) Rise

Over

Setting

Time

shoot

Time

PID

0.297

0.03

PSO-ID2

38.4e

PSO-ID3

1.6e

(5)

ISE behavior that provides a better tracking performance and suitable for analytical and computational purpose and IAE provides good response and useful for computer simulation studies. The system will have a better performance as the error is smaller. 5.

Table 1 Transient Response of Output Substrate (e=×10−8 )

Controller

Performance index will indicate the effectiveness of the system performances. The objective function will be chosen in this research are Integral Square Error (ISE) and Integral Absolute Error (IAE) [3]. 𝑇𝑇𝑇𝑇 IAE = ∫0 |𝐾𝐾𝐾𝐾(𝑡𝑡𝑡𝑡)|𝑑𝑑𝑑𝑑𝑡𝑡𝑡𝑡 𝑇𝑇𝑇𝑇 ISE= ∫0 𝐾𝐾𝐾𝐾 2 (𝑡𝑡𝑡𝑡)𝑑𝑑𝑑𝑑𝑡𝑡𝑡𝑡

substrate and dissolve oxygen has the lowest IAE and ISE error. The rise time fastest and the overshoot lowest among other PID. The overshoot value for PSO nearest to the zero after optimize the gain of non linear kn and the gains of PID control.

IAE

ISE

0.512

67.3

1214

0.00

68.4e

143

38.64

0.00

2.85e

56.7

41.51

Table 4 Optimize Gains

PSO-PID 3

Kn2

K2

Kp

Ki

Kd

1.98

0.74

6.15

6.34

0.0669

4.

CONCLUSION PSO algorithm is the optimization technique that has been proposed in this research. By using this algorithm, the optimal gains parameter of the nonlinear PID will be obtained. In the result of the simulation, the IAE and ISE are obtained lowest error and the transient responses can be analyzed. The optimal parameters are tuned in the simulation rather than in real plant to avoid the risk of damage. REFERENCES [1]

Figure 2 Comparison PSO-PID Input/Output Substrate (mg/L) and Dissolve Oxygen (mg/L) There have differences number of iteration and particles has been used in PSO algorithm to evaluate to optimal parameter of NPID which are PID 2 (p=20, i=5), PID 3 (p=100, i=25) in this research. The input value of substrate and dissolve oxygen is set 41.2348mg/L and 6.1148mg/L respectively. Based on the result, the PSO-PID with highest number of iteration and particles (PID 3) for both transient responses of the © Faculty of Electronic and Computer Engineering, FKEKK 104 100

[2]

[3]

N.A. Selamat, N.A. Wahab, and S. Sahlan, “Particle Swarm Optimization for multivariable PID controller tuning,” Proc. - 2013 IEEE 9th Int. Colloq. Signal Process. its Appl. CSPA vol. 2013, no. 1, pp. 170–175, 2013. M.A. Shamseldin, M.A.A. Ghany, and A.M.A. Ghany, “Performance study of enhanced nonlinear PID control applied on brushless DC motor,” Int. J. Power Electron. Drive Syst., vol. 9, no. 2, pp. 536–545, 2018. M.I. Solihin, L.F. Tack, and M.L. Kean, “Tuning of PID Controller Using Particle Swarm Optimization (PSO),” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 1, no. 4, pp. 458, 2011.

Proceedings Innovationand and Technology Technology Competition (INOTEK) 20212021, Proceedings ofofInnovation Competition (INOTEK) Melaka, Malaysia, pp. 101-102

The Design of Wireless Power Transfer for Drone Charging Low Kuan Yik, Yusmarnita Yusop

Faculty of Electronic and Computer Engineering (FKEKK), Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia *Corresponding author’s email: [email protected] ABSTRACT — This paper focus on the design of a wireless power transfer system based on class D CLL topology. Wireless Power Transfer (WPT) system is the communication of power source and electrical load without any medium such as cables. The method used in this paper is Inductive Power Transfer (IPT) due to the electromagnetic field and inductive coupling which is not affected by dust. The class D converter is used in this project because of the lower losses in power consumption and it can increase the efficiency to 100% theoretically. The simulation and calculation of class D converter are included in this paper. Keywords: WPT; Drone; Charging 1.

INTRODUCTION

Wireless Power Transfer (WPT) has become a hot topic that most people are interested in. The concept of wireless transmission is carried out by Nikola Tesla. One of the basic concepts for wireless power transfer to work is according to Faraday’s Law of induction which can produce AC current when the changes of the magnetic field. WPT enables the power to supply through an air gap without any transmission medium [1]. The advantages of this technology caused it can be used in many technologies. With the development of drones to this day, battery life is still one of the factors affecting flight time and user comfort. Thus, the used of WPT in drone charging is very important to increase the use efficiency. The types of WPT system are listed in Figure 1. Among these types, IPT is selected as electromagnetic field is used in IPT. The concept of IPT is based on mutual inductance. When two conductors are configured to induce a voltage at both ends of the wire through the current change of wire, they are known as inductive coupling or also known as electromagnetic coupling.

2.

METHODOLOGY

2.1 Design of Class D inverter The inverter used in this project is known as class D CLL resonant inverter. A class D inverter’s operating principle is actually quite simple, but it is extremely effective [3]. To design a inverter, the output power is set at 10W and the operating frequency is 100kHz. The DC input is set to 24V, load resistor is 150 Ω and quality factor is assumed as 10. The calculation of capacitor, inductor, output voltage and output current are listed below: 𝑄𝑄𝑄𝑄𝐿𝐿𝐿𝐿

𝐶𝐶𝐶𝐶 =

𝑤𝑤𝑤𝑤𝑂𝑂𝑂𝑂 Ri 𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖

𝐿𝐿𝐿𝐿 =

𝑤𝑤𝑤𝑤𝑂𝑂𝑂𝑂 𝑄𝑄𝑄𝑄𝐿𝐿𝐿𝐿

=

=

𝐿𝐿𝐿𝐿1 = 𝐿𝐿𝐿𝐿2 =

10

2𝜋𝜋𝜋𝜋(100𝑘𝑘𝑘𝑘𝑘𝑘𝑘𝑘𝑘𝑘𝑘𝑘)(150Ω) 150Ω

2𝜋𝜋𝜋𝜋(100𝑘𝑘𝑘𝑘𝑘𝑘𝑘𝑘𝑘𝑘𝑘𝑘)(10) 𝐿𝐿𝐿𝐿 23.87𝑢𝑢𝑢𝑢𝑘𝑘𝑘𝑘 1 𝐴𝐴𝐴𝐴

1+

=

1 1

1+

= 106.1𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛

= 23.87𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢

= 11.94𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢

𝑉𝑉𝑉𝑉𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 = �𝑃𝑃𝑃𝑃𝑜𝑜𝑜𝑜 R i = �10𝑊𝑊𝑊𝑊(150Ω) = 38.73𝑉𝑉𝑉𝑉

𝐼𝐼𝐼𝐼𝑜𝑜𝑜𝑜 =

𝑉𝑉𝑉𝑉𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 Ri

=

38.73𝑉𝑉𝑉𝑉 150Ω

= 0.258𝐴𝐴𝐴𝐴

© Faculty

of Electronic and Computer Engineering, FKEKK 105 101

(2) (3) (4) (5)

2.2 Design of Full Bridge Rectifier Assume the voltage requirement for a drone is 40V. By using Watt’s law, output current can calculate: 𝐼𝐼𝐼𝐼 =

𝐼𝐼𝐼𝐼 =

𝑃𝑃𝑃𝑃

(6)

𝑉𝑉𝑉𝑉 10𝑊𝑊𝑊𝑊

(7)

40𝑉𝑉𝑉𝑉

(8)

𝐼𝐼𝐼𝐼 = 0.25𝐴𝐴𝐴𝐴

2.3 PSPICE simulation

Figure 2 Circuit for Class D CLL inverter

Figure 1 Categories of WPT

(1)

Proceedings of Innovation and Technology Competition (INOTEK) 2021

Lau and Yusmarnita, 2021

Figure 5 Sample view for two coupled coils Figure 3 Circuit for Full Bridge Rectifier 2.4 ANSYS Maxwell

Figure 6 Graph Coupling Coefficient vs Spacing REFERENCES [1] Figure 4 Maxwell 3D design of coupled coils 3.

RESULTS & DISCUSSION

As the result, the output voltage is converted from DC to AC before transfer through coupled coils where the transferred voltage has been converted back to DC after passing through the rectifier. The simulation output power is calculated using Watt’s Law which get 𝑃𝑃𝑃𝑃 = 40.35 ( 269𝑚𝑚𝑚𝑚) . Thus, 𝑃𝑃𝑃𝑃 = 10.785𝑊𝑊𝑊𝑊 . The simulation result is almost same as calculated result which is 10W. Figure 5 is an example view which shows how the magnetic field flow between two coupled coils. Next, Figure 6 shows the relationship between coupling coefficient and distance between two coupled coils. We observed that the farther the distance between two coils, the lower the coupling coefficient. 4.

CONCLUSION

As a conclusion, the design of wireless power transfer system based on class D CLL topology has simulated. But the simulation results are slightly difference with the expected results. Nevertheless, the result still shows that the class D CLL topology can be used for wireless power transfer system. Other than that, the rectangular spiral shaped of charging coil has drawn by using ANSYS Maxwell 3D software. The difference of distance between coil are clearly seen in the results. The farther the distance between two coils, the weaker the magnetic field. The circuit using ANSYS Simplorer has also been drawn but it failed to obtain the same data as the expected result.

© Faculty of Electronic and Computer Engineering, FKEKK 106 102

[2]

[3]

[4] [5]

A. Abdolkhani, “Fundamentals of Inductively Coupled Wireless Power Transfer Systems,”, Wireless Power Transfer - Fundamentals and Technologies, InTech, pp. 3-25, 2016. “All About a Multirotor FPV Drone Battery Charger | GetFPV Learn.” https://www.getfpv.com/learn/new-to-fpv/allabout-multirotor-fpv-drone-battery-charger/ (accessed May 29, 2021). “Class-D Sinewave Inverter Circuit | Homemade Circuit Projects.” https://www.homemadecircuits.com/sinewave-inverter-using-class-damplifier-circuit/ (accessed May 29, 2021). J. V. Joseph Maldonado, “Class-D Power,”, pp. 1-26, 2010. F. Gregorio, G. González, C. Schmidt, and J. Cousseau, “Practical Approaches for RF Impairments Reduction”, Signal Processing Techniques for Power Efficient Wireless Communication Systems, pp. 73-104, 2020.

Proceedings of Innovation and Technology Competition (INOTEK) 2021, Proceedings of Innovation and Technology Melaka, Malaysia, pp.Competition 103-104, (INOTEK) 2021

Design Improvement of an IoT Visitor Counter for Smart Building L. M. Heng1, Z. A. Mutalip1* 1

Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia Corresponding author’s email: [email protected]

*

ABSTRACT: Determining the numbers of person or visitors has been a difficulty for building management in order to ensure the presence and safety of those during critical or emergency time. In this project, a design improvement of an IOT Visitor Counter for smart building was conducted. Object tracking and detection was implemented in according to the machine learning model trained to calculate the visitor flows. The core achievement of this project was the detection of multiple people in a frame. Finally, the model was utilized and run in Raspberry Pi 4 to perform the detection and counting.

detection boxes precision, detection boxes recall, regularization loss etc. After the model done training, it was exported to save_model.pb and ready to be test with picture and video on the accuracy detection of the model. The model was then converted into model.tflite which are more suitable for android type to be used in Raspberry Pi 4. 3.

For this project, in order to ensure the accuracy and stability of the detection, an accuracy test has been carried out with the detection of only 1 person, 2 person and 3 persons in an image. Besides the detection, the confidence level of the model on the detection has also been recorded. Confidence level is the percentage chance that an object is detected based on the classes set. In this accuracy test, a total of 483 which categorized as 1person, 2 person and 3 person testing images were tested.

Keywords: visitor counter; object detection; object tracking. 1.

INTRODUCTION

Due to high amount of visitor flows on places such as malls, restaurants, libraries etc., a visitor counter is needed to calculate and analyse those frequently visited places. Besides, visitor counter can ensure the correct number of visitors during emergency. Based on [1], ultrasonic sensor was proposed in detecting visitors, but the accuracy drops when more than one person passes through the sensor. Same with [1], Infrared sensor was proposed as the medium in detecting person in [2]. From previous work, infrared sensor was used, and the accuracy is not satisfied for the counting of the visitor. Therefore, on design improvement of an IOT visitor counter for smart building are conducted by developing visitor counter for multiple access point integrating with Internet of Thing while analysing multiple visitor entry. In this project, camera was chosen as the medium to detect person implemented with machine learning. 2.

Table 1 Images categorized to number of persons. No of person Total 1 person 381 2 persons 85 3 persons 17 Table 2 Accuracy test of the detection of the model.

Confidence Level (%)

Detection >90 >80 >70 >60 >50

FORMAT

True 298 21 2 60 -

1

Number of person in image 2 3 False True False True False 141 3 25 4 21 11 2 2 2 2 1 1

Based on the test, there was no false detection for 1 person, instead there is 2 false detections in the 2-person image where one of the images detected 3 persons while the other detects 1 person. For the 3-person image detection, there are 4 errors where 3 images detect only 2 persons while 1 detects 1 person. For the model to do undergo counting part, object detection and object tracking algorithm are needed to achieve it. From the figure show below, is the window showing a counting part of a recorded video. The yellow line in the center act as the point to determine whether the person is going in or out. When there are person entering, the up counter will increase and vice versa.

For the starting, custom tensorflow 2 object detector model were obtained from [3]. then, the model was trained using Tensorflow with own custom overhead of person dataset based on the step listed in [3]. The model used is mobilenet SSD (single shot detection) v2 320x320 which works perfectly for camera with resolution of 640x480. Before training, a virtual environment was required to create using anaconda with the required library installed in it. Then the dataset collected were converted into xml file using labelimg software by labelling the person in every picture. During training, tensorboard was used to monitor training and visualize the training metrics such as © Faculty of Electronic and Computer Engineering, FKEKK

RESULTS AND DISCUSSION

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simplification of object tracking process includes the process of detecting the initial object, then a special ID is allocated for each of the detected object. The object is then tracked frame by frame as they move around in the video while preserving the ID initially assigned to them. 4.

In summary, a visitor counter involving the object detection and tracking are not suitable to be implemented in the Raspberry Pi as the processor are high enough to allow it to be carried out via real time. The problem encountered throughout this project is that suitable dataset is not available in the beginning and need to collect dataset with own method. Besides, during training model process, the training process using CPU are too slow, therefore CPU and GPU are utilized simultaneously to train the model. From this project, improvement can be made by implementing the model on suitable device with high processor to ensure it runs smoothly. The increment in dataset no matter in number or variety position and number of people in an image provides better training result for the model. Lastly, the supremacy of this project is in its object detection where it could detect more than two or even 3 if enough dataset were provided.

Figure 2 Object detection and tracking of recorded video.

ACKNOWLEDGEMENT

Figure 3 Model assigning ID to the person and tracking it.

Sincere gratitude to Universiti Teknikal Malaysia Melaka for the facilities supported and supervisor for her patience and guidance throughout this project.

From the recorded video, the model able to detect and track the person entering and exiting the premise. The counter for entering and exiting are shown in the bottom left corner or the window frame show as figure 2 and 1. The data of the counter was then writing into a csv file in raspberry pi after the video was processed.

REFERENCES [1]

[2]

[3]

Figure 4 Counter wrote into a csv file in raspberry.

The information in csv includes the time and counter up and down. The counter was updated for every time there is a change in up or down counter along with the time it was updated. However, the model has low fps when running the video as it requires high processor. The object tracking may sound easy, but the algorithm behind it requires a high amount of process running. This causes the video recorded tracked very slow at around 9fps. The © Faculty of Electronic and Computer Engineering, FKEKK

CONCLUSION

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Carvalho, Bruno & Silva, Caio & Silva, Alessandra & Buiati, Fábio & de Sousa Junior, Rafael, “Evaluation of an Arduino-based IoT Person Counter”, 2016. Y. Vivekananth, R. Kalpana, G. Malarvizhi, P. Mounika, and S. Muniyappan, “Bidirectional Visitor Counter Using IoT”, International Journal of Innovative Research in Computer and Communication Engineering, vol. 5, no. 3, pp. 4952-4956, 2017. Armaanpriyadarshan/training-a-CustomTensorFlow-2.X-Object-Detector. (n.d.). [Online]. Available https://github.com/armaanpriyadarshan/Trainin g-a-Custom-TensorFlow-2.X-Object-Detector

Proceedings of Innovation and Technology Competition (INOTEK) 2021, Proceedings of Innovation andMalaysia, Technology Melaka, pp.Competition 105-106, (INOTEK) 2021

Analysis of Graphene Oxide using Hummer's Method, Counter Electrode for Dye-Sensitized Solar Cell Z. A. F. M. Napiah1*, M. I. M. Azhar1, M. I. Idris1 1

Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia Corresponding author’s email: [email protected]

*

ABSTRACT: Instead of using simply the chemical process, this work uses a combination of chemical and mechanical techniques to produce graphene oxide. In addition to using chemical reagents, mechanical sonication was used to help exfoliate graphene oxide (GO). The actions of sonication cause GO to be synthesized by exfoliating the graphite layer and simultaneously exposing the layer to an oxidizing agent, in this case potassium permanganate (KMn O4 ).The properties and characterization of GO was using XRD and UV Visible. The important characteristics peak was observed at 11.6° for XRD and the spectrum of GO has an absorption peak at 240 nm. The GO then coated at ITO substrate to obtain GO films and act as counter electrode (CE) for DSSC (Dye sensitized solar cell).

including low cost, wide scale, and easy operation. [3]. METHODOLOGY 2.1 Chemical and materials Graphite with purity 99%, sulfuric acid (H2 SO4, 98% purity), Potassium permanganate (KMn O4 ) and hydrochloric acid (97% purity) purchase from Chemiz company. Hydrogen peroxide (30% purity) from Merck. 2.2 Preparation of Graphene oxide The modified Hummer's method was used to make graphene oxide since it is one of the most environmentally friendly methods due to the removal of the carcinogenic chemical sodium nitrate (NaNO3) [4]. Usually, 5g graphite powder was combined with 200 ml (H2 SO4 ) in a beaker and stirred for 1 hour in an ice bath. Then 30 g of (KMn O4 ) was gradually added, and stirring was continued for another 24 hours to complete the oxidation reaction. To homogenize the solution and make it appear dark brown, it was stirred for 2 hours. After that, 100 mL distilled water was gradually added, and the solution was sonicated at 30°C for more than 4 hours. The liquid was heated at 90° for 1 hour after the sonication technique, then 250ml distilled water was added and stirred. To stop the process, 30 ml hydrogen peroxide was added. Adjust pH to 7 by washing the mixture of 1M HCL and distilled water. Brown pastry material of Graphene oxide was obtained. After that dry in oven overnight at 60°c.

Keywords: Graphene oxide (GO); Counter electrode (CE); Dye sensitized solar cell (DSSC) INTRODUCTION Nowadays, the alternative to implement a clean energy sources or renewable energy is necessary for the sustainable development. The dye-sensitized solar cells (DSSCs), which are the third generation of solar cells, most researchers are interested in it because of its simple fabrication techniques, environmental friendliness, and plasticity. Anode, dye-sensitizer, electrolyte solution, and cathode are the four components of a DSSC. Anodes made of titanium dioxide (TiO2) or zinc oxide are used in the construction of DSSCs (ZnO). Recently, graphene has been studied to synthesis the composite material for enhancement of DSSCs efficiency. With sp2 -hybridized carbon atoms arranged into a hexagon shape; graphene is a potential one-atom thickness material. Graphene has a large surface area, is highly conductive, has a high carrier mobility, and is biocompatible. Among the synthesis routes of graphene, reduction of graphene oxide (GO) is an efficient, low-cost, and simple method [1]. In transparent conductive films, graphene and its variants have been employed [2], as well as in organic photovoltaic (PV) cells and as a potential family of effective hole- and electron-extraction materials. Graphene films can be made using a variety of techniques, such as chemical vapors deposition (CVD), micromechanical exfoliation of graphite, solution-based chemical reduction of graphite to graphene oxide, and magnetron sputtering, which has a number of benefits, © Faculty of Electronic and Computer Engineering, FKEKK

(a)

(b)

Figure 1: (a) before filter (b) after filter 2.3

Characterization and measurement

2.3.1 X-ray Diffraction (XRD) analysis X-Ray Diffraction (XRD) was utilized for the physical construction of the GO. X-ray diffraction measurement with data analysis was used to classify the 105 109

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crystalline phase of the materials and to disclose details on unit cell measurements. Cathode ray tube and filter produced by X-ray generate monochromatic radiation and concentrate directly on the sample. X-ray diffraction peaks are created by constructive interference of a monochromatic X-ray beam scattered at a particular angle.

now successfully embedded by internally on graphene oxide.

2.3.2 UV Vis Spectroscopy UV-Visible spectroscopy refers to absorption or reflectance spectroscopy in the ultraviolet and adjacent visible spectrum regions. The technique of UV-visible absorption spectroscopy is commonly used to investigate the optical characteristics of nanoparticles. The UV and visible light range between 400 nm to 700 nm in the test tube generally do the electronic transitions where the molecules can absorb the light in the UV and visible region

Figure 3 UV Vis Graphene oxide 2.

In conclusion, that commercial graphite powder can be oxidized into graphene oxide through chemical and mechanical method. The characterize of GO only have been done on Xray-Diffraction analysis and UV Visible spectroscopy. The oxidation process has been proved by a result of spectroscopic and optical technique. Unfortunately for GO film process for DSSC cannot be done due to the current MCO which does not allow student enter the campus.

2.4

Preparation of GO films for counter electrode Firstly, 0.5mg GO powders were dissolved in 1ml deionized water. The solution then sonicates for 30 minute and stir for 2 hours. Then, the solution spin coated on ITO substrate at 1000 rpm for 15s and followed by 2500 rpm for 45s to obtain GO film. The GO film was annealed at 100°C in the oven for 1 hour. 1.

CONCLUSION

RESULT AND DISCUSSION

ACKNOWLEDGEMENT

The treatment of graphite with sulfuric acid and potassium permanganate produced brownish graphene oxide (GO) (Figure 1). The GO readily forms stable colloidal suspension in water. When graphite is exposed to oxidizing agent (KMn O4 ) and intercalant agents (H2 SO4), the graphite interlayer d-spacing expands dramatically, weakening the link between layers. This is referred to as graphite intercalated compound (GIC). The peak disappeared once the graphite was oxidized to GO, and a new peak emerged at the lower 2 theta degree, indicating that the graphite was totally converted to GO [5]. The existence of an oxygen functional group is indicated by a peak at 2=11.6°, which corresponds to an interlayer spacing (Figure2).

The authors would like to thank Universiti Teknikal Malaysia Melaka (UTeM) and Fakluti Kejuruteraan Elektronik & Kejuruteraan Komputer (FKEKK). REFERENCES [1] H. Wang & Y. H. Hu, “Graphene is a counter electrode material for desensitized solar cells”, Energy & Environmental Science, vol. 5, no. 8, p. 8182. 2012. [2] T. T. N. Le and V. C. Le “Synthesis of Zinc Oxide/Reduced Graphene Oxide Composites for Fabrication of Anodes in Dye-Sensitized Solar Cells”, 2020. [3] Q. Zheng, Z. Li and J. Yang, “Graphene oxidebased transparent conductive films”, 2014. [4] J. Chen, B. Yao, C. Li and G. Shi,”J. Carbon N. Y.” vol. 64, p. 225, 2013. [5] J. Li, X. Zeng, T. Ren and V. D. Heide,”J. Lubricants 2”, p. 137, 2014.

Figure 2 XRD Graphene oxide Figure 3 shows the Ultraviolet-visible spectra of graphene oxide. The spectrum of GO has an absorption peak at 240nm. The absorption peak at 240nm is attributed to transition bond between Carbon-Oxygen © Faculty of Electronic and Computer Engineering, FKEKK

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Proceedings of Innovation and Technology Competition (INOTEK) 2021, Proceedings of Innovation andMalaysia, Technology Melaka, pp.Competition 107-108, (INOTEK) 2021

Study of Zinc Dioxide (ZnO) And Titanium Dioxide (TiO2) Photoanode For Solar Cell Applications using Silvaco TCAD M. A. Syawal1, Z. A. F. M. Napiah1* , F. Arith1 1

Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia Corresponding author’s email: [email protected]

*

ABSTRACT: The works studied the ZnO and TiO2 photoanode of DSSC to obtain the optimum parameter, and hence increase the efficiency of the DSSC. Silvaco TCAD simulator has been used for the analysis. By simulating the ZnO and TiO2 in Silvaco software, all the required data such as IV characteristics, spectral response, and layer thickness are analysed. These results will be a guide to the researcher for further fabrication process. Keywords: DSSC; ZnO; TiO2; Silvaco TCAD 1.

INTRODUCTION

A photoelectric solar cell or commonly depicted as a solar cell is one of the best alternatives compared to hydroelectric in producing electricity [1]. The solar cell has been intensively developed and currently it is already emerges from first-generation into the third generation [2][3]. Thereby, DSSCs are eco-friendly, cost-effective, and can be grown on elastic thin films. DSSCs consists of five main components, including photoanode, dye, electrolyte, counter electrode, and the transparent conductive substrate [4][5]. Dye-sensitized solar cells (DSSCs) are now known as special third-generation photovoltaic cells that give the optimum conversion of fluorescent light into electrical energy. The photoanode is completely fabricated of a thin semiconductor nanoparticle film deposited on a transparent conductive support that absorbs a dye; the working principle of dye mostly to extend the absorption spectrum of the semiconductor in the spectral light and inject an electron from the excited singlet state D* into the conduction of nanoparticles The electrolyte is made up of a solvent and a redox pair that acts as charge mediators and reduces the dye's oxidation state. The counter-electrode is typically made by coating a conductive substrate with a catalyst material which capable of converting triiodide to iodide and transferring electrons from the external circuit to the dye via the electrolyte [4][5]. For this project, TiO2 and ZnO will be the main component to be analysed using Silvaco simulation software. Several selected parameters are compulsory to be inserted into the command of ATLAS to generate recombination rate such as I-V characteristic and J-V characteristic

© Faculty of Electronic and Computer Engineering, FKEKK

Table 1. Parameter of TiO2 and ZnO Parameter Bandgap (eV) Permittivity Affinity (eV) Conduction band effective density of states, NC (cm-3) Valence band effective density of states, NV (cm-3) 2.

TiO2

ZnO

3.2 10 4.2

3.35 9 4.5

2.0 × 1017

6.0 × 1017

2.2 × 1018

1.8 ×1019

METHODOLOGY

Silvaco TCAD is the software applied in this project that provides a comprehensive and well-integrated numerical simulation [6]. For solar cell simulation, CAD modules provided S-Pisces, Blaze, Luminous, TFT, Device3D, Luminous3D, and TFT3D. Based on the simulated fabrication of its physical structure, the extraction of the features of a solar cell can be done by using the ATLAS device simulator [7]. The parameters such as IV characteristic performance, mapping of photogeneration and spectral response are being provided through the simulation. Integration of energy balance transport equations and drift-diffusion are conducted in silicon-based enhancement which implementing both ATLAS and SPisces as a computer simulator [7]. The advanced device module which applies the Luminous deployed together with ATLAS is a module regarding to concentrates on modelling photogeneration and absorption of light either in a semiconductor planar or non-planar system. There are several steps required for the simulation which are specified in five sections. Firstly, defines structure specification which includes mesh, region, electrode, and doping. Then, followed by material model specification where any material used needs to be determined its models, contacts and interface. Next, numerical method selection and solution specification Syawal et al., 2021 consist of the log, solve, load and save. Lastly, extraction results analysis where the Tonyplot will be generated as a result of the simulation. The sourc itself and angle bea 3. RESULT AND DISCUSSION 107 standard 111 The DSSC was used to simulate the effect of TiO2 solar cell and ZnO layer separately by varying the parameter of the TiO2 and ZnO layer. 4. CO

Syawal et al., 2021 Proceedings of Innovation and Technology Competition (INOTEK) 2021

Syawal et al.,The 2021 source beam basically located in between of the cell itself and 2μm from the cell. It is necessary to set the angle beam that normal to the cell which at 90°. Plus, The 3. RESULT AND DISCUSSION a result of the simulation. The source beam basically located in between of the cell standard spectrum at the earth surface is clarified to test The DSSC was used to simulate the effect of TiO2 itself and 2μm from the cell. It is necessary to set the solar cell which indicating as Air Mass 1.5 (AM 1.5). and ZnO layer separately by varying the parameter of the angle beam that normal to the cell which at 90°. Plus, The 3. RESULT AND DISCUSSION TiO2 and ZnO layer. standard spectrum at the earth surface is clarified to test 4. CONCLUSION The DSSC was used to simulate the effect of TiO2 solar cell which indicating as Air Mass 1.5 (AM 1.5). and ZnO layer separately by varying the parameter of the As a conclusion, it was succesful to simulate the TiO2 and ZnO layer. structure of DSSC which consist of TiO2 and ZnO 4. CONCLUSION photoanode separately by using Silvaco software. Here, As aand conclusion, it wasare succesful the ZnO TiO2 material suitabletotosimulate be used the as structure of DSSC which consist of TiO 2 and ZnO photoanode of the DSSCs. Results proved that the cell photoanode separately by using Silvaco software. Here, are working fine under illumination and unillumination the ZnO and TiO2 material are suitable to be used as analysis. The open circuit voltage was also obtained, photoanode of the DSSCs. Results proved that the cell showing that the maximum voltage presented in the are working fine under illumination and unillumination DSSCs. analysis. The open circuit voltage was also obtained, showing that the maximum voltage presented in the 5. ACKNOWLEDGEMENT DSSCs. This work was supported by Universiti Teknikal Figure 1 Construction of DSSC doping and geometry Malaysia Melaka and Ministry of Education Malaysia 5. ACKNOWLEDGEMENT under RACER/2019/FKEKK-CETRI/F00403. This work was supported by Universiti Teknikal Figure 1 Construction of DSSC doping and geometry Malaysia Melaka and Ministry of Education Malaysia REFERENCES under RACER/2019/FKEKK-CETRI/F00403. a result of the simulation.

Figure 2 Illuminated and unilluminated IV characteristics Figure 2 Illuminated and unilluminated IV characteristics

Figure 3 Simulation of open circuit voltage

From the graphs above, the structure of DSSC was of open circuit voltage displayedFigure as 3a Simulation FTO/TiO2&Dye/Electrolyte/Platinum denoted with color to differentiate every layers. The DyeFrom the graphs above, the structure of DSSC was Sensitized Solar Cell consists of two regions which are displayed as a FTO/TiO2&Dye/Electrolyte/Platinum the bulk heterojunction region and the electrolyte region. denoted with color to differentiate every layers. The DyeTiO2 particles are coupled with dye, which is a lightSensitized Solar Cell consists of two regions which are absorbing substance, in the bulk heterojunction area. The the bulk heterojunction region and the electrolyte region. anode and cathode to be an electrical contact in order to TiO2 particles are coupled with dye, which is a lightsimulate the electrical properties with the work function absorbing substance, in the bulk heterojunction area. The is set to be 4.4 eV for FTO and 6.35 eV for platinum. anode and cathode to be an electrical contact in order to ATLAS is also used to simulate the IV characteristic of simulate the electrical properties with the work function the device. Before initializing the simulation, several is set to be 4.4 eV for FTO and 6.35 eV for platinum. preparations are compulsory as it will affects the output. ATLAS is also used to simulate the IV characteristic of the device. Before initializing the simulation, several © Faculty of Electronic and Computer Engineering, FKEKK preparations are compulsory as it will affects the output. 108 © Faculty of Electronic and Computer Engineering, FKEKK

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[1] M. A. Green, Solar Cells: Operating Principles, REFERENCES Technology, and System Applications, Prentice Hall., 1982. [1] M. A. Green, Solar Cells: Operating Principles, [2] O. V. Aliyaselvam, F. Arith, A. N. Mustafa, M. Technology, and System Applications, Prentice K. Nor and O. Al-Ani, "Solution processed of Hall., 1982. solid state HTL of CuSCN layer at low [2] O. V. Aliyaselvam, F. Arith, A. N. Mustafa, M. annealing temperature for emerging solar cell," K. Nor and O. Al-Ani, "Solution processed of Int. J. Renew. Energy Res., vol. 11(2), pp.869solid state HTL of CuSCN layer at low 878, 2021 annealing temperature cell," [3] A. N. Mustafa, F. Arith,for I. J.emerging Rong, M.solar Zaimi, A. Int. J. Renew. Energy Res., vol. 11(2), pp.869S. Rahimi, and S. Saat, “Investigation of 878, 2021 Copper(I)Thiocyanate (CuSCN) as a hole [3] A. N. Mustafa, F. Arith, I. J. Rong, M. Zaimi, A. transporting layer for perovskite solar cells S. Rahimi, and S. Saat, “Investigation of application.” J. Adv. Res. Fluid Mech. Therm. Copper(I)Thiocyanate (CuSCN) as a hole Sci. vol. 78(2), pp. 153–159, 2021. transporting layer for perovskite solar cells [4] O'Regan, B., Grätzel, M. A low-cost, highapplication.” J. Adv. Res. Fluid Mech. Therm. efficiency solar cell based on dye-sensitized Sci. vol. 78(2), pp. 153–159, 2021. colloidal TiO2 films. Nature 353, pp. 737–740 [4] O'Regan, B., Grätzel, M. A low-cost, high1991. efficiency solar cell based [5] M. A. Azhari, F. Arith, F. Ali,onS. dye-sensitized Rodzi, and K. colloidal TiO 2 films. Nature 353, pp. 737–740 Karim, “Fabrication of low cost sensitized solar 1991. cell using natural plant pigment dyes.” ARPN J. [5] M. A. Azhari, F. Arith, F. Ali, S. Rodzi, and K. Eng. Appl. Sci. vol. 10(16), pp.7092–7096, Karim, “Fabrication of low cost sensitized solar 2015. cell using natural plant pigment dyes.” ARPN J. [6] Z.A.F.M. Napiah, N. Makhtar, M. A. Othman, et Eng. Appl. Sci. vol. 10(16), pp.7092–7096, al. “Characterization of vertical strain silicon 2015. MOSFET incorporating dielectric pocket (SDP[6] Z.A.F.M. Napiah, N. Makhtar, M. A. Othman, et VMOSFET)” AIP Conference Proceedings, al. “Characterization of vertical strain silicon 1586, pp. 161-165, 2014. MOSFET incorporating dielectric pocket (SDP[7] M. Bavir and A. Fattah, “An investigation and VMOSFET)” AIP Conference Proceedings, simulation of the graphene performance in dye1586, pp. 161-165, 2014. sensitized solar cell,” Opt. Quantum Electron., [7] M. Bavir and A. Fattah, “An investigation and vol. 48 (559), pp. 1–18, 2016. simulation of the graphene performance in dyesensitized solar cell,” Opt. Quantum Electron., vol. 48 (559), pp. 1–18, 2016.

Proceedings of Innovation and Technology Competition (INOTEK) 2021, Melaka, Malaysia, pp.Competition 109-110, (INOTEK) 2021 Proceedings of Innovation and Technology

Old Document Handwritten Text Extraction Using Machine Learning W. K. Jing1, M. R. Kamarudin1* 1

Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia Corresponding author’s email: [email protected]

*

ABSTRACT: The images are mixed with four different types of noises in the process of digitization text extraction. The majority of the available algorithm can only use to remove single type of noise. For this condition, three image filtering techniques will have carried up to remove the noise that present in the image will be carried out. An ensemble model combined with the previous denoise technique is put forward for removing the noise. Meanwhile, simulation and results show that the method can effectively clear the noise.

preprocessing, feature extraction, ensemble selection, and output. There are two types of training data involved in this research such as training data with noises and validation data. For the first part of the research, three denoise technique will be used for single training image, which are 1. Median Filter 2. Canny Edge Detection Algorithm 3. Adaptive Thresholding

Keywords: Ensemble Model; Old-Document Handwritten Text Extraction; Machine Learning

The best parameter in the denoise method will be inserted into the feature model for feature extraction of the ensemble model. The ensemble model that chosen for training the images are 1. Gradient Boosting Regressor 2. Adaptive Boosting Regressor

1

INTRODUCTION

From electronic books to online medical records, the necessity for digitization is rapidly increasing. However, many printed documents remain stuck in the past: coffee stains, wrinkles, and fade sun spots which prevent optical character recognition from recognizing all the text. Therefore, a machine learning algorithm for cleaning up these documents and introduce them to the digital world is required. This project is to pre-process the captured image to filter out the background image noise without significantly remove the original text information. Based on the study on an adaptive local binarization method and comparing with 3 types of thresholding method for document images based on a novel thresholding method and dynamic window. The PSNR value of the Niblack method is 6.5, Sauvola’s method is 11.61, and the Nick method is 11.76. [1] Based on the study on image-denoising based on biro wavelet transform and median filter. The PSNR of the median filter at 0.02 noise is at a range of 18.86 to 19.12.[2] Based on the study by Angalaparrameswari Rajasekaran, image denoising using median filter with canny edge detection can remove salt and pepper noise from the corrupted image.[3] Based on the study on efficient Gaussian noise reduction technique for noisy images, the performance of median filter will be affected by the variance range. PSNR value increase from 12.48 to 24.12 for variance 0.01 to 1.00. [4] 2.

Figure 1 Flow chart of progress testing 3.

RESULT

To verify the effectiveness of the method, an image affected by the noise is chosen. Then median filter, edge detection dilation, and erosion, adaptive thresholding, and ensemble model are used to denoise. The first 3 denoise method adopts the 3x3 window, while the max depth level of the ensemble model is 4 layers. Figures 2 and 3 show the results after noise removal.

DATA AND INSTRUMENTATION

Figure 1 shows the flow chart of the progress testing. There are five phases which are input, © Faculty of Electronic and Computer Engineering, FKEKK

Figure 2 After and Before Filtering Type1 Noise Image

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Where an image is considered to have M number of lines and N number of segments in the above equation x and y are pixel index values.[5] Universal Quality Index (UQI) The UQI defined as per equation (3) below

𝑈𝑈𝑈𝑈𝑈𝑈 =

Figure 3 After Removing Noise by Ensemble Model

4𝜎𝜎𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 (𝜇𝜇𝐼𝐼𝑟𝑟 +𝜇𝜇𝐼𝐼𝑓𝑓 )

2 +𝜎𝜎 2 )(𝜇𝜇 2 +𝜇𝜇 2 ) (𝜎𝜎𝐼𝐼𝐼𝐼 𝐼𝐼𝐼𝐼 𝐼𝐼𝐼𝐼 𝐼𝐼𝐼𝐼

(3)

Where µ is mean and σ2 is a variance for an M x N image size[5]

Table1 Performance Test of Ensemble Model Accuracy

Time Spent (s)

Gradient Boosting

97.6%

12.4

4.

Adaptive Boosting

96.3%

22.8

In this paper, the median filter method is the best among the 3 denoise techniques. Based on tables 1 and 2, denoise using the ensemble model of gradient boosting regressor is the best for different types of noise removal due to high accuracy, shorter time spent and good quality test performance. The noise removal performance improved using machine learning.

Table 2 Quality Performance Test Type of noise Median Filtering

Quality Test RMSE UQI PSNR RMSE UQI PSNR RMSE UQI PSNR RMSE UQI PSNR RMSE UQI PSNR

EDE method Adaptive

Thresholding

Gradient Boosting (Average) Adaptive Boosting (Average)

1

2

21 99.4 21 121 71.8 6 35 98.6 17

3

17 23 99.6 99.2 23 20 125 120 69.1 71.9 6 6 34 35 98.7 98.6 17 17 18 99.6 23 19 99.5 23

4 22 99.3 21 126 70.0 6 35 98.6 17

REFERENCES [1]

[2] [3]

[4]

To compare the denoising effects of the different filtering methods, peak signal to noise ratio, root mean square error, and universal quality index can be used as the standard for evaluation. [5]

Peak Signal to Noise Ratio (PSNR) The PSNR defined as per equation (1) below

𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 = 20𝑙𝑙𝑙𝑙𝑙𝑙10 (

𝑀𝑀𝑀𝑀𝑀𝑀 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 √𝑀𝑀𝑀𝑀𝑀𝑀

(1)

)

Where MSE is Mean Square Error which is defined as the squared intensity of the original image pixels to the output image pixels.[4] Root Mean Square Error (RMSE) The RMSE defined as per equation (2) below 1

𝑀𝑀 𝑅𝑅𝑅𝑅𝑅𝑅𝐸𝐸 = √(𝑀𝑀𝑀𝑀) ∑𝑀𝑀 𝑥𝑥=1 ∑𝑦𝑦=1 (𝐼𝐼𝑟𝑟 (𝑥𝑥, 𝑦𝑦) − 𝐼𝐼𝑓𝑓 (𝑥𝑥, 𝑦𝑦))

© Faculty of Electronic and Computer Engineering, FKEKK

2

CONCLUSION

(2)

114 110

B. Bataineh, S. N. H. S. Abdullah, and K. Omar, “An adaptive local binarization method for document images based on a novel thresholding method and dynamic windows,” Pattern Recognit. Lett., vol. 32, no. 14, pp. 1805–1813, 2011, doi: 10.1016/j.patrec.2011.08.001. H. Dong and F. Wang, “and Median Filter,” no. 1, pp. 2–4, 2012. A. Rajasekaran1, “Image Denoising Using Median Filter with Edge Detection Using Canny Operator,” Int. J. Sci. Res., vol. 3, no. 2, pp. 30– 34, 2014, [Online]. Available: www.ijsr.net. S. C. Kumain, M. Singh, N. Singh, and K. Kumar, “An efficient Gaussian Noise Reduction Technique for Noisy Images using optimized filter approach,” ICSCCC 2018 - 1st Int. Conf. Secur. Cyber Comput. Commun., pp. 243–248, 2018, doi: 10.1109/ICSCCC.2018.8703305. M. Kumar, “Image fusion based on evolutionary optimization algorithm Sample Report On Image Fusion based on Evolutionary Optimization Algorithm Prepared By MI Research Lab , Kota,” October, 2018, doi: 10.13140/RG.2.2.13146.59845.

Proceedings of Innovation and Technology Competition (INOTEK) 2021, Proceedings of Innovation andMalaysia, Technology Competition Melaka, pp. 111-112, (INOTEK) 2021

Solar Powered For Aquaponic System Apparatus S. H. Husin1*, M. N. M. Aiman 1 1

Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia Corresponding author’s email: [email protected]

*

ABSTRACT: Aquaponics is one of the famous water recirculation systems for the growth of the plants. The aquaponic system used nowadays are supplied by the conventional main power, which uses hydroelectric as the primary source to generate electricity. As a result, whenever a large energy of electricity used, the cost will be high, expensive and has limited reservoirs. Aquaponics system is a process of manually caring. Therefore, a high amount of worker energy consumption, the high cost will contribute in order to proceed with the monitoring progress. By introducing this solar power is to provide an alternative source because solar power generates electricity by collecting solar radiation. Plus, the solar power is much more affordable as it uses renewable energy comes from sunlight. This project will also be improved by using the IoT. This technology function to send a message to the user that the process is completely done. Also, this technology also functions to signal if the process is not working correctly to the user through message. This project designed the boost converter that uses solar charge controller functions to supply energy to the battery and loads, including water pump and microcontroller. The solar power will have an output of DC power, so in order to run the water pump that is using AC power source to make sure the pressure flow of water will be high enough, inverter circuit will be designed to invert DC power source from solar power to AC power source through the water pump. The power consumption produced are freely protected the environment, reducing the energy and cost of electricity used in an agricultural system. The agricultural techniques applied to aquaponics has more advantages compared with traditional farming.

spell and environmental change, aquaponic frameworks have pulled in expanding consideration because of their asset investment funds, high proficiency and low utilization. They have become the pattern and bearing of current agrarian advancement [2]. As one of the significant patrons of hydroponics creation on the planet, China has the most significant hydroponics industry. Additionally, its hydroponics creation surpassed 50 million tons, representing over 60% of the world's hydroponics creation in 2018. Be that as it may, simultaneously as the fast mechanical turn of events, a few issues happened in various zones, for example, the absurd conveyance of hydroponics, genuine contamination from hydroponics in certain zones and a low level of scale and association. The conventional techniques for hydroponics in China are as yet portrayed by high thickness, high goading rates and high water trade rates [3]. It is assessed that 52-95% of nitrogen, 85% phosphorus and 60% of feed put into hydroponics will ultimately be changed over into particulate matter, broken up synthetic compounds or gases. Ultimately, different feeding residues and animal excretions will appear in the water due to different feeding types and techniques [4]. 2.

In this part, the advancement of the whole task is clarified carefully step by step. The process of project is summed up in type of flowcharts to explain effectively. The figure 1 shows the flowchart of the whole project development that starting with project proposal, implementing the Arduino program combining with hardware materials to the addition the end result. Next, the Figure 2 shows the process of this project. The process begins by initiating the Nodemcu Esp32 to perform the Arduino programming. The both Nodemcu Esp32 and Arduino software need to establish the connection to the internet, if the internet is not connected then the error messages will appear on program. Meanwhile, if the internet connected, the Arduino will generate an IP address to user through email. Moreover, the system will receive the analogue input data sensor from Moist Sensor, Light Detector (LDR) Sensor, pH Sensor and Real-Time Clock (RTC) module. Next, the analogue input data will send to Blynk IoT platform

Keywords: Aquaponic; Smart Solar Powered; Internet of Things(IoT) 1.

INTRODUCTION

Hydroponics is another innovation in present day farming creation that joins hydroponics with aquaculture. Accordingly, vegetable planting no longer requires preparation, and fish societies needn't bother with water changes now and again. This change permits fish, developed yields and microorganisms to frame commonly valuable advantageous interaction and amicable conjunction of natural equilibrium connections. It is a working method of manageable sound food creation [1]. Notwithstanding soil contamination, dry © Faculty of Electronic and Computer Engineering, FKEKK

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i.

Figure 3. Most likely the amount of water for AM and PM are same and the growth of plant shows an increasing pattern.

Start

Project proposal

No accepted

Yes Designing a DC to AC converter for AC water pump Run simulation circuit

Figure 4 Water the Plant(AM/PM) against Growth of Plant(cm)

Implement design on circuit board

4.

No

Pass

Circuit test

Finally, the sensor, microcontroller, actuator, and smartphone device were used to plan and create the IOT Based Aquaponics Monitoring System. There are several methods for monitoring the aquaponics system that can be used. One of them is IoT-based, which is used in this project to monitor the system's sensors and actuators. This would be due to the effortless incorporation of multiple sensors for consistent plant growth and water fish tank measuring actuators for automatic ambient temperature management, such a device can continually track and operate the unit without user interaction, making it easier for the user to operate the farming process until the plant and fish can be harvested. Finally, rather than farming aquaponics, this project can serve as a catalyst for other researchers to develop more reliable systems and expand this to ensure that people begin producing their own food in their quadratic everyday routines. The outcomes can then be measured and compared.Technology.

Implement solar panel, DC to AC converter and water level sensor into Arduino IDE

End

Figure 1 Project Completion Procedure

Figure 2 Functioning of block diagram. 3.

ACKNOWLEDGEMENT The authors would like to thank Universiti Teknikal Malaysia Melaka (UTeM) and Fakulti Kejuruteraan Elektronik & Kejuruteraan Komputer (FKEKK).

RESULT AND DISCUSSION

Figure 3 shows a result of Fish Feeding (AM/PM) against Growth of Fish (cm) from 27th April until 30th May. It’s showed that amount of fish feeding increasing start from 4th May and the amount of feeding become to be almost a flat curve onwards.

REFERENCES [1]

[2]

[3]

Figure 3 Fish Feeding(AM/PM) against Growth of Fish(cm)

[4]

Figure 4 shows a result of Water the Plant (AM/PM) against Growth of Plant (cm) for the same period in © Faculty of Electronic and Computer Engineering, FKEKK

CONCLUSION

116 112

K. N. Azad, M. A. Salam, and K. N. Azad, ‘‘Aquaponics in Bangladesh: Current status and future prospects,’’ J. Biosci. Agricult. Res., vol. 7, no. 2, pp. 669–677, 2016. N. Mchunu, G. Lagerwall, and A. Senzanje, ‘‘Aquaponics in South Africa: Results of a national survey,’’ Aquaculture Rep., vol. 12, pp. 12–19, Nov. 2018. D. Klinger and R. Naylor, ‘‘Searching for solutions in Aquaculture: Charting a sustainable course,’’ Annu. Rev. Environ. Resour., vol. 37, pp. 247–276, Nov. 2012. M. T. Gutierrez-Wing and R. F. Malone, ‘‘Biological filters in aquaculture: Trends and research directions for freshwater and marine applications,’’ Aquacultural Eng., vol. 34, no. 3, pp. 163–171, 2006.

Proceedings of Innovation and Technology Competition (INOTEK) 2021, Proceedings of Innovation and Technology Melaka, Malaysia, pp.Competition 113-114, (INOTEK) 2021

IoT Based Control and Monitoring for Aquaponic System Z. B. Zamani1*, M. S. H. Shamsuri1 1

Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia Corresponding author’s email: [email protected]

*

ABSTRACT: Aquaponic farming is an unbelievably productive approach to develop natural vegetables, greens, herbs and organic products without utilizing any synthetic compounds with the additional advantage of fresh fish and healthy source of protein. Integrate this system with the renewable energy sources has been proven to be reliable and widely accepted as the greatest option for fulfilling our growing energy demand. This project aims to develop an aquaponic system that power up by solar energy that able to measure the pH, current, voltage and light intensity integrated with Internet of Thing (IoT). By using a ESP32 microcontroller all the data obtain will be send to the cloud. This will give user the real time information of the data collected. Keywords: Internet microcontroller 1.

of

Things(IoT);

will transferred to the ThingSpeak which it will enable to be excessed through anywhere 2.

This system is capable to sensing four different parameters which are pH, voltage, current and light intensity and capable to transfer the data obtained to the cloud. The users able to monitor the system remotely away from the field. Solar Panel voltage is obtained from the voltage sensor. The voltage sensor measurement is based on a voltage divider circuit that comprises of a sensor and a reference resistor. Solar panel current is obtaining based on Hall effect in the current sensor ASC712. When the current flows through the copper conductor and is detected by the Hall Effect sensor therefore the magnetic field is created. The magnetic field will be converted in to an appropriate voltage by the Hall Effect sensor. The light intensity will be sensed by LDR sensor. The data obtained will be processed by the NodeMCU ESP32 microcontroller and will be sent the data over to the internet. ThingSpeak server is used as a platform to storage the data obtain from the microcontroller for future reference and review. This data will be able to viewed any time at anywhere by using an internet. The schematic diagram for this system is shown in Figure 1 below:

ESP32

INTRODUCTION

Nowadays, Internet of Things (IoT) is a revolution in the field of electronics. It provides us to transfer data via a network without person-to-person or person-tocomputer interaction. Also, user can access the data any time at anywhere. The main objective is to reduce the involvement of person as much as possible. Solar energy most abundant and the cleanest renewable energy source. The energy generated by the solar panel is easily affected by change oi the solar irradiation, weather condition and other factors[1]. An aquaponic is an alternative way for food production technique. An aquaponic system is a closed loop system which consist of hydroponic and aquaculture element would counter these issues. Periodic monitoring of aquaponic system needs to do because of the synergetic uptake and release of effluent from fish to plants[2] Hence, the monitoring on the power generated from the solar panel are needed. Also, monitoring on the water quality are needed for the aquaponic system. IoT based is needed for transfer the data obtain from the sensor to the cloud. This experimental setup is consisting of solar panel, voltage sensor, current sensor, LDR sensor pH sensor, ESP32 microcontroller. The programming codes are developed on Arduino IDE and the data visualization is carried out on ThingSpeak. This system has the ability to measure voltage, current, light intensity and pH value. All the data obtain © Faculty of Electronic and Computer Engineering, FKEKK

METHODOLOGY

Figure 1 Schematic Diagram of Solar powered Aquaponic monitoring system

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CONCLUSION

As conclusion, the traditional sources of power become depleted, humanity will need to rely in the renewable energy sources such as solar and wind energy to survive. Solar energy is an excellent choice as a source of energy since it is clean and abundant. The cost of devices is rapidly falling as result of technical improvements[3]. As a result, all we need is a solid, upto-date monitoring system that can conduct main function without human intervention and deliver data to user whenever and wherever it is required. IoT is the greatest way for monitoring solar and aquaponic system in order to keep up with quickly evolving technology. IoT based control and monitor for aquaponic system will become cost saving system because it will decrease the labor charge[4]. ACKNOWLEDGEMENT The authors would like to thank Universiti Teknikal Malaysia Melaka (UTeM) and Fakluti Kejuruteraan Elektronik & Kejuruteraan Komputer (FKEKK). REFERENCES [1]

[2] Figure 2 Flow of project IoT based control and monitoring aquaponic system 3.

[3]

RESULT AND DISCUSSION

[4]

Users can view the live data from the ThingSpeak website showed in Figure 3. All parameter reading will be transferred to the ThingSpeak approximately every 10 second. Due to unstable internet, sometimes the data cannot be transferred to the ThingSpeak. So, the reading that displayed at ThingSpeak will remain same until new data able to transferred to it.

Figure 3 Data obtained from the sensor in ThingSpeak The data is collected where the aquaponic system is located at Alor Gajah. © Faculty of Electronic and Computer Engineering, FKEKK

114 118

D. Lau et al., “Hybrid solar energy harvesting and storage devices: The promises and challenges”, Mater. Today Energy, vol. 13, pp. 22–44, 2019. A. M. Nagayo, C. Mendoza, E. Vega, R. K. S. Al Izki, and R. S. Jamisola, “An automated solarpowered aquaponics system towards agricultural sustainability in the Sultanate of Oman”, 2017. S. R. I. of T. Anila, R. Nandhini, S. Poovizhi, P. Jose, and R. Ranjitha, “Arduino Based Smart Irrigation System Using IOT”, p. 5, December 2017. G. K. Panigrahi, S. Panda and S. N. Padhi, “Aquaponics : An innovative approach of symbiotic farming”, pp. 4808–4814, 2016.

HUMAN INTERACTION TECHNOLOGY

Proceedings of Innovation and Technology Competition (INOTEK) 2021, Proceedings of Innovation andMalaysia, Technology Competition Melaka, pp. 115-116 (INOTEK) 2021

Study On Performance of Face Recognition Using Convolutional Neural Network M.A. Rohaizi1, A.A. Basari1

1

Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia Corresponding author’s email: [email protected] 2.

ABSTRACT: Face recognition has been known worldwide, with daily usage and can be found everywhere. Every person has different features on their face is the reason face recognition is viable. Covid-19 has change how people live in daily life. People need to wear facemask every time they step out of their house. Wearing a facemask can limit the features on the face for face recognition. Thus, in this paper, Convolutional Neural Network (CNN) architecture will be developed and evaluate the performance of the developed CNN in making correct identification of the different type of faces including face that is wearing a facemask. The process will involve setting up datasets, which consist of five categories with four types of face, determining the best settings in CNN and analyzing the results. The results of five different tests were recorded and the final CNN architecture is able to obtain up to 96.2% in validation accuracy. This study shows the performance of CNN in making classification of people wearing facemask is highly accurate.

In in paper, there are five different test. This is the process to find the best possible CNN architecture to obtain the highest validation accuracy. 2.1 Preparing datasets Dataset is the input for training. The datasets are photo of faces. There are five categories of datasets, each contains 4 types of faces, 100 samples in each categories. The total of the samples are 500. Every sample will be edit to reduce any noise (unwanted background or lighting). Cropping the picture is the simplest way to do to get rid of the background. All the datasets will be insert into a folder with the name that will be read by the system, Matlab. The four types of faces are; i. Face without any mask on ii. Face with transparent mask iii. Face with light colour mask iv. Face with dark colour mask

Keywords: Face recognition; Convolutional Neural Network; CNN 1.

METHODOLOGY

INTRODUCTION

Face recognition has been one of the most use technology of human interaction. In these modern days, face recognition technology can be found in phone, building and websites. It also currently the one of the more secured way to replace id verification such as using as payment method and password [1].

2.2 Train using initial CNN architecture No

Yann LeCun discovered convolutional neural network in 1989. He developed LeNet-5 consisted of two hidden layers with two rectifier linear unit (ReLU). The input dataset used are characters of 0-9 with the total of 1000 samples. Every each of the samples are 32x32 in size [2]. In late 2019, covid-19 virus has shocked the world. People are obligate to wear facemask outside their house. Facemask can reduce the risk of someone spreading or getting the virus from others [3]. However, wearing a facemask covers many key features on a person face.

1 2

Image Input Convolution

3 4 5

ReLU Max Pooling Convolution

6 7 8

ReLU Max Pooling Fully Connected Softmax Classification Output

9 10

In this paper, the main objectives is to classify several input data of people who wear facemask. Next, to develop CNN architecture for recognizing identity of the people who wear mask and evaluate the performance of developed CNN architecture in masking correct identification.

Table 1 Initial CNN architecture Name Input Activation 270x220x3 6 filters; 5x5x3; stride [1 1] ReLU 2x2; stride [2 2] 16 filters; 5x5x6; stride [1 1] ReLU 2x2; stride [2 2] 2 fully connected layer softmax Output classes of 2

270x220x3 133x108x6 133x108x6 132x107x6 64x52x16 64x52x16 63x51x16 1x1x2 1x1x2 -

2.3 Developing a better architecture of CNN In this step, the focus is on the CNN architecture itself. All the preferences in the CNN need to take into consideration in order to find the best possible way to obtain high accuracy in validation. Increasing the number of epochs and number of hidden

© Faculty of Electronic and Computer Engineering, FKEKK

115 121

y several Next, to ity of the mance of correct

K

Output

2.3 Developing a better architecture of CNN In this step, the focus is on the CNN architecture itself. All the preferences in the CNN need to take into Rohaizi Basari, 2021 Proceedings Innovation Competition (INOTEK) 2021 consideration in order to find the best ofpossible wayandto&Technology obtain high accuracy in validation. Rohaizi & Basari, 2021 layers mean more features can be extract from the Increasing the face. number of so, epochs of hidden samples of the Even this and doesnumber not mean more layers mean more features can be extract from the accuracy, too high or too low values can lean the results samples of the face. Even so, this does not mean more toward over-fitting or under-fitting respectively. 115 accuracy, too high or too low values can lean the results toward over-fitting or under-fitting respectively. 2.4 Increasing the dataset The earlier test will involve small dataset, two categories. 2.4 Increasing the dataset After the accuracy is recorded, and if the accuracy is The earlier test will involve small dataset, two categories. approximately 90%, the test will proceed to add more After the accuracy is recorded, and if the accuracy is dataset, five categories. Figure 2 Live recognition using webcam approximately 90%, the test will proceed to add more dataset, five categories. 2.5 Results analysis The results of this experiment is based on the validation 2.5 Results analysis accuracy obtain after every training. 16 samples will be The results of this experiment is based on the validation chosen randomly for the system to make the prediction. accuracy obtain after every training. 16 samples will be Accuracy will be calculated using; chosen randomly for the system to make the prediction. Accuracy will be calculated using; 𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 𝑜𝑜𝑜𝑜 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 = (1) 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛 𝑜𝑜𝑜𝑜 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 𝑜𝑜𝑜𝑜 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝

(1) 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 = 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛 𝑜𝑜𝑜𝑜 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 Lastly, webcam will be use for the live recognition along with the live top 5 prediction. Lastly, webcam will be use for the live recognition along with the live top 5 prediction. 3. RESULTS AND DISCUSSION 3. results RESULTS AND on DISCUSSION The are based the percentage of validation accuracy; the accuracies from various tests were The results are based on the percentage of validation compiled and compared. accuracy; the accuracies from various tests were compiled and compared. There are five different tests involved; Test 1 – Initial CNN architecture There are five different tests involved; Test 2 – Improved settings in hidden layers Test 1 – Initial CNN architecture Test 3 – Increased number of dataset categories Test 2 – Improved settings in hidden layers Test 4 – Added a new hidden layer Test 3 – Increased number of dataset categories Test 5 – Optimum number of max epochs Test 4 – Added a new hidden layer Test 5 – Optimum number of max epochs For test 1 and 2, only two categories were used. Five categories were used in test 3, 4 and 5. Furthermore, test For test 1 and 2, only two categories were used. Five 1, 2 and 3 used 2 hidden layers while test 4 and 5 used 3 categories were used in test 3, 4 and 5. Furthermore, test hidden layers. All the tests were trained with 0.0001 1, 2 and 3 used 2 hidden layers while test 4 and 5 used 3 initial learn rate. hidden layers. All the tests were trained with 0.0001 initial learn rate. Table 2 The results of different tests Test Epoch Runresults Time (min) Accuracy Table 2 The of different tests (%) 1 5 00:35 58.1 (%) Test Epoch Run Time (min) Accuracy 21 55 01:33 97.7 00:35 58.1 32 55 04:20 54.6 01:33 97.7 43 55 04.29 64.9 04:20 54.6 54 255 23.36 96.2 04.29 64.9 5

25

23.36

96.2

Figure 1 Prediction made by the system.

Figure 2 Live recognition using webcam Table 2 Final CNN architecture No Name Input Activation Table 2 Final CNN architecture 1 Image Input 270x220x3 270x220x3 No Name Input Activation 2 Convolution 32 filters; 5x5x3; 266x216x32 1 Image Input 270x220x3 270x220x3 stride [1 1] 2 Convolution 32 filters; 5x5x3; 266x216x32 3 ReLU ReLU 266x216x32 stride [1 1] 4 Max Pooling 2x2; stride [3 3] 89x72x32 3 ReLU ReLU 266x216x32 54 Convolution 64 filters; 5x5x6; 85x68x64 Max Pooling 2x2; stride [3 3] 89x72x32 stride [1 1] 5 Convolution 64 filters; 5x5x6; 85x68x64 6 ReLU ReLU 85x68x64 stride [1 1] 7 Max Pooling 2x2; stride[3 3] 28x23x64 6 ReLU ReLU 85x68x64 8 Convolution 128 filters; 5x5x6; 24x19x128 7 Max Pooling 2x2; stride[3 3] 28x23x64 stride [1 1] 8 Convolution 128 filters; 5x5x6; 24x19x128 9 ReLU ReLU 24x19x128 stride [1 1] 10 Max Pooling 2x2; stride [3 3] 8x6x128 9 ReLU ReLU 24x19x128 11 Fully 2 fully connected 1x1x5 10 Max Pooling 2x2; stride [3 3] 8x6x128 Connected layer 11 Fully 2 fully connected 1x1x5 12 Softmax softmax 1x1x5 Connected layer 13 Classificatio Output classes of 5 12 Softmax softmax 1x1x5 n Output 13 Classificatio Output classes of 5 n Output 4. CONCLUSION

In4.thisCONCLUSION paper, we were able to carry out a study about CNN. The developed CNN architecture was able to In this paper, we were able to carry out a study about classify the input data samples of people wearing CNN. The developed CNN architecture was able to facemask. The performance of developed CNN achieved classify the input data samples of people wearing up to 96.2% validation accuracy. facemask. The performance of developed CNN achieved up to 96.2% validation accuracy. ACKNOWLEDGEMENT ACKNOWLEDGEMENT The authors would like to thank Faculty of Electronic and Computer Engineering and Universiti Teknikal Malaysia The authors would like to thank Faculty of Electronic and Melaka for the financial support. Computer Engineering and Universiti Teknikal Malaysia Melaka for the financial support. REFERENCES REFERENCES [1] K. Elleithy and T. Sobh, “New trends in networking, computing, e-learning, systems sciences, and [1] K. Elleithy and T. Sobh, “New trends in networking, engineering,” Lect. Notes Electr. Eng., vol. 312, pp. computing, e-learning, systems sciences, and 343–348, 2015, doi: 10.1007/978-3-319-06764-3. engineering,” Lect. Notes Electr. Eng., vol. 312, pp. [2] Y. LeCun, P. Haffner, L. Bottou, and Y. Bengio, 343–348, 2015, doi: 10.1007/978-3-319-06764-3. “Learning, Object recognition with gradient[2] Y. LeCun, P. Haffner, L. Bottou, and Y. Bengio, based,” Shape, contour Group. Comput. Vis., pp. “Learning, Object recognition with gradient319–345, 1999. based,” Shape, contour Group. Comput. Vis., pp. [3] D. Lepelletier et al., “What face mask for what use 319–345, 1999. in the context of the COVID-19 pandemic? The [3] D. Lepelletier et al., “What face mask for what use French guidelines,” J. Hosp. Infect., vol. 105, no. 3, in the context of the COVID-19 pandemic? The 2020, doi: pp. 414–418, French guidelines,” J. Hosp. Infect., vol. 105, no. 3, 10.1016/j.jhin.2020.04.036. pp. 414–418, 2020, doi: 10.1016/j.jhin.2020.04.036.

Figure 1 Prediction made by the system.

© Faculty of Electronic and Computer Engineering, FKEKK

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FACIAL EXPRESSION RECOGNITION USING AI TECHNIQUE Mawardi. A. A1, A.A. Basari1 Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia

1

Corresponding author’s email: [email protected]

*

ABSTRACT: In this world face expression can tell a lot meaning. But one thing for sure each expression is the way human express their feeling. Even though each human has different face features but with artificial intelligence(AI) technology we can detect the expression. The objective is to create an AI with face expression recognition. The method use is creating Convolutional Neural Network (CNN) to train the dataset and achieve high accuracy of prediction. The accurateness can be determined from live view using camera.

disgust, fear, and surprise. All the image’s totals are 660 where 110 for each expression. 2.2 Cropping the dataset (input) The input database is the image of human expression from several humans. The input dataset (images) must be crop first before going through the training. It will reduce size of the images 1280 x 960 to 180 x 180. The dataset needs to be cropped. This is due to reduce background and the noise of the image and only focus the main part of expression which eyes, nose, and mouth.

Keywords: Convolutional Neural Networks, Face Expression, Face Recognition. 1.0 INTRODUCTION Facial expressions are important aspects of how we interact and how we make interpretations of the people around us. In The Expression of Emotion in Man and Animals, Charles Darwin suggested that facial expressions develop to rapidly convey emotional states that are vital to social survival. He believed that such facial gestures are inherent, and thus uniformly communicated and accepted in all cultures. Face detection efficiency is a key concern, so strategies for coping with non-frontal facial detection are addressed. Subspace simulation and learning-based dimensionreduction approaches are central to many modern facerecognition techniques. The implications of findings from this project is that this technique can be widely use in the future. For example, detective can use it during interrogation to detect human behaviour understanding. Doctors also can use it to detect mental disorders and synthetic human expression.

Figure 2.2 Figure of cropped dataset

2.3 Resize the dataset. All the input needs to be resized into a same size. The resizing is done by using a function in MATLAB where the images will be automatically resized before going through training layer. This is due to allow the images to be train and to avoid the maximum capacity, allow by the laptop to train the images.

2.0 METHODOLOGY In this paper there are six steps included for the methodology of this project. 2.1 Preparing dataset (input) Figure 2.3 Resize dataset.

Figure 2.3 shows the dataset is being resize to [180 140 3]. This is the maximum input that can be received by the laptop to proceed the training process.

Figure 2.1: Figure of example of datasets(input)

The total dataset(input) that use in this project are six faces expression which is angry, happiness, sad, © Faculty of Electronic and Computer Engineering, FKEKK

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2.4 Applying formula. To be able to find the accuracy of the training, a formula for accuracy must be apply. The accuracy apply is: 𝑨𝑨 =

𝑻𝑻𝑻𝑻𝑻𝑻𝑻𝑻𝑻𝑻 𝒏𝒏𝒏𝒏.𝒐𝒐𝒐𝒐 𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄 𝒑𝒑𝒑𝒑𝒑𝒑𝒑𝒑𝒑𝒑𝒑𝒑𝒑𝒑𝒑𝒑𝒑𝒑 𝒅𝒅𝒅𝒅𝒅𝒅𝒅𝒅𝒅𝒅𝒅𝒅𝒅𝒅 𝑻𝑻𝑻𝑻𝑻𝑻𝑻𝑻𝑻𝑻 𝒏𝒏𝒏𝒏𝒏𝒏𝒏𝒏𝒏𝒏𝒏𝒏 𝒐𝒐𝒐𝒐 𝒅𝒅𝒅𝒅𝒅𝒅𝒅𝒅𝒅𝒅𝒅𝒅𝒅𝒅 𝒕𝒕𝒕𝒕𝒕𝒕𝒕𝒕𝒕𝒕𝒕𝒕𝒕𝒕

(1) Figure 3.1: Result of images being predict by system and live view.

2.5 Building CNN for training layer. Table 1 CNN uses for the training purposes.

No. 1 2 3 4 5

Name Dataset (input) Convolution2D ReLU Max pooling2D Fully connected

6 7

Softmax Classification

Input 180x140x3 6 layers (5x20x3) ReLU 10x10; stride[10 10] 6 fully connected layer Softmax -

Figure 3.2: Result of the accuracy

2.6 Increase dataset (optional) 4.0 CONCLUSION

The last step is increasing dataset which is optional but recommended. In this case, at first the dataset uses only 330 images but then it has been doubled up to 660 images.

A technology that can be uses for various reason, helping investigator to detect the expression of suspect and doctors for mental-ill person. This technology can be helpful in the future when the system is flawless. In this paper, the proposal was designing a machine learning tool for recognition of facial expression through artificial intelligence. The machine learning was used to train hundreds of datasets until it can get the highest accuracy possible. The more the images used the higher the accuracy it will be as it will have enough important features that are strong predictors of the target. But it also depends on the layer and epoch used in the CNN. Because these two is heavily related in getting the highest frequency for training.

3.0 RESULT AND DISCUSSION The result recorded is based on highest accuracy from five different test. The result is then compared, and the highest accuracy will be chosen. The four tests are: (i) Using 5,10,15 and 20 epochs (ii) Increasing the number training dataset and validation (iii) Increased number of dataset categories (iv) Adding layers on CNN Notes: For test (i) and (ii) only two categories dataset were used. For test (iii) three and five categories were used and for test (iv) five and six dataset were used.

REFERENCES [1]

Table 2: Results of accuracy of the test.

Test

Epoch

Time

1 2 3 4

20 10 10 10

18.40 1.45 5.46 6.10

Accuracy (%) 21.8750 75 91.0714 98.4848

© Faculty of Electronic and Computer Engineering, FKEKK

[2]

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B. R. Ilyas, B. Mohammed, M. Khaled, A. T. Ahmed and A. Ihsen, "Facial Expression Recognition Based on DWT Feature for Deep CNN," 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT), 2019, pp. 344-348, doi: 10.1109/CoDIT.2019.8820410. Bendjillali, R.I.; Beladgham, M.; Merit, K.; TalebAhmed, A. Improved Facial Expression Recognition Based on DWT Feature for Deep CNN. Electronics 2019, 8, 324.

Proceedings of Innovation and Technology Competition (INOTEK) 2021, Proceedings of Innovation and Technology Melaka, Malaysia, pp.Competition 119-120 (INOTEK) 2021

IoT Based Hypoglycemic Early Detection via ECG Signals M. S. Jefri1, A.S. Mohd Zain1

Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia

1

Corresponding author’s email: [email protected]

ABSTRACT: Hypoglycemia is a common and dangerous side effect of insulin therapy in patients with diabetes. Hypoglycemia should be diagnosed and predicted early, which can help with treatment and prevention. Besides that, the discomfort of finger pricking will deter patients from routinely checking their blood glucose levels. This project aims to design an electrocardiogram (ECG) circuit for early detection of hypoglycemia using ECG signals. Specifically, it analyzes the ECG signal's activity when hypoglycemia exists. To determine the hypoglycemia behavior, a circuit is designed that consists of instrumentation amplifier, driven right leg, low pass filter and DC offset cancelation. Then, an Internet of Things (IoT) platform was built to display the ECG signals. Besides that, the ECG was analyzed from the data of 3 people. The results showed the hypoglycemia occurs when the ECG graph showed higher Q-wave amplitude than normal. These results determine that hypoglycemia can be predicted early by ECG signals. An accident also can be prevented early by getting the notification. So, the family or doctor can take appropriate action to prevent accidents from happening.

Non-invasive methods are proposed such as infrared/near-infrared spectroscopy, iontophoresis, skin conductance, etc. None of these have proven to be accurate or unobtrusive enough. An efficient and responsive method for non-invasive monitoring of hypoglycemia has recently been developed using physiological parameters such as heart rate, skin impedance and electrocardiography (ECG). Based on this achievement, ECG provides a faster, more ubiquitous, non-invasive clinical and research screen than other physiological signals for the early detection of hypoglycemia and hyperglycemia [2]. From the electrocardiogram, the repolarization period is usually defined as the time interval from the Qwave onset to the T-wave offset, for example, QT interval. Figure 1.1 shows the most common QT interval measurements and the impact of hypoglycemia on these parameters [3]. In Laitinen et al., QT time prolongation and T-wave flattening were found to result in hypoglycemia [4].

Keywords: ECG; Hypoglycemia; IoT 1.

INTRODUCTION

The Internet of Things is not a new phenomenon, but it is a big issue throughout the world. Unsurprisingly, the Internet of Things (IoT) connects 18.2 billion devices globally. This includes all of the world's IoT categories. In essence, IoT refers to the internetworking of electronic equipment that allows data sharing between devices for specific domain applications. The internet of things (IoT) concept of internetworking simplifies human lives more than ever before. To some extent, current technologies used in the diabetes diagnostic testing and self-monitoring industry have also been expanded. However, with the use of novel design concepts, technology developments in the industry are expected to provide non-invasive glucose meters. There are a small number of non-invasive blood glucose monitoring systems on the market, but each of them has its own disadvantages in terms of function, cost, reliability and obtrusiveness. Intensive study has been devoted to the development of hypoglycemic warnings, leveraging concepts ranging from electrocardiography (ECG) identification or changes in skin conductance (due to sweating) to measurements of subcutaneous tissue glucose levels by the glucose sensor [1]. © Faculty of Electronic and Computer Engineering, FKEKK

Figure 1.1 The ECG Signal [4] An ECG is normally acquired by using metal electrodes put on the body's surface. The raw ECG data straight from the electrodes, on the other hand, are fundamentally unreliable 0.001mV-100mV with a normal value of 1mV. Noise and other interruptions, such as power line interference, pulse noise, electrostatic potential, stray capacitance, and adjacent electrical equipment, are readily polluted. Signal disruptions can also be introduced through an ECG by subject movement and muscular tension. A well-designed circuit may greatly improve computation precision, reliability, and repeatability [5].

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2.

METHODOLOGY

Person C are normal. As we can see from Figure 3.1, the graph for Person A is higher than the graph for Person B and Person C in Figure 3.2 and Figure 3.3 respectively. This is because the hypoglycemic occurred on Person A when the data is collected at a certain time. Furthermore, the graph in Figure 3.1 showed that the R-wave amplitude exceeded the normal number which is 2.96 mV. Besides that, the graphs in Figure 3.2 and Figure 3.3 are at normal values. 4.

CONCLUSION

As a conclusion, IoT based hypoglycemic detection via ECG signals has been successfully designed and developed. Then, the ECG circuit that consists of instrumentation amplifier, driven right leg, low pass filter and DC offset cancelation had been designed. Furthermore, the proposed project can analyze the behavior of the ECG signals when hypoglycemia occurs. When someone get the notification, an early action can be taken to prevent any accidents. Besides that, this project can be used for someone that has diabetes and is afraid of finger pricking. In addition, the project can be used in hospitals, clinics or homes. Note that the project does not have flash memory to store the data from the patients. So, the data from the patients are stored in the cloud.

Figure 2.1 The Flow Chart of The Research Methodology Figure 2.1 shows the flow chart that consists of 5 phase. First phase is designing the ECG circuit. Second phase id PCB fabrication. Third phase is developing the prototype. Fourth phase is analyzing the ECG signal from the data of 3 people. Lastly, designing the IoT prototype. 3.

Jefri & Mohd Zain, 2021

RESULTS AND DISCUSSION

ACKNOWLEDGEMENT The authors would like to thank Faculty of Electronic and Computer Engineering and Universiti Teknikal Malaysia Melaka for the financial support. REFERENCES [1]

Figure 3.1 ECG graph for Person A

[2]

[3]

Figure 3.2 ECG graph for Person B

[4]

Figure 3.3 ECG graph for Person C

[5]

The data are collected from three people which are Person A, Person B and Person C at night. Person A had a history of diabetes in his or her family. Person B and © Faculty of Electronic and Computer Engineering, FKEKK

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P. P. San, S. H. Ling, and H. T. Nguyen, “Block based neural network for hypoglycemia detection,” Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS, pp. 5666–5669, 2011, doi: 10.1109/IEMBS.2011.6091371. L. L. Nguyen, S. Su, and H. T. Nguyen, “Identification of Hypoglycemia and Hyperglycemia in Type 1 Diabetic patients using ECG parameters,” Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS, pp. 2716–2719, 2012, doi: 10.1109/EMBC.2012.6346525. J. A. Lipponen et al., “Dynamic estimation of cardiac repolarization characteristics during hypoglycemia in healthy and diabetic subjects,” Physiol. Meas., vol. 32, no. 6, pp. 649–660, 2011, doi: 10.1088/0967-3334/32/6/003. T. Laitinen et al., “Electrocardiographic alterations during hyperinsulinemic hypoglycemia in healthy subjects,” Ann. Noninvasive Electrocardiol., vol. 13, no. 2, pp. 97–105, 2008, doi: 10.1111/j.1542474X.2008.00208.x. W. Y DU, “Design of an ECG Sensor Circuitry for Cardiovascular Disease Diagnosis,” Int. J. Biosens. Bioelectron., vol. 2, no. 4, pp. 120–125, 2017, doi: 10.15406/ijbsbe.2017.02.00032.

Proceedings of Innovation and Technology Competition (INOTEK) 2021, Proceedings of Innovation andMalaysia, Technologypp. Competition Melaka, 121-122 (INOTEK) 2021

Design and Analysis of Vehicle Tracking System for Emergency Department N.S. Mahmood1, F. Idris1 Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia

1

Corresponding author’s email: [email protected]

ABSTRACT: An emergency is a situation that puts health, life, property or environment at immediate risk. Most emergencies require urgent intervention to prevent worsening the situation. Most of the developed or developing countries have within them several emergency services, whose purpose is to assist in dealing with any emergency. The most common emergency services include the police – to deal with situations involving violence and illegal misconduct of the law, fire, and rescue department – to manage emergencies involving fire, though they typically have a secondary rescue duties and first-aid emergency treatment and lastly, the ambulance – to provide medical assistance during emergencies. This project intended to design a tracking system for the fire and rescue department in Malaysia, to aid the emergency service to monitor and track the whereabouts of their vehicular assets in times of emergencies. The tracking system is designed by implementing a microcontroller and a GPS module to provide the location of the emergency vehicle in realtime and send the location data to an IoT platform for monitoring.

final destination via multiple routes. 2.

METHODOLOGY

The subsequent diagram provides the general workflow of this project.

Figure 1: Flow of the project The hardware configuration of the project is as below:

Keywords: Emergency; GPS; Tracking system. 1.

INTRODUCTION

This project focuses on the Fire and Rescue Department of Malaysia or most known by the locals as BOMBA. A vehicle tracking system for the BOMBA’s vehicular assets – the fire engines – is designed to track and monitor the location and whereabouts of the emergency vehicle, in real-time. The groundwork of this project is made up of two essential components namely a microcontroller and a GPS module. The microcontroller used in this project is an Arduino NodeMCU and the GPS module model is the NEO 6M GPS module. The project is proposed to provide a solution to some problems that can be faced by the emergency department such as a stolen emergency vehicle or delayed arrival to the emergency destination and to achieve the highlighted objectives. The objectives of this project are: i. To design a vehicle tracking system for the emergency department. ii. To provide and display the real-time location of the emergency vehicle in an IoT platform. iii. To measure and record the time taken for the emergency vehicle to arrive from the initial to © Faculty of Electronic and Computer Engineering, FKEKK

Figure 2: Hardware configuration For this project, when the Arduino NodeMCU is connected and powered on, the GPS module will provide the current location of the emergency vehicle when the push button is pressed. The LEDs are placed to indicate which button is pressed, the initial button or the final destination button. If the button is pressed, the initial location will be provided. Then, after arriving to the emergency destination, the second button will be pressed to indicate arrival and send the latest location to the IoT platform. 3.

RESULTS AND DISCUSSION

For the first part of the project, which is to determine the coordinates or location of the tracking system, I was able to display the location in the webpage. Next is to display the location of the emergency vehicle in an IoT platform which is the ThingSpeak.

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Table 2 Calculated time

Route Morning hours Route 1 Route 2 Route 3 (8am - 11am) 12.13333333 19.53333333 23.86666667

Time difference (in minutes)

(a)

(b)

Figure 3 (a) Coordinates displayed in webpage, (b) coordinates displayed in ThingSpeak in graphical form.

Lunch hour (12pm - 3pm)

Route 1 Route 2 Route 3 10.58333333 14.13333333 31.7

After hours (6pm - 9pm)

Route 1

Average (minutes)

4.

Then, by using the coordinates provided, the precise location can be viewed in Google Maps to correctly indicate the whereabouts of the emergency vehicle, as in Figure 4.

16.4

Route 2

14.25

Route 3

9.3

13.03888889 15.97222222 21.62222222

CONCLUSION

Emergencies and accidents, as one knows, have caused significant loss of lives and properties. Emergency departments are so crucial in that they must be given great care and security to ensure they can improve their operation to respond to emergencies. Cases of vehicular manslaughter involving emergency vehicle are no stranger to the society and the proposed project model serves as the minimal criterion to keep track of the emergency vehicle when it is out and about tending to emergencies. ACKNOWLEDGEMENT The authors would like to thank Faculty of Electronic and Computer Engineering and Universiti Teknikal Malaysia Melaka for the financial support.

Figure 4 Google Maps visual of one of the coordinates The data from ThingSpeak is then exported to.csv file to be analyzed. Fulfilling the third and final objective of this project, which is to measure the time taken for the emergency vehicle to arrive at the final destination via multiple routes, the time difference between the initial and final location is calculated. Table 1 illustrates the data collected in the database, exported to Excel. From the Table 2, it is observed that out of all the three routes used to test this tracking device, the best route is Route 1, averaging at 13 minutes as opposed to Route 2 and Route 3 with a total of 16 minutes and 23 minutes, respectively. Although, after working hours, the best route is ultimately Route 3, which took less than 10 minutes to arrive at the final destination.

REFERENCES [1]

[2] [3] [4]

Table 1 Exported data from database

©

Time Lat Long LAT (DMS) LONG (DMS) Route 8:28:56 AM 2.70453 101.99015 ° 00' 00'' ° 00' 04'' 8:33:34 AM 2.71276 102.00071 ° 00' 00'' ° 00' 04'' Route 1 8:39:45 AM 2.73904 101.9835 ° 00' 00'' ° 00' 04'' 8:41:04 AM 2.75685 101.98203 ° 00' 00'' ° 00' 04'' 9:19:15 AM 2.7038 101.98967 ° 00' 00'' ° 00' 04'' 9:27:38 AM 2.71916 101.97968 ° 00' 00'' ° 00' 04'' 9:32:58 AM 2.72576 101.95925 ° 00' 00'' ° 00' 04'' Route 2 9:38:01 AM 2.75011 101.9744 ° 00' 00'' ° 00' 04'' 9:38:47 AM 2.75671 101.98211 ° 00' 00'' ° 00' 04'' 10:26:27 AM 2.70338 101.98979 ° 00' 00'' ° 00' 04'' 10:31:44 AM 2.71392 102.00074 ° 00' 00'' ° 00' 04'' 10:37:23 AM 2.72897 101.99095 ° 00' 00'' ° 00' 04'' Route 3 10:44:21 AM 2.73958 101.97767 ° 00' 00'' ° 00' 04'' 10:50:19 AM 2.75694 101.98196 ° 00' 00'' ° 00' 04'' 12:04:19 PM 2.70376 101.98968 ° 00' 00'' ° 00' 04'' 12:09:24 PM 2.71456 101.97872 ° 00' 00'' ° 00' 04'' 12:16:01 PM 2.72486 101.98104 ° 00' 00'' ° 00' 04'' 12:20:01 PM 2.7257 101.96672 ° 00' 00'' ° 00' 04'' Route 3 12:28:30 PM 2.72362 101.96245 ° 00' 00'' ° 00' 04'' 12:34:22 PM 2.74334 101.96788 ° 00' 00'' ° 00' 04'' 12:36:01 PM 2.75714 101.9817 ° 00' 00'' ° 00' 04'' 1:12:10 PM 2.70351 101.98966 ° 00' 00'' ° 00' 04'' 1:18:23 PM 2.73978 101.98877 ° 00' 00'' ° 00' 04'' Route 1 1:22:45 PM 2.75711 101.98174 ° 00' 00'' ° 00' 04'' Faculty of Electronic and101.98964 Computer° 00' Engineering, FKEKK 2:26:09 PM 2.70387 00'' ° 00' 04'' 2:31:16 PM 2.72342 101.99282 ° 00' 00'' ° 00' 04'' Route 2 2:36:35 PM 2.73859 101.98458 ° 00' 00'' ° 00' 04'' 2:40:17 PM 2.75687 101.98203 ° 00' 00'' ° 00' 04'' 6:02:13 PM 2.70363 101.98958 ° 00' 00'' ° 00' 04'' 6:07:03 PM 2.72249 101.98022 ° 00' 00'' ° 00' 04'' Route 2 6:13:00 PM 2.73372 101.96529 ° 00' 00'' ° 00' 04'' 6:16:28 PM 2.75696 101.98186 ° 00' 00'' ° 00' 04''

[5]

[6]

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A. Press, “Michigan firefighter charged in fatal crash in Minnesota,” Detroit Free Press, 30[Online]. Available: Aug-2016. https://www.freep.com/story/news/local/michig an/2016/08/29/michigan-driver-jailed-fatalfire-truck-crash-minnesota/89539024/. [Accessed: 30-May-2021]. Ahmed, S., Rahman, S., & Costa, S. E. (2015). Real-time vehicle tracking system (Doctoral dissertation, BRAC University). Clark, J. R. (2017). Vehicular Manslaughter. Air medical journal, 36(5), 229-230. Dinkar, A. S., & Shaikh, S. A. (2011). Design and implementation of vehicle tracking system using GPS. Journal of Information Engineering and Applications, 1(3), 1-7. Pooja, S. (2013). Vehicle tracking system using GPS. International Journal of Science and Research (IJSR), India Online ISSN, 23197064. S. Lee, G. Tewolde and J. Kwon, "Design and implementation of vehicle tracking system using GPS/GSM/GPRS technology and smartphone application," 2014 IEEE World Forum on Internet of Things (WF-IoT), 2014, pp. 353-358, doi: 10.1109/WFIoT.2014.6803187.

Proceedings of Innovation and Technology Competition (INOTEK) 2021, Proceedings of Innovation and Technology Melaka, Malaysia, pp.Competition 123-124 (INOTEK) 2021

Region-of-Interest source localization analysis for cognitive impairment during Public Speaking Anxiety D.S. Bala1, F.S. Feroz1 1

Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia Corresponding author’s email: [email protected]

*

ABSTRACT: The purpose of this study is investigate the temporal dynamics of the dorsal anterior cingulate cortex (dACC) and the rostral ventral (rv) ACC during the interaction of the Stroop task with cognitive control in people who have public speaking anxiety (PSA). Using a modified Stroop paradigm, we investigated the time course of neuronal activations within the dACC and rvACC using event-related potentials (ERP) and standardized low-resolution electromagnetic tomography (sLORETA) region of interest (ROI) source localization studies. The dACC and rvACC had very distinct time courses of brain activation, with the rvACC having more dramatic initial responses and the dACC having enhanced activity primarily in the late negative window. We observed the rVACC in HPSA group has the highest current density compared to the rVACC in LPSA group which is slightly lower in current density within the rvACC, a post-hoc Bonferroni test revealed that the incongruent condition had significantly higher mean current density than the congruent condition. During the N450 window, there is a substantial negative correlation between trait anxiety and current density in the incongruent condition within the rvACC for HPSA subjects, but not for LPSA subjects. Keywords: rVACC, dACC, Stroop Task, Public Speaking Anxiety 1.

perform the Stroop task individually. Before analyzing EEG raw data, the EEG preprocessing needed to be done by ICA in MATLAB plugin (EEGLAB) and sLORETA. Lastly, the interpretation of data can be carried out to conclude the findings regarding this experiment to be recorded in the report. 2.1 Experiment Paradigm Participants in the Stroop Experiment were given different coloured words, such as ‘Hijau' (green), ‘Merah' (red), and ‘Kuning' (blue) (Yellow). When the colours were responded to, a black blank screen appeared for 1500ms before another fixation point appeared for 500ms. The procedure was repeated until all of the colours were visible. In the congruent trial, the same colour will be used with the same word, whereas in the incongruent trial, a different colour will be used with a different word.

INTRODUCTION

The dynamic interplay between emotional and cognitive functioning is apparent in our daily lives. Unbalanced cognitive impairment, such as that seen in anxiety and mood disorders, can be harmful. PSA subjects, more concise have feelings of dread and anxiety when they find themselves in situations where they are the centre of attention, resulting in nausea and excessive sweating. The significance of this study is to identify and observe the brain activity of PSA subjects during the Stroop Task to detect aberrant emotional-conflict modulation. By conducting this analysis, we discover new knowledge which is the source localization analysis for aberrant emotional-conflict modulation in PSA subjects. An opportunity has been taken to study this selected title to make a scientific contribution in the field of cognitive neuroscience.

The adjusted Greenhouse-Geisser (GG) correction to the univariate repeated measures ANOVA p values, the unadjusted degrees of freedom, and epsilon values were provided throughout this research to comply with the sphericity criterion of the repeated measures analysis of variance (ANOVA). The Bonferroni t technique was employed in all multiple comparison tests in this work because it is resistant to sphericity violations.

2.

3.

Figure 1 Experiment Scenario (Congruent) 2.2 Statistical Analysis.

METHODOLOGY

In both participants, there was a significant ROI group at effect [F (1,22) = 3.184, GG epsilon = 1.00, partial, p =.088].

Twenty-two data were analyzed, in which 12 of them were categorized with low PSA and the rest with high PSA during the Stroop Task. They were required to © Faculty of Electronic and Computer Engineering, FKEKK

RESULTS AND DISCUSSION

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that, there was a significant ROI × congruence × group [2] effect at the N450 window [F (1,22) = 21.90, GG epsilon = 1.00, partial ƞ2 = 0.09, p < .001]. Post-hoc Bonferroni test indicated significant higher mean current density in theCompetition incongruent compared to the congruent condition Proceedings of Innovation and Technology (INOTEK) 2021 Bala & Feroz, 2021 within the rvACC (t (22.42) = 2.074, p < .001) in the Bala & Feroz, 2021 LPSA group. between the mean current density of rVACC incongruent The scatterplots (Figure 3) depict the association condition within the trait score and the trait score itself between the mean current density of rVACC incongruent (vertical axis; calculated at theEngineering, N450 window). During © Faculty of Electronic Computer FKEKK condition within theandtrait score and the trait score itself the N450 window, there is a substantial negative 124 (vertical axis; calculated at the N450 window). During correlation between trait anxiety and current density the N450 window, there is a substantial negative inside the rvACC for HPSA patients, but not for LPSA correlation between trait anxiety and current density patients. [r = -.6524, p = .021]. inside the rvACC for HPSA patients, but not for LPSA patients. [r = -.6524, p = .021]. 3.1 Discussion. 3.1 Within Discussion. the rvACC, the incongruent condition had a larger mean current density than the congruent condition. Within the rvACC, the incongruent condition had a Previous research has found conflict activation in the larger mean current density than the congruent condition. dorsal anterior cingulate cortex (dACC), which is an Previous research has found conflict activation in the important element of the executive control network. The dorsal anterior cingulate cortex (dACC), which is an right ventral ACC (vACC) was activated for conflict important element of the executive control network. The processing in emotional stimuli, implying that it is also right ventral ACC (vACC) was activated for conflict activated for emotional conflict processing [1]. The most processing in emotional stimuli, implying that it is also remarkable finding of this research is that the HPSA activated for emotional conflict processing [1]. The most group exhibits unusual behaviour. The HPSA group had remarkable finding of this research is that the HPSA no Stroop effect, as seen by the stronger negative (smaller group exhibits unusual behaviour. The HPSA group had amplitude) in congruent than incongruent conditions. no Stroop effect, as seen by the stronger negative (smaller Our findings are backed up by [2] studies, which show amplitude) in congruent than incongruent conditions. that worry can have a negative impact on working Our findings are backed up by [2] studies, which show memory. The correlation analysis revealed a positive that worry can have a negative impact on working relationship between anxiety and brain activation in the memory. The correlation analysis revealed a positive pregenual ACC (rvACC) under moderate threat but a relationship between anxiety and brain activation in the definite negative relationship with strong threat. pregenual ACC (rvACC) under moderate threat but a definite negative relationship with strong threat. 4. CONCLUSION

(a) (a)

4. This CONCLUSION study revealed that the current density of the rVACC in the HPSA group is highest at 535.16ms, while This study revealed that the current density of the the current density of the rVACC in the LPSA group is rVACC in the HPSA group is highest at 535.16ms, while slightly lower. When comparing current density within the current density of the rVACC in the LPSA group is dACC and rvACC, it is obvious that rvACC has a larger slightly lower. When comparing current density within current density. In the LPSA incongruent condition, dACC and rvACC, it is obvious that rvACC has a larger however, there was a tendency toward significance at current density. In the LPSA incongruent condition, rvACC. As for the ROI correlation, there is a negative however, there was a tendency toward significance at correlation between Trait anxiety and the current density rvACC. As for the ROI correlation, there is a negative within the rvACC in the incongruent condition. correlation between Trait anxiety and the current density within the rvACC in the incongruent condition. ACKNOWLEDGEMENT

(b) of Brain Activity within the Figure 2 (a) Time Course (b) dACC and rvACC for HPSA and LPSA. (b) The N450 Figure 2 (a) Time Course of Brain Activity within the ROI x congruence x group dACC and rvACC for HPSA and LPSA. (b) The N450 ROI x congruence x group

ACKNOWLEDGEMENT The authors would like to thank Faculty of Electronic and Computer Engineering and Universiti Teknikal Malaysia The authors would like to thank Faculty of Electronic and Melaka for the financial support. Computer Engineering and Universiti Teknikal Malaysia Melaka for the financial support. REFERENCES REFERENCES [1] P. R. Santhana Gopalan, O. Loberg, J. A. Hämäläinen, and P. H. T. Leppänen, [1] P. R. Santhana Gopalan, O. Loberg, J. A. “Attentional processes in typically developing Hämäläinen, and P. H. T. Leppänen, children as revealed using brain event-related “Attentional processes in typically developing potentials and their source localization in children as revealed using brain event-related Attention Network Test,” Sci. Rep., vol. 9, no. 1, potentials and their source localization in pp. 1–13, 2019, Attention Network Test,” Sci. Rep., vol. 9, no. 1, [2] C. M. MacLeod, “Half a century of research on pp. 1–13, 2019, the Stroop effect: An integrative review.,” [2] C. M. MacLeod, “Half a century of research on Psychol. Bull., vol.109, no. 2, pp.163–203, 1991 the Stroop effect: An integrative review.,” Psychol. Bull., vol.109, no. 2, pp.163–203, 1991

Figure 3 Scatterplots of the N450 Mean Current Density with Trait Score vs rVACC incongruent. Figure 3 Scatterplots of the N450 Mean Current Density Trait Score rVACCinincongruent. Thewith figure shows thatvsrVACC the LPSA group had an initial rise in activity peaking at 82.03ms. Other than The figure shows that rVACC in the LPSA group had that, there was a significant ROI × congruence × group an initial rise in activity peaking at 82.03ms. Other than effect at the N450 window [F (1,22) = 21.90, GG epsilon that, there was a significant ROI × congruence × group = 1.00, partial ƞ2 = 0.09, p < .001]. Post-hoc Bonferroni effect at the N450 window [F (1,22) = 21.90, GG epsilon test indicated significant higher mean current density in = 1.00, partial ƞ2 = 0.09, p < .001]. Post-hoc Bonferroni the incongruent compared to the congruent condition test indicated significant higher mean current density in within the rvACC (t (22.42) = 2.074, p < .001) in the the incongruent compared to the congruent condition LPSA group. within the rvACC (t (22.42) = 2.074, p < .001) in the LPSA group. The scatterplots (Figure 3) depict the association The scatterplots (Figure 3) depict the association

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Proceedings of Innovation and Technology Competition (INOTEK) 2021, Proceedings of Innovation andMalaysia, Technology Melaka, pp.Competition 125-126 (INOTEK) 2021

Performance analysis of notification alert system using arduino for emergency responders A. I. I. A. Yazid, J. M. Sultan* Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia Corresponding author’s email: [email protected]

*

ABSTRACT: Time management is a method of organizing and planning how to divide the time spent on various activities. It is the most critical element that will affect frontline workers since they must save victims' lives while ensuring their safety in dangerous situations, such as firefighters. This study is about a supplemental device that can help firemen overcome the problem of time delays while also increasing their time response operations as a time management approach. The system uses Arduino, ESP8266 Wi-Fi Shield, and Blynk to notify the firefighter on the inside about the remaining time, while the buzzer alerts the supervisory structure outside that the firefighter on the inside only has one minute left. The device also has a red LED that indicates the critical time and an LCD that displays the countdown. The delay is investigated utilizing the findings of many tests that have been undertaken.

Shield is stacked atop the Arduino Uno, while jumper wires link the push button, buzzer, red LED, and 220ohm resistor to the breadboard. The system is set up so that the countdown begins 30 minutes after the button is pressed on a 162 LCD with I2C. During the countdown, the Arduino IDE configuration allows the Blynk app on the communication device to deliver alerts every 30, 20, 10, and 1 minute. Because the SCBA is only expected to be useful for 30 minutes, the notifications serve as a reminder of how much time is left. The buzzer and red LED will be turned on one minute before the timer expires. The buzzer serves as an alarm siren, indicating the critical moment if the firefighter inside does not come out, while the red LED serves as a warning indication and is especially useful at night. 3.

Keywords: Firefighters; Blynk; Notify 1.

3.1 Indoor environment Table 1 Distance between the project and the communication device at indoor environment Distance (m) Delay (sec) 1 1 2 2 2 3 2 2 1 2 5 2 2 2 1 7 2 2 2 2 10 2 1 2 2 15 2 2 1 2

INTRODUCTION

Firefighters who use the Self-Contained Breathing Apparatus (SCBA) to enter a space with little to no oxygen to save people's lives must be able to manage their time well. The device usually takes 30 minutes to finish the air tank, so they only have 10 minutes to find the victims and another 20 minutes to get in and out of that perilous location. The project will give the firefighter an estimate of how much time they have until they return to the main center operation. After the firefighter presses the button to enter the building, the countdown is started using Arduino. After pressing the button, the firefighter will take his phone with him to get notifications from the ESP8266 Wi-Fi Shield and Blynk. The device also has an LCD screen that displays the countdown, a buzzer that serves as an alert, and red LED lights that indicate the critical time, which is one minute before the timer expires. The buzzer will continue to beep until a firefighter emerges from the building and presses the button to indicate that the mission is complete. 2.

METHODOLOGY

Figure 1 Graph delay at indoor environment

The project's hardware includes an ESP8266 Wi-Fi Shield, an Arduino UNO, a 16X2 LCD with I2C, a push button, a buzzer, a red LED, a 220-ohm resistor, a breadboard, and jumper wires. The ESP8266 Wi-Fi © Faculty of Electronic and Computer Engineering, FKEKK

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According to the graphs in the three cases, the notification delays are nearly consistent, ranging from 2 seconds to 15 seconds at lengths of 1 meter to 15 meters. As long as the project and the communication device are linked to the Internet, the alerts will be received.

3.2 Outdoor environment Table 2 Distance between the project and the communication device at outdoor environment Distance (m) Delay (sec) 1 2 2 1 2 3 2 2 2 1 5 2 1 2 2 7 2 2 2 2 10 2 2 1 2 15 2 2 2 2

4.

CONCLUSIONS

One of the project's goals, to design and create a notification alert system for emergency responders using Arduino, has been met because the project can deliver messages several times. On the other hand, the second objective was accomplished using the testing graphs to analyze and assess the notification system based on time delay and distance effect. This supplemental equipment will assist firemen in overcoming time delays, as well as increasing their time response operations as a time management approach. Furthermore, the goods employed in the project posed no risk to the environment or humans. As long as the emergency department uses SCBA, the project might last for a long period. ACKNOWLEDGEMENT The authors would like to thank Faculty of Electronic and Computer Engineering (FKEKK) and Universiti Teknikal Malaysia Melaka for the financial support.

Figure 2 Graph delay at outdoor environment 3.3 Indoor and outdoor environment

REFERENCES

Table 3 Distance between the project and the communication device at indoor and outdoor environment Distance (m) Delay (sec) 1 2 2 2 2 3 2 2 2 2 5 2 2 2 2 7 2 2 2 2 10 2 2 2 2 15 2 2 1 2

[1]

[2]

[3]

[4]

Figure 3 Graph delay at indoor and outdoor environment

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A. Amin and M. N. A. Khan, “A survey of GSM technology to control remote devices”, International Journal of u-and eService, Science and Technology, vol. 7, no. 6, pp. 153–162, 2014. M. Betz and V. Wulf, “EmergencyMessenger: a text-based communication concept for indoor firefighting”, in proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1515–1524, 2014. Chief Fire & Rescue Adviser. Fire and Rescue Service: Operational Guidance Incidents Involving Hazardous Materials. Norwich, UK: TSO (The Stationery Office), 2012. F. Marques et al., “FIREMAN: FIRefighter team brEathing Management system using ANdroid”, in proceedings of the 2013 International Symposium on Wearable Computers (ISWC '13), Association for Computing Machinery, New York, NY, USA, pp. 133–134, 2013.

Proceedings of Innovation and Technology Competition (INOTEK) 2021, Proceedings of Innovation andMalaysia, Technology Competition Melaka, pp. 127-128 (INOTEK) 2021

Analysis of inertial measurement accuracy using complementary filter for MPU6050 sensor N. H. Zani, J. M. Sultan* Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia *

Corresponding author’s email: [email protected]

ABSTRACT: This research includes finding in improving the accuracy of the Inertial Measurement from the MPU6050 Sensor. Sensors are the main item in collecting information for measuring and transmit data based on production. However, different costs of sensors lead to varying levels of accuracy. MPU6050 is a lowcost Inertial Measurement Units (IMU) containing a 3axis accelerometer and 3-axis gyroscope for complete 3axis orientation measurement. It may be a straightforward sensor in managing with extricating exact data. In this ponder, primary issues were analysed was fathomed that appear in most sensors when getting a precise angle of the Sensor with the presence of vibration. A digital filter is used to solve the problem to achieve accurate internal measurement during any application. The Sensor is connected to ESP8266 NodeMCU, and later on, the data is displayed in the IoT platform.

ESP8266 NodeMCU and communicates through the I2C protocol. A printed circuit board is developed to provide a stable base for components. The MPU6050 sensor is placed horizontally next to the ESP8266. As for the finishing, the PCB was then put into an enclosure box, as shown in Figure 1.

Keywords: Inertial measurement units; MPU6050; Complimentary filter. 1.

Figure 1 Printed Circuit Board (PCB) for the project 2.2 Testing setup for accurate reading of MPU6050 Sensor by using a Goniometer tool The dynamic analysis comprised tests where the IMU-sensor was settled on a straight surface that can pivot 360°. The setup of the tests can be seen in Figure 2. The Goniometer tool is used to measure the accuracy of the X, Y, and Z-axis reading. The Sensor was settled in a position that measures yaw, pitch, and roll when revolutions are presented around the circuit board.

INTRODUCTION

An Inertial Measurement Unit (IMU) is the centre of inertial positioning and navigation systems. Every IMU comprises at any rate three accelerometers and three gyroscopes (angular-rate sensors) [1]. Before IMUs are collected, gyroscopes and accelerometers are dependent upon independent tests [2]. In any case, it is essential to decide their boundaries when they are parts of IMUs on the grounds that the yield boundaries of gyroscopes and accelerometers are fixed to the reference axes of the IMU. The angular rate signal of MEMS gyroscope must be integrated about time in arrange to produce readable value where these will be caused float issues [3]. Merging yields of two or multi-sensors through digital filtering set to adjust the float issue of MEMS gyroscope [4]. Hence, building the over, due to incompetency of MEMS gyroscope that can be effortlessly influenced by float, a method by blending output of MEMS gyroscope and accelerometer through a complementary filter is proposed in this research. 2.

METHODOLOGY Figure 2 Setup for measuring the accuracy of the Yaxis

2.1 Hardware setup The 9V of power supply from the DC battery is connected to ESP8266 NodeMCU. MPU6050 will be activated with a 5V power supply from the VCC pin of © Faculty of Electronic and Computer Engineering, FKEKK

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RESULTS AND DISCUSSION

pass filter Gl(z). The filter that high pass gyro measurement is a high pass filter Gh(z) as Equation 1.

The data that is produced by MPU6050 Sensor obtain from experiment section 2 is stored in excel. It is consists of value X-axis (Roll), Y-axis (Pitch), and Z-axis (Yaw). Clearly, experimental results in Table 1 show that the one programmed with a complimentary filter proven accuracy of approximately ±1.5° rather than with the raw IMU reading from the MPU6050 sensor. This result verifies that both the gyroscope and accelerometer require a filter to ensure the output free of interference and achieve desirable accuracy.

𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 = 𝐺𝐺𝐺𝐺𝐺𝐺 × 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 𝑜𝑜𝑜𝑜 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 + 𝐺𝐺ℎ(𝑧𝑧) × 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 𝑜𝑜𝑜𝑜 𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺

(1)

The filtered value is then prompt onto IoT Platform (Blynk). This way, any user implementing this system can monitor the angle accuracy of their application remotely. Figure 4 is the Blynk interface from the data connected to ESP8266 NodeMcu.

Table 1 Result of axis reading from MPU6050 Sensor No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 ∑

Threshold (⸰) X, Y, Z 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 1710

Raw Output Reading (⸰) X Y Z 1.5 0.16 0 2.51 8.85 7.05 1 14.88 -10.02 1 16.38 27.55 4.51 19.89 31.05 5.01 13.38 46.11 5.01 19.9 38.6 5.01 32.43 36.59 7.02 32.93 34.59 9.02 59.48 35.09 9.02 54.96 33.09 11.52 56.97 31.08 12.53 51.46 31.08 12.53 63.48 29.08 12.53 68 27.07 10.03 62.49 31.07 10.53 46.96 44.63 4.52 10.88 46.63 14.06 10.88 26.55 138.9 644.36 546.9

Filtered Output Reading (⸰) X Y Z 0.796 0.419 0.271 9.231 10.325 11.314 19.25 20.515 20.831 28.89 30.274 30.304 38.2 39.907 41.726 47.61 50.133 49.847 57.35 60.872 60.741 70.13 70.655 70.413 78.85 78.905 81.308 92.91 90.503 91.381 102.36 100.809 102.254 111.35 109.608 110.502 123.39 122.304 122.243 129.58 130.711 132.054 138.38 142.738 141.097 148.86 150.158 151.07 158.16 162.246 160.63 171.96 171.779 168.63 179.36 179.838 180.961 1706.6 1722.7 1727.58

Figure 4 IoT platform interface on Blynk application 4.

CONCLUSION

The presented work illustrates combining the MPU6050 gyroscope and accelerometers through a complimentary digital filter with calculation, a = 0.97 proven to discover the accurate value of angle and has been effectively created. Based on the dynamic analysis testing and utilising the goniometer tool, the raw deal coming from the MPU6050 sensor must be filtered using the complementary filter. Besides, the field test demonstrates that the developed framework accomplishes a precision of ±1.5° concerning the true north.

Figure 3 shows the Serial Plotter Arduino IDE graph for the X, Y, and Z-axis reading with the same rotation applied.

REFERENCES [1]

[2] [3]

[4]

Figure 3 Graph before complimentary filter (Top) and graph after complimentary filter is applied (Bottom) From the graph above, raw reading produces unnecessary plotting and an inaccurate value of the angle. A complimentary filter fixes these problems by making the accelerometer measurement pass through the low © Faculty of Electronic and Computer Engineering, FKEKK

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S. Askari, M. H. Asadian and A. M. Shkel, “High quality factor MEMS gyroscope with whole angle mode of operation,” 2018 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL), 2018, pp. 1–4. V. Ziemann, “Gyroscope tracking 3D-motion via WIFI”, Uppsala University, 2017, pp. 35. A. M. Kamal, S. H. Hemel, and M. U. Ahmad, "Comparison of Linear Displacement Measurements Between A Mems Accelerometer and Hc-Sr04 Low-Cost Ultrasonic Sensor," 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), 2019, pp. 1–6. R. Maulana, W. Kurniawan and H. Z. Fahmi, “Noise Reduction on the Tilt Sensor for the Humanoid Robot Balancing System Using Complementary Filter”, 2018 The 2nd International Conference on Mechanical, System and Control Engineering (ICMSC 2018), vol. 220, pp. 1–5, 2018.

Proceedings of Innovation and Technology Competition (INOTEK) 2021, Proceedings of Innovation andMalaysia, Technology Melaka, pp.Competition 131-132 (INOTEK) 2021

Non-invasive bilirubin screening using color skin detection M. F. Ayob, M. M. M. Aminuddin* Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia Corresponding author’s email: [email protected]

*

ABSTRACT: Hyperbilirubinemia is a yellowish or sometimes yellow-brownish color of an infant's body. To verify the accurate bilirubin level, blood samples are often collected, and several laboratory procedures are undertaken. Since the process is repetitious, it causes trauma to newborns, and it also required the involvement of professionals to carry out this test [1]. The objective of this project is to design the non – invasive method, which is the jaundice detector by using the color sensor to detect jaundice. Next is to detect the jaundice presence in the mixed color between human skin color shade with bilirubin reference color tone in the term of RGB values. In this project, the suggested jaundice detection system uses Asian Skin Color Shade. No babies are involved in this project. Random bilirubin color represents the input data that mix with the human skin color shades via an online tool called the Miracle Color Tool. The non – jaundice detected at (255, 255, 0) while the jaundice is below the (255, 255, 0) in terms of RGB values. The detection is done by using the Arduino platform along with the LDR sensor and RGB LED. The RGB values are displayed on the LCD.

2.

2.1 Simulation/online process Four human skin color shades were mixed with bilirubin reference color tones B1, B2, B3, and B4, respectively. Two types of human skin color shades were mixed with B2 by using an online tool to find the weight value. B2W1 + X1W2 = Z1 B2W1 + X2W2 = Z2

where, W1 = W2 = weight B2 = bilirubin color X1 & X2 = skin color Z1 & Z2 = mixed color in RGB values

2.2 Hardware/practical process All hardware was bought and prepared accordingly. Hardware was installed accordingly on a breadboard with the right connections, as shown in Figure 1. Arduino IDE software for Windows was installed inside the computer. Arduino codes were compiled and uploaded into Arduino UNO by using the Arduino IDE software. The color sensor (jaundice detector) was calibrated by using B2 jaundice reference color. All the test from the previous simulation/online tool procedure was tested by using the jaundice detector.

INTRODUCTION

Hyperbilirubinemia is a yellowish or sometimes yellow-brownish color of an infant’s body when the bilirubin level is beyond the normal level, under 5.2 mg/dL, due to internal organs that have just begun to mature [2]. It is formed as the result of the red-blood-cell breakdown in bone marrow cells and the liver. The quantity of bilirubin produced relates directly to the volume of killed blood cells [3]. Usually, jaundice occurs 2 to 3 days after the baby is born and goes away within the first few weeks. Sooner or much later, certain forms can turn up [4]. There are two types of methods to detect jaundice: using an invasive and non-invasive method. This project focus on the non – invasive method, which is by using skin color detection. The detection is done by using the Arduino platform along with the LDR sensor and RGB LED. The RGB values are displayed on the LCD. It results that all the non – jaundice detected at (255, 255, 0) while the jaundice is below the (255, 255, 0) in terms of RGB values. The paper is structured as follows. Section 2 is the methodology, Section 3 is for results and discussion, while Section 4 is the conclusion.

Figure 1 Hardware installed on a breadboard © Faculty of Electronic and Computer Engineering, FKEKK

(1) (2)

The weight value was calculated by using the elimination method on both Equations 1 and 2. The weight value W1 and W2 was obtained and recorded. The other human skin color shades were mixed with the bilirubin reference color tone. All the mixed colors were recorded and ready to use for the next step.

Keywords: Hyperbilirubinemia; Bilirubin; Arduino 1.

METHODOLOGY

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RESULTS AND DISCUSSION

Based on Tables 1 and 2, it is proved that all the skin colors mixed with B1 don’t have jaundice conditions. RGB values, which are (255, 255, 0) were detected because the hardware was calibrated by using B2. B2 is FFFF00 in hexadecimal, converted into RGB values will get (255, 255, 0). It means that the highest RGB values that the hardware can detect in the non – jaundice condition is (255, 255, 0). Below than (255, 255, 0) of the RGB values is for the jaundice condition.

Table 1 Simulation/online results Mixture (Simulation/online tool) RGB Values Hex Decimal R G B FFF2CA FFEDC5 FFE8C0 FFE3BC FFF28D FFED86 FFE87F FFE378 FFDC8D FFD786 FFD17F FFCB78 FFDE8D FFD986 FFD37F FFCD78

16773834 16772549 16771264 16769980 16773773 16772486 16771199 16769912 16768141 16766854 16765311 16763768 16768653 16767366 16765823 16764280

255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255

242 237 232 227 242 237 232 227 220 215 209 203 222 217 211 205

202 197 192 188 141 134 127 120 141 134 127 120 141 134 127 120

4.

In this project, jaundice presence was detected by using the non – invasive method. A jaundice detector has been successfully designed to detect the jaundice presence inside a baby’s skin. But in this project, human skin color and bilirubin reference color shade were represented as the baby’s skin color and the jaundiced color, respectively. All the simulation or online tool results were used to detect any jaundice presence in the color mixture by using the jaundice detector. It results that all the non – jaundice detected at (255, 255, 0) while the jaundice is below the (255, 255, 0) in terms of RGB values. The detection is done by using the Arduino platform along with the LDR sensor and RGB LED. The RGB values are displayed on the LCD. This shows that the jaundice detector is working properly. ACKNOWLEDGEMENT

Table 2 Hardware/practical results Mixture (Hardware/practical) Jaundice Hex Decimal RGB Values R G B FFFF00 16776960 255 255 0 No FFFF00 16776960 255 255 0 No FFFF00 16776960 255 255 0 No FFFF00 16776960 255 255 0 No FFE400 16770048 255 228 0 Yes FFD400 16765952 255 212 0 Yes FFBD00 16760064 255 189 0 Yes FFBC00 16759808 255 188 0 Yes FFAC00 16755712 255 172 0 Yes FFAA00 16755200 255 170 0 Yes FFA700 16754432 255 167 0 Yes FFB300 16757504 255 179 0 Yes FFE400 16770048 255 228 0 Yes FFCC00 16763904 255 204 0 Yes FFD100 16765184 255 209 0 Yes FFBF00 16760576 255 191 0 Yes

The authors would like to thank Universiti Teknikal Malaysia Melaka for the financial support. REFERENCES [1]

[2]

[3] [4]

Table 3 Weight value Weights, W W1 = 0.5445712738 W2 = 0.4554140127

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A. K. Chowdhary, S. Dutta, and R. Ghosh, “Neonatal Jaundice Detection Using Colour Detection Method,” International Advanced Research Journal in Science, Engineering and Technology, vol. 4, no. 7, pp. 197–203, 2017, doi: 10.17148/IARJSET.2017.4733. A. H. Abu Bakar, M. N. Mohd Hassan, A. Zakaria, and A. A. Abdul Halim, “Pearson’s Correlation Coefficient Analysis of non-invasive Jaundice Detection based on Colour Card Technique,” J. Phys. Conf. Ser., vol. 1372, no. 1, 2019, doi: 10.1088/1742-6596/1372/1/012012. “Bilirubin | biochemistry | Britannica.” https://www.britannica.com/science/bilirubin (accessed Dec. 17, 2020). “Does My Baby Have Jaundice? What Are the Treatments?” https://www.webmd.com/parenting/baby/digesti ve-diseases-jaundice#1 (accessed Jan. 03, 2021).

Proceedings of Innovation and Technology Competition (INOTEK) 2021, Proceedings of Innovation and Technology Melaka, Malaysia, pp.Competition 133-134 (INOTEK) 2021

Performance analysis of IoT-based solution for abandoned kids in the vehicle P. Ganesan, M. H. Mohamad* Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia Malaysia Corresponding author’s email: [email protected]

*

ABSTRACT: In recent years death toll in vehicles has been rising drastically throughout the world. This death toll is mainly toddlers who are the most victim caused by heatstroke, where the toddler is left in the car in hightemperature weather. In last year 2020 alone in the United States of America, there is already a death count of 24 children dying in the car because of heatstroke and is still in the updating phase; on average, there are 38 deaths. The project is about to design a safety alert system using ESP32 that can be plugged into available car seats. Further, to integrate the system with an IoT application to able feature of sending an alert message to the smartphone. Besides that, a performance analysis of the developed safety vehicle system was done. A system using IoT has been built to alert parents by sending an alert message to the smartphone to overcome this problem. The finding shows that the system can detect temperature ranges from 40°C to 80°C. The system also can notify the user by using Blynk application. Finally, the system response time is analyzed by using different types of internet connections.

focuses on social neuroscience, particularly the neurobiology of "Forgotten Baby Syndrome." His theory suggested that malfunction in the memory system is the reason behind the causes. There is a process called "prospective memory," which, he wrote, contains the purpose of remembering to accomplish things from the usual routine, and then there's a "habit memory" system, which is equivalent to being on autopilot. When an adult fails to remember an infant is in a car is when the prospective system fails, and at this point, the habit will take charge added by him in his research.[2] [3]. This issue can be solved by designing a system to give awareness to the parents when their child is left alone in the car. This system can be designed to consist of sensors, buzzers, and micro-controller, which can alert the parents. 2.

METHODOLOGY

This part shows the components and sensors used with its roles as inputs or outputs, as shown in the functioning block diagram in Figure 1.

Keywords: Abandoned kids; IoT; vehicle 1.

INTRODUCTION

Reports of kids being left in the vehicle and died because of heatstroke have increased year after year. These events occur because few factors such as parents being very busy and rushing, parents lack of awareness towards their children in the car where they easily get distracted, failure of prospective memory of parent, which is doing tasks out of routine, and in even worst scenario parents who are trying save time by leaving their children unattended in the vehicle. These are the few factors that can be the cause of a child's death as a result of heatstroke. This can be seen from the statistic which is shown by Jan Null from the CMM Department of Meteorology, and Climate Science San Jose State University explains that over the course of period of 22 years from 1998 to 2019, the cause of children to be in such a dangerous situation is 54.2% forgotten by parents which is equal to 460 cases, further parents aware of leaving the child in the car which is 19.1% which equal to 162 cases [1]. There is a theory deduced for some of the action above to occur, which Professor of Psychology does, David Diamond from the University of South Florida, worked together with KidsAndCars.org. He © Faculty of Electronic and Computer Engineering, FKEKK

Figure 1 Functioning block diagram An ESP32 is a main control system that controls the processing inputs and outputs of the system. The sensors used as inputs read real-time condition, which is later the data is sent to the ESP32 system, which will process the data based on the programming done for it. The proper action is taken where the output will be produced in terms of buzzer is triggered, and the IoT application will send a notification to the Smart Phone. These sensors are DHT 22, a temperature sensor that detects the temperature change, and the For Sensitive Resistive Sensor, FSR sensors that detect the weight or rather the pressure of the infant or toddler from the head and the sitting place to indicate there is a load.

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3.

RESULTS AND DISCUSSION

3.3 Effect of internet connection to the system response time The Table 1 shows the types of internet connection with the system response time where it is between the WiFi and mobile data. As it can be seen, the system takes 5s to respond to Wi-Fi. Meanwhile, for the mobile data Digi, Maxis, and U mobile for 2s.

This part shows the outcome of the result which has been achieved based on the system which has been built using the components which are stated, reading of the sensor which is the temperature sensor, and the difference of the system to detection based on the internet connection between the WIFI and the mobile data.

Table 1 Types of internet connection and system response time Source of Internet System response time WIFI (UNIFI) 4s DIGI 2s MAXIS 2s U-MOBILE 2s

3.1 Prototype of the system This shows the system developed with components that have been stated and used for the system, which can be seen in Figure 2. Based on Figure 2 (a) Front view, (b) Back view, (c) Pressure sensor, and (d) IoT notification. The system functions when the Pressure sensor(c) detects load, which is the toddler, and the temperature sensor which is at the Back view (b) detect a threshold above 40°C this will send the date to ESP32 and trigger the buzzer and also sends a notification to parent smartphone with a IoT notification(d).

4.

CONCLUSION

In conclusion, the system is built to ensure the safety of a toddler, which has been jeopardized by the parent's forget fullness or rather the “Forgotten baby syndrome” through the use of the ESP32 and the sensor which are DHT 22 temperature sensor and also the FSR sensors. Once the sensors detect the thresholds of 40°C, the output device, the buzzer, will trigger, and the alert message will be sent to the parents' smartphone. The result shows the impact when a toddler is being left in a hot day in two different colours of the car, which supports the system's capability to be used in the vehicle to ensure the life of the toddler to be saved. ACKNOWLEDGEMENT The authors would like to thank Faculty of Electronic and Computer Engineering (FKEKK) and Universiti Teknikal Malaysia Melaka for the financial support.

Figure 2 The prototype of the system with (a) front view (b) Back view (c) Pressure sensor (d) IoT Notification

REFERENCES

3.2 Effect of the colour on vehicle to the temperature changes Figure 3 showed that a range of 120 minutes takes, which is equal to 2 hours. The result is achieved by letting the temperature sensor in the two types of cars, which are black and silver to show the difference in temperature change on a hot day. The black car detected higher temperature rise compared to the silver car, which as a lower reading which are black car 53°C and silver car 45°C. This occurs because of the black car, which has the capability to absorb heat, and the silver surface, which reflects sunlight, shows a rather weak heat absorption capability [4].

[1]

[2] [3]

[4]

Figure 3 Graph of comparison

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C. McLaren, J. Null, and J. Quinn, “Heat Stress from Enclosed Vehicles: Moderate Ambient Temperatures Cause Significant Temperature Rise in Enclosed Vehicles”, Pediatrics, vol. 116, no. 1, pp, e109-e112, 2005. doi: 10.1542/peds.2004-2368. D. Diamond, “Children dying in hot cars: a tragedy that can be prevented”, The Conversation, 2016. D. M. Diamond, “When a Child Dies of Heatstroke after a Parent or Caretaker Unknowingly Leaves the Child in a Car: How Does It Happen and Is It a Crime?” Medicine, Science and the Law, vol. 59, no. 2, pp. 115–126, Apr. 2019. doi:10.1177/0025802419831529. S. M. Akyol and M. Kilic, “Dynamic simulation of HVAC system thermal loads in an automobile compartment”, International journal of vehicle design, vol. 52, no. 1/2/3/4, pp. 177–198, 2010.

Proceedings of Innovation and Technology Competition (INOTEK) 2021, Proceedings of Innovation andMalaysia, Technology Competition Melaka, pp. 135-136 (INOTEK) 2021

Extracting information from identity card into electronic form using image processing technique N. S. Yusman, M. M. Ibrahim* Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia Corresponding author’s email: [email protected]

*

ABSTRACT: Due to the development of information technology, the research has been implemented and used to accomplish human works optimally. An example of the development of information technology in business is how to check-in hotels. If customers want to check in hotel, all the data about customers can be obtained from their ID card. However, the customer's data has been inputted manually. That is not efficient process because we need a lot of time to input data one by one. This project is about extracting information from identity card into electronic form using image processing technique. Information extraction is a part of text mining which is used to extract information to make the data structured from unstructured data. The customer is expected to submit an ID card and the information will be extract like name, address, etc., and enter it into a data entry software.

data from ID cards using Python. 2.

The implementation of this project to achieve its objectives are described and explained in this section. 2.1 Develop algorithm character detector To develop algorithm character detector, a suitable algorithm must be selected for the project to be executed and complement these project objectives. In this section, the information is being extracted using Tesseract OCR algorithm. First, to detect the presence of text in an image, use OpenCV's EAST text detection model. Then, using simple image cropping/NumPy array slicing to extract the text Region of Interest (ROI) from the image. 2.2 Integrate algorithm with data identification As for the next stage, the character detection algorithm uses Pytesseract to detect the text on the ID cards before the information has been extracted. The detection algorithms will extract the information that only visible on ID cards. The first step for the face extracting the information is to acquire or scan an ID card from a webcam. The second step is extracting the information from the acquired ID card. After extracting the information is successful, all the data will be inserted in data entry.

Keywords: Extracting information from identity cards; Text mining. 1.

INTRODUCTION

This project aims to develop an algorithm to extract information from identification card (ID) using image processing techniques. As for the second objective is to integrate the extracted information from ID into data entry software. Lastly is to validate the performance of the developed algorithm and integration of algorithm and data entry software. Up until today, there are various techniques and methods developed to extract data. For example, the Convolutional Neural Networks and the latest one is Graph Convolutional Network techniques. The project is using the image processing technique. The software that has been used to develop and integrate the algorithm for this project in Python. OpenCV and DLIB is the specific library that been used from Python. The algorithm is started by extracting the data from the identification card (ID) into electronic form, then all the data will be transmitted into data entry. This project is using OCR techniques. Out of these, one commonly used OCR engine is Tesseract. When extracting data from ID cards, it only needs only a few essential attributes like Name, ID number, and Address. OCR only extracts text wherever identified, so it must use a deep learning algorithm to make OCR more intelligent. Hardware that has been used in this project is a webcam for a better resolution. The project will cover only designing and developing the algorithm to extract © Faculty of Electronic and Computer Engineering, FKEKK

METHODOLOGY

3.

RESULTS AND DISCUSSION

When extracting information from narrative text documents, the context of the concepts extracted plays a critical role. In this project, based on Figure 1 a Software Requirement is used in this project. PyCharm is an integrated development environment (IDE) that is primarily used to construct code programming for the Python language. To process the text recognition algorithm, open-source python libraries such as OpenCV, NumPy, and Pytesseract are used. OpenCV (OpenSource Computer Vision Library) is a software library for open-source computer vision and machine learning. OpenCV has been created to provide a shared infrastructure for computer vision applications and speed up the use of machine perception in consumer products. The OpenCV library also offers the counting function mentioned before to count the pixel from the threshold to obtain the value of 1 and 0 from the frame. The vital library that is in use is the DLIB library.

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Figure 3 Data entry software Figure 1 Coding to identifies the identity cards

4.

NumPy is a Python numerical open-source library. A multi-dimensional array and matrix data structures are used in NumPy. A variety of mathematical operations on arrays, such as trigonometric, statistical, and algebraic routines, can be used. Therefore, a large number of mathematical, algebraic, and transformation functions are in the library. NumPy is the library used to set data into preferred bits. Figure 2 shows the outcome results after identifying the customer identity. It will locate the identity of the customers by extracting the information from the ID cards, such as name, address, etc.

CONCLUSION

In some organisations and business models, ID card digitization saves a lot of time and work. From the project it shown that how to extract words from national ID cards using a simple projection approach. This approach has a lot of room for development. However, the implementation of a recognition mechanism is required. ACKNOWLEDGEMENT The authors would like to thank Faculty of Electronic and Computer Engineering (FKEKK) and Universiti Teknikal Malaysia Melaka for the financial support. REFERENCES [1]

[2] Figure 2 Result outcome after identifying the customer identity The Pytesseract is a Tesseract-OCR Engine wrapper. As a stand-alone invocation script to tesseract, it is also useful as it can read all image forms provided by the imaging libraries of Pillow and Leptonica, including jpeg, png, gif, bmp, tiff, and others. Make sure the image is properly pre-processed to prevent the output of tesseract precision will decrease. Using any of the following python functions or follow the OpenCV documentation to preprocess the image for OCR. Figure 3 shows the data entry software that builds to store the data of the customers. It builds so, the extract information from the identity cards will store into it automatically.

© Faculty of Electronic and Computer Engineering, FKEKK

[3]

[4]

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M. R. Akhter, M. H. Bhuiyan, and M. S. Uddin, “Extraction of words from the national ID cards for automated recognition,” in International Conference on Graphic and Image Processing (ICGIP 2011), vol. 8285, 2011. doi:10.1117/12.913478. W. Satyawan et al., “Citizen Id Card Detection using Image Processing and Optical Character Recognition,” Journal of Physics: Conference Series, vol. 1235, no. 1, 2019. doi: 10.1088/1742-6596/1235/1/012049. N. Thanh Cong, N. Dinh Tuan, and T. Quoc Long, “Information Extraction From Id Card Via Computer Vision Techniques,” Tehnical Report. VNU University of Engineering and Technology, 2018. Q. Wu, Y. Zhou and G. Liang, “A Text Detection and Recognition System Based on an End-toEnd Trainable Framework from UAV Imagery,” 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2018, pp. 736–741, doi: 10.1109/ROBIO.2018.8665259.

Proceedings of Innovation and Technology Competition (INOTEK) 2021, Proceedings of Innovation andMalaysia, Technologypp. Competition Melaka, 137-138 (INOTEK) 2021

Analysis on prediction of rubber crop production using machine learning H. J. Pang, M.N.S. Zainudin* Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia Corresponding author’s email: [email protected]

*

ABSTRACT: Agriculture is backbone of Malaysia’s economy. Malaysia has exported rubber products to various countries. The prediction system of rubber crop production is important for making financial decisions earlier if the shortage of crop production is estimated. Four prediction machine learning algorithms; Random Forest, Linear Regression, Decision Tree, and Neural Network, are applied and evaluated.

2.

According to the block diagram shown in Figure 1, the agriculture dataset is collected from the official website of the Department of Statistic Malaysia and Open Government Data Malaysia. Then, the dataset is split into training and testing subsets. The data are standardized in data pre-processing step. The final pre-processed training data fit each machine learning algorithm (Random Forest, Linear Regression, Decision Tree, and Neural Network). Testing subset is used to evaluate the performance of prediction using trained model. Lastly, the prediction results are analysed, and the accuracy of each model is compared.

Keywords: Machine learning; Prediction; Rubber crop 1.

INTRODUCTION

Agriculture plays a major role in economic growth of countries to vast majority of population from developing countries by reducing the rate of unemployment and improve the national income level as well as living standards. Some countries earn foreign exchange by trading their raw materials as natural resources. In 2018, RM 99.5 billion has contributed by agriculture sector in Gross Domestic Product (GDP) of Malaysia, and 2.8% of GDP in agriculture sector is contributed by rubber [1]. Malaysia is one of the top 10 rubber traders because our country is the 3rd largest rubber producer in the world during 2017 [2]. This study aims to develop a system using machine learning for predicting rubber’s crop yield. Human-based prediction methods include various techniques: crop cuts, farmer’s surveys, and expert assessment. These techniques are considered labour and time-consuming. Inappropriate measurement in crop cuts method will cause overestimation. In some cases, insufficient recall data by farmer’s survey will affect the accuracy of prediction results. A large range of crops is challenging for experts and it highly relies on level of expertise [3]. The accuracy of prediction achieved using machine learning is 71.88% and definitely higher than using a human-based method, which is 65.5% [4]. Hence, machine learning is applied on prediction system while the crops data is analysed as the input data to predict the outcome [5], and the accuracy performance is compared. The machine learning algorithms applied are Random Forest, Decision Tree, Linear Regression and Neural Network. Four parameters are chosen for predicting rubber crop yield: temperature, rainfall, humidity, and planted area. Then, the performance of each algorithm is evaluated with Mean Squared Error (MSE) and Mean Absolute Error (MAE).

© Faculty of Electronic and Computer Engineering, FKEKK

METHODOLOGY

Figure 1 Block diagram of the system 2.1 Software Implementation and Data Preparation PyCharm IDE using Python is used in this study. All datasets from the year 2000 to 2011 are collected from different states: Melaka, Perak, Pahang, and Johor. 2.2 Data Splitting 80% of data are split for training data while 20% of data are used for testing. The split process is done by dividing the data from year 2000 to 2008 for training, while from year 2009 to 2011 are used for resting. 2.3 Data Pre-processing Data normalisation is used as the method of feature normalisation. The training and testing data are standardised to ensure the variables of dataset lie within a specific range. 2.4 Training, Testing and Evaluating Process Hyperparameter optimisation is a method to choose 137 141

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4.

the suitable parameter for each machine learning algorithms. The optimum performance of algorithm is related to the value of parameter chosen. Fine tuning is a process to figure out which hyperparameters are optimal for each algorithm by enabling the machine learning algorithms to provide the most decisive output as it has variable numerical parameters that are adjusted through iterative optimisation. [6]. The trained model is produced once the training data is fit into model. Lastly, the performance of prediction models is evaluated by regression metric, which are MSE and MAE. The equations of MSE and MAE are given as below. 1 (1) 𝑀𝑀𝑀𝑀𝑀𝑀 = ∑𝑛𝑛𝑗𝑗=1(𝐴𝐴𝑗𝑗 − 𝑃𝑃𝑗𝑗 )2 𝑛𝑛 1

3.

𝑀𝑀𝑀𝑀𝑀𝑀 = ∑𝑛𝑛𝑗𝑗=1(|𝐴𝐴𝑗𝑗 − 𝑃𝑃𝑗𝑗 |) 𝑛𝑛

The prediction of rubber crop yield using machine learning is successfully implemented and analysed. Random Forest, Decision Tree, Linear Regression and Neural Network are applied to undergo this study. Data normalisation is required to ensure all the variables of dataset are within a specific range. Hyperparameter optimisation is crucial step to ensure the training data fit to improve the level of prediction model. As conclusion, Linear Regression is believed and able to provide the most accurate prediction among the other algorithms since it has the least value of MAE. ACKNOWLEDGEMENT

(2)

The authors would like to thank Universiti Teknikal Malaysia Melaka for the financial support through PJP/2020/FKEKK/PP/S01787.

RESULT AND DISCUSSION

The prediction results of rubber production are computed according to Melaka, Perak, Pahang, and Johor. Four different machine learning algorithms are applied in this work. The prediction results in Melaka are shown as Table 1. The average values of MSE and MAE are tabulated along with the type of model. From Table 2, the MAE is achieved the lowest error and it is considered better than MSE. Due to the MSE is computed by sum of squared error divided by n, the variance associated with the frequency distribution of error magnitudes will grow along with total square error [8]. In comparison with learning model, Linear Regression has the least value of MAE, and it could be concluded as the most accurate prediction results.

REFERENCES [1]

[2]

[3]

Table 1 Prediction results in Melaka by different algorithms Type of Year Actual Predicted model Result of Result of Rubber Rubber Production Production (tonnes) (tonnes) Random 2009 2003 1919.5 Forest 2010 1387 2020.25 2011 1479 3272.6 Decision 2009 2003 1919.5 Tree 2010 1387 2114 2011 1479 1919.5 Linear 2009 2003 2072.94 Regression 2010 1387 1182.33 2011 1479 1666.08 Neural 2009 2003 1856.81 Network 2010 1387 2007.7 2011 1479 1421.27

[4]

[5]

[6]

[7]

Table 2 Comparison of MSE and MAE between different algorithms Type of model Average Average MAE MSE value value Random Forest 0.2448 0.4169 Decision Tree 0.1828 0.3757 Liner Regression 0.0569 0.1711 Neural Network 0.0528 0.1757 © Faculty of Electronic and Computer Engineering, FKEKK

CONCLUSION

[8]

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Department of statistics malaysia, “Press Release Selected Agricultural Indicators, Malaysia”, 2019. [Online]. Available: https://www.dosm.gov.my/v1/index.php?r= column/pdfPrev&id=SEUxMEE3VFdBcDJhd UhPZVUxa2pKdz09 A. A. Khin et al., “Challenges of the Export for Natural Rubber Latex in the ASEAN Market,” in IOP Conf. Series: Materials Science and Eng., vol. 548, no. 1, pp. 1–9, Aug. 2019. Food and Algriculture Organisation of the United Nations, “Methodology for Estimation of Crop Area and Crop Yield under Mixed and Continuous Cropping Publication prepared in the framework of the Global Strategy to improve Agricultural and Rural Statistics,” 2017. [Online]. Available: http://www.fao.org/3/ca6514en/ca6514en.pdf P. Charoen-Ung and P. Mittrapiyanuruk, “Sugarcane yield grade prediction using random forest with forward feature selection and hyperparameter tuning,” in Adv. in Intelligent Systems and Computing, vol. 769, pp. 33–42, 2019. B. Fulkerson, D. Michie, D. J. Spiegelhalter, and C. C. Taylor, “Machine Learning, Neural and Statistical Classification,” Technometrics, vol. 37, no. 4, pp. 459, Nov. 1995. I. el Naqa and M. J. Murphy, “What Is Machine Learning?,” in Machine Learning in Radiation Oncology, Springer International Publishing, pp. 3–11, 2015. A. Botchkarev, “Evaluating performance of regression machine learning models using multiple error metrics in Azure Machine Learning Studio”, 2018. [Online]. Available: https://ssrn.com/abstract=3177507. C. J. Willmott and K. Matsuura, “Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance,” Climate Research, vol. 30, pp. 79–82, Dec. 2005.

Proceedings of Innovation and Technology Competition (INOTEK) 2021, Proceedings of Innovation and Technology Melaka, Malaysia, pp.Competition 139-140, (INOTEK) 2021

Arabic Character Recognition Using Spiking Neural Network K. Ganesan1 , M.R. Kamarudin1, * Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia

1

Corresponding author’s email: [email protected]

*

ABSTRACT: A spiking neural network model is used to identify and detect characters in a Arabic character set With enhanced character resolution, the network is effectively trained. Arabic is one of a group of Semitic alphabetical systems in which consonants are primarily expressed in writing, with vowel markings (via diacritics) being optional and infrequently used. Certain problem has been detect from Arabic character which is due to their cursive nature and unavailability of sources such as Arabic text database makes more difficult to develop Arabic characters recognition. The purpose of this project is to apply spiking neural Arabic dataset with spiking neural network model and to validate the accuracy of spiking neural network (SNN) compare with others neural network model. BindsNET is a spiking neural network simulation package aimed at developing physiologically inspired machine learning methods. The spiking neural network is used to train an actual Arabic recognition through BindsNET although many neural networks have been used before this. In conclusion. This project will be easier among all to recognize the Arabic characters

2.

Figure 1 The proposed spiking neural network architecture for Arabic character classification 2.1 Characters Recognition The recognition approaches applied to Arabic character recognition which can be classify as Spiking Neural Network . For Spiking Neural Network (SNN) modeling and analysis, this project have describe a new Python package for the simulation. The software is called BindsNET. Function of the software is to builds around PyTorch and enables rapid building of rich simulation of spiking networks in a concise syntax. BindsNET is based on the PyTorch deep neural networks toolkit, which makes spiking neural networks easier to develop on fast CPU and GPU platforms. In this project a database will be introduced which is Mnist. Mnist is a dataset of 60,000 small square 28×28pixel grayscale images of handwritten single digits between 0 and 9. MNIST is also like roman words but appears in number and MNIST extended is for full roman character and number. Next, Kaggle is an is online database platform that can be downloaded with different dataset. Arabic dataset with 28 classes that represent with variety type of isolated Arabic handwritten alphabet of 32x32 pixels have been downloaded. The database is divided into two sets: a training set (13,440 characters to 480 images per class) and a test set (3,360 characters to 120 images per class)

Keywords: Spiking Neural Network, Arabic Character 1. INTRODUCTION It is commonly known that biological neurons encode information using pulses or spikes. Biological neurons also retain information in the timing of spikes, according to studies. Spiking neural networks are the third generation of neural networks, and they employ spikes to represent information flow, just like their biological counterparts. Character recognition is the task of automatically structuring a set of text documents into different categories according to a group structure that is known in advance. The Arabic alphabet is widely used by many people from different countries including all Arab countries in addition to being used in the Persian, Urdu and Pashto languages. Spiking Neural Networks (SNNs) is introduced to recognize more easily alphabet on Arabic character in this project. The reason for selecting this network is because it brings new learning algorithms for unsupervised learning. First, information can be transmitted using very weak signals as rate encoding is very robust to noise. Second, they bring new learning algorithms for unsupervised learning.

© Faculty of Electronic and Computer Engineering, FKEKK

METHODOLOGY

3.

RESULT AND DISCUSSION

The different results have been showed for different type of label numbers consist of Arabic alphabet . For example in the first label , it contain with “Alif” character. Followed up by different character. For this project 10 types classes of Arabic character have been choose to train and test. Each classes have 120 image for testing and 480 for training .The accuracy records for different types of classes or character shown below.

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Figure 2: Arabic Character of “Taa”

Figure 5 Results of the Arabic Character Accuracy using Reservior.py 4.

In this work ,spiking neural network has been used to identify the images of Arabic characters. The network's training process continues until the output of neurons matches the output of learning neurons changes. The accuracy have been gathered by this project. All trained characters of character set are successfully recognized.

Figure 3 Input activity and spikes from the Input and Output layer Table 1 Results of different Classes Number of classes One classes Five classes Ten classes

Training and Testing data 120 test image 480 train images 600 test image 2880 train images 1200 test image 4800 train images

5.

Accuracy

ACKNOWLEDGEMENT

I extremely thankful and pay my gratitude to my lecturer electronic engineering and computer engineering faculty (FKEKK) for valuable guidance and support on completion of specific knowledge that have been teach. I extend my gratitude to University Teknikal Malaysia Melaka (UTeM) for giving opportunity to learn in this university.

100% 80.44% 56.89%

REFERENCES [1]

The spike neural network has been initialized and trained for the Arabic character set consist of 10 classes which is 1200 test image and 4800 train image using reservoir.py The initial weight values are assigned to a random number, causing all output neurons to spike during the first training iteration. Each character is delivered to the spike neural structure one by one throughout the training process. The network was trained and test for 100 epochs. The less image or character have a high accuracy in the table shown. The losses also will be less among the others.

[2]

[3] [4]

Figure 4 Voltages for Neuron

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CONCLUSION

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W. Maass, “Networks of spiking neurons: the third generation of neural network models,” Neural Networks, vol. 10, no. 9, pp. 1659-1671, 1997. A. Gupta and L. Long, “Character Recognition using Spiking Neural Networks,” in International Joint Conference on Neural Networks., Orlando., Florida, USA, 2007. W. Gerstner and W. M. Kistler, Spiking Neuron Models: Single Neurons, Populations, Plasticity. Cambridge University Press, 2002. Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., et al. (2015). TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Available online at: tensorflow.org

Proceedings of Innovation and Technology Competition (INOTEK) 2021, Proceedings of Innovation andMalaysia, Technology Melaka, pp.Competition 141-142, (INOTEK) 2021

Analyze on Fingerprint Devices For Motorcycle Starter And Tracking System Using IoT M.K. M Noor 1, M. M Said 1, * Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia

1

*Corresponding author’s email: [email protected] ABSTRACT: Fingerprint identification is one of the most top and popular publicized biometrics. In this digitalization era where application development becomes emerging trends of technology, fingerprint have been used for identification for over century and becoming biometric due to advancements in computing capabilities. In this project will focuses about developing prototype of vehicle ignition using fingerprint scanner and GPS tracker on motorcycle. Additional method to turn ON/OFF the engine using smart phone via IoT platform.

This project uses the Arduino UNO as a microcontroller that integrated with fingerprint module, GPS module and Wi-Fi module. The fingerprint module will replace the normal motorcycle stater and the GPS module enable user to track their motorcycle anywhere as long the WiFi in connected to the internet. For the Wi-Fi service can use any kind Wi-Fi service that can stand alone like broadband or etc. Additional state the user can turn on the engine using the application same as the GPS by simply replace for the key ignition. Figure 2 below is the basic circuit design for the system. Arduino microcontroller will be programmed to Wi-Fi module and fingerprint module. The Wi-Fi module will be connected to the IoT cloud which is control using smartphone application. In addition, GPS module will also link to the smartphone application. So, user can monitor their motorcycle anytime. Next, fingerprint module will be replaced normal starter on motorcycle. The fingerprint signal will be sent to the Arduino and give instruction to start the motorcycle. Table 1 Type of fingerprint scanner Type Name

Keyword: GPS tracker, IoT, Fingerprint scanner. 1.

INTRODUCTION

Statistic cases of motorcycle theft in Malaysia still at high level judging from the news and media social. In The Star newspaper by Meng Yew Choong, Saturday, 28 December 2019 reported statistics by the Vehicle Theft Reduction Council of Malaysia Bhd (VTREC) showed that in 2018, more than 7,500 motorbikes were stolen, with the majority of these being the rather popular underbone motorcycles – also called “kapchai” with engines below 150cc [1]. Fingerprint scanner are used for security applications because of every fingerprint is different from any human in the world and it became countless combinations for identification [2]. For example, the most popular application of fingerprint scanner is on the smartphone. The main idea of the project is for having a fingerprint scanner that will detect rather the user is an authorized or unauthorized person and a real-time GPS tracking system for motorcycles by using Wi-Fi module. The project will be incorporated with the monitoring and control system via IoT platform. 2.

R307 capacitive fingerprint sensor R503 capacitive fingerprint sensor GT-521F32 optical fingerprint sensor Based on table 1 there are 3 type of fingerprint scanner will be tested in this project. Comparison between the model will show on the result.

METHODOLOGY

3.

User need to enrol their fingerprint first to able use the fingerprint module. There are 3 type fingerprints was used in this print which is model R307 capacitive fingerprint sensor, R503 capacitive fingerprint sensor and GT-521F32 optical fingerprint sensor. For all the fingerprint sensor, only 2 of 3 success to test and 1 of 3 originally cannot use because of manufactured problem. How the system works simple as turn on the engine key button and start using user finger that has been enrolled. From time-to-time user can track the motorcycle using blynk application whenever the user wants.

Figure 1 Block diagram of the project © Faculty of Electronic and Computer Engineering, FKEKK

RESULTS AND DISCUSSION

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Table 2 Comparison between fingerprint scanner.

Figure 2 Fingerprint data using demo software.

R307

R503

Power supply Resolution

4.2-6 V 500 DPI

Current consumption Scanning speed

80% from position reference (100mm). Analysis for both control strategies are summarized in Table 1. © Faculty of Electronic and Computer Engineering, FKEKK

0.008s

The authors would like to thank Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka for the financial support.

The stability of the pole-placement controller is guarantee based on its closed-loop poles location. Therefore, in order to ensure the stability of the pneumatic positioning control system using poleplacement controller, this study assigned the poles to be placed inside the unit circle (0.01, 0.1, 0.5, and 0.9). 3.

0.008s

ACKNOWLEDGEMENT

Equation (2) is the algorithm for pole-placement controller. 𝐹𝐹𝐹𝐹

PID

This paper presents the positioning control of pneumatic actuator system. ARX model structure based on system identification technique was used to represent the pneumatic system’s behaviour. Meanwhile, poleplacement controller was proposed in order to provide accurate positioning control of the pneumatic system used in this study. The aim of this study was to demonstrate the effective of applying the pole-placement controller algorithm in the transient response performance. Simulation result shows that the transient response performance using pole-placement strategy was improved, especially in overshoot, compared to PID control.

Figure 2 The pole-placement controller block diagram

𝐻𝐻𝐻𝐻

CONTROL STRATEGY PP PP PP (0.9) (0.1) (0.5)

*PP refers to pole-placement controller

2.2 Controller Design and Simulation Test A pole-placement controller was proposed as a new control strategy for pneumatic positioning system in this study. The main aim is to improve the transient response performance of pneumatic positioning system, as well as to provide pneumatic positioning control system with accuracy. Figure 2 illustrates the block diagram of poleplacement controller.

𝑢𝑢𝑢𝑢(𝑡𝑡𝑡𝑡) =

PP (0.01)

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Rahmat, M. F., Sunar, N. H., Sy Salim, S. N., Zainal Abidin, M. S., Fauzi, A. A., & Ismail, Z. H. (2011). Review on modeling and controller design in pneumatic actuator control system. International Journal on Smart Sensing & Intelligent Systems, 4(4). Jamian, S., Salim, S. N. S., Kamarudin, M. N., Zainon, M., Mohamad, M. S., Abdullah, L., & Hanafiah, M. A. M. (2020). Review on controller design in pneumatic actuator drive system. Telkomnika, 18(1), 332-342. Syed Salim, S. N., Rahmat, M. F. A., Faudzi, M., Ismail, Z. H., & Sunar, N. (2014). Position control of pneumatic actuator using self-regulation nonlinear PID. Mathematical Problems in Engineering, 2014. C. Muñoz-Poblete, "Pole placement controller applied to a Rotary Inverted Pendulum System. A didactic view," 2018 IEEE International Conference on Automation/XXIII Congress of the Chilean Association of Automatic Control (ICAACCA), 2018, pp. 1-6, doi: 10.1109/ICAACCA.2018.8609824.

Proceedings of Innovation and Technology Competition (INOTEK) 2021, Proceedings of Innovation andMalaysia, Technology Melaka, pp.Competition 225-226, (INOTEK) 2021

Enhancement of the PID Controller Design for a Pneumatic Actuator System N. S. Khairul Nizal1, S. I. Samsudin1* 1

Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia *Corresponding e-mail: [email protected]

ABSTRACT: This paper presents an enhancement of the PID controller design for an intelligent pneumatic actuator system (IPA). Improved position of the pneumatic actuator is aimed in this design. A self-tuning PID technique has first developed and applied to improve the performance of the actuator as it exhibits highly nonlinear characteristics. The optimal self-tuning with particle swarm optimization (PSO) algorithm is next tested to optimally tune the parameter of the controller. System identification technique has been employed to represent the pneumatic system. The control performances based on error, integral absolute error, integral square error and percentage are investigated. The simulation works done in Matlab/Simulink platform. Keywords: Self-tuning; actuator system 1.

optimization;

2.

METHODOLOGY

This study consisted of four primary stages, such as literature review, system modelling, controller design and simulation test and experimental validation and performance analysis. Figure 1 illustrates the methodological flow of this study.

pneumatic

INTRODUCTION

Nowadays, many of industrial applications are widely use pneumatic actuators in their production such as robot manipulator, pick-and-place motion and rivet machine. There are many benefits shown by this pneumatic such as can maintain easily, has low cost, free from spark operation, fast, robustness, high power-toweight ratio, high number of cycles per day of work, and free of overheating in the presence of constant load. However, this actuator exhibits highly nonlinear characteristics because of its high friction forces, air compressibility, and dead band of the spool movement in the valve. These nonlinearities and uncertainties in parameters have become the biggest challenge to achieve a precise position control of a pneumatic system[1]. One of the control methods often implemented in industries is the proportional- integral-derivative (PID) system. Even with the advancements of modern control techniques, the prevailing use of PID control is undeniably the same. The PID controller, however, still faces a major challenge in controlling a nonlinear system, especially with the randomness of external disturbances. The classic PID can have difficulties performing well in high-performance control with changes in operating conditions. Besides, in the classical PID controller, the fixed control parameters lead to poor transient response efficiency[2]. In conjunction to these issues, enhancement of PID controller with optimal self-tuning capabilities is proposed to result a good accuracy of actuator’s position. © Faculty of Electronic and Computer Engineering, FKEKK

Figure1 Flowchart of methodology framework The main focus of this research is to develop a selftuning controller then optimally tune the parameter with the ability to provide a rapid response with or without minimal overshoot. Besides, this proposed study is to achieve improved steady-state efficiency while keeping the pneumatic actuator system stroke at the desired position[3]. With these objectives in mind, in order to compensate for the nonlinearities and uncertainties in the system parameters, an additional feature known as a nonlinear gain function was integrated with the observer system. The integration of this feature into the observer system reduced the system error significantly while improving the transient response by reducing the overshoot in the managed system. 225 227

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Nizal & Samsudin, 2021

3.

RESULTS AND DISCUSSION a.

Development of self-tuning PID controller

Figure 2

Figure 4 Self-tuning PID plant with PSO algorithm

Development of the PID controller

Table 2 Different values of errors for 5 max particles Start IAE ISE Output error range max 10 0.02188 0.3657 0.001222 50 0.03041 0.5333 0.001185 100 0.01354 0.2177 0.007373 500 0.00182 0.06885 0.002545 1000 0.02188 0.3657 0.001222 4.

Figure 3

This paper presents an enhancement of PID controller design for a pneumatic actuator system with the aim to reduce the errors (IAE, ISE) and overshoot of the system response. The first objective of this project has achieved which to develop a self-tuning PID controller design by using Simulink. Besides, an optimization has been done by using Particle Swarm Optimization (PSO) algorithm to optimally tune the parameters for self-tuning PID controller. The value of epoch, maximum particles and start range maximum in the PSO algorithm has been set with the random value to observe the performance of the system.

Self-tuning PID algorithm

Table 1 Different values of errors depending by gamma’s value Gamma, Kg

IAE

ISE

0.05 0.5 5 50 500

0.2684 0.09714 0.02681 0.009847 0.003474

3.573 1.407 0.4829 0.1506 0.03395

b.

Output Error 0.01409 0.01065 -0.001304 0.01315 0.00314

REFERENCES [1]

Development of optimal self-tuning PID controller [2]

[3]

© Faculty of Electronic and Computer Engineering, FKEKK

CONCLUSION

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S. N. S. Salim, M. F. Rahmat, A. ‘Athif M. Faudzi, and Z. H. Ismail, “Position control of pneumatic actuator using an enhancement of NPID controller based on the characteristic of rate variation nonlinear gain,” Int. J. Adv. Manuf. Technol., vol. 75, no. 1–4, pp. 181–195, 2014. Bingi, Kishore & Ibrahim, Rosdiazli & Karsiti, Mohd & Chung, Tran & Hassan, Sabo. (2016). Optimal PID control of pH neutralization plant. 1-6. S. F. Sulaiman, M. F. Rahmat, A. A. M. Faudzi, and K. Osman, “A new technique to reduce overshoot in pneumatic positioning system,” vol. 17, no. 5, pp. 2607–2616, 2019.

Proceedings of Innovation and Technology Competition (INOTEK) 2021, Proceedings of Innovation and Technology Melaka, Malaysia, pp.Competition 227-228, (INOTEK) 2021

Class E Inverter-Impedance Matching Network Integration for 40 kHz Ultrasonic Air Transducer N. R. M. Aziz1, S. H. Husin1* Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia

1

*

Corresponding author’s email: [email protected]

ABSTRACT: This paper discussed on the emerging engineering technologies which involved in wireless power transfer system (WPT). Unlike from the existing WPT existing system which are inductive power transfer (IPT) and capacitive power transfer (CPT), the new alternative technique is proposed names as acoustic energy transfer (AET). The AET system utilizes the vibration or sound waves propagation to transfer the power from the transmitter unit to receiver unit. The compensation network design will be incorporate with ultrasonic transducer to generate the acoustic wave which electromagnetic free for the low power application. This project focus on analysis and design on power conversion performance in Class E ZVS inverter at transmitter unit and the suitable circuit designed in impedance matching to overcome the impedance variation.

Energy Transfer is still new under a research, the results from experimental project in the past have already proven that AET can exceed the limit of IPT and CPT. AET is a technology that uses sound waves or vibrations to transfer energy wirelessly instead use of electromagnetic fields like CPT and IPT. Hence, energy can be transmitted through metal wall which overcoming the major drawback of IPT, in open air as well as in living human tissue. 1.1 Objectives a. To design an efficient Acoustic Energy Transfer system using Class E ZVS inverter at the transmitter unit in order to reduce or eliminate switching amplifier losses. b. To design impedance matching circuit to overcome the impedance mismatch between the purely resistive load and ultrasonic transducer, thus high efficiency of Class E ZVS can be maintained. c. To design the power efficiency of Acoustic Energy Transfer system at the receiver unit for power transmitter efficiency.

Keywords: Acoustic energy transfer; class E ZVS; ultrasonic transducer 1.

INTRODUCTION

In the beginning of the technology, every machine, electrical equipment and many other devices such as implantable devices, mobile phone needs power to activate them. The power transfer has proven that it plays a major part in powering the devices. However, there are limitation of these old methods which one of limit is they are bounded to transfer the power using cable or wire. It is undeniable that power transfer via wire or cable are much more direct and the possibility of power loss is very low. Unfortunately, not every application usage of wire can be guarantee as convenient and safe. Thus, the industry came out with different approach to replace wired power transfer with wireless power transfer (WPT) as the alternative way to be explored. The technology of wireless power transfer (WPT) systems is the technique by transfer the power source in transmitter to the load in receiver by eliminates wires or cables. Therefore, the development and study of wireless power transfer of which involve Acoustic Energy Transfer (AET) which uses sound waves to propagate energy without relying on electrical contact, Inductive Power Transfer (IPT) that utilizes magnetic coupling and Capacitive Power Transfer system that use electrical field coupling in capacitive plate become flexible. Because of that idea, wireless power transfer (WPT) is more portable and convenient in order to run the system. Although Acoustic © Faculty of Electronic and Computer Engineering, FKEKK

2.

METHODOLOGY

The project starts with research and background study on previous work regarding to AET was conducted in order to get an idea and prepared for the draft proposal project. Apart from that, the AET circuit was designed and simulated along with the right value for each component by using MATLAB via Simulink. The designing and simulation would be in three parts which were the first one was to design basic construction of standard class E circuit. The most importantly, each of part would consists on evaluation regarding to ZVS condition whether it was achieved and overlapping. If the result showed overlapping, there would be troubleshooting process. Secondly, the class E circuit would be combined with ultrasonic transducer, which would cause impedance variation because the impedance value in ultrasonic transducer made the circuit no longer purely resistive. Because of that, the next step would be on impedance matching process that can be overcome the impedance variation problem. During the evaluation, beside zero voltage switching observation, there would be several measurements that need to be taken to analyze the performance of AET system which were the power

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Aziz & Husin, 2021 Proceedings of Innovation and& Technology Competition (INOTEK) 2021 Aziz Husin, 2021

input, power output and total efficiency that managed to achieved by the system. input, power output and total efficiency that managed to achieved by the system. 3. RESULTS AND DISCUSSION

impedance for optimum operation.

The Acoustic Energy Transfer (AET) system of the 3. RESULTS AND DISCUSSION project is designed by Class E ZVS has been chosen at The Acoustic Energy (AET)assystem of the the transmitter circuit whichTransfer is the design an amplifier project is designed by Class E ZVS has been chosen for the maximum power. In addition, Class E ZVS at is the transmitter circuit is the designTransfer as an amplifier proposed to drive thewhich Acoustic Energy (AET) for the caused maximum In addition, Class In E figure ZVS is1 system to itspower. low switching capability. proposed to drive the Acoustic Energy Transfer (AET) shows the circuit designed of a Class E in MATLAB system caused to its low switching capability. In figure 1 software as a converter circuit which consists of choke shows the circuit designed of a Class E in MATLAB inductor (Lf) and shunt capacitor (Cp). Choke inductor software a converter which consists of choke (Lf) is to as reduce current circuit ripple through the circuit while inductor (Lf) and shunt capacitor (Cp). Choke inductor shunt capacitor (Cp) is to shape and modify the drain (Lf) is toand reduce current ripple through current voltage waveform. Classthe E circuit circuitwhile also shunt capacitor is to (Cseries) shape andand modify drain consists of series(Cp) capacitor seriesthe inductor current voltage waveform.act Class circuit also (Lseries)and which the components as a E filter to reduce consists of series capacitor (Cseries) and series inductor the number of the harmonic effects in waveform. The (Lseries) which the advantages componentsfrom act as filter to reduce class E have many thea other converter the number of the harmonic effects in waveform. The because it is simple passive purely and working operation class E have many advantages from the other converter has no overlap between current and voltage. because passiveof purely and working operation Forit is thesimple evaluation system performance, the has no overlap between current and voltage. values that can be analyse are: For Zero the voltage evaluation of system (1) switching, ZVS performance, the values that can be analyse are: (2) Power input, Pi (1) ZVS (3) Zero Powervoltage output,switching, Po (2) Power input, Pi (4) Efficiency (3) Power output, Po (4) Efficiency

Figure 3 ZVS for Class E + Ultrasonic transducer (PZT) Figure 3 ZVS for Class E + Ultrasonic transducer The model class E(PZT) inverter that inverted with

π1a impedance matching is simulated in Figure 4. The The model class Ebetween inverter the thatswitch inverted with non-overlapping transition current π1a impedance matching is simulated in Figure 4. and voltage waveform must be fulfilled all the time. The The non-overlapping transition between the switch simulation results managed to satisfy thecurrent ZVS and voltage waveform must be fulfilled all theoutput time. The requirement which obtained 0.70W of power and simulation results managed to satisfy the ZVS 100% efficiency. requirement which obtained 0.70W of power output and 100% efficiency.

Figure 4 ZVS for Class E + PZT + π1a impedance matching Figure 4 ZVS for Class E + PZT + π1a impedance 4. CONCLUSION matching As conclusion, the performance of transmitter unit 4. CONCLUSION consists of Class E inverter with load, R=470 Ohm and As conclusion, the performance of integrated transmitter with unit piezoelectric transducer, following by consists of Class E inverter with load, R=470 Ohm π1a impedance matching at the operating frequencyand of piezoelectric followingusing by integrated 39.8 kHz is transducer, studied, simulated Simulink with via π1a impedance at As the operating of MATLAB and matching compared. a prove, frequency this project 39.8 kHz is studied, simulated using Simulink via managed to achieve efficiency of the system is 100% MATLAB andthat compared. As amet prove, project which prove AET system the this requirement managed to achieve efficiency of the system is 100% benchmark 80% of total efficiency. In order to achieve which prove that AET system met the requirement high efficiency inverter, the natures of class E ZVS must benchmark 80% of total efficiency. In order to achieve be always satisfied. high efficiency inverter, the natures of class E ZVS must be always satisfied. REFERENCES

Figure 1 Simulation of Class E inverter 1 Simulation TheFigure simulation result of in Class figureE 2inverter illustrated the accomplishment of ZVS condition, there is no The simulation in figure 2 illustrated the overlapping of switchresult current and voltage waveform accomplishment of ZVS condition, there is no during the switching time intervals, thus the switching overlapping of switch current and voltage waveform losses are zero, producing high efficiency. The power during switching time is intervals, switching output the voltage obtained 0.69W, thus withthe98.57% of losses are zero, producing high efficiency. The power efficiency. output voltage obtained is 0.69W, with 98.57% of efficiency.

[1] Aldhaher, S., Luk, P. C. K., Bati, A., & Whidborne, REFERENCES J. F. (2014). Wireless power transfer using class E [1] Aldhaher, S., Luk, P. C. K., Bati, A.,inductor. & Whidborne, inverter with saturable DC-feed IEEE J. F. (2014). Wireless power transfer E Transactions on Industry Applications,using 50(4),class 2710– inverter with saturable DC-feed inductor. IEEE 2718. on&Industry Applications, 50(4), 2710– [2] Transactions Dumbrava, V., Svilainis, L. (2007). Evaluation of 2718. the ultrasonic transducer electrical matching [2] Dumbrava, V.,Ultrasound, & Svilainis,62(4), L. (2007). Evaluation of performance. 16–21. the ultrasonic transducer electrical matching [3] Garcia-Rodriguez, M., Garcia-Alvarez, J., Yañez, performance. Ultrasound, 62(4), 16–21. Y., Garcia-Hernandez, J., Turo, A., & Chavez, J. A. [3] Garcia-Rodriguez, M., Garcia-Alvarez, Yañez, (2010). Low cost matching network for J., ultrasonic Y., Garcia-Hernandez, J., Turo, A., & Chavez, J. A. transducers. Physics Procedia, 3(1), 1025–1031. (2010). Low cost matching network for ultrasonic transducers. Physics Procedia, 3(1), 1025–1031.

Figure 2 ZVS for Class E + 470 Ohm

2 ZVS for 470 Ohmof the Figure 3Figure illustrates the Class ZVSE + condition simulation result after the addition of PZT in the circuit. Figure 3 illustrates of the Small distortion occurred atthe the ZVS switchcondition voltage waveform simulation result after the addition of PZT in the circuit. and only managed to get 0.83W. The changed of resulted Small distortion occurred at the switch voltage waveform waveform is expected, reducing the efficiency to 95%. only managedistomainly get 0.83W. of resulted Aziz & Husin,and 2021 This reduction dueThe to changed the mismatch of waveform is expected, reducing the efficiency to 95%. This reduction is mainly due to the mismatch of © Faculty of Electronic and Computer Engineering, FKEKK anaged to impedance for optimum operation.

em of the chosen at amplifier

impedance for optimum operation.

© Faculty of Electronic and Computer Engineering, FKEKK

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Proceedings Proceedings of Innovation of Innovation and and Technology Technology Competition Competition (INOTEK) (INOTEK) 2021, 2021, ProceedingsMelaka, of Innovation and Technology Competition (INOTEK) 2021 Melaka, Malaysia, Malaysia, pp. 229-230, pp. 229-230, Proceedings of Innovation and Technology Competition (INOTEK) 2021, Melaka, Malaysia, pp. 229-230,

Design Design andand Development Development of aofSingle a Single Display Display Pedestrian Pedestrian Traffic Traffic Light Light System System forfor Colour Colour Vision Vision Deficiency Deficiency Drivers Drivers Design and Development of a Single Display Pedestrian Traffic Light System for Colour Vision1 Deficiency Drivers 11 1* 1* 1* F. M. F.Rafee M. Rafee , S. K. , S.Subramaniam K. Subramaniam

1

F. M. Rafee1, S. K. Subramaniam1* 11 Fakulti Fakulti Kejuruteraan Kejuruteraan Elektronik Elektronik dan dan Kejuruteraan Kejuruteraan Komputer, Komputer, Universiti Universiti Teknikal Teknikal Malaysia Malaysia Melaka, Melaka, Hang Hang TuahTuah Jaya,Jaya, 76100 76100 Durian Durian Tunggal, Tunggal, Melaka, Melaka, Malaysia Malaysia 1 Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia *Corresponding *Corresponding email: email: [email protected] [email protected] *Corresponding email: [email protected]

ABSTRACT: ABSTRACT: The The single single display display traffic traffic lightlight will will revolutionize revolutionize the entire the threethree colour colour traffic traffic light light system. system. ABSTRACT: Theentire single display traffic light will There There are many are many drawbacks drawbacks of the of current thetraffic current system system which which revolutionize the entire three colour light system. drive drive into into the development the development of this of this single single display display traffic traffic There are many drawbacks of the current system which light light andinto and thethethe most most significant significant is the is the cost. cost. Since Since the the drive development of this single display traffic light and and the and most significant is theencourage cost. Since the government government many many organizations organizations encourage Green Green government many organizations encourage Green Technology Technology as and aassubstitute a substitute for for the the existing existing system system in in Technology aswastages a substitute theoptimize existing system in afford afford to save to save wastages andfor and optimize our our available available afford to save wastages and optimize our available resources resources towards towards a greener a greener environment. environment. The The single single resources towards a greener The single display display traffic traffic lightlight system system willenvironment. will reduce reduce manufacturing manufacturing display traffic light system will reducerequired manufacturing cost, cost, maintenance maintenance cost,cost, less less components components required for the for the cost, maintenance cost,this lesswill components required for theeven same same operation operation and and this will make make recycle recycle process process even same and recycle process better better inoperation the in long the long run.this run. Onewill One of make the of added the added features features ofeven the of the better in the long run. One of the added features of the single single display display traffic traffic lightlight is colour is colour vision vision deficiency deficiency single display traffic light is colour vision deficiency friendly. friendly. The The display display is built is built withwith the the conventional conventional friendly. The display is built with the conventional colours colours with with added added symbols symbols to differentiate to differentiate the status the status of of colours with added symbols to differentiate the status of thethe display thedisplay display shown shown to the to drivers the drivers even even to the to unfortunate the unfortunate shown to the drivers even to the unfortunate (colour (colour vision vision deficiency deficiency drivers). drivers). This This single single display display (colour vision deficiency drivers). This single display traffic traffic light light required required low low power power totooperate to operate so it makes traffic light required low power operate so ititsomakes makes very very much much easier easier toto beto be powered bybybattery by battery during during power power very much easier bepowered powered battery during power interruptions. interruptions. Another Another added added feature feature in the in single the single display display interruptions. Another added feature in the single display traffic traffic light light system system is ispowered is powered bybysolar by solar as as primary the primary traffic light system powered solar as the the primary power power source source and and thetheAC the land land power power asasthe as secondary the secondary power source and ACAC land power the secondary source. source. This This system system enables enables the the traffic light to to operate source. This system enables thetraffic traffic lightlight to operate operate with renewable energy most the time. with with renewable renewable energy energy forformost for most ofofthe of time. the time.

It makes It makes the single the single screen screen traffic traffic indication indication become become moremore practical. practical.

situation demands a reflection on relationship themore relationship between ThisThis situation demands a reflection onbecome the between It makes the single screen traffic indication practical. traffic signs and lights for drivers colour vision deficiency. traffic signs anddemands lights for drivers with colour vision deficiency. This situation a reflection on with the relationship between the ability to see shapes and textures through visual TheyThey have the to drivers see shapes and textures through visual traffic signshave and ability lights for with colour vision deficiency. They have the seewhat shapes and textures through visual perception. So, notomatter what traffic sign colour shown, it will perception. So,ability no matter traffic sign colour shown, it will perception. So, no what traffic sign colour shown,the it will notdifficult be difficult a colour-blind person to understand the colour not be formatter afor colour-blind person to understand colour not be difficult for a colour-blind person to understand the colour meaning. meaning. meaning. METHODOLOGY 2. 2.METHODOLOGY METHODOLOGY

2.

StartStart

Start

Connect Connect to the to controller the controller

Connect to the controller (Arduino (Arduino Uno)Uno) to the to the (Arduino Uno) to the pedestrian pedestrian cross cross light light circuit pedestrian cross light circuitcircuit

No No No

Keywords: Wireless traffic light; pedestrian crossing Keywords: Keywords: Wireless Wireless traffic traffic light; light; pedestrian pedestrian crossing crossing INTRODUCTION 1. 1.1. INTRODUCTION INTRODUCTION Single Pedestrian Light all-in-one pedestrian TheThe The Single Single Pedestrian Pedestrian Light Light isisanan isall-in-one an all-in-one pedestrian pedestrian light that can simultaneously display details about walk lightlight that that can can simultaneously simultaneously display display details details about about aa walk a walk sign same pedestrian light screen. The The or or stop orstop stop sign sign ononthe onthethe same same pedestrian pedestrian light light screen. screen. The findings show the implementation of the single findings findings showshow the the implementation implementation of of the the single single pedestrian light with the Green Policy. Therefore, the pedestrian pedestrian lightlight withwith the the Green Green Policy. Policy. Therefore, Therefore, the the single pedestrian traffic light is capable to replacing the single single pedestrian pedestrian traffic traffic lightlight is capable is capable to replacing to replacing the the conventional pedestrian lights on the road. That means conventional conventional pedestrian pedestrian lights lights on the on road. the road. That That means the era of unified standard traffic display has come. means the era the era unified of unified standard standard traffic traffic display display come. has come. Theof single display traffic light show ahas multi-function The The single single display display traffic traffic light showshow a multi-function ashow multi-function display which capable of light simultaneously green, display display which capable of simultaneously ofthesimultaneously showshow green, amber andwhich redcapable traffic lights, directions such asgreen, arrow amber amber andand and redcross traffic red traffic lights, thethe directions thesame directions suchsuch as as arrow for go forlights, stop on display of arrow traffic forlight. go for and go and crosscross stop forelectricity, stop on the on same the same display of traffic of traffic In order tofor save protect thedisplay environment light. light. Inincrease order In order tohighway save to save electricity, electricity, protect protect thedisplay environment the environment and safety, a new single traffic andlight and increase increase highway safety, a new a new single single display display traffic traffic method is highway beingsafety, proposed. This features drivers with colour vision deficiency drivers who suffer fromdrivers colour light light method method is being is being proposed. proposed. This This features features drivers withwith blindness and has difficult to distinguish thefrom red, amber colour colour vision vision deficiency deficiency drivers drivers whowho suffer suffer from colour colour and green thedifficult sametodisplay trafficthe light. blindness and to distinguish the red, amber blindness andlight hasinhas difficult distinguish red, amber green in same the same display traffic and and green lightlight in the display traffic light.light. © Faculty of Electronic and Computer Engineering, FKEKK

© Faculty © of Electronic of and Computer and Engineering, Engineering, FKEKK FKEKK © Faculty Faculty of Electronic Electronic and Computer Computer Engineering, FKEKK

229

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Button Button Button Pressed? Pressed? Pressed? Yes Yes Yes

Pause 30 Pause Pause 30seconds seconds 30 seconds Red Redlight Red lightON light ON ON Yellow light Yellow Yellow lightOFF light OFFOFF Green light OFF Green Green light light OFFOFF Pause 30 seconds Pause Pause 30 seconds 30 seconds

Pause 30 seconds Pause Pause 30 seconds 30 seconds

Pause 30 seconds

Red light OFF RedRed light OFF Yellow lightlight OFFOFF Yellow Yellow lightON light OFFOFF Green light Green Green light ON ON Pause 30light seconds

Pause Pause 30 seconds 30 seconds

Red light OFF RedRed light light OFF Yellow light ON OFF (flashing) Yellow Yellow lightlight ON ON Green light OFF (flashing) (flashing)

Green Green lightlight OFFOFF

Red light ON Redlight Red light light ON ON Yellow OFF Green light OFF Yellow Yellow light light OFFOFF

Green Green lightlight OFFOFF

End

EndEnd

Figure 1 Flowchart of the project

Figure Figure 1 Flowchart 1 Flowchart of the of project the project

Rafee & Subramaniam, 2021

Proceedings of Innovation and Technology Competition (INOTEK) 2021

a worthwhile investment and also environment-friendly. The brightness consumers low power compared to the traditional tungsten halogens bulbs.

3. RESULT AND DISCUSSION

4. CONCLUSION In conclusion, the project objectives are to design single display pedestrian traffic light system for colour vision deficiency drivers. They have ability from visual perception about shapes. In this project, the traffic light was designed special for colour vision deficiency drivers which is for red light with cross sign is to indicate a stop sign for pedestrian, for green light with straight arrow sign will indicate them to go and for yellow light with flashing cross sign will indicate them to get ready to stop. Next, the government will minimize the expenses of developed and maintaining the pedestrian traffic light. The brightness consumers low power compared to the traditional tungsten halogens bulbs and it can reduce current usage and it can make less carbon uses. The project also can save the cost of electric bills and maintenance cost because this project is powered by solar as the primary power source and the AC land power as the secondary source.

Figure 2 Voltage reading of RGB LED traffic light display

ACKNOWLEDGEMENT The authors would like to thank Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka for the financial support. REFERENCES [1]

Figure 3 Power consumption of RGB LED For this project, the project was designed with four group of LED because of the intersection at the middle was joined. For red LED, LED from the group 1, group 3 and group 4 will be active, while LED in group 2 is inactive. Then, for green LED the group in active mode are LED from group 1 and group 2 while group 3 and group 4 are inactive. Next, for yellow, the LED from the group 1, group 3 and group 4 will be active, while red LED in group 2 is inactive. Therefore, it will get the measured 0 for the inactive group. Other than that, the maximum LED voltage for this project are 3.5 V. So, all the measured value gets below 3.5V. The light produced by low voltage is sharper and looks more natural as all people can see. The blue LED show the result 0 V. It is because the blue LED are in mode off and not active. To produce the yellow LED, red and green must be set as low and blue must be set as high in a coding at Arduino. From this project it can reduce current usage and it can make less carbon uses. Single pedestrian traffic light has become an efficient and effective alternative compared to traditional traffic light. It is because single display pedestrian traffic light are very low power consumption and very long life compared to the power consumption of traditional traffic light. For RGB led, based on the research, it is about eight years of 24 hour run time at full brightness. In addition, to the low energy usage, the long life of LED signals means low maintenance costs, which makes LED signals © Faculty of Electronic and Computer Engineering, FKEKK

[2]

[3]

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Shinde, Nikita. “Microcontroller Based Intelligent Traffic Signal Light Control System.” International Journal for Research in Applied Science and Engineering Technology, vol. 6, no. 4, 2018, pp. 1106–1111., Al-Nabulsi, J., Mesleh, A., & Yunis, A. (2017). Traffic light detection for colorblind individuals. 2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies(AEECT). Sharma, P. (2021). Smart Traffic Light and Street Light Management System. International Journal for Research in Applied Science and Engineering Technology, 9(1), 392–397.

Proceedings of Innovation and Technology Competition (INOTEK) 2021, Proceedings of Innovation and Technology Melaka, Malaysia, pp.Competition 233-234, (INOTEK) 2021

Delta Robot Arm Simulation for Pick and Place the Object F. N. I. Ramlee1, W. H. M. Saad1 * 1

Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia Corresponding author’s email: [email protected]

*

2.

ABSTRACT: This work was aimed to simulate the pick and place of a delta robot. The mechanical design utilised in the delta robot, together with the creation of a custom URDF file, are presented in order to simulate a delta robot on ROS Environment. To convert it into the URDF, transfer it into ROS Environment and execute simulations with the same URDF on the basis of the Delta Robots that was model design using solid works software. This is done by generating separate packages and launch files to visualize and simulate the model. The movement simulations are done in Gazebo, which is shown in RViz.

In this chapter, the approach employed in this project is described. The following major elements must be discussed. As a result, this chapter should detail all of the major aspects employed in this project. This is required for a correct comprehension of the project as well as the possibility of subsequent development of the experiment using the same criteria. 2.1 Project Environment ROS (Robot Operating System) Environment was involved in this project as the method for completing the delta robot pick and place object with simulation. The preparation of the methods used for the overall project simulation was explained in more detailed.

Keywords: URDF; ROS Environment; Solid work 1.

Table 1 Fundamentals of the simulated environments TOOLS CONCEPT VirtualBox Oracle VirtualBox is a framework for cross-platform virtualization Ubuntu The key operating system (OS) that is. 20.04 Because of its high degree of configurability, Ubuntu is the OS of choice, which facilitates the installation of all constituent components to build the simulation environment and enforce the proposed solution for delta robot arm-pick and place object. ROS The Ubuntu 20.04 (Focal) release is NOETIC the primary target for ROS Noetic NINJEMYS Ninjemys, however other operations are controlled to varied degrees. URDF In ROS, a robot model may be seen using Unified Robot Description Format files (URDF). The URDF files are XML-based and meant to achieve a genuine robot in 3D. Different features of robots such as form, colour, joints etc may be simulated using these kind of files. Gazebo Gazebo has been selected for its ease of model portability and ease of connectivity between ROS and virtualization software modules, resources, and libraries. It can be very time consuming and costly to test on physical hardware.

INTRODUCTION

Robots are programmable devices that have been created to carry out many jobs. Previous robots have been operated mostly by hand. Robots are part of future progress in the field of IT. Robots are getting ever more flexible because of constant research and development in robotic software and hardware. In every element of life, robots become prevalent. Robots are utilized in medical operations, human aid, automation of the factory, etc. To build the real robot in some robotics fields, for example, agricultural robotics, certain hardware settings may result in saturation of the actuator or may experience harmful conditions. Simulation approaches can offer an economical framework to experiment with multiple sensing and operational mechanisms to check the robot's performance in diverse circumstances to quicken that pace. The Simulation technique offers a reliable way to overcome the gap between creative ideas and laboratory. Furthermore, ROS (robot operating system) was employed in this study as part of the experimental platform for building the Delta Robot. Based on its existing CAD model, the open-source robot 'Delta Robot' joint control system was designed. The model was converted into a format that allowed for the creation of joints as well as the definition of parent and child links. The model was then run with the appropriate inertia and forces. The Delta robot models were created in Rviz to give models for visual demonstration to conduct gazebo simulations.

© Faculty of Electronic and Computer Engineering, FKEKK

METHODOLOGY

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3.

RESULTS AND DISCUSSION

4.

A URDF is used to produce 3D representations of actual robots, as stated in the approach. It is possible to change properties such as form and colour while creating a 3D model using URDF.

CONCLUSION

As a result, a comprehensive simulation-ready model is built (export using URDF) utilizing the CAD model to be performed in ROS (Robot Operating System). Dynamic joints are formed and suitable controllers are given based on pre-set limits on movement. In addition, two separate launch files based on the URDF are prepared for RVIZ visualization and Gazebo dynamic simulations. REFERENCES [1] [2]

[3] Figure 1 Export to URDF

[4]

Figure 2 Open in Moveit Setup Assistant

Figure 3 URDF to Graphiz

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R. T. Arrazate, “Development of a URDF file for simulation and programming of a delta robot using ROS,” no. February, 2017. J. Kerr and K. Nickels, “Robot operating systems: Bridging the gap between human and robot,” Proc. Annu. Southeast. Symp. Syst. Theory, pp. 99–104, 2012, doi: 10.1109/SSST.2012.6195127. A. Domel, S. Kriegel, M. Brucker, and M. Suppa, “Autonomous pick and place operations in industrial production,” vol. 39, no. 2, pp. 2015, doi: 356–356, 10.1109/urai.2015.7358978. F. A. Azad, M. Reza, H. Yazdi, and M. T. Masouleh, “Parallel Robot Based on the Screw Theory and,” 2019 5th Conf. Knowl. Based Eng. Innov., pp. 717–724, 2019.

Proceedings of Innovation and Technology Competition (INOTEK) 2021, Proceedings of Innovation Technology Melaka,and Malaysia, pp.Competition 235-236, (INOTEK) 2021

Power Saving Analog Reservoir Computing System Design C. Y. Yuen1, W. Y. Chiew1* Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia

1

Corresponding author’s email: [email protected]

*

ABSTRACT: The aim of this project is to design an Analog power saving Spike-Based Delayed Feedback Reservoir Computing System and analyse its performance. The developed system can make the integrated circuit (IC) achieve power saving so that can solve the problem excessive power consumption of supercomputer with good performance. Moore's prediction is coming to an end as things are starting to slow down. Over the last few years, reservoir computing has evolved as a revolutionary notion in the field of machine learning. To process temporal data, reservoir computing combines the memory and Spatio-temporal processing capabilities of recurrent neural networks. Silterra 130nm technology is used to create a new class of computationally efficient spike timing-dependent encoders and delay-based reservoirs within reservoir networks. The new method eliminates the need for power-hungry analog-to-digital converters (ADCs) and operational amplifiers (Op-AMPs), resulting in lower power consumption and a smaller design footprint.

Figure 1 Schematic in Cadence Virtuoso Figure 1 shows the whole schematic in Cadence Virtuoso, the ECG dataset is put into the V0 as the input signal of the project. After that, the signal will across a non-linear transformation circuit and process an output voltage, VNT (Voltage Non-linear Transformation). Then the VNT across a buffer and process a signal input (Sin) of first encoder circuit (Encoder_1).

Keywords: reservoir computing; temporal encoder; spike-based; delay feedback reservoir

Figure 2 Encoder

1. INTRODUCTION

As shown in Figure 2, the signal will across the encoder circuit and process an output spike train signal. The output signal of Encoder_1 (Sout_1) will become the input signal of the second encoder circuit (Sin_2). Then, The Sin_2 will across the second, third and fourth encoder circuits (Encoder_2, 3 & 4) and process a spike train.

Due to the underlying performance restrictions of the chips, the pace of increase is starting to saturate and slow down, indicating the end of Moore's prediction. A supercomputer with good performance needs a lot of power consumption. The need to break through barriers has led researchers in multiple directions, such as novel computing architectures. Reservoir computing is a technique for speeding up machine learning algorithms. Only a nonlinear neuron and a delay loop are used in the most recent reservoir computing model, delayed feedback reservoir (DFR) computing. It not only provides easy-to-implement hardware, but it also allows the inherent delay and its rich intrinsic dynamics to contribute to optimal performance. The schematic of the circuit is designed by using software Cadence Virtuoso. Analog implementations would be more power-efficient and take up less space because power-hungry peripheral components such as ADC and Op-AMP are not included.

1.1. Non-linear Circuit

Figure 3 Non-Linear Circuit Figure 3 shows the simplified design scheme of nonlinear. This circuit included two input triggers (PM0 and PM1), non-linear transformer (NM2, NM3 and C1), a feedback current mirror (PM2, PM3 and NM1), an output current mirror (PM5, PM6 and NM0), and a buffer (PM7, NM6 and C4). During reset period, the input charged to VDD, such that (VDD −Vin) < Vthp. The input trigger will be

2. METHODOLOGY ECG (Electrocardiogram) signal, the input dataset of this system is collected from PhysioNet website. All the datasets will be the input of the project. The project is created by using Silterra 130nm technology in Cadence Virtuoso software. © Faculty of Electronic and Computer Engineering, FKEKK

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deactivated, and the nonlinear transformer will be discharged, resetting the output to zero. During the decision-making, the nonlinear transformer read the input signal and charge up its intrinsic capacitor to regulate the VNT. Then, the VDS increase rapidly until its saturation level is reached. When the VNT is smaller than the threshold, the 𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉 of NM0 would be 0, such that the output current mirror PM5 diode-connected structure fully enables the output current mirror to achieve the maximum output current. When the voltage 𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉 reaches its saturation level, the transistor PM5 would drops into its sub-threshold region and decrease the output current (Iout). Meanwhile, the feedback current mirrors form a positive feedback loop to generate a high voltage at VFB, disabling the input trigger and keeping the nonlinear node in a state of rest until the next input data arrives. At the same time, the VNT also across a buffer and process the signal (Sin_1) at the output.

Then, we prove the relationship between time delay(τ) and the calibration current (Ical). Next, we calculated the total power consumption and proved it can be power saving. In Figure 6, since the Ical 1,3,5,7 are set in 10uA,20uA,30uA and 40uA, so we can observe that the distance between Sin_2, 3, 4 and output, align with the formula in [1].

Figure 6 Distance between spike Next, the total power consumption is calculated by using power static formula. The Pstatic is equal to Istatic*VDD, and Istatic is equal to sum of the dc current from each branch. Since the sum of the total static current is 226.9µA and the VDD is equal 1.2V, therefore the static power, Pstatic = (226.9µA) * (1.2V) = 272µW. The power consumption of the developed system just using 272µW, demonstrating the power saving of the system.

1.2. Encoder Circuit

Table 1 Benchmarking of power consumption Project Power (W) ECG SIMULATOR 4mW Solar-Powered Portable ECG Device 3.99mW A low-power and miniaturized 29.74mW electrocardiograph data collection system Automatic analysis method for 1.86W long-term ECG This work 272µW

Figure 4 Encoder Circuit During operation, the sensing capacitance Cs (C0) continually tracks and charges up the calibration current Ical (I18) generated by the delay calibration module. When the Vthc exceeds the threshold level of both of input transistor, the two cascading inverters (PM1, NM2 & PM3, NM4) fire an output spike. At the same time, the positive feedback loop provides a high voltage at Vreset, triggering the reset transistor (NM0), allowing the sensing capacitor to discharge completely. The delay time is regulated by the integrating time of sensing capacitor. The delay time constant can be written as follows: τdelay = Cs * (Vthc/Ical) For making the system could predict the signal, 4encoders are used because it can form a short-term memory for the system.

4. CONCLUSION The developed analog DFR computing system was developed with the standard Silterra 130nm CMOS technology. As shown in the simulated results, the produced analog DFR chip has a rich dynamic behavior, indicating that the proposed DFR architecture, which can replicate the nervous system with only 272µW of power consumption, has been successfully implemented. The developed design has achieved design an analog spikebased nonlinear processor directly processes spike signals, and the power consumption is greatly reduced because components such as analog-to-digital converters (ADCs) and operational amplifiers (Op-AMPs) are not required.

3. RESULTS AND DISCUSSION In Figure 5, we observed that the R point (the highest point of an ECG signal) from high to low. This proves the higher the voltage, the increase the number of spikes. Four different values of Ical are used to cover different pattern of input signal.

REFERENCES [1]

Figure 5 Result of ECG signal_2 © Faculty of Electronic and Computer Engineering, FKEKK

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K. Bai & Y. Yi, “DFR: An energy-efficient analog delay feedback reservoir computing system for brain-inspired computing,” ACM Journal on Emerging Technologies in Computing Systems (JETC), 14(4), 1-22, 2018.

Proceedings of Innovation and Technology Competition (INOTEK) 2021, Proceedings of Innovation and Technology Melaka, Malaysia, pp.Competition 237-238, (INOTEK) 2021

The Design of Poultry Egg Incubator Control and Monitoring System M. A. M. Zain1, Y. Yusop1* Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia

1

Corresponding author’s e-mail: [email protected]

*

ABSTRACT: Poultry egg incubator can be found on easily with many kind of features that are offered with the amount of hatching capacity is diverse, but some of those incubator will still work manually. The stability of the temperature and humidity in poultry egg hatchery will be less effective if the monitoring and controlling will be done manually. The manual monitor and control process are not efficient anymore for the poultry farmers as it spends much of time. Therefore, in order to overcome the problem and improve the technology, it needs a modern poultry egg incubator tools that makes it easier for the poultry farmers to handle it anytime and anywhere. Technology of “Internet of Things” allows the poultry farmers to control and monitor the incubator in distance by using the internet optimally. By using the Microcontroller of Arduino integrate with NodeMCU ESP8266 and ESP32Cam which is combined with temperature and humidity sensors, servo motor, webcam and relay to produce control and monitoring system of chicken eggs incubator machine which can be accessed through website.

will be implemented in the poultry egg design which is the used of “Internet of Things (IoT)” to allow the poultry farmers control and monitor the incubator in distance by using the internet optimally in their job. Integration of Arduino Microcontroller with ESP8266 an ESP32Cam combining with temperature and humidity sensor, servo motor, webcam, and relay to produce the best control and monitoring system of incubator which can be accessed through the website or via smart phone applications. 2.

This project will be design using Simulation of MATLAB Software to choose and pick the best module of solar panel for the system. In this research, the best parameters of solar panel is 18V of maximum output voltage and 10W of output power created by the solar module. For hardware implementation, this project using Arduino microcontroller with ESP8266 and ESP32 Cam for control and monitoring system. This project design will be analyzed for the solar module system analysis and the percentage of egg hatching using this poultry egg design incubator.

Keywords: Arduino Microcontroller; Chicken Egg Incubator; Internet of Things 1.

METHODOLOGY

INTRODUCTION

Poultry egg incubator is an insulated enclosure within which temperature, humidity and other environmental conditions can be regulated at optimal level for growth, hatching, reproduction and it has been used to keep the fertilized eggs of poultry warm until they are ready to hatch. There are three main elements for incubating process which is temperature, humidity and turning. All of these elements related to each other to maintain the egg hatching process. The optimum range of temperature is 37.7°C with humidity of 65% [1]. Manual poultry egg incubator system operation contribute to a lot of disabilities includes spends much of time for the poultry farmers to control and monitor and the unstable environment related with temperature and humidity in the surroundings. Therefore, the new modern technology egg incubator will be built in order to make it easier for the poultry farmers in control and monitoring the incubator. The new design of poultry egg incubator designed using the Photovoltaic (PV) source as a power supply due to the incubator needs 24 hours of supply to maintain the temperature and humidity of surroundings. Besides that, the new modern technology © Faculty of Electronic and Computer Engineering, FKEKK

Figure 1 General Block Diagram of Control and Monitoring System

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Figure 4 The IoT system of control and monitoring using Blynk Application

Figure 2 Flowchart of project implementation 3.

RESULT AND DISCUSSION

The analysis has been recorded and test for around 30 days. The result and data were recorded directly using the design of Poultry Egg Incubator Prototype. Figure 5 the full prototype design of Poultry Egg incubator

Table 1 Hatching percentage of chicken eggs.

4.

CONCLUSION

This research project has achieved both two main objectives which is to design the poultry egg incubator and monitoring system with integration of photovoltaic (PV) energy system and IoT Technology and to analyze the proposed system performance in term of poultry egg hatching percentage and overall system efficiency. The IoT platform using Blynk application has been integrate in order to monitor the temperature and humidity of the incubator and also the live camera to monitor the condition of incubator surrounding. Besides that, the control system also capable in IoT using Blynk application to control on output based of candescent bulb and fan to on or off it anytime and anywhere by the user. ACKNOWLEDGEMENT Authors would like to thank Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka for the equipment and apparatus provided for this research. REFERENCES

Figure 3 Charging Process of Battery by Solar module system

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[1]

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S. S. Abiola, "Effects of Turning Frequency of Hen's Eggs in Electric Table Type Incubator on Weight Loss, Hatchability and Mortality,” AFR J. Biotechnol, 7(23): 4310-4313p, 2008.

Proceedings of Innovation and Technology Competition (INOTEK) 2021, Proceedings of Innovation andMalaysia, Technology Melaka, pp.Competition 239-240, (INOTEK) 2021

Solid Characterization using Planar Microwave Resonator Sensor R. S. Aswir1, Z. Zakaria1*

Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia

1

*

Corresponding author’s email: [email protected]

ABSTRACT: This paper presents a planar microwave sensor for detecting and characterizing the dielectric properties in common solid material. The design is fabricated on Roger 5880 and operates at 2.27GHz in the range of 1 GHz to 3GHz. The sensor is designed using computer simulation technology (CST) and analyzed by a vector network analyzer (VNA). The sensor produces narrow resonances and a high Q-factor. The accuracy achieved is more than 85%, which qualifies the sensor for industrial use in monitoring the quality and safety of food, pharmaceuticals, and other products.

parameter of designation. 𝑓𝑓𝑓𝑓𝑜𝑜𝑜𝑜 =

𝑐𝑐𝑐𝑐

(1)

2 𝑥𝑥𝑥𝑥𝑥𝑥𝑥𝑥 𝑥𝑥𝑥𝑥�𝜀𝜀𝜀𝜀𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒

Where 𝑓𝑓𝑓𝑓𝑜𝑜𝑜𝑜 is the resonant frequency and 𝜀𝜀𝜀𝜀𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 is the effective permittivity. 𝜀𝜀𝜀𝜀𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 =

𝜀𝜀𝜀𝜀𝑟𝑟𝑟𝑟 +1 2

+

B. Simulation Process

𝜀𝜀𝜀𝜀𝑟𝑟𝑟𝑟 −1 2

�

1

1+12

ℎ 𝑤𝑤𝑤𝑤

�

(2)

Keywords: Microwave resonator sensor; solid sample; Q-factor 1.

INTRODUCTION

Microwave technology is most popular for monitoring the safety and quality of foods, chemical and pharmaceutical products. Sensing technique studies the interaction between each material with electrical activity. Almost all quality and safety of food products are important to prevent consumers from diseases because of certain ingredients. It is important to ensure that the products that are to be marketed are safe and have acceptable quality before deciding to sell them to customers [1]. Many methods have been proposed and used for material characterization. The researchers presented microwave resonator sensors such as dielectric, coaxial, and waveguide sensors in detecting material characterization [2]. However, traditionally resonators are bulky in size, high -cost manufacturing, and complex design structure. So, microwave planar resonator is suggested developing a sensor with compact, low cost and easy fabrication. Nevertheless, the technique also gives a disadvantage which low Q value and limits the range of material characterization. Thus, in this paper, we will present the high Q of resonator based on the planar microwave resonator sensor. 2.

Figure 1 Designation on CST

C. Measurement Process Final process after fabrication is measurement process. The microwave resonator sensor is measured by using vector network analyzer. The VNA will analyzed S11(dB), S21(dB) of microwave sensor. The range of frequency for this sensor 1GHz to 3GHz.The sample that used are Roger 5880.Roger 4350, and Fr4.

METHODOLOGY

A. Design Structure In this study, the resonant frequency of the microwave resonator sensor operated at 2.272GHz in the range of 1GHz to 3GHz for testing material characterization solid. The sensor is designed by using computer simulation technology (CST). Before the simulation process, there are mathematical analysis for © Faculty of Electronic and Computer Engineering, FKEKK

Figure 2 The measurement setup using VNA

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3.

RESULT AND DISCUSSION

Table 1 Comparison simulation with MUT MUT QS21( BW 𝑓𝑓𝑓𝑓𝑜𝑜𝑜𝑜 ∆𝑓𝑓𝑓𝑓 Error fact dB) (%accur or acy) AIR 240 -10.9 0.02 2.4 0 0 ROG 140 -7.17 0.03 2.31 0. 3.75(96. ER 29 09 3) 5880 ROG 64.2 -8.25 0.06 2.21 0. 7.916(9 ER 875 19 2.08) 4350 FR4 168 -14.0 0.02 2.11 0. 12.08(8 5 29 7.91)

A. Resonant Frequency Shifting Analysis The resonant frequency shifting determined the accuracy of the microwave sensor. During simulation, double ring resonator sensor illustrates that resonance frequency shifting at 2.272 GHz. During measurements, the frequency is shifted at 2.4 GHz. The obtained resonant frequency from simulation is found in good agreement. However, the simulation and measurement results are slightly different due to a misalignment between the feed lines and the SMA connector.

C. Dielectric Constant The relationship between the shifting of the resonant frequency and the standard permittivity may be approximated using a second order polynomial approach using measured data. Figure 3 Comparison of S21(dB)

Figure 5 Polynomial curve fitting permittivity

Figure 4 Comparison different MUT in measurement

4.

With MUT for both simulation and measurement three sample Roger 5880, Roger 4350, Fr4, with different thickness whereas 0.79 mm,0.508mm and 1.6mm with respectively. In both design it shows that the FR4 shifting frequency is further from operating frequency while Roger 5880 shifting is nearer to the operating frequency which mean the different thickness of sample also affect the resonant frequency shifting with respect to fringing field will be weaken which will not interfere with the permittivity.

A microwave resonator sensor at frequency 2.27GHz with high Q-factor is designed, simulated, fabricated and measured. In simulation result the Qfactor achieved 110.05. For measurement result Q-factor achieved 240.The percent of accuracy for this sensor is more than 85% which qualifies the sensor for industrial use in monitoring the quality and safety of food, pharmaceuticals, and other products. On top of that, this sensor can be used because it is cost effective, simple and easy to fabricate and easy to simulate.

B. Q-Factor Analysis

REFERENCES

Table 1 Comparison simulation with MUT MUT QS21( BW 𝑓𝑓𝑓𝑓𝑜𝑜𝑜𝑜 ∆𝑓𝑓𝑓𝑓 Error fact dB) (%acc or uracy) AIR 110 -1.79 0.04 2.27 0 0 ROG 110. -1.86 0.03 2.15 0. 5.106 ER 14 915 11 (94.89) 5880 6 ROG 103. -2.24 0.04 2.10 0. 7.8424 ER 98 043 17 (92.52) 4350 FR4 72.6 -4.93 0.05 1.95 0. 14.084 373 32 (85.92) © Faculty of Electronic and Computer Engineering, FKEKK

CONCLUSION

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[1]

R. A. Alahnomi, Z. Zakaria, E. Ruslan, A. A. M. Bahar, and N. A. Shairi, “A novel microwave sensor with high-Q symmetrical split ring resonator for material properties measurement,” J. Teknol., vol. 78, no. 10–3, pp. 37–42, Oct. 2016.

[2]

U. Schwerthoeffer, R. Weigel, and D. Kissinger, “Microwave Sensor for Precise Permittivity Characterization of Liquids Used for Aqueous Glucose Detection in Medical Applications," GeMiC 2014; German Microwave Conference, pp. 1-2, 2014.

Proceedings of Innovation and Technology Competition (INOTEK) 2021, Proceedings of Innovation andMalaysia, Technology Melaka, pp.Competition 241-242, (INOTEK) 2021

Design, Analysis, and Implementation of a Vehicle-to-Vehicle Driver Warning System for Rear-End Collision Avoidance in a Same Lane Braking Situation N. S. A. Jalil1, I. Ibrahim1* Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia

1

Corresponding author’s email: [email protected]

*

ABSTRACT: Road accidents become common in our daily life. Many factors that cause road accidents. One of them is because of careless drivers on the road. This careless behaviour will lead driver to sudden hit the brake pedal that can endanger other drivers on the road. However, with the advance in technology, we can design the system that can help drivers to avoid or reduce the number of accidents. Because of this, a system is proposed for the drivers on the road in the same lane. Driver warning system is design for the front driver to transmit the warning signal when he or she is hit the brake pedal to the drivers in car behind. Overall, this project is divided into three categories; transmitter to transmit the signal, receiver to receive the signal and lastly for the warning signal to be displayed on LCD. This project use IR transmitter and IR receiver for the communication between vehicles. The project starts with designing the transmitter and receiver circuit in Multisim to find the right components and proceed with connecting components on the breadboard and then lastly to design the driver warning system prototype.

the microcontroller. The microcontroller will send the alert signal to the output LCD display. This project includes the implementation of Arduino Microcontroller. Furthermore, the vehicle in the range of (0-10 m) position only will receive the signal. This is because, the warning system might interface with the other vehicle and will be distract other drivers which can lead to any accidents. 1.1 Objectives a. To design a transmitter that can transmit a signal to receiver when emergency brake is applied at some distance. b. To develop a system programming using Arduino microcontroller and display the Liquid Crystal Display (LCD) a warning message as an alert to the recipient. c. To develop a directional transceiver hardware on the prototype demonstrating the situation. 2.

The project starts with designing transmitter and receiver circuit along with the LCD circuit, with microcontroller. Suitable components determined and decided by using Multisim. After that, Arduino coding is developed to implement it into the transceiver circuit. In hardware development, the connection of the components on the breadboard is done based on the circuit designed from Multisim. Circuit is constructed combining microcontroller, transmitter, and sensor. Lastly, the working system of transmitter and receiver are tested and having connection between each other with the implementation coding of Arduino compiler. Then, analysis is done to test the hardware functionality.

Keywords: IR Transmitter; IR Receiver; Liquid Crystal Display 1.

INTRODUCTION

Road accidents are the leading cause of death by injury and the tenth-leading the cause of all deaths globally [1]. 2018 was the most dangerous year for drivers on Malaysian roads since 2012, with a seven-year high in the number of road accidents [1]. Approximately, there are 1.35 million people die each year as a result of road traffic accidents. More than half of all road traffic accidents are among vulnerable road users [2], which are pedestrians, cyclist and motorcyclists. Based on some research, the common factor contributing to rear-end collision is because of driver inattention which can lead to sudden hitting of the brake pedal which causes a panic stop on the road. This will cause the car behind it does not have time to brake and collides with it. Besides, even though we are fully focus on the road, there might other people that are lack of attention. This can lead to sudden hit the brake pedal without any notice. This project is planned, whenever a vehicle hits the brake pedal, automatically the transmitter will transmit an alert signal and received by the car behind it. After received the data, the receiver sends the signal towards © Faculty of Electronic and Computer Engineering, FKEKK

METHODOLOGY

2.1 System block diagram In this project, to illustrate the brake pedal is hit, a switch is connected in the transmitter circuit. When the switch is closed, it means that the brake pedal is hit and LED in transmitter circuit is on. This will cause the transmitter circuit emits an infrared light to be received by the IR receiver. Besides, in IR receiver circuit, TSOP1738 is connected to detect the modulated IR light. When the signal is received, the signal is sent to microcontroller for further processing, and the signal can be displayed on LCD.

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Brake Pedal Hit

IR TRANSMITTER

IR RECEIVER

MICROCONTROLLER (ARDUINO)

LCD

Figure 1 System block diagram Project includes two parts which are hardware and software part. The project is done part by part to check any errors before constructing the last part, which is designing the prototype. 3.

Figure 4 Transceiver circuit testing When push button is pressed, indicates that the brake pedal is hit, TSOP 1738 receive the signal and LED is turned on. The system was thoroughly tested by checking the transmitter and receiver circuits. The efficacy of this system approach is determined on the maximum distance between transmitter and receiver can approach. Based on datasheet, maximum range over which the signal can be transmitted by the transmitter is around 3 to 10 metres. In figure 4, receiver is able to receive the signal from transmitter, but it did not send the signal to LCD due to many errors occurs while connecting it with Arduino system. Based on circuit testing, IR receiver can receive signal from transmitter at maximum 1.84 meters which is not approximately accurate to compare from theory.

RESULTS AND DISCUSSION

The circuit is run first in Multisim to test the operation of the circuit and to determine suitable components to connect at LED avoiding it from any damage or burn. The value for each component is chose based on the datasheet to give a successful result to operate the transmitter and receiver circuit.

4.

CONCLUSION

Designing transceiver for vehicle emergency braking warning system is applied the combination of software and hardware to complete the project. Two objectives of the project are achieved. Second objective is not able to complete because of IR receiver manages to receive the signal but it is does not send the alert message to LCD display. This is due to errors occur while connect it with Arduino system. To conclude, the project is not fully functioning. Only the signal sends by IR transmitter to IR receiver in a range of 10 meters. The receiver successfully receive the signal interpret it on LED blinking.

Figure 2 Transceiver Circuit In hardware design, the circuit from Multisim simulation is connected on the breadboard to test either the design is working or not. For the first stage, transmitter circuit is connected on the breadboard, and then receiver circuit, and lastly testing for the transmitter able to send signal and receiver receive the signals.

ACKNOWLEDGEMENT Authors would like to thank Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka for the equipment and apparatus provided for this research.

Figure 3 LED turned ON when push button pressed in transmitter circuit.

REFERENCES [1]

Figure 3 above shows when push button pressed, red LED is turned on, and when push button is not pressed, LED is not turned on. The button is used to illustrate the brake pedal hit situation. It shows that the circuit is successfully connected based on the circuit simulation in Multisim. After confirming the IR transmitter circuit is functioning well, red LED is replaced by IR LED so that the signal from transmitter circuit can be received by IR receiver. Red LED cannot be used as IR transmitter because it cannot send the signal to receiver. Transmitter and receiver circuit are tested together to show both circuits are working or not. IR LED is faced directly to TSOP 1738 sensor, so that the IR sensor can detect the signal send from transmitter. © Faculty of Electronic and Computer Engineering, FKEKK

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M. M. A. Manan, A. Várhelyi, A. K. Çelik, and H. H. Hashim, “Road characteristics and environment factors associated with motorcycle fatal crashes in Malaysia,” IATSS research, 42(4), pp. 207-220, 2018. J. Doble, Introduction to radio propagation for fixed and mobile communications. Artech House, Inc., 1996.