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.
Development of a Traffic Light Control System Using ARM-Based Microcontroller
Traffic lights are widely used at road intersections to regulate vehicle movement through red, yellow, and green signals. However, the absence of a clear time indication for each phase can cause driver uncertainty, sudden braking, and red-light violations. To address these issues, this project develops a traffic light control system with a single countdown timer as an indicator to road users while reducing implementation costs and simplifying hardware interconnections. The system is divided into two main parts: software development and hardware implementation. The C programming language is used to compute the traffic control program. The program is written, tested, debugged, and then programmed onto the NXP LPC1768 development board using the Keil uVision Integrated Development Environment (IDE). The developed program controls the timing sequence of the traffic lights, manages the countdown display, and determines the pedestrian crossing phase according to a predefined fixed-time schedule. The hardware part involves assembling components such as 7-segment displays, Light-Emitting Diode (LED) traffic lights, and the microcontroller development board. The traffic light system is designed to implement a 30s green light phase, followed by yellow and red light phases. During the final 10s, the countdown from 9 to 0 is displayed using a single 7-segment. In addition, a dedicated pedestrian crossing phase is activated at the end of each full cycle, during which all vehicle signals are red to allow pedestrians to cross safely. The project demonstrates a fully functional traffic control system with an effective countdown display and smooth integration of the software and hardware components. The novelty of this project is a low-cost traffic control system that uses a single 7-segment display for the countdown phase, which reduces hardware cost, simplifies the circuit design, and cutting required microcontroller pins.
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Analysis of the Effect of the Age of Postpaid Single-Phase kWh-meters on Measurement Accuracy
The background of this research stems from the need to ensure that the age of single-phase post-paid kWh meters significantly influences consumer electricity consumption. The accuracy of kWh meter measurements in recording electrical energy usage is a critical factor for PT. PLN (Perusahaan Listrik Negara) is to provide satisfactory service to customers in Indonesia while ensuring no party is disadvantaged. To achieve this objective, routine inspections are conducted on customer kWh meters, particularly those installed beyond the standard limit of 10 years or that exhibit an error deviation exceeding 5%. Testing results from 10 selected customers revealed that 6 customers fell into the normal category with inspection results below 5%, while 4 customers fell into the normal category with results exceeding 5%. The impact of a kWh meter age exceeding the standard limit is a loss for PLN and a gain for the customer if the deviation is "Negative (-)"; in such cases, the customer effectively pays less than the energy consumed. Conversely, it results in a loss for the customer and a gain for PLN if the error deviation is "Positive (+)", requiring PLN to compensate for the energy surplus paid for but not consumed. Therefore, consistent monitoring by PT. PLN as the producer and customer awareness as consumers are essential for maintaining kWh meters to minimise measurement inaccuracies.
Establishment of an Intelligent Wireless EV Charging System Using the Wireless Power Transfer (WPT) Method
This paper discusses the development of a smart parking system with inductive power transfer for an electric vehicle (EV) prototype using a circular coil configuration. The system uses an induction-based wireless energy transfer technology to provide continuous charging from the parking location to a vehicle. An ESP32 microcontroller is installed in the parking space as the control centre, handling infrared sensor detection, relay module control, and charging startup. Once a vehicle enters the parking lot and its presence is detected by the infrared sensors, the transmitter coil is switched on along with a cooling fan for thermal control. In an electric vehicle, when another ESP32 acts as the main controller and is connected to a charging module (TP4056), an INA219 DC sensor, and a DC voltage sensor to view input voltage, charging current, battery voltage, and battery %. Parameters are examined in real time through the Blynk platform, and the battery level is displayed on an onboard OLED screen. The study also explores coil alignment in inductive wireless power transfer, where increasing the gap between the transmitter and receiver reduces both the input voltage and the charging efficiency. Experimental findings highlighted that coil spacing greatly affects efficiency, with noticeable differences in charging performance between 0.2 mm and 0.6 mm, and no measurable charging beyond that range. The overall system presents a working IoT-based smart parking and wireless charging solution with integrated monitoring and control features.
IoT-Based Flood Monitoring System with LoRa Communication and Automatic Gate Barrier
Flooding remains a major natural disaster in Malaysia, causing extensive property damage and threatening human safety, particularly in flood-prone residential areas. Existing flood mitigation systems are often manually operated, costly, or limited in coverage, leading to delayed responses during sudden flood events. This study presents an IoT-based smart flood monitoring system utilising LoRa communication and an automatic gate barrier for real-time flood mitigation. The architecture employs a decentralised design: a detection unit utilising discrete float-switch sensors, a LoRa-integrated transmitter for long-range data propagation, and a receiver node for real-time actuation and Telegram notification. Managed by an ESP32 microcontroller, the system implements a tri-level threshold logic comprising Normal, Warning, and Danger states. At the Warning level, the gate is partially deployed to 45° and a Telegram alert is issued. Meanwhile, at the Danger level, full deployment to 90° is executed. Experimental results indicate stable indoor LoRa communication up to 10 meters with RSSI values ranging from –53 dBm to –67 dBm and SNR up to 10.50 dB. Under outdoor line-of-sight (LOS) conditions, LoRa communication was successfully maintained up to 50 m, with RSSI decreasing to –90 dBm and SNR between 1.50 dB and 5.75 dB. Although signal degradation increased with distance, alert notifications were still received at 50 m, while communication failed beyond 60 m due to the receiver's sensitivity limits. The findings suggest that this integrated framework offers a scalable and cost-effective solution for autonomous flood management and disaster risk reduction.
Solar-Powered IoT Gas Monitoring System with Vibration Alerts for Deaf Users
This research focuses on developing a Solar-Powered IoT Gas Monitoring System with Vibration Alerts for Deaf Users to enhance safety in residential and commercial environments. The system utilises solar energy with battery backup to ensure continuous operation during power outages, while the alternating current (AC) supply serves as the primary power source. An automatic power transfer mechanism is implemented to maintain uninterrupted operation of critical components, particularly the gas detection and IoT monitoring modules. The Internet of Things (IoT) technology helps continuously monitor gas levels and sends early notifications to users. In cases of hazardous gas levels, various safety features are triggered, such as an alarm buzzer, an LED alarm, the starting of exhaust fans, and automatic control of a gas solenoid valve to restrict gas leakage. In addition, a vibration-based alert bracelet is incorporated for vibration alarms for deaf and hard-of-hearing individuals. This ensures effective non-auditory alerts for the deaf. Results from experiments confirm that the system can detect gas leakage and hazardous gas levels, as well as activate safety alarms and issue vibration alerts for deaf individuals, in real time. Overall, the developed system demonstrates strong potential as a reliable, inclusive, and sustainable safety solution for modern home and commercial applications.
Design and Performance Evaluation of a Compact Single-Element LTE/5G Patch Antenna for Vehicular Communication
This paper presents the design, fabrication and experimental evaluation of a compact single-element microstrip patch antenna intended for LTE and sub-6 GHz 5G vehicular communication. The proposed antenna operates at 2.6 GHz and exhibits a higher-order resonance around 3.5 GHz, enabling dual-band operation without additional slotting or parasitic structures. The antenna geometry was optimised using full-wave electromagnetic simulation to determine the exact performance parameters, including resonant frequency, impedance matching and return loss characteristics. Following optimisation, the antenna was fabricated on an FR4 substrate and experimentally validated using a Vector Network Analyzer (VNA) to measure the reflection coefficient (S11). Measured results show good agreement with simulation, achieving return loss below −10 dB across the targeted LTE band. The results demonstrate that the proposed single-element antenna provides reliable impedance matching and stable performance suitable for vehicular LTE communication.
System Design and Development for Early Detection of HFMD Symptoms Using AI and Infrared Thermometer Sensor
Hand, Foot, and Mouth Disease (HFMD) is a highly infectious disease that mainly occurs in children under six years of age and frequently breaks out in Malaysia, making it a major public health concern in those regions. For this project, an automated screening system is introduced using a combination of Artificial Intelligence and an infrared sensor to detect symptoms at an early stage. The project uses an MLX90614 infrared sensor to scan body temperatures without direct contact, and a YOLOv8 object detection algorithm to identify skin rash on a child's hand using a digital camera. Using IoT capabilities, the real-time scan results and notifications are stored in a cloud Firebase Realtime database and can also be viewed in a Streamlit application interface. The application of this system will serve as a proactive "first line of defence" in childcare centres or kindergartens to effectively control HFMD.
Design and Implementation of a Dual-Band Microstrip Patch Antenna for Wireless Bridging at 2.4 GHz and 5.0 GHz
This study presents the design and implementation of a compact dual-band microstrip patch antenna incorporating rectangular-shaped and U-slots for wireless bridging at 2.4 GHz and 5.0 GHz. The antenna was designed and simulated using CST Studio software and fabricated on a low-cost FR‑4 substrate with a dielectric relative permittivity constant of 4.3 and a thickness of 1.6 mm, with overall dimensions of 76 x 59 mm2. Simulation results showed resonant frequencies at 2.4 GHz and 5.0 GHz, with return losses of -15.9 dB and -20.5 dB, and bandwidths of 80 MHz and 180 MHz, respectively. The measured results closely matched the simulation, with return losses of -15.9 dB at 2.4 GHz and -10.0 dB at 5.1 GHz. The antenna exhibited directional radiation patterns and low VSWR below 2.0 in both bands. It was applied in a 20-meter point-to-point wireless bridging system using MikroTik L11UG-5HaxD devices, achieving 120 Mbps at the access point and 74 Mbps at the client. Results confirm the antenna’s compact size, efficiency and suitability for reliable Wi‑Fi and short-range wireless bridging.
Design and Development of EMG Monitoring Based Human-Machine Interface Using Nextion Display
Many rehabilitation devices use button-based interface that limit real-time feedback, accessibility and user interaction, reducing compliance and effectiveness in home-based monitoring. This project develops a user-friendly Human-Machine Interface (HMI) to replace buttons, creates a communication algorithm between a Nextion HMI display and microcontroller, and evaluates real-time system response. Methodology included hardware development using EMG sensor, a 3D-printed casing using Fusion 360, Canva-designed for GUI layouts, and UART implementation. EMG testing assessed the developed system under three muscle contraction with load. Average maximum amplitudes were 0.80 mV, 1.55 mV, and 2.68 mV respectively, confirming increased muscle activity. Higher EMG amplitudes appeared in overweight subjects, but underweight subjects also showed increased activity under load, indicating physiological variation beyond BMI affects activation. Results validate that the EMG system reliably records muscle activation patterns in real-time via web interface. While full real-time visualization was not implemented, the GUI integration shows future potential. This research confirms the HMI-based EMG monitoring system is user-friendly for smart rehabilitation. Future work will target full real-time integration, muscle fatigue analysis, and improved home-based usability.
Development of a Mobile Robot Based on ROS2 Architecture
The primary aims of this work were to create an autonomous path planner for an unknown environment and to implement Simultaneous Localisation and Mapping (SLAM), both achieved through the development and construction of a differential-drive mobile robot. Its approach is step-by-step, starting with kinematic and sensor validation of a Unified Robot Description Format (URDF) model in a Gazebo simulation environment, followed by physical hardware integration. The hardware is designed around the Raspberry Pi 4 Model B as the central processing unit, with the Arduino Nano connected via a serial bridge for low-level motor control. The sensor is the RPLIDAR A1, which perceives the environment. It is built on the ROS 2 Humble Hawksbill release, the SLAM Toolbox to create a 2D occupancy grid, and Navigation 2 (Nav2) to plan both global and local paths. The experimental results demonstrate that the system generates high-quality static maps and autonomously navigates while avoiding dynamic obstacles. Lastly, this low-cost, easily copiable platform offers a handy testbed and has a high likelihood of producing educational robotics and applied ROS 2 research.
AI-Driven Optimization and Performance Analysis of LoRaWAN Network Deployment in Campus Non-Line-of-Sight (NLOS) Environment
LoRaWAN is widely used in low-power wide-area network (LPWAN) applications. However, network planning in a campus environment is challenging because of non-line-of-sight (NLOS) conditions due to buildings and obstacles. Conventional propagation models are not always capable of modeling the irregular behavior of wireless signals in such environments. In this research, the LoRaWAN signal performance in NLOS scenarios is predicted using a machine learning based approach. Field measurement data were collected by drive testing in a university campus. K-Nearest Neighbors (KNN) and Random Forest (RF) algorithms used to predict received signal strength indicator (RSSI) and signal to noise ratio (SNR). The results demonstrate the ability of the machine learning models to successfully learn real world signal variations in NLOS conditions. This approach offers a more practical way to enhance LoRaWAN network planning and deployment in campus environments.
Smart Plant Monitoring System Using LoRa Antenna
Effective, long-range, low-power monitoring devices are required in smart agriculture to continuously monitor environmental conditions. This project works on designing a smart plant monitoring system using LoRa antenna technology for long-range communication. In this project, an ESP32 microcontroller is combined with temperature, humidity, and soil moisture sensors to acquire environmental data. To make it work in an independent outdoor setting, a solar-powered power supply system has been established. LoRa transceivers help in transmitting data from sensors to a receiver node for online monitoring. The results of this experiment confirm effective data transmission from sensors and reliable long-range communication. In the field of smart agriculture for plant monitoring, this proposed system offers an economical, power-efficient alternative.
Application of ROS 2-Based Linorobot2 Framework on A Boxy Mobile Robot
This project implements the linorobot2 framework, integrated with Robot Operating System 2 (ROS 2), on a custom-built Boxy mobile robot. The main goal is to create a reliable system for manually controlling this differential-drive robot indoors. A Raspberry Pi 4 running ROS 2 Humble serves as the main computer, managing high-level ROS 2 nodes for communication and control. Simultaneously, a Raspberry Pi Pico microcontroller handles low-level motor control and processes data from wheel encoders, communicating with the ROS 2 system using micro-ROS. The robot utilises a YD LiDAR sensor for environmental data acquisition, supporting future mapping and localisation. While the current focus is on manual control via keyboard teleoperation, the project lays the foundation for fully autonomous navigation using the Navigation2 (Nav2) framework. The results demonstrate that the Linorobot2 framework provides a modular and scalable platform for low-cost mobile robotics in education and research.
IoT-Based Fire Automatic Surveillance System with Sprinkler Control Systems
The principal objective of this project is to develop an Internet of Things (IoT) based fire automatic surveillance system with sprinkler control for early fire detection and suppression in indoor environments. To limit damage and improve response times, the system is designed to continuously monitor fire-related factors, including temperature, smoke concentration, and flame presence. Conventional fire detection systems often rely on single sensors and human intervention, which can lead to greater danger and delayed response. The suggested device combines an ESP8266 NodeMCU microcontroller with an IR flame sensor, MQ-2 smoke sensor, and BMP280 temperature sensor to get around this limit. When set threshold values are exceeded, the system is designed to automatically activate a buzzer alarm and a spray of water. The system also uses Wi-Fi connectivity and the Blynk IoT platform for mobile notifications and real-time monitoring. The system can identify fire conditions with an average response time of 7.13 seconds and a cumulative detection accuracy of 66.91%, according to experimental testing. This intelligent fire monitoring system provides a dependable, affordable, and useful way to improve fire safety in homes, workplaces, and laboratories.
Portable LSPR Sensing Platform with Cloud-Based Data Logging for Malathion Monitoring in Agricultural Food Samples
Excessive use of malathion in agriculture can result in food contamination, environmental pollution, and health risks to consumers. Conventional detection methods such as High-Performance Liquid Chromatography (HPLC) and Gas Chromatography-Mass Spectrometry (GC-MS) offer high accuracy but require expensive equipment and laboratory-based analysis. This work presents a portable Localised Surface Plasmon Resonance (LSPR) sensing platform with integrated cloud-based data logging for the monitoring of malathion in agricultural food samples. The developed system combines gold nanobipyramids (GNBPs), an AS7265x spectroscopy sensor, and ESP32-enabled wireless communication to support spectral acquisition, real-time monitoring, and centralised cloud data storage. Experimental evaluations were conducted using chilli, tomato, and cabbage extracts at a resonance wavelength of 860 nm. Chilli extract showed an increase in intensity from 20.172 to 69.056, with an R² value of 0.9963, while cabbage extract increased from 63.895 to 86.507, with an R² value of 0.993, following the addition of GNBPs and malathion. In contrast, tomato extract showed a decreasing intensity trend from 106.305 to 82.379, possibly due to nanoparticle aggregation influenced by the acidic properties of the sample matrix. The obtained results demonstrated distinguishable spectral variations between pesticide-free and malathion-treated samples, indicating the feasibility of the proposed sensing platform for preliminary pesticide screening. In addition, the implemented cloud logging framework enabled real-time wireless storage of spectral datasets, improving data accessibility, traceability, and long-term record management for future agricultural monitoring applications.
Analysing the Effect of Humidity and Rain on LoRa Signal Performance
This project attempts to analyse the effects of humidity and rain on LoRa (Long-Range) signal performance. The range of the LoRa signal influenced by humidity and rain is considered for outdoor conditions up to 10 meters. The experimental design includes two Arduino Uno boards: the transmitter node connected to the DHT22 sensor for measuring humidity and rain, while the receiver node incorporates the LoRa SX1278 to measure the RSSI and SNR values. In dry conditions (humidity of 73-74%), the RSSI values measured -114 to -121 dBm at 1 to 4 meters. In moderate rain conditions (rain intensity of 70-95%), the RSSI values were within -115 to -121 dBm at 1 to 6 meters. In heavy rain conditions (rain intensity of 99-100% at a humidity level of 91-94%), the RSSI values steadily indicated -115 to -121 dBm at 1 to 10 meters. The experiment indicates that the signal became weak due to intense weather conditions while maintaining a stable range.
Performance Analysis of Solar PV System with Tilt Angle Optimization to Enhance Energy Efficiency
Solar energy is a key component of clean, green energy technology to meet the rising demand for electricity in today's world. Solar system efficiency is dependent on many factors, including installation quality, measurement instruments, and cost analysis. Solar system installation plays a vital role in energy production. Poor installation leads to lower energy output, high cost and system failure. The accuracy of measurement instrumentation helps identify key parameters such as solar irradiance, voltage, and current. The main objective of this research work is to analyse the relationship between installation quality, measuring instruments, and their cost. Simulation tools are used to model the real-world case study scenario and analyse the effect of the tilt angle on solar system performance. The findings indicate that high installation quality can enhance measurement precision, leading to more reliable performance and better financial outcomes. Accurate instrumentation lowers maintenance costs and increases operational efficiency, thereby promoting the long-term sustainability of the solar system project.
Developing a Multi-Level Nitrogen Malnutrition Detection on Paddy Leaf Using YOLOv11
This project focuses on developing a YOLOv11 deep learning model to classify leaf nitrogen levels to support efficient crop health monitoring in precision agriculture. A dataset of 6,000 annotated images, representing nitrogen levels 2 to 5 based on the Leaf Colour Chart (LCC), was split into training (80%), validation (10%), and testing (10%) sets. The model achieved peak stability and accuracy at Epoch 130. For practical field use, the model was deployed into an Android mobile application via TensorFlow Lite. Real-time testing showed detection accuracy of 86% for Level 5 and 74% for Level 2, while intermediate levels 3 and 4 achieved lower rates of 56% and 46% due to visual color similarities. These findings demonstrate that YOLOv11 is a reliable tool for automated nutrient monitoring.
Solar-Powered Smart Irrigation for Chilli Plants
The project aims to develop a Solar-power Smart Irrigation System for chilli plants, which are sensitive to water management. This smart water irrigation system should reduce water waste and manual intervention, and encourage the use of renewable energy. It is equipped with multiple sensors, including a soil moisture sensor, a pH sensor, and a close-range ultrasonic water sensor, which are managed by an ESP32 microcontroller to detect real-time soil and environmental conditions. Using the system, water can be optimised for chilli plant growth because it automatically irrigates in accordance with set soil moisture levels. It is solar-powered, using a solar photovoltaic panel and a battery-charging module, making it independent of the grid. It has also used an Internet of Things (IoT) platform, the Blynk application, for real-time tracking and triggering alerts to users via a mobile interface. Empirical observations indicate that this system can ensure the best level of soil moisture, energy efficiency, and non-pollution, and is usable across different sun exposure regimes. It can be helpful for small-scale farming, urban farming, and at-home gardens, and it focuses on the potential of solar energy and smart irrigation for sustainable chilli cultivation.
Inshyrah Harlina Alyea Laila Md Armaludin
Bit Error Rate (BER) Performance of Hybrid Beamforming for mmWave in 5G Massive MIMO Systems
mmWave massive MIMO is a promising technology for enhancing overall network performance in 5G and beyond 5G. Beamforming is employed in massive MIMO systems to spatially direct the transmitted signal toward intended users while suppressing interference. While analogue beamforming (ABF) offers a simpler signal implementation, its performance is inferior to that of fully digital beamforming (DBF). To overcome these issues, hybrid beamforming (HBF) is proposed to balance performance and hardware efficiency by combining ABF and DBF. The objective of the project is to develop a 5G mmWave massive MIMO system with hybrid beamforming that employs Joint Spatial Division Multiplexing with Per-Group Processing (JSDM-PGP). Next, the investigation of the effects of the number of transmit antennas in the multi-user system on overall performance. Finally, the system performance is measured in terms of bit error rate (BER). Results obtained using the JSDM-PGP technique indicate that increasing the number of transmit antennas from 128 to 512 leads to a noticeable reduction of 19.51% for BER. In addition, the proposed JSDM-PGP scheme is benchmarked against JSDM-JGP to assess the relative improvements in BER under different transmit antenna configurations. For future work, this study can be extended to more realistic mmWave communication scenarios by considering user mobility, dynamic blockage, imperfect CSI, adaptive bandwidth and power allocation, to further evaluate the robustness and practical suitability of JSDM-PGP and JSDM-JGP in next-generation wireless networks.
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
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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.