Evolution in Electrical and Electronic Engineering
Evolution in Electrical and Electronic Engineering (EEEE) is a peer-reviewed, open-access Periodical which is aimed to help disseminate knowledge among the community. EEEE welcomes article submission for any original work and development in the field of electrical and electronics engineering that includes (but not limited to) Electrical Power, Communication, Control, Robotics, Computer, Networks, Microelectronics and Electronics Engineering. It is published twice a year (April and October) and there is no publication fee for the accepted manuscript.
Wireless EMG Monitoring System for Biceps Muscle Recovery
Muscle recovery is an essential process in the treatment of patient with muscle injuries, especially the biceps muscle. However, current monitoring methods are still not very accurate or reable in tracking patient progress during rehab sessions, this make it hard to know if the treatment is working and can lower the patient’s motivation. Additionally, patient who live far away often have trouble going to rehab session regularly. This can cause low attendance and slower recovery. This project aims to create a wireless EMG monitoring system to track biceps muscle activity in real time more effectively. The main system uses an ESP32, EMG sensor and OLED display and the Blynk IoT platform for monitoring. The result show that the system can accurately record biceps muscle activity with peak EMG readings reaching 4095 bit and 100% signal detection accuracy
Copyright (c) 2025 Evolution in Electrical and Electronic Engineering
Plant-Based Air Purification with Real-Time Monitoring: A Hybrid Hydroponic Sensor System for IAQ Enhancement
This study addresses the growing concern of poor indoor air quality (IAQ) in urban environments, which has been associated with various health problems. Many conventional air purification systems are energy-intensive and less effective against certain gaseous pollutants. To overcome these challenges, a hybrid hydroponic-sensor system is developed, combining air purification by plants with IoT-enabled real-time environmental monitoring. Spider plants, golden pothos, and philodendron gold, known for their natural air-cleaning capabilities, were cultivated using a nutrient film technique (NFT) setup. Environmental parameters including temperature, humidity, CO₂, total volatile organic compounds (TVOCs), and pH were monitored using an ESP32 microcontroller integrated with SHT31, CJMCU-811, and analog pH sensors. Data was displayed on an LCD and accessible via the Blynk mobile application. A three-week experimental evaluation demonstrated a reduction of 20–30% in CO₂ and 35–50% in TVOC concentrations, while maintaining optimal plant growth conditions. This affordable and low-power consumption solution enhances IAQ and contributes to healthier indoor environments in homes, offices, and classrooms. The findings highlight the potential of integrating plant-based phytoremediation with off-the-shelf IoT technology for sustainable and scalable indoor air management.
FPGA-Based Traffic Density Monitoring System
The FPGA-based Traffic Density Monitoring System is designed to provide a real-time solution for monitoring and analyzing traffic conditions. This project aims to address the inefficiencies in traditional traffic management systems by utilizing the processing capabilities of Field-Programmable Gate Arrays (FPGAs). The primary objective is to enhance the efficiency of traffic flow by providing accurate and timely data on vehicle density. The methodology involves capturing live traffic data using advanced image sensors, which are then processed using FPGA hardware for real-time traffic density estimation. The system employs sophisticated image processing algorithms to detect and count vehicles within the monitored area, generating outputs that can assist traffic authorities in optimizing signal timings and mitigating congestion. Verilog programming and hardware optimization techniques are extensively utilized to ensure high-speed processing and accuracy in the system's operations. The results demonstrate that the YOLOv8-based vehicle detection consistently achieved an accuracy of 95–98% under normal lighting conditions, while the FPGA finite state machine (FSM) was successfully validated in both simulation and hardware testing. The system maintained real-time responsiveness, with average end-to-end processing latency below 100 milliseconds even under high traffic loads. The system’s ability to operate in real-time makes it a viable and scalable solution for dynamic traffic management scenarios. Moreover, its cost-effective implementation offers the potential for widespread adoption in urban settings. In conclusion, this project highlights the transformative potential of FPGA technology in revolutionizing traffic monitoring systems. By offering a scalable, efficient, and cost-effective solution, it addresses modern urban challenges, paving the way for smarter and more sustainable cities.
Home Environmental Monitoring System and Medicine Reminder for Elderly Assistance
The Home Environmental Monitoring System and Medicine Reminder for Elderly Assistance project aims to provide caregivers with an accessible and efficient system for remotely monitoring the home environment and medication schedules of their elderly parents. The system utilizes sensors, including BME280, MLX90614, AHT21, MQ-2, MQ-7, MQ-135, and RTC DS3231, integrated with an ESP32 microcontroller, to continuously track key environmental parameters such as temperature, humidity, and air quality while managing timely medication reminders. Google Sheets serves as a bridge for seamless communication between the hardware and a customized Kodular-developed application, enabling caregivers to receive immediate reminders and notifications on their mobile phones to address missed medication timings. Powered by a simple 5V supply, the device is easily installable and maintainable in a home setting. This integrated system enhances the health and well-being of older individuals while offering caregivers peace of mind and improved management of their relatives' health, thereby increasing daily care levels. Evaluation reveals a 98.3% success rate for the medicine reminder system and stable hardware performance with 99.5% sensor accuracy over 24-hour operation.
Dual Multiphase Boost Converter Using Pi Controller with Texas Instruments Microcontroller
This project focuses on the design and development of a dual multiphase boost converter that can regulate output voltage when the load changes. This system is created to solve common problems found in single-phase boost converters, such as low efficiency, poor voltage regulation, and unstable performance. By using two phases working together, the converter can share current more evenly and improve overall system stability. The converter is controlled by a Proportional Integral or PI controller, which is programmed on a Texas Instruments TMS320F28335 microcontroller. The controller adjusts the duty cycle in real time to keep the output voltage steady. The project starts with simulation in MATLAB Simulink to test how the converter works with and without the controller. Then, the controller code is developed using Code Composer Studio to communicate with the microcontroller. The hardware, including the converter circuit and gate drivers, is designed using Proteus software and built on a printed circuit board or PCB. Testing is done to check if the system performs well when connected to a load. With a fixed input of 5 volts, the converter successfully increased the voltage to around 10.8 volts with stable output. The results show that this converter works well in practice and can be used in systems that need reliable voltage control.
An Analysis of Surface Charging of Satellite in Orbit
The Spacecraft Plasma Interaction Software (SPIS) has been employed to perform surface charging analysis of a generic GEO satellite in orbit. This study focuses on investigating the surface charging behaviour of a spacecraft under varying space plasma conditions, with particular emphasis on comparing Single Maxwellian and Bi-Maxwellian plasma distributions and analysing the effect of photoemission across various conductive surface materials. For each distribution type, simulation are carried out under both normal and worst-case plasma conditions to examine the extent of surface potential buildup. In addition, the effects of enabling and disabling photoemission which represent the presence and absence of Sun (eclipse) are analysed to understand their role in mitigating or exacerbating spacecraft charging. This study provides insight into how different plasma environments and emission mechanisms influence charging behavious on spacecraft surfaces.
FPGA Implementation for Character of License Plate Recognition System using CNN
License plate recognition, which uses optical character recognition (OCR) techniques to identify vehicle registration numbers, has become an essential component of modern intelligent transportation systems. This research focuses on creating a Character of License Plate Recognition (CLPR) system using a Convolutional Neural Network (CNN) model to address applications in parking management, toll automation, traffic monitoring, and law enforcement. The objective is to create a CNN-based character recognition system and implement it on an FPGA for real-time embedded applications. A dataset of 35,500 grayscale, 28 x 28 pixel pictures from European license plates was used for testing and training. With 94% accuracy, the CNN model was created and trained in Google Colab using Python. Verilog HDL in Intel Quartus Prime 23.1 is used to extract and convert the trained weights and biases into decimal and hexadecimal text files for FPGA implementation. The model is validated through an Excel-based simulation, and floating-point parameters are changed to integers to guarantee compatibility. The Altera DE2-115 FPGA board houses the CNN module, which runs at a clock frequency of 50MHz and requires 40µs for character recognition and 8ms for LCD display. With a power consumption of just 137.24mW, the system is ideal for low-power, real-time embedded systems, according to power analysis.
IoT Fertigation Dashboard for Precision Agriculture Monitoring and Control
This research involves the development of an IoT-based fertigation dashboard for precision agriculture. A single RS-485 soil sensor was used to measure temperature, moisture, pH, and electrical conductivity. The ESP32 sent this data over Wi-Fi using the MQTT protocol to a Raspberry Pi 5, which acted as the MQTT broker and hosted a real-time dashboard using Node-RED. The system provided live monitoring and remote access. Sensor readings were accurate after calibration, with values like moisture around 10–12%, pH 5.7–6.8, and temperature around 28°C. InfluxDB Cloud was used to store the data online, and it could be downloaded as a CSV file for analysis in Excel. The system was tested successfully, proving to be low-cost, reliable, and suitable for smart farming.
Maximum Power Point Tracking System for Enhanced Photovoltaic Efficiency Using FPGA
This project presents the design, simulation, and evaluation of an FPGA-based Maximum Power Point Tracking (MPPT) controller using the Perturb and Observe (P&O) algorithm, implemented in Very High-Speed Integrated Circuit Hardware Description Language (VHDL). The primary objective is to develop an optimum controller to maximize power extraction from a simulated photovoltaic (PV) panel, validated through RTL-level simulation without hardware deployment. The system incorporates a sequencer module that emulates a realistic PV panel I-V curve, with input voltage ranging from 10V to 59V and current from 5A to 0A, mirroring real-world behavior. The controller, designed with an 8-bit resolution and a constrained 10%–90% duty cycle, is synthesized using Altera Quartus II and tested with ModelSim, achieving a Maximum Power Point (MPP) at approximately 20V–26V with a peak power of 104W. Analysis of Power vs. Voltage, Current vs. Voltage, and Efficiency vs. Voltage graphs confirms effective MPP tracking and high efficiency (88%–100%) near the MPP, though post-MPP efficiency anomalies such as 410% are identified as simulation artifacts. In conclusion, the project successfully meets its goals of designing and validating the MPPT controller within the VHDL environment, providing a robust simulation framework. Recommendations for future work include refining the P&O algorithm with adaptive step sizes, enhancing the dcdc_converter model, and planning for hardware-in-the-loop testing to address limitations and transition toward real-world deployment.
MPPT, Perturb & Observe (P&O) algorithm, Incremental Conductance (InC) algorithm
Study on Electrical and Physical Properties of Lithium Cobalt Oxide Thin Films for Renewable Energy Application
The work described in this paper seeks to understand the impact of post-deposition treatments on the electrical, mechanical and physical properties of lithium cobalt oxide (LiCoO2) thin films in order to improve their applicability as cathode materials in lithium-ion batteries. The thin films of LiCoO2 were deposited on nickel substrates by RF magnetron sputtering at room temperature, and then thermal annealed at 200 °C temperature during 1 hour and reactive ion etching (RIE) at 100 W RF power, 20 sccm Ar flow, 100 mTorr working pressure, for the duration of 2 minutes. The characterization of surfaces by FESEM and AFM showed that annealing boosted the average grain size (34.2nm untreated to 39.0nm) and roughness (Ra 5.2nm to 7.4nm) and etching further promoted grain size to 42.3nm and roughness to 9.8nm. Local conductivity was found to increase sixfold as measured by conductive AFM, 0.3 nA (bare) to 1.9 nA (etched). Contact angle measurements showed an increase in wettability after annealing (~25) and a decrease in wettability after etching (~50). The combination of these morphological and electrical improvements leads to an increase in the electrochemically active surface area, optimization of lithium-ion transport pathways, and a decrease in interfacial resistance - leading to improved charge storage and cycling stability. These results indicate the performance of LiCoO2 thin films can be greatly enhanced by adjusting the surface structure by controlled annealing and etching to meet the requirements of advanced energy storage devices.
Infant Oxygen Supply Automatic Monitoring System
Infants and young children, especially those born prematurely or with breathing problems, often struggle to breathe properly because their lungs are not fully developed. Oxygen therapy is essential to help them survive and grow. However, manually checking their oxygen levels takes time and leads to human errors. These mistakes can cause serious complications like hypoxia (too little oxygen) or oxygen toxicity (too much oxygen), which may affect the child’s long-term health. To reduce the risk, developed the Infant and Child Oxygen Supply Automatic Monitoring System. This system is designed to continuously monitor vital signs such as heart rate, oxygen saturation (SpO2), and temperature in real-time. It is built using the MAX30102 sensor, ESP32 microcontroller, and piezoelectric sensor, which helps provide the right amount of oxygen based on the child’s health condition. The system also includes a mobile app that allows caregivers and patients to view live data and receive alerts if there are any irregularities, so can take quick action even from a distance. This smart system offers better support in neonatal and pediatric care especially in settings where proper oxygen monitoring tools are lacking. It helps reduce the workload for healthcare staff and gives families peace of mind.
A Drone-Based Parking Enforcement System Using Ardu Pilot and Raspberry Pi 4
Accelerated urbanization has exacerbated parking-related challenges in urban centers, as inadequate enforcement leads to congestion, increased fuel use, and environmental deterioration. Conventional enforcement techniques, dependent on manual inspection, are labor-intensive, prone to errors, and lack scalability. This study introduces an automated drone-based parking enforcement system utilizing a 3DR Quadrotor equipped with the ArduPilot Navio2 autopilot and a Raspberry Pi 4 onboard computer. The platform utilizes high-resolution image sensors for real-time vehicle identification and license plate recognition (LPR). Flight dynamics and navigation are regulated by ArduPilot firmware, whilst LPR algorithms implemented on the Raspberry Pi analyze collected photos. Identified license plate information is delivered by secure Wi-Fi to a cloud-based Node-RED interface for analysis, visualization, and enforcement measures. The suggested system seeks to provide an effective and scalable solution that guarantees precise detection, dependable data transfer, and autonomous functionality in open-area parking lots. Experimental validation confirms its capability to surpass the constraints of traditional approaches, providing enhanced accuracy, efficiency, and adaptability for smart city applications.
Alcohol Detection and Engine Cut-Off System using MQ-3 Sensor with GPS and Telegram Alert
This project is proposed to enhance road safety by preventing drunk driving via an automated method. The proposed method detects alcohol on the driver's breath using an alcohol sensor, a GPS module, a Telegram notification, and a microcontroller (ESP32). When the system detects the issue, it cuts off the vehicle's engine and transmits real-time notifications with position information to chosen contacts. The process includes designing and integrating hardware components, programming the microcontroller for alcohol detection, and implementing GPS tracking and Telegram alert integration. Testing happens in controlled situations to confirm the performance and dependability of the alcohol detecting system, engine lock, GPS accuracy, and Telegram notifications. The findings show that the system successfully prevents vehicle operation while under the influence of alcohol and provides timely notifications, guaranteeing increased safety for drivers and the public. In conclusion, this project demonstrates that integrating alcohol detection with engine control and communication technologies can be an effective solution to reduce drunk-driving incidents and enhance road safety.
Smart Pesticide Sprayering Robot for Fertigation Farm
This Smart Pesticide Spraying Robot is designed to improve pesticide use in fertigation systems by supporting more accurate and eco-friendly farming practices. It uses embedded AI on an ESP32-CAM to detect crops grown in polybags and spray them as needed. The object detection model was trained using Edge Impulse with three types of datasets: black polybags (V1), white polybags (V2), and a combined version (V3), labelled through Roboflow. The model trained on V3 achieved 93.06% accuracy and 95% precision in polybag detection. Once a polybag is detected, the robot activates a spray pump using a relay and controls its movement using two DC motors through an MDD3A driver, guided by the object’s location in the video frame. The system runs fully on-device without needing cloud access, thanks to Edge Impulse’s optimized inference engine. However, during testing, some issues were observed. The robot sometimes failed to respond or sprayed incorrectly, especially when nearing a target. This was likely due to the ESP32-CAM’s limited processing power and the absence of advanced motor control systems like PID. Despite these limitations, the project proves that low-cost, AI-powered robots can be used for smart farming. For future improvements, it is recommended to use a more powerful processor like a Raspberry Pi, improve motion control, expand the training dataset, and add more sensors for better navigation and accuracy.
Design of 32-Bit RISC-V Architecture Using Verilog Hardware Description Language
RISC-V architecture is an open standard instruction set architecture that is established based on of RISC architecture. The RISC-V architecture is an open source, free, user-friendly access, and easy to modify. RISC-V offers a reduced instruction set that is lightweight and modular. Thus, its architecture is less complex and easy to be customised. Hence, this paper presents the design and development of a 32-bit RISC-V processor architecture from fundamental concepts, encompassing instruction set definition, datapath design and control logic implementation. Verilog HDL language is used to design the architecture that comprises combination of behavioural modelling and structural modelling structure. The processor is designed based on the RV32I instruction set where the key components include Program Counter, Instruction Memory, Register File, ALU, Immediate Generator, Data Memory, Branch Comparator, Multiplexer and Control Unit. The design is expected to perform operations for instruction types of R, I, B, S, U and J. Eleven instructions were chosen for the implementation and validation. The functionality of the design was tested using ModelSim-Altera, to ensure the architecture able to produce the desired results. After the functional test, a timing performance analysis is conducted to ensure that the processor meets the time requirements. The simulation results indicate that the designed architecture able to produce correct results for all the selected operations. In addition, the timing analysis shows that the processor can operate with a maximum operating frequency of 40.88 MHz. As conclusion, the processor met all the design objectives and functions as expected. For future improvement, it is recommended to extend the work with FPGA prototyping and pipelining structure to improve throughput performance. By doing so, the research could move from theory to real-world application, making the processor more reliable and handle more instructions per cycle, reducing delays and improving processing speed.
Experimental Analysis of DC Water Pumps with Non-Geared and Geared Mechanism in Air Cooler Prototypes
The paper includes the results of the conducted experiment on DC water pumps performance, which have geared and non-geared mechanisms built into prototypes of air coolers. The research is intended to analyze electrical and thermal performance such as power usage, the stability of current, temperature condition, and efficiency at a specific test condition. A monitoring system was developed with the ESP32 microcontroller using real-time data sensors and uploaded in Google sheets through Wi-Fi. Experimental results showed that non-geared pumps achieved an average efficiency between 80–85% with lower current usage (0.36–0.38 A) and more stable temperature (21.81–26.50 °C), while geared pumps showed lower efficiency (70–75%) and higher current draw (0.41–0.46 A). This suggests non-geared pumps are more suitable for energy-limited or tropical locations in sustainable air-cooling systems.
Single-Axis Solar-Powered Water Quality Monitoring with Internet of Things
This project focuses on the design and implementation of a single-axis solar-powered water quality monitoring system integrated with Internet of Things (IoT) technology to enable real-time data collection, transmission, and analysis of key water parameters. The aim of this project is to provide an efficient, reliable, and sustainable solution for monitoring water resources, particularly in remote or off-grid areas where traditional manual sampling methods are time-consuming, costly, and often result in delayed responses. The system incorporates a single-axis solar tracking mechanism designed to maximize energy generation by continuously adjusting the position of the solar panel toward the sun throughout the day. During testing at UTHM lake, the solar tracking system successfully produced an output power of 21.04 W, demonstrating its ability to supply continuous energy to power the IoT-based sensors. These sensors recorded key water quality parameters, including pH levels ranging from 6.29 to 6.39, turbidity measured at 42.5 NTU, and water temperature between 28.3°C and 32.3°C. The collected data was transmitted in real time to a cloud platform for storage, monitoring, and analysis, enabling timely detection of changes in water quality. The findings of this project demonstrate that the system can operate effectively under actual environmental conditions, offering a practical and environmentally friendly solution for continuous water quality monitoring and proactive water resource management.
Automated PV Irrigation System for Red Chili
This paper presents the development of an automated photovoltaic (PV) irrigation system designed specifically for red chili cultivation. The system combines renewable energy with IoT-based real-time monitoring to enhance water efficiency and support sustainable farming. A 50W solar panel powers essential components including the ESP32 microcontroller, soil moisture and rain sensors, and a DC water pump. The system intelligently controls irrigation based on real-time soil moisture and rain detection, reducing water wastage. Monitoring and alerts are managed via Blynk and Telegram. Experimental results under different solar panel orientations (horizontal, vertical, 45°) show that optimal performance is achieved at a 45° angle. This integration enables low-cost, efficient irrigation suitable for small-scale farmers, aligning with sustainable agricultural goals
Smart Solar Power System with Load Prioritization for Rural Areas and Islands
Solar energy is a free, renewable, and clean energy source that can be an alternative to diesel generators in rural electrification. But it is highly weather-dependent, making it unreliable, especially without a proper energy management system and uncontrolled load usage. This project proposes a smart solar power system with a load prioritization mechanism that aims to improve reliability by gradually shedding lower-priority loads as the battery’s State of Charge (SoC) decreases, preserving power for critical loads. Using a voltage divider circuit, the programmed Arduino UNO safely monitors the State of Charge (SoC) of the battery and smartly controls relays connected to the loads. The system's voltage measuring accuracy and relay response to load prioritization were tested before conducting an experiment to evaluate the efficiency and reliability improvements between the traditional and designed systems. The accuracy of the measured voltage values has been proven high, with each measurement having only a minimal deviation (<1%), and the relays' control also operated as intended. The experiment results concluded that the designed system has reduced the voltage depletion rate by approximately 44.44%, implying an improvement in the efficiency and reliability of the solar energy management system, creating an option for rural electrification.
Smart Energy Saving Lamp with Ambient Light Detection for Automatic Brightness Adjustment
This project aims to develop a smart energy-saving lamp that automatically adjusts its brightness based on ambient light levels, providing an energy-efficient lighting solution for residential and commercial spaces. The standard lighting systems operate at full strength hence consuming exorbitant amounts of surplus energy. While further, such actions decrease the lamp's life. The proposed system integrates a Light Dependent Resistor (LDR) to sense ambient light intensity and an Arduino Uno microcontroller to process sensor data. An AC dimmer module was used for tuning the brightness level of a 220–240V dimmable LED lamp during system testing and calibration. The system is designed for relatively small rooms or office spaces, usually between 15 and 20 square meters. The recommended illumination level here is 500 lux. With minimum daylight, the lamp will be in full brightness. Increase in light level causes proportional dimming of the lamp so as to give adequate light and simultaneously save on energy cost. On the software level, an algorithm to control the brightness (easing out and in to the desired value in order to not produce sudden changes that could cause visual discomfort) has been used by the Arduino software. Tests and analysis show a potential energy saving up to 45%-50% with the proposed system in medium light conditions with respect to alternative light sources. The new system also can extend the lamp life by decreasing a thermal stress, while adjusting the brightness according to an actual light environment in real time. This project demonstrates the feasibility of adoption of smart control systems in lighting design in actual installations to conserve energy in residential and commercial buildings, and ensure sustainable development.
Smart Solar Powered Plant Irrigation Water Sprinkler Agriculture Robot
This work intends to solve many problems like reducing wastage of water, harnessing natural energy and helping human do work more efficiently. As a result, it has taken the initiative to prevent water waste by developing a smart solar power plant irrigation water sprinkler plant. The project created an automated plant watering system and has distributed wireless network of sensor that are placed in mobile. An algorithm was developed with threshold values of sensor that was programmed into microcontroller-based gateway to control water quantity. Besides by integrating a solar panel provides a renewable energy source, reducing the reliance on external power supplies such as electricity or batteries. This makes the irrigation robot self-sustaining and capable of operating in remote or off-grid areas. As a result, the project is a success and able to provide sufficient water to the plants. Besides, water waste and energy consumption can be reduced since the project uses renewable energy so it is eco-friendly.
GUI-Based Maintenance Reporting System for Electrical Substations Integrated with PyTesseract OCR
This work focuses on developing a graphical user interface (GUI) system integrated with Optical Character Recognition (OCR) for generating electric substation maintenance reports. The main goal is to automate the process of extracting temperature data from thermal images captured during Condition-Based Monitoring (CBM) tasks using FLUKE equipment. PyTesseract, an OCR engine in Python, reads annotated temperature values such as SP1, SP2, and AVG from the thermal images. The GUI is developed using Tkinter and OpenCV, allowing users to load images, perform OCR on the whole image or by selecting a Region of Interest (ROI), and validate extracted readings. The validated data is then inserted into a structured table and exported into a Microsoft Word report. Evaluation shows that ROI-based OCR significantly improves accuracy compared to whole-image OCR. This system offers a more efficient, user-friendly, and accurate approach for substation data reporting, reducing human error and improving documentation speed for maintenance workflows.
Development of a Child Fall Detection System based on YOLOv9 using Transfer Learning from Adult Fall Datasets
Falls detection systems are vital for ensuring safety, particularly among vulnerable groups such as children. However, most existing systems are trained using adult-specific datasets, limiting their effectiveness for child fall scenarios due to differences in body size, movement patterns, and fall dynamics. In this study, a child fall detection system was developed using YOLOv9, a state-of-the-art object detection algorithm, with transfer learning applied from adult fall datasets. The system was built through several stages including dataset collection, annotation using Roboflow, model training in Google Colab, and deployment via a user interface created using Gradio. Three experimental setups were conducted: training and testing on adult datasets, testing on child fall images using a model trained solely on adult data, and testing on multi-subject child scenarios. The YOLOv9 model was trained and evaluated at multiple epochs (30, 50, 70, and 100) using standard performance metrics such as precision, recall, F1 score, and mAP. Results showed that while the model achieved high accuracy on adult data (mAP@0.5 up to 0.995), it also demonstrated reasonable performance in detecting child falls, especially at 100 epochs, with a precision of 0.98 and recall of 1.00 in single-child scenarios. Although performance declined slightly in multi-subject scenes, the findings confirm that the developed system can generalize from adult to child fall detection with acceptable accuracy. This study demonstrates the possibility of using transfer learning in child safety as well as providing a cost-effective solution in those settings where a child-specific data is scarce.
Performance Testing of Breakdown High Voltage Contactor
The aim of this paper is to study the behaviour of a breakdown high voltage (HV) contactor with different insulating materials. The analysis of breakdown voltage and electric field distribution by means of experimental testing and numerical simulation is considered in the study. The purpose is to test the dielectric strength of various insulating materials that is to say acrylic heat shrink rods (Polyolefin), Teflon and uninsulated rods under high voltage stress. The experimental analysis was in a high-voltage laboratory and simulation analysis by using Comsol Multiphysics software. Based on the results, it was found out that the value of the dielectric strength went up sharply with the use of insulation. Precisely, there were low breakdown voltages by bare rods of 23 kV (at 12 mm gap) and 32 kV (at 16 mm gap) whereas the acrylic and Polyolefin insulators had more than 50 kV to break. The same respective distances after being tested, showed a breakdown voltage of 47kV and 49kV with teflon. The simulation results further justified that when you increase the electrode gap it decreases the strength of the electric field, and also full insulation coverage results in widespread field distribution in comparison to partial insulation. This highlights the devastating effect of the insulation material used, how well one insulates, and the spacing between the electrodes of the dielectric behavior. In summary, the reliability and safety of high-voltage contactor systems could be increased greatly by insulating using suitable materials and arrangements.
Application of Artificial Neural Network (ANN) for Solar Photovoltaic Power Forecasting
The integration of renewable energy sources, particularly solar photovoltaic (PV) systems, is crucial in the global fight against climate change and the reduction of dependence on fossil fuels. However, the variability in environmental conditions makes accurate forecasting of PV system power output challenging. This project investigates the application of Artificial Neural Networks (ANN) to predict PV system power output using historical and real-time environmental data. Data were collected over 20 days at Lorong Haji Idris, Parit Raja, Batu Pahat, Johor, with measurements taken five times daily. The ANN model effectively addresses the nonlinear relationships and complexities of PV performance. Statistical metrics such as Mean Squared Error (MSE) were used to validate the model's accuracy. The findings show that the ANN model achieved high accuracy with R = 0.98661, MSE = 5.37 W², and RMSE = 2.32 W, with most predictions within ±5% of measured values. In comparison, the calculation method produced higher errors wih MSE = 26.89 W² and RMSE = 5.19 W, confirming that ANN is more reliable for PV power forecasting.
Refinement of Outer Rotor Permanent Magnet Flux Switching Machine for Downhole Application
Electric machinery in downhole oilfield environments endure severe high-pressure, high-temperature (HPHT) conditions. This study examines a 12-slot/22-pole outer rotor Permanent Magnet Flux Switching Machine (PMFSM) optimized for these applications. JMAG Designer software was employed to model and simulate diverse combinations. A deterministic optimization method was utilized to enhance the design parameters, including rotor geometry, permanent magnet size, and coil turn quantity. The optimized model demonstrated a 102.6% augmentation in torque (from 16.58 Nm to 33.57 Nm), a diminution in cogging torque (from 3.2 Nm to 1.76 Nm), and a 37.3% reduction in magnet weight. These improvements validate the suitability of outer rotor PMFSMs for efficient and reliable downhole operations.
Performance Comparison Grounding System using Aluminium and Copper
This study presents a comparative analysis of aluminum and copper as grounding materials by evaluating their effectiveness through soil resistance measurements. Prototype grounding systems—including single rod, triple parallel rods, and grid configurations—were installed at a depth of 1.5 meters at an open field site at Universiti Tun Hussein Onn Malaysia (UTHM). Resistance values were recorded daily over 30 days using the Fall-of-Potential method. The results indicate that for the single rod configuration, copper exhibited a slightly lower average resistance (4.50 Ω) compared to aluminum (4.95 Ω). In the triple parallel rod setup, aluminum outperformed copper with an average resistance of 1.17 Ω versus 1.41 Ω. For the grid configuration, aluminum again demonstrated superior performance with an average resistance of 2.52 Ω, while copper recorded 3.51 Ω. These findings suggest that aluminum is a viable and often more effective alternative to copper in certain grounding system configurations, particularly in grid installations.
Ensuring the safety of building occupants and keeping track of their presence during emergencies can be a challenging task, especially in office or high-traffic environments. This project presents a practical and cost-effective RFID-based system designed to manage attendance and support evacuation processes. Using the ESP8266 microcontroller and long-range UHF RFID readers, the system records check-in and check-out data in real time, identifying individuals who are still inside during an evacuation. The data is stored locally using SPIFFS, allowing the system to function reliably even without internet access. A simple web-based interface is used to display the status of occupants and access attendance logs. Additionally, the system includes an automatic control logic that focuses on managing energy consumption, particularly for air conditioning units, based on occupancy. The system promotes electricity savings by integrating automatic load switching, particularly for air conditioning units. This ensures that electrical loads are only active when necessary, helping to reduce energy consumption in unoccupied areas.
Smart Cane: A Safety and Mobility Enhancement Tool for the Elderly and Visually Impaired
The Smart Cane project introduces an advanced mobility aid designed to enhance safety and independence for the visually impaired and elderly. It features obstacle detection using an ToF sensor, LED illumination for dark areas, and an emergency alert system that sends real-time location notifications to family or close contacts. It also includes water detection to prevent slips on wet surfaces. Unlike conventional mobility aids, this device integrates assistive technology that gives haptic feedback through vibrations when obstacles are detected. The Smart Cane bridges the gap between traditional aids and the growing needs of users in today’s dynamic world.
Smart Fan Temperature and Motion Detection
This project is to design and develop a “ the microcontroller temperature based fan speed controller with motion sensor”. This project will present the design, development, control and analysis that can be implemented for home automation system. The home automation system is PIC microcontroller based project which focused on a system to automatically control the speed of a ceiling fan according to the surrounding temperature and the motion sensor detects on human presence. This ceiling fan system and motion sensor contains combination of sensor, controller, driver and motor with integration of embedded controlled programming which means in this case using pic microcontroller as the main controller. This project also presents the expected performance of the automatic fan system and motion sensor, construction of hardware and software development to gather the performance data. Finally, this system performance will be analysed by comparing performance data to the theoretical. End of this project will produce an advance technology with programmable features which control the speed of the fan is depending on the changes in room temperature and the motion sensor detect the presence of a person.