Journal of Electronic Voltage and Application
The Journal of Electronic Voltage and Application (JEVA) is a peer reviewed, open-access journal which is aimed to help disseminate knowledge among the community. JEVA welcomes article submission for any original work and development in the field of electrical and electronics engineering that includes (but not limited to) Electrical Engineering, Power System, Communication System, Control and Robotics System, Computer Engineering, Networks System and Electronics Engineering. In addition, JEVA also publishes detailed case studies and review articles, as well as short communication and discussion. It is published twice a year (June and November) and there is no publication fee for the accepted manuscript. More Info
Detection Method for Authenticity Assessment of Malaysian Tualang Honey Using Absorbance and Transmittance Analysis with combination of Application of Artificial Neural Network - Optical Properties (ANN-OP) Classification
Honey is a highly valued natural sweetener, frequently subjected to adulteration due to its economic importance. It is commonly consumed for its perceived health benefits and as a substitute for refined sugar in a wide range of food products. In Malaysia, the widespread availability of diluted honey poses a significant challenge in distinguishing between pure and adulterated forms. This study presents a simple spectrophotometric approach using absorbance and transmittance measurements to differentiate honey authenticity. Eight honey samples, both pure and adulterated, were analysed. Results showed that the darker honey samples exhibit higher absorbance values, while lighter samples demonstrate faster signal responses in transmittance measurements. Optimal wavelengths were identified at 480 nm for absorbance and 630 nm for transmittance. These wavelength-specific responses provide an effective means of identifying adulterated honey. This method offers a practical solution for consumers and industry professionals seeking to verify honey purity, especially for health and dietary applications.
Faculty of Electrical and Electronics Engineering
Copyright (c) 2026 Journal of Electronic Voltage and Application https://creativecommons.org/licenses/by-nc-sa/4.0
Performance of 5G mmWave Massive MIMO System with Hybrid Beamforming
Massive MIMO integrated with beamforming constitutes a fundamental physical layer mechanism in 5G, leveraging large-scale antenna arrays to realize spatial multiplexing gains and array gain for enhanced spectral efficiency (SE), particularly in high-frequency (mmWave) bands suffering from severe path loss. Hybrid beamforming (HBF) has the potential to reduce cost and power consumption compared to fully digital beamforming (DBF). This paper investigates the effectiveness of hybrid beamforming integrated with Joint Spatial Division Multiplexing using Joint Group Processing (JSDM-JGP) in reducing the number of required radio frequency (RF) chains. A comparative analysis between JSDM-JGP and DBF is conducted to examine the trade-offs between reduced system complexity and potential performance enhancements. Our numerical results demonstrate that HBF with JSDM-JGP can significantly improve system energy efficiency (EE) performance with a lower complexity than DBF system. On the other hand, the spectral efficiency (SE) of HBF is slightly lower compared to DBF. In terms of bit error rate (BER), both HDF and DBF show extremely similar results.
YOLOv8-Based Infrared Intrusion Detection and Classification Model Using the OTCBVS Benchmark Dataset
Infrared imaging provides a reliable technique of monitoring and detecting intrusions in low-light and harsh environmental situations. This paper presents an optimized deep learning model for infrared intrusion detection and classification utilizing the You Only Look Once version 8 (YOLOv8) framework. The model was developed using the OTCBVS Benchmark Dataset Collection, pre-processed and annotated with Roboflow, and trained in a Google Colab Pro GPU environment. Multiple hyperparameter tuning and picture augmentation procedures were used to enhance detection robustness in a variety of environmental and operational conditions. The numerical evaluation revealed a precision of 0.999, recall of 0.929, mAP50 of 0.937, and mAP50-95 of 0.70, as against the non-optimized YOLOv8 (baseline YOLOv8) with precision of 0.740, recall of 0.650, mAP50 of o.721, and mAP50-95 of 0.453, respectively. This shows outstanding accuracy and generalization across various test conditions. The smooth convergence of the training and validation loss curves shows the model's stability and lack of overfitting. Visualizing bounding box predictions and confusion matrices proves the efficacy of intrusion target localization and classification. The results establish the proposed YOLOv8-based model as a highly efficient and precise framework for infrared-based intelligent surveillance and smart power infrastructure protection.
A Solar-Powered Automated Alternating Wetting and Drying System for Sustainable Paddy Field Irrigation
This paper presents the design and implementation of a solar-powered automated irrigation system based on the alternating wetting and drying (AWD) technique for paddy field cultivation. The system is designed to enhance water-use efficiency and reduce reliance on manual irrigation by harnessing renewable solar energy. Water level sensors are employed to monitor field conditions and automatically regulate irrigation cycles in accordance with AWD principles. A solar photovoltaic (PV) system provides renewable power to the ESP32 microcontroller and sensor, enabling reliable operation in off-grid and remote areas. Prototype testing using a scaled paddy field model is conducted to evaluate the irrigation control strategy and PV system performance. The solar PV system demonstrated a voltage range of 12.5-15.6 V, with peak power output of 99.8 W under clear-sky conditions, and exhibited a strong linear relationship between irradiance and power output (R² = 1.00). The automated AWD control using an ultrasonic water-level sensor achieved reliable irrigation triggering with measurement errors below 10%, enabling accurate detection of dry and normal field conditions. These results confirm that the solar-powered AWD system provides reliable off-grid operation while maintaining effective and precise water management for paddy cultivation.
IoT-Based Smart Helmet for Real-Time Crash Detection, Prevention and Rider Safety Monitoring
Motorcyclists remain among the most vulnerable road users, with high fatality rates often linked to delayed emergency response and unsafe riding practices. This project presents the design and development of an IoT-based smart helmet for real-time crash detection, accident prevention, and rider safety monitoring. The system integrates multiple sensors, including the MPU6050 accelerometer and gyroscope for crash detection, MQ- 3 alcohol sensor for intoxication monitoring, TCRT5000 IR sensor for drowsiness detection, ultrasonic sensor for obstacle awareness, and IR sensor for helmet- wearing verification. An ESP32 microcontroller serves as the central unit, coordinating sensor inputs and outputs, while GSM and GPS modules enable automatic emergency communication through SMS, calls, and location sharing. Testing results demonstrate that the helmet reliably detects unsafe conditions such as crashes exceeding a 2.5 g threshold, alcohol presence above set limits, prolonged eye closure, and nearby obstacles within one meter. The system initiates alerts via buzzer, Blynk mobile application, Gmail notifications, and GSM communication, with a 10- second countdown provided to cancel false crash triggers. Findings confirm that the smart helmet offers both preventive and responsive safety features, reducing accident risks and improving emergency response times. This work highlights the potential of IoT technology to enhance motorcycle safety by integrating real-time monitoring, rider condition assessment, and automated emergency alerts into a single wearable device. The prototype demonstrates a practical and scalable solution that can contribute to reducing motorcycle fatalities and advancing intelligent road safety systems.
Simulation-Based Arduino Basic Electrical Monitoring System for Educational Applications
This paper presents a fully simulation-based and low-cost electrical monitoring system for educational applications in embedded systems laboratories, developed entirely in Tinkercad using Arduino Uno simulation. The methodology employs Arduino Uno simulation in Tinkercad with a voltage divider circuit for input voltage measurement, potentiometer (connected to A0) for variable load conditions and LED brightness control, 16x2 LCD (D4-D11) for real-time display of input voltage, load voltage, power consumption and three LEDs (red, yellow, green) for load indication based on voltage thresholds, validated using Tinkercad's built-in multimeter. Results show accurate voltage and power monitoring with close agreement between LCD values and built-in multimeter readings, plus correct LED status responses (green for low load <3V, yellow for medium 3-5V, red for high >5V). This project offers a fully virtual, accessible, and hands-on learning experience, helping students build foundational knowledge in electrical monitoring, circuit interfacing, and embedded programming using simulation-based tools.
Conductive Fabric Study and Its Application for Wearable Devices
The integration of electronics into clothes has created demand for aesthetic, flexible and conductive materials that can serve as reliable circuit paths. Among these, conductive fabrics have emerged as a promising alternative to conventional wires in wearable devices due to their lightweight structure, comfort and ability to follow the movement. This study investigates the performance of a silver-plated nylon fabric, MedTex130 as a conductive fabric in a simple wearable LED circuit application. A series of experimental tests were conducted to evaluate the fabric’s electrical behaviour under real-world conditions. Resistance was measured across various strip lengths 1 cm to 8 cm, widths 0.3 cm on shiny and non-shiny surfaces. Additional tests evaluated how stretching up to 100% affected resistance value, how the fabric responded to 0Hz frequency direct current (DC) voltage up to 12 V and how thermal stress from soldering impacted conductivity and material integrity. The results showed that resistance increased with length and decreased with width, consistent with Ohm’s law. Interestingly, resistance dropped significantly when the fabric was stretched, particularly on the non-shiny side, with some samples showing up to 94% reduction. Although the fabric supported LED activation up to 12 V, significant heat build-up was observed above 5 V. Furthermore, soldering at typical temperatures ≥350°C caused permanent damage to the silver coating, leading to a drastic increase in resistance. The observed resistance variation and thermal degradation directly affect power efficiency, user safety and long-term reliability in wearable devices, indicating that conductive fabrics are suitable only for low-voltage, low-current applications with careful thermal management. Overall, the findings confirm the fabric’s capability for low-voltage wearable circuits but also highlight limitations in heat tolerance and the need for alternative joining methods.
Developing a Smart Child Presence Detection in a Vehicles Using Thermal Camera
Children left unattended in parked cars face severe risks due to rapidly rising cabin temperatures, often leading to heatstroke or fatalities. Even on moderately warm days, interiors can reach life-threatening levels within minutes, and despite awareness campaigns, these incidents continue, highlighting the urgent need for proactive detection systems.This project presents a smart child presence detection system for vehicles using thermal imaging and deep learning, designed on preventing fatalities caused by vehicular heatstroke. The system integrates the MLX90640 thermal camera with a Raspberry Pi 5 and leverages the YOLOv8s object detection model to identify human heat signatures in real time. Unlike conventional camera-based or multi-sensor solutions, this work introduces a lightweight, cost-effective approach that ensures reliable detection regardless of lighting conditions. A key novelty of this system lies in its fully embedded implementation, performing all processing locally on the Raspberry Pi to enable fast, autonomous operation without external infrastructure. The system is designed to trigger an alert when a human is detected in the vehicle cabin, activating a buzzer and sending a thermal image notification to the caregiver via a Telegram bot. A five-minute delay mechanism is included to prevent repeated alerts unless manually reset. The YOLOv8s model was trained on a custom thermal dataset and tested under varying distances, angles, and body positions to simulate realistic in-vehicle conditions. Comparative evaluation with a commercial infrared thermometer confirmed the MLX90640’s capability in detecting human heat signatures. The results demonstrate the feasibility and potential of combining thermal sensing with on-device AI for real-time child presence detection in vehicles.
Copyright (c) 2025 Journal of Electronic Voltage and Application https://creativecommons.org/licenses/by-nc-sa/4.0
Evaluating Interoperability in EV Wireless Charging Systems: A Framework-Based Comparative Approach
Wireless Power Transfer (WPT) technology has emerged as a promising solution for convenient and safe Electric Vehicle (EV) charging. However, the lack of interoperability between wireless charging systems developed by different manufacturers due to the diversity of international standards such as SAE J2954, IEC 61980, and ISO 15118 poses significant barriers to large-scale adoption. This paper presents a comprehensive review of the current WPT standard and its technical specifications, and is followed by an analysis of interoperability challenges in EV charging systems. Based on these identified gaps, we propose a conceptual framework to evaluate technical interoperability across various WPT implementations. This framework certainly also considers key parameters such as frequency alignment, coil compatibility, power level, communication protocol, and safety features. The results of this study provide insights for developers, manufacturers, and regulators in aligning the WPT system to support EV charging infrastructure that can be operated in the future.
Article Advances in Feature Extraction and Selection for Iris Recognition Systems: A Review
The growing demand for security and reliable authentication methods has positioned biometric recognition systems at the forefront of modern security infrastructure. Among various biometric modalities, including facial and fingerprint, iris recognition has emerged as the most accurate and reliable, due to its rich texture, stability over time, and resistance to ageing and surgical alteration. However, existing iris recognition systems are primarily optimized for frontal, high-quality images and often fail to accurately process angular or partially captured iris images. To overcome these issues, techniques such as feature extraction, segmentation, and normalization need to be improved. Feature selection is essential in dimensionality reduction, and when combined with feature extraction techniques, the process will improve both time complexity and accuracy. This review explores the advancements in iris recognition, focusing on feature extraction and selection techniques that are crucial for improving system accuracy and efficiency. This study offers a comprehensive overview of various feature extraction and selection techniques, serving as a valuable reference for researchers.
Analysis of Symmetric and Asymmetric Multilevel Inverter Topologies with Focus on THD Performance
Inverters are important components in modern power conversion systems, including renewable energy applications and electric vehicles, where the conversion of DC power to high-quality AC output is essential. With the growing demand for power quality, minimizing Total Harmonic Distortion (THD) is crucial, as high levels can degrade system performance and cause equipment failure. This project aims to simulate and compare symmetric and asymmetric cascaded H-bridge (CHB) multilevel inverters to determine the most efficient topology for power electronics applications. The study involves developing separate PLECS models for a 5-level symmetric and a 7-level asymmetric CHB inverter, implementing Sinusoidal Pulse Width Modulation (SPWM) and Level-Shifted SPWM (LSSPWM), respectively, and applying an LCL filter to the output of each. The results show that the unfiltered THD for the symmetrical 5-level inverter was 27.00%, which was reduced to 0.13% after LCL filtering. The asymmetrical 7-level inverter inherently performed better, with an initial THD of 17.42% that improved to 4.38% after filtering. These findings conclude that both inverter designs, particularly the asymmetric topology with their finer voltage steps, effectively meet harmonic standards below the required 5% threshold after filtering, validating their suitability for real-world systems.
Comparison of Traditional PWM and Phase Disposition PWM for a 5-Level Cascaded H-Bridge (CHB) Inverter in Low-Voltage Applications
Multilevel inverters play an important role in power systems as they provide better output waveform quality and help reduce harmonic distortion (THD). In this paper, a 5-level Cascaded H-Bridge (CHB) inverter is designed and tested using MATLAB Simulink. The inverter consists of two H-bridge cells, with each cell supplied by an equal DC voltage source. The 5-level configuration is chosen because it requires fewer switching devices compared to a 7-level inverter and is easier to implement, particularly for small low-voltage systems. Two modulation methods are used in this simulation. The first is Traditional PWM (TPWM), which employs one sine wave and one triangular carrier wave. The second is Phase Disposition PWM (PDPWM), where multiple triangular carriers are all in the same phase. Both PWM methods are tested under the same conditions, using a 50 Hz fundamental frequency to match the Malaysian power supply standard. To analyze the effect of filtering, the inverter is tested before and after applying a passive LC filter. Simulation results show that THD is reduced from 57.40% to 5.81% for TPWM, and from 27.38% to 1.83% for PDPWM after filtering. These results demonstrate that PDPWM is more effective when used with a passive filter. This work highlights the potential of PDPWM for applications in renewable energy systems, motor drives, and electric vehicles, and suggests opportunities for future exploration of advanced PWM strategies such as Modified PWM (MPWM) and AI-based modulation.
Performance Evaluation of Three-Phase Multilevel Inverter with R-Load using SPWM
This project focuses on the design and performance evaluation of a three-phase symmetric multilevel inverter using Sinusoidal Pulse Width Modulation (SPWM) and an RL load. The system is developed and simulated using PLECS software with a maximum DC input of 1000 VDC and a load resistance of 10 Ω. To improve output waveform quality and meet Malaysian power standards, an LC filter is designed using a 4.7 µF capacitor and a 5.4 mH inductor, achieving a cutoff frequency of 1000 Hz. The analysis confirms that the Total Harmonic Distortion (THD) of the output voltage is reduced from approximately 68 % to below 1.5%, demonstrating the filter's effectiveness and the inverter’s suitability for clean power delivery. This result confirms that the proposed design effectively meets the power quality standards with a low-distortion sinusoidal output.
Sinusoidal Pulse Width Modulation (SPWM)
Fathullah Mohammed Fathullah Alqamoudi Alqamoudi
Performance Analysis of YOLOv8, YOLOv9, and YOLOv11 for Corn Leaf Disease Detection
Corn is a crucial crop for agricultural yield and food security, yet it faces significant threats from various foliar diseases that can diminish both growth and quality. Conventional visual assessment techniques often require substantial labor and are susceptible to errors in diagnosis. This study introduces a Corn Leaf Disease Detection System that employs Convolutional Neural Networks (CNNs) and evaluates the performance of three models: YOLOv8, YOLOv9, and YOLOv11. The methodology involves capturing high-resolution images of corn leaves afflicted by diseases such as Northern Leaf Blight and Common Rust. These images undergo a pre-processing phase to enhance clarity and are standardized for input into the detection framework. CNN is employed for intricate classification tasks, while YOLOv11 is utilised for real-time disease detection. The dataset comprises 3,000 images, which are augmented to expand the training set to 6,000 samples. Among the evaluated models, YOLOv11 demonstrated superior performance, achieving an F1-score of 0.93, with precision at 0.94 and recall at 0.92 by epoch 100. These findings highlight the system's operational efficiency and robustness in effectively detecting corn leaf diseases.
Optimal Sizing and Location of Multi-Unit Photovoltaic System on Distribution Network Using Particle Swarm Optimization
This paper investigates the optimal sizing and location of multi-unit photovoltaic (PV) systems in a distribution network using Particle Swarm Optimization (PSO). The goal is to reduce power losses and improve voltage stability by installation PV as distributed generator (DG). The IEEE 33-bus and 69-bus radial distribution systems were used as test system. Power flow analysis was conducted using the Newton-Raphson method in MATLAB software. Simulation results revealed that integrating three PV units provided the best performance, reducing power losses by 65.52% and 69.16% for the IEEE 33-bus and 69-bus systems respectively. A scenario with a 20% increase in total load was also analyzed to simulate the impact of electric vehicle charging station (EVCS) to PV sizing and location. It found that, proper sizing and placement of PV integration can improve the network losses when EVCS connected to the distribution system.
Power Grid Synchronous Detection Based on Voltage and Frequency
The power grid system is a complex electrical network that supply electricity from power generation to consumers. Required the variation of voltage and frequency are maintained within the normative limits. The aim of this research is to design a circuit model that simulate a power grid synchronous system using Proteus 8.17 software. This research had designed and built a model that can detect grid condition based on voltage and frequency variation. Arduino UNO microcontroller had been used in this research to control the overall operation flow. The functionality of the model had been examined through software simulation testing. Six different conditions had been set to test the functionality of the model. The assigned conditions include Normal Voltage (between 207V and 253V), Under Voltage (<207V), Over Voltage (>253V), Normal Frequency (between 49.5Hz and 50.5Hz), Under Frequency (<49.5Hz) and Over Frequency (>50.5Hz). Overall, the developed model in simulation using Proteus 8.17 software had a accuracy when detecting grid based on over/under voltage and over/under frequency variation.
Self-Sustaining Solar Panel Cleaning Solution with Integrated IoT Monitoring
Deposition of dust on photovoltaic panels significantly reduces their power output efficiency, posing a major hindrance to optimal solar energy generation. In this article, the development of a self-sustaining solar panel cleaning system with an inbuilt Internet of Things (IoT) platform for real-time monitoring and control is reported. The system design suggested herein combines a wiper mechanism and a centrifugal air blower powered by a monocrystalline solar panel for effective removal of dust and debris. The cleaning cycle is made automatic using a dust sensor and real-time clock (RTC) module with an override facility through the Blynk IoT interface. Environmental and electrical parameters such as dust density, panel output voltage, current, and power are also monitored by the system. Results indicated a considerable power output increase after cleaning operations, validating the effectiveness and feasibility of the system as a cost-effective, environmentally friendly means of domestic solar panel cleaning.
Predictive Simulation of Wind Turbine Outputs at Mersing, Johor
Wind power potential in tropical nations such as Malaysia is typically having low and fluctuating wind speeds, rendering traditional Horizontal Axis Wind Turbines (HAWTs) not as effective. This project solves the dilemma of power output calculation of Vertical Axis Wind Turbines (VAWTs) confidently, which are more appropriate for low-wind regions but for which there are no applicable tools of assessment. This study tries to develop a simulation model that calculates power output of a VAWT based on real environmental readings for example, wind speed, temperature, and relative humidity. Dynamic estimation of power output and air density were included in a working mathematical block model and performed using ideal wind power equation. Environmental readings measured from two Malaysian sites, Batu Pahat and Mersing, were employed in order to explore and test the model. Outputs shows that Mersing whose average wind speed met and exceeded that of turbine cut-in, held up maximum energy output of 34,930 Wh in February, in comparison to Batu Pahat which was likely to fall short of power delivery due to low wind speed. Simulation outcomes also concluded that temperature and humidity significantly influence air density, and hence, turbine efficiency. Lastly, aforesaid proposed model constitutes a certain tool in assessing VAWT viability on the basis of low-wind, high-humidity areas. It upholds early-stage planning, site selection, and early-stage feasibility determination because it provides credible simulations of wind energy output in compliance with changing tropical conditions.
Design and Implementation of a Smart Energy Management System (SEMS) for Food Truck DC Loads in Off-Grid Photovoltaic (OGPV)
Mobile food trucks need diesel generators or gasoline to produce power to operate essential appliances. This leads to increased fuel expenditures, emissions, and noise pollution, which are harms to the environment, as well as customers’ and employees' experience. To reduce these burdens, this project was to design and develop a Smart Energy Management System (SEMS) to replace the need for a diesel generator by integrating it with an Off-Grid Photovoltaic (OGPV) to power DC loads more efficiently. An energy demand analysis was conducted on four food trucks located in Kuala Lumpur; the data collected from the appliances was analyzed to create an Excel-based tool that allowed us to determine the optimal sizes of PV panels, batteries, and MPPT charge controllers that provided a two-day autonomy (for cloudy days). The tools were validated using the PVsyst software and the analysis showed an average load demand error of lower than 0.4% which validated the tool's effectiveness. A SEMS prototype with an ESP32 microcontroller was built that utilized voltage, current, light, and rain sensors, and the prototype was able to automate lighting control and monitor real-time data using an LCD display and Arduino IoT Cloud. Testing in the day, night, and rain proved the system's capacity to manage energy very efficiently along with reliable operations and the ability to adapt to variable environments. To summarize, the integrated SEMS-OGPV system provides a real-world user outcome as a practical, sustainable, and scalable energy solution for mobile food operations considering the accurate sizing, optimized efficiencies, and greater environmental considerations.
Development of a Driver Drowsiness Detection System Using Infrared, Tilt and Heart Rate Sensors
Driver drowsiness is a major contributor to road accidents, making early detection systems essential for enhancing vehicle safety. This paper presents the design and evaluation of a wearable drowsiness detection prototype integrating an infrared (IR) sensor for eye blink monitoring, an MPU6050 accelerometer for head tilt detection, and a MAX30100 sensor for heart rate and oxygen saturation measurement. Laboratory validation was first carried out using servo motors and oscilloscope readings to simulate eyelid movement and head tilts. The system was then tested in-vehicle under real driving conditions, where real-time monitoring of eye status, head posture, and heart rate was displayed on an LCD with buzzer and LED alerts. Results demonstrated reliable detection of prolonged eye closure, abnormal head tilts, and reduced heart rate, with an estimated overall accuracy of approximately 85%. These findings confirm that combining behavioral and physiological indicators enhances detection robustness compared to single-parameter systems. The prototype demonstrates a low-cost, portable, and non-intrusive solution for drowsiness detection, making it suitable for vehicles lacking advanced driver-assistance systems. Furthermore, the use of easily available components allows for wide applicability and integration into various vehicle types. Future research will focus on refining adaptive thresholds, conducting long-term driving trials, and applying machine learning techniques to further improve detection reliability.
Development of a Solar-Powered Grass Cutter with Dual Mode Operation and Solar Tracking for Residential Use
This paper presents the design and development of an automated grass cutter powered by a solar system for residential applications. The system, named MOWI ECO-BLADE which integrates dual operation modes, manual and autonomous along with a single-axis solar tracking mechanism to maximize energy efficiency. The primary objective is to offer an environmentally friendly and alternative solution to conventional gasoline-powered lawnmowers, which contribute to noise and air pollution. The proposed MOWI ECO-BLADE system incorporates a 20W photovoltaic panel, a 12V battery, and an ESP8266 (Wi-Fi enabled microcontroller) based IoT control system utilizing Blynk technology. In automatic mode, ultrasonic sensors enable obstacle detection and zigzag path navigation, while the solar tracker uses LDR (Light Dependent Resistor) sensors and a servo motor to follow the sun’s position. Key performance metrics include runtime duration, energy consumption, solar charging efficiency, and navigation effectiveness. Testing conducted in outdoor residential conditions demonstrates that the system can operate autonomously for one hour with an additional hour of energy reserve. This project highlights the potential of combining green energy and IoT for sustainable and user-friendly lawn maintenance solutions.
High-Performance Bootstrapped Sample & Hold Circuit for ADCs: Achieving Low Power and Enhanced SNR/SNDR in 45nm CMOS Technology
A sample and hold (S/H) system is commonly used alongside Analog to Digital Converters (ADCs) to accurately convert analog signals into digital form. This paper presents a low-power bootstrapped S/H circuit designed to improve signal-to-noise ratio (SNR), signal-to-noise and distortion ratio (SNDR), and power efficiency in both low and high-frequency applications. By charging the bootstrap capacitor through a connection between the transistor's source and body, the input range is increased, which lowers power consumption without affecting the SNDR. The proposed design, developed using the bootstrapped method, maintains the functionality of the sample and hold circuit while boosting the differential input signal amplitude to 300mV in 45nm CMOS technology. The design demonstrates minimal power consumption of 0.21, 0.027, and 0.23µW, while achieving higher SNR values of 72.43, 66.79, and 77.57dB at 50, 100, and 500MS/s, respectively. Additionally, the SNDR shows improved values of 52.47, 52.95, and 51.22dB at those same sampling rates. The proposed bootstrapped S/H circuit was simulated using LT Spice IV in 45nm CMOS technology.
Anomaly Detection Using Artificial Intelligence Methods: Support Vector Machine and Decision Tree Algorithms
For anyone using a network, safeguarding personal or sensitive information is a top priority. Anomaly detection plays a crucial role in spotting irregular patterns that could signal malicious behaviour and help to prevent data breaches, defend against cyberattacks, and ensure the integrity of network resources. Hence, this study explores how machine learning can be used to detect unusual or suspicious activity in network traffic, a key step in protecting systems from threats like Denial of Service (DoS) attacks, unauthorised access, and probing attempts. Using the improved NSL-KDD dataset, which builds on the original KDD Cup 1999 dataset, two popular algorithms: Decision Trees and Support Vector Machines (SVM) with a polynomial kernel were tested. The models were applied to classify network traffic as either normal or potentially harmful, helping assess their effectiveness in identifying anomalies and supporting stronger network security. The Decision Tree model demonstrated a remarkable accuracy of 99.7% with an F-score of 0.997, showcasing its robustness in detecting prevalent attack patterns with high precision. Conversely, the Polynomial Kernel SVM exhibited a slightly lower accuracy of 99.5% but demonstrated stronger generalisation across various classes, reflected by a macro average F-score of 0.72. This indicates its superior capability to handle diverse and complex anomalies. The findings highlighted that both models are highly effective for intrusion detection, with the Decision Tree excelling in overall accuracy and the SVM offering a more balanced performance across a range of attack types. This research contributes to the ongoing development of intrusion detection systems, providing valuable insights into the trade-offs between model complexity, computational efficiency, and detection accuracy in real-time network security contexts.
Economic Viability Analysis of Electric Bikes over Conventional Bikes
In India, two-wheelers have taken over the transportation industry, with a large portion of the vehicles sold in the country and being used as a major means of transportation. The trend of sustainable transport in the world has made electric vehicles (EVs) an attractive alternative to internal combustion engine (ICE) vehicles, which can potentially save energy and minimize greenhouse gas emissions. Nevertheless, the lack of clarity in the total cost of ownership (TCO) is a barrier to EV adoption in India. The paper is a comparison of TCO of electric two-wheelers (E2Ws) and ICE bikes based on real-world models such as the Revolt RV400 and Honda CB Shine 125cc. The initial costs, running costs (electricity/fuel), and the cost of replacing the battery are calculated using a methodological cost framework, which is supported with the EV Calculator Pro application. The findings indicate that EV running costs (0.25-0.98/km) are 31-67 percent less than ICE costs (1.42-2.00/km), and the Revolt RV400 saves 67 percent compared to the Honda CB Shine. The costs of battery replacement (0.40-0.48/km) are also a major consideration. The paper finds that EVs can be cost-effective and is environmentally friendly and suggests that the policy measures, such as subsidies and charging networks, should be implemented to increase adoption.
Analysis of Total Harmonic Distortion in Inverter Systems: A Comparative Study of Square Wave, Phase Shift and Multilevel Inverters
This study investigates the total harmonic distortion (THD) in inverter systems, comparing square wave, phase shift, and multilevel inverters. The performance of inverters is crucial for efficiently converting direct current (DC) to alternating current (AC), especially in renewable energy applications and electric vehicles. The research identifies gaps in understanding THD performance across different inverter configurations under various input conditions and modulation techniques. Utilizing MATLAB/Simulink, the study simulates a proposed multilevel inverter's output voltage characteristics in both filtered and unfiltered scenarios. The objectives include stimulating THD performance under square wave inputs, SPWM and phase shifts while analyzing the multilevel inverter's output voltage. Findings aim to enhance inverter designs for better power quality and efficiency, highlighting multilevel inverters' advantages over traditional two-level inverters. The implications of filtering techniques on voltage quality are also discussed, contributing valuable insights to the field of power electronics.
Banana Leaf Disease Classification Using SqueezeNet, AlexNet and MobileNet
Banana production is a vital component of global agriculture, facing significant challenges due to various leaf diseases. These diseases can cause substantial yield losses, impacting both farmer livelihoods and industry development. Early and accurate disease detection is crucial for implementing effective management strategies. This study explores the application of Convolutional Neural Network (CNN) for banana leaf disease classification. Three pre-trained CNN architectures, SqueezeNet, AlexNet, and MobileNet, were evaluated for their ability to distinguish between Black Sigatoka, Fusarium Wilt, and healthy banana leaves. A comprehensive dataset containing 3000 images per class was employed for training and testing, with an 8:1:1 train-validation-test split. Performance evaluation metrics included accuracy and loss rate. Among the evaluated models, MobileNet achieved the highest accuracy (94.89%) and the lowest loss (0.1484), demonstrating its effectiveness in banana leaf disease detection. These findings suggest the potential of CNNs as a valuable tool for precision agriculture applications.
Design of an Off-Grid Hybrid Power Generation System for a Telecommunication Base-Transceiver Station: A Case Study of Cell Site at Oke-Imobi, Nigeria
Hybrid of energy resources has proved to be an innovative means of providing off-grid power to infrastructures in remote locations where there is limited access to the national power supply grid. Adopting a mix of renewable energy resources with battery backup, and gas generator, to determine the optimum mix of the energy resources to meet particular load demands is a major challenge. Load profile for the case cell site was used as input to the simulation software, HOMER (evaluation version), to simulate the mix of energy resources to meet optimum power supply requirement. For cost effective power solution, the optimum period of 4–6 hours per day and a converter of 3 kW were used to obtain the following results for various mix: use of solar, wind and gas generator with 78.4 hours Autonomy produced 6,319 kWh/yr, in excess by 1694 kWh/yr (26.8%), Zero Unmet load and Capacity Shortage; use of solar and gas generator with 85.3 hours Autonomy produced 6,126 kWh/yr., in excess by 1526 kWh/yr (24.8%), with Zero Unmet load and Capacity Shortage; use of wind and gas generator with 177 hours Autonomy produced 5043 kWh/yr in excess by 198 kWh/yr (3.92%), Zero Unmet load and Zero Capacity Shortage; and, the use of solar and wind with 19.7 hours Autonomy produced 4,509 kWh/yr in excess by 632 kWh/yr, (14%), Unmet load of 621 kWh/yr and Capacity Shortage of 811 kWh/yr. Therefore, for the study area, the use of wind and gas generator produced the optimal supply of highest autonomy and minimal excess power with unmet load and zero capacity shortage.
Analyzing Voltage Stability in IEEE 57-Bus System Using Voltage Collapse Proximity Index (VCPI)
The comprehensive study highlights how power systems have evolved over the decades to meet the growing demand for electricity. Increasing energy consumption has made maintaining voltage stability a significant challenge. As a result, voltage stability has become a critical parameter that requires in-depth analysis in modern power systems. This research aims to improve voltage stability analysis in the IEEE 57-bus system using VCPI analysis to identify the weakest buses, enhancing the accuracy of stability assessments and reducing the risk of voltage instability. The proposed methodology utilizes MATLAB software to conduct the power flow analysis, followed by the Voltage Collapse Proximity Index (VCPI) analysis to pinpoint the weakest bus in the 57-bus network. Bus 18 was observed to have the highest VCPI value of 1.0359 pu, signifying it as the weakest bus in the network. It is recommended to apply voltage stability techniques like VCPI for a more comprehensive analysis. Instead of relying on initial values such as voltage magnitude, VCPI provides a reliable measure for assessing the risk of voltage instability, offering valuable insights for future research.
IoT-Enabled Bus Tracking System for Real-Time Campus Transportation
Transportation is a utilitarian means by which individuals may move from one location to another. In the present epoch of globalisation, transport plays a vital role in the economic development of a country. The statutory basis for accountability of a public service corporation is now ensuring the delivery of excellent and high-quality service. Currently, students, particularly those who do not have their own means of mobility, rely on school transportation to navigate the campus. However, an issue emerged when students were unable to retrieve the position and location while the bus was in motion. Users have difficulty due to the uncertainty of the bus's arrival schedule, which necessitates an indefinite waiting period. We developed an Internet of Things (IoT) based bus tracking system for Universiti Tun Hussein Onn Malaysia (UTHM) to resolve the problem. This system enables users to conveniently receive real-time information about the bus's current position, location, and estimated arrival time at each stop. The researcher used the Design Thinking Process (DTP) paradigm in this study. The process consists of five steps: empathy, deciding, ideation, prototyping, and testing. The analysis's results validate the successful development and planned operation of the system. Furthermore, the system has the capability to display the precise position and location of the bus, in addition to providing consumers with useful information. The accompanying questionnaire supports this claim, indicating that 32 students, which accounts for 95% of the target audience, have confidence in the product's ability to work well. Ultimately, the IoT-enabled bus tracking system implemented at UTHM effectively accomplished all of its predetermined objectives.
IoT-Based Automated Cabinet System for Drugs Storage and Accurate Retrieval for Operation Theater Application
Modern healthcare facilities, especially operation theaters, rely on a multitude of medications to ensure the well-being of patients during surgical procedures. In traditional operation theater environments, medications are typically stored in locked cabinets or storage units, with healthcare professionals manually retrieving and dispensing them as needed. This manual approach can be time-consuming, error-prone, and may lead to delays in urgent medical procedures. Thus, the development and implementation of an "IoT-Based Automated Cabinet System for Drugs Storage and Accurate Retrieval for Operation Theater Application” tailored for drug storage and retrieval in operation theaters becomes imperative and is proposed in this work. The system comprises three main components: a storage compartment system, a storage locking system, and a drugs retrieval system. These components are seamlessly integrated with an ESP32 Wi-Fi module and two Arduino Uno microcontrollers. In order, to enhance security, the automated cabinet system incorporates a three-level security feature. This includes AS608 fingerprint sensors and an S-60 electromagnetic lock, ensuring that only registered users can access the cabinet. The system leverages the Kodular application, which integrates with a Google Sheet Database for user authentication and administration. Experimental results demonstrate that it efficiently retrieves correct and substitute drugs, allowing healthcare professionals to monitor drug availability. In addition, it is shown that the HC-SR04 ultrasonic sensor system is efficient in tracking intended drugs within the storage compartment. The system also updates the remaining drug balance using Firebase Database, providing a reliable and efficient solution for operation theater applications. In a nutshell, by integrating various sensors and IoT technologies, this automated cabinet system significantly improves drug retrieval accuracy and efficiency, thereby enhancing patient safety and minimizing the risk of medication errors.