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Mohamad, Roslina
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Hybrid encryption based on a generative adversarial network Amir, Iqbal; Suhaimi, Hamizan; Mohamad, Roslina; Abdullah, Ezmin; Pu, Chuan-Hsian
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp971-978

Abstract

In today’s world, encryption is crucial for protecting sensitive data. Neural networks can provide security against adversarial attacks, but meticulous training and vulnerability analysis are required to ensure their effectiveness. Hence, this research explores hybrid encryption based on a generative adversarial network (GAN) for improved message encryption. A neural network was trained using the GAN method to defend against adversarial attacks. Various GAN training parameters were tested to identify the best model system, and various models were evaluated concerning their accuracy against different configurations. Neural network models were developed for Alice, Bob, and Eve using random datasets and encryption. The models were trained adversarially using the GAN to find optimal parameters, and their performance was analyzed by studying Bob’s and Eve’s accuracy and bits error. The parameters of 8,000 epochs, a batch size of 4,096, and a learning rate of 0.0008 resulted in 100% accuracy for Bob and 52.14% accuracy for Eve. This implies that Alice and Bob’s neural network effectively secured the messages from Eve’s neural network. The findings highlight the advantages of employing neural network-based encryption methods, providing valuable insights for advancing the field of secure communication and data protection.
Colour sorting ROS-based robot evaluation under different lights and camera angles Saaid, Mohammad Farid; Thamrin, Norashikin M.; Misnan, Mohamad Farid; Mohamad, Roslina; Romli, Nurul A’qilah
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1807-1815

Abstract

Automated colour sorting, aided by mobile robots, is widely prevalent in the current manufacturing industry. Obstacles, such as fluctuating light conditions and camera angles, frequently hinder this procedure. Creating a colour sorting robot is a complex and time-consuming task, especially due to the vulnerability of the RGB colour space to detection errors in extreme brightness or darkness. In response to these concerns, we introduce a mobile robot that operates on the robot operating system (ROS) platform and incorporates OpenCV. This robot employs the hue, saturation, and value (HSV) colour space model for its image processing capabilities in recognising the colours and Welzl’s algorithm for the ball’s diameter estimation. The robot’s performance was assessed across various luminous fluxes and camera tilt angles. It demonstrated exceptional performance at 64 lm and a tilt angle of 40 degrees, achieving an average accuracy of 87.5% for detecting the colour of the ball, and 81.25% for determining its location based on colour. For the ball’s diameter estimation, it was found that the best estimation was received at 64 lm and 30 degrees, with both 96.32%.
Solar-powered irrigation and monitoring system for okra cultivation Soekarno, Mohamad Syfiq; Mohamad, Roslina; Thamrin, Norashikin M.; W. Muhamad, Wan Norsyafizan
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp469-477

Abstract

Eco-friendly and cost-effective irrigation systems are essential for sustainable agriculture. Traditional irrigation systems are unsustainable due to the high cost of operation and environmental pollution associated with fossil fuels. A possible solution for farmers is the use of solar-powered irrigation systems. This research aims to develop a solar-powered irrigation and a real-time monitoring system for okra cultivation. The irrigation system was powered by a monocrystalline solar panel and controlled by a Node MicroController Unit ESP8266 microcontroller unit. A 12 V pneumatic diaphragm water pump was utilized to irrigate the okra plants efficiently. The real-time monitoring system using Blynk allowed for the remote monitoring of the system's performance. The irrigation system was deployed on an okra farm, and the results showed that the system could sustain the soil moisture level for the okra plants, with an average soil moisture sensor reading of over 80%. The system delivered power effectively, with an average voltage measurement exceeding 12 V, average current readings above 180 mA, and average power readings exceeding 2 W. These results demonstrate that the solar-powered irrigation system is a viable and sustainable solution for farmers, researchers, and engineers to enhance the performance of conventional irrigation systems.
EMG-based hand gesture classification using Myo Armband with feedforward neural network Mohd Said, Sofea Anastasia; Thamrin, Norashikin M.; Amin Megat Ali, Megat Syahirul; Hussin, Mohamad Fahmi; Mohamad, Roslina
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp159-166

Abstract

This paper presents the development of an electromyography (EMG)-based hand gesture identification system for remote-controlled applications. Even though the Myo Armband is no longer commercially supported, the research discusses its use in EMG data collecting. Open-source libraries were utilized to capture EMG data from this device to solve this problem. Using the developed data acquisition platform, data was collected from 30 participants who performed three (3) gestures - a fist, an open hand, and a pinch. The energy spectral density (ESD) and power ratio (pRatio) were extracted to describe gesture-specific patterns. A feedforward neural network (FFNN) was implemented for classification, initially configured with 10 hidden neurons and later optimized to 40 neurons to improve the performance. The box plot analysis showed channels CH1, CH4, CH5, and CH7 as the most significant for enhancing classification accuracy. The optimized FFNN achieved 80% and 70% for the training and testing accuracies, respectively. However, the results suggest that implementing a systematic protocol during data acquisition to reduce signal overlap between movements could improve classification accuracy. In conclusion, the study successfully developed an open-source EMG data acquisition platform for MYO Armband and demonstrated acceptable hand gesture recognition using an optimized FFNN.
Co-Authors Abdullah, Ezmin Amin Megat Ali, Megat Syahirul Amir, Iqbal Hussin, Mohamad Fahmi Misnan, Mohamad Farid Mohd Said, Sofea Anastasia Pu, Chuan-Hsian Romli, Nurul A’qilah Saaid, Mohammad Farid Soekarno, Mohamad Syfiq Suhaimi, Hamizan Thamrin, Norashikin M. W. Muhamad, Wan Norsyafizan