W. S. Mada Sanjaya

A portable giant magnetoresistance (GMR) biosensing platform integrating an Fe 3 O 4 /PVA–glucose oxidase functional layer, dual-coil magnetic excitation, embedded signal acquisition, and machine-learning-assisted prediction was developed for quantitative glucose sensing. The system exhibited stable concentration-dependent GMR responses over 0.02–0.15 mg ml − 1 . Linear Regression (LR) and support vector regression with a radial basis function kernel (SVR-RBF) were evaluated using development-set analysis, leave-one-concentration-out validation, independent external testing, and prepared-saliva samples. On an independent 10-concentration dataset, SVR-RBF achieved R 2 , MAE = 0.0029 mg ml − 1 , and RMSE = 0.0040 mg ml − 1 , outperforming LR ( R 2 , MAE = 0.0048 mg ml − 1 , RMSE = 0.0059 mg ml − 1 ). In prepared saliva, the fixed SVR-RBF model maintained low prediction errors (MAE = 0.0043 mg ml − 1 ; RMSE = 0.0050 mg ml − 1 ). These results demonstrate the feasibility of combining magnetic GMR transduction, functional magnetic nanomaterials, and nonlinear regression within a compact platform for quantitative glucose sensing, while further validation in authentic biological samples remains necessary.

This article explores techniques for the detection and classification of fish as an integral part of underwater environmental monitoring systems. Employing an innovative approach, the study focuses on developing real-time methods for high-precision fish detection and classification. The implementation of cutting-edge technologies, such as YOLO (You Only Look Once) V5, forms the basis for an efficient and responsive system. The study also evaluates various approaches in the context of deep learning to compare the performance and accuracy of fish detection and classification. The results of this research are expected to contribute to the development of more advanced and effective aquatic monitoring systems for understanding underwater ecosystems and conservation efforts.

This study presents the development of an Electronic Nose system using Arduino Mega and Raspberry Pi 4, capable of accurately detecting odors and gases. Previously, sensor analysis revealed that variations in meatball and formalin mixtures yield diverse sensor responses, with some sensors exhibiting high sensitivity to formalin. Additionally, Linear Discriminant Analysis (LDA) demonstrated clear separation among different classes, facilitating comprehensive data analysis. The study also evaluated the performance of the SVM model, showing precise SVM parameter optimization with high accuracy for classification, achieving up to 100% accuracy at C = 0.1, kernel RBF, and gamma 0.1. These findings highlight the potential of the developed system to effectively detect formalin in meatballs, providing valuable insights for ensuring food safety and quality assurance. Overall, the optimal selection of the C parameter plays a key role in enhancing SVM model performance and contributes to advancing detection technology in food industry applications.

In this study, we investigate the application of the Arneodo-Coullet-Tresser chaotic system in both image and voice encryption, considering the increasing importance of data security in the digital age. The analysis of the chaotic system involves exploring phase diagrams, time series, bifurcation diagrams, Lyapunov exponent analysis, and Poincaré maps to understand its dynamics. For image encryption, we evaluate the effectiveness of the system through various analyses, including histogram analysis, correlation analysis, entropy analysis, NPCR and UACI analysis, and noise attack analysis. Similarly, for voice encryption, we assess it through various analyses, including waveform plots, FFT, spectrograms, correlation coefficients, entropy analysis, and RMSE. The research findings demonstrate the suitability of the Arneodo-Coullet-Tresser chaotic system for both image and voice encryption, suggesting its potential as a data transmission masking technique. The research includes numerical simulations conducted using Python to support the proposed approach.

In detecting epileptic activity, medical experts examine the visual result of Electroencephalography signals. The visual analysis will take a lot of time and effort, due to a large amount of data. Furthermore, there are some errors in concluding the analysis result. One of the ways to analyze this quickly is to use Machine Learning (ML) methods. This study aims to evaluate the performance of 1D-CNN in identifying the given data. First, the signal will go through pre-processing using EEGLAB Toolbox which is then classified to identify epilepsy and non-epilepsy with the 1D-CNN algorithm. The results showed that the proposed method obtained high accuracy values, respectively 99,078% for the training data and 82,069% for the validation results. From the evaluation by a confusion matrix, an average accuracy of 99,31% was obtained. Based on this evaluation, the proposed model can be used as an efficient method in the process of automatic classification, detection, or identification of epileptic activity.

Ibn al-Haytham or better known as Alhazen (965-1040 AD) was a great Muslim scientist who mastered many disciplines. One of his works entitled "Qawl fi Samt al-Qibla bi al-Hisab" describes a mathematical method for determining the direction of Qibla. In this paper, Ibn al-Haytham's algorithm is modified, thus the algorithm can determine the Qibla direction on the entire surface of the globe. Then, Ibn al-Haytham's mathematical method is compared with modern spherical trigonometry methods and our previous research using al-Biruni’s method. The computational results show that all methods have the same accuracy, and show that ibn al-Haytham's method is still relevant now. Therefore, the modified ibn al-Haytham method algorithm was implemented to develop a Qibla direction device and interface based on Raspberry Pi 4, GPS module, digital compass, and Processing 3 software. The implementation results show that the device can display the Qibla direction interface according to numerical calculations with high accuracy, real-time, and dynamic interface.

Colored object detection using 5 dof robot arm based adaptive neuro-fuzzy method

In this study, we investigate the facial tracking and expression recognition to developed Social Robot called SyPEHUL (System of Physics, Electronics, HUmanoid robot and machine Learning). This social robot contains by 12 Degree of Freedom (DoF) for actuating robotic head based on Arduino microcontroller to display four type different facial expressions and tracking of the human face. In this research, expression recognition processed by an algorithm based on Python 2.7 (with OpenCV library) using Cascade Classification and LBPH Face Recognizer. The result shows that the implementation of facial tracking and expression recognition to Social Robot show a good accuracy of recognition rate and works well for Human-Robot Interaction.

RC circuit is the basic physics experiment show the charging and discharging on capacitor electronic component. In this paper, we have investigated the numerical simulation from the mathematical model of charging and discharging of capacitor, also we developed experimentally RC circuit using Raspberry Pi single board microprocessor and make interfaces using Python software. The benefit of this system that all of them was open Source and can help the physics experiment easily at the laboratory. To get data acquisition from RC circuit we used IC MCP3008 which have converted an analog to digital data. The experimental result showed the best agreement with the numerical simulation result.

In this paper, we describe a new 3-D chaotic system with line equilibrium. A detailed analysis of the proposed chaotic system is provided with MATLAB phase portraits, equilibrium points, symmetry, invariance, Lyapunov exponents, Kaplan-Yorke dimension, bifurcation diagram and a Poincarè map. For specific values of the parameters, the proposed system displays chaotic behaviour. We apply adaptive control method for the global chaos synchronization of the new chaotic systems with unknown system parameters. In addition, a new circuit implementation of the new chaotic system is reported and examined in MultiSIM. A good qualitative agreement is shown between the simulations and the MultiSIM results.