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VOLUME 14, ISSUE 2, FEBRUARY 2026
DESIGN AND CONSTRUCTION OF A MOVABLE MODULAR ENERGY STORAGE SYSTEM FOR INSTITUTIONAL ENERGY RELIABILITY
Victor Arobor Nsaba, Musa Yakubu, Ajayi Oluwafemi Ezekiel
Deep Learning Based Channel State Estimation and Signal Detection for Orthogonal Frequency Division Multiplexing Wireless Systems
Nukala Mounika, Dr. G. Krishna Reddy
AI monitored Wireless SCADA for Remote Industrial Monitoring using CNN and OCR
I Aadhil Mohamed, R Satheesh, R Rohit Devkar, R Karthivasan
Load Flow Study of a 69-Bus Radial Distribution Network with Optimal Reactive Power Compensation
Rohit Kumar Verma
Power Quality Improvement Techniques in Electronic Load Controllers for Standalone Micro-Hydro Systems
Rohit Kumar Verma
A Spirometer-like System, Providing Indication About Lung Health, Using Air flow Sensor, FPGAs and VHDL
Dr Evangelos I. Dimitriadis, Leonidas Dimitriadis
Design and Implementation IOT Based Energy Monitoring & Controlling System
Dnyaneshwari Pundlik Borkute, Dhiraj Mohan Mothankar, Rohit Ashok Vaidhya, Shekhar Digambar Kawale, Sahil Purushottam Tale, Prof. Rajendra Bhombe, Mr. Harshal Makde
GaN-on-SiC Heterostructures for High-Power RF Applications
Ramsundar. R, Dr. K.G. Padmasine, Bharathi.P
REAL-TIME MULTI-SENSOR FUSION FRAMEWORK FOR EARLY FAULT DETECTION IN ELECTRIC VEHICLE LITHIUM-ION BATTERIES
Gokul. C, Dr. S. Rathinavel
QUALITY ANALYSIS OF FABRICS USING LABVIEW
Reshma M Menon, Dr. K.G. Padmasine, Bharathi P
AN INTELLIGENT TRANSPORTATION SAFETY SYSTEM USING REAL-TIME IMAGE ANALYTICS ON EMBEDDED HARDWARE
M.MOULEESWARAN, Dr. S. RATHINAVEL
A SURVEY FOOTBALL INJURIES ACCORDING TO PLAYING POSITION AMONG ELITE FOOTBALL PLAYERS
Kuljeet Singh, Sinku Kumar Singh
Effect of Weight Training on Speed Performance of Handball Players
Dilip Dattataryarao Bhadke
Abstract
DESIGN AND CONSTRUCTION OF A MOVABLE MODULAR ENERGY STORAGE SYSTEM FOR INSTITUTIONAL ENERGY RELIABILITY
Victor Arobor Nsaba, Musa Yakubu, Ajayi Oluwafemi Ezekiel
DOI: 10.17148/IJIREEICE.2026.14201
Abstract: Consistent electricity supply remains a major challenge in many tertiary institutions located in off grid and semi grid regions of Nigeria. Although solar photovoltaic systems are increasingly adopted as a clean energy solution, their intermittent nature limits their ability to provide uninterrupted power without adequate energy storage (Divya & Østergaard, 2009; Dunn et al., 2011). This study presents the design, construction, and performance evaluation of a movable modular energy storage system developed for the Federal College of Education (FCE), Gidan Madi, Sokoto State. The proposed system integrates electrochemical energy storage with a pure sine wave inverter housed in a compact, mobile enclosure, allowing flexible deployment across different campus locations. The system was designed to complement existing solar infrastructure and to provide temporary backup power for newly constructed facilities awaiting permanent electrification. Construction involved mechanical frame fabrication, electrical wiring, protection device integration, and thermal management provisions. Performance testing revealed a stable alternating current output of 220 V at 10 A, confirming the system’s suitability for powering typical academic and administrative loads. The results demonstrate that the developed system offers a practical, scalable, and cost-effective approach to improving energy reliability in educational institutions. The study contributes to sustainable energy solutions for off grid environments and provides a replicable model for similar institutions in developing regions.
Keywords: Modular energy storage, solar energy, inverter system, renewable energy, off-grid power, educational institutions.
Keywords: Modular energy storage, solar energy, inverter system, renewable energy, off-grid power, educational institutions.
Abstract
Deep Learning Based Channel State Estimation and Signal Detection for Orthogonal Frequency Division Multiplexing Wireless Systems
Nukala Mounika, Dr. G. Krishna Reddy
DOI: 10.17148/IJIREEICE.2026.14202
Abstract: Orthogonal Frequency Division Multiplexing (OFDM) remains the dominant waveform for broadband wireless systems such as Wi-Fi, LTE/5G NR variants, and IoT links. Accurate channel state information (CSI) and robust symbol detection are crucial for optimal OFDM receiver performance. In recent years, deep learning (DL) techniques both purely data driven and model-driven (hybrid) have shown significant potential in improving channel estimation (CE) and signal detection, especially under severe channel impairments, nonlinearities, and hardware imperfections. This paper presents a comprehensive review of the state of the art in deep-learning-based CSI estimation and signal detection for OFDM systems. It summarizes conventional estimation techniques (LS, MMSE), explains various DL architectures (CNNs, RNNs/LSTMs, Transformers, and unfolded networks), and examines model-driven hybrid approaches such as COMNET, CSNET, and unfolded DETNET-style frameworks. Additionally, it discusses commonly used datasets and evaluation metrics, compares reported performances, highlights practical deployment challenges (including computational complexity, generalization ability, dataset bias, and model interpretability), and outlines promising future research directions such as meta-learning, domain adaptation, lightweight inference, and end-to-end joint estimation– detection learning.
The review consolidates and analyses 25 representative references encompassing foundational studies, methodological papers, surveys, and recent advancements in this rapidly evolving research area.
Keywords: OFDM, Channel Estimation, Signal Detection, Deep Learning, CNN, RNN, Model-Driven Deep Learning, Unfolded Networks, MIMO
The review consolidates and analyses 25 representative references encompassing foundational studies, methodological papers, surveys, and recent advancements in this rapidly evolving research area.
Keywords: OFDM, Channel Estimation, Signal Detection, Deep Learning, CNN, RNN, Model-Driven Deep Learning, Unfolded Networks, MIMO
Abstract
AI monitored Wireless SCADA for Remote Industrial Monitoring using CNN and OCR
I Aadhil Mohamed, R Satheesh, R Rohit Devkar, R Karthivasan
DOI: 10.17148/IJIREEICE.2026.14203
Abstract: This paper presents a new low-cost wireless SCADA (Supervisory Control and Data Acquisition) System using an image-based monitoring module for automatically determining values on Grafana Dashboard Graphs via a Convolutional Neural Network (CNN). The system captures an image of the dashboard screen at pre-determined intervals, crops the image to extract just the graph area, and submits the graph image to a lightweight CNN to obtain the value of the Current Process Variable from the curve. This value is then compared against a user-defined setpoint with the output reflecting the state of the dashboard either “GOOD” (Green) or “BAD” (Red) based on whether the current value falls within the defined tolerance. This non-invasive monitoring approach does not require a direct data API, but instead provides visually verifiable support for displayed trends, as well as support of legacy systems where programmatic access to process variables may be limited. The results of the experimental tests performed with both synthetic and actual dashboard images indicated very high recognition accuracy (>94%) and near real-time inference speed (on an embedded host), demonstrating that an image-based supervisory strategy can provide an additional safety measure for training labs and small installations due to its use of graphical images as an external means of tracking process values in industrial settings.
Keywords: Convolutional Neural Network; Chart Image Recognition; Dashboard Monitoring; Grafana; SCADA; Image-based Meter Reading; Setpoint Verification; Edge AI; Real-time Monitoring; Industrial Automation; OCR – Optical Character Recognition.
Keywords: Convolutional Neural Network; Chart Image Recognition; Dashboard Monitoring; Grafana; SCADA; Image-based Meter Reading; Setpoint Verification; Edge AI; Real-time Monitoring; Industrial Automation; OCR – Optical Character Recognition.
Abstract
Load Flow Study of a 69-Bus Radial Distribution Network with Optimal Reactive Power Compensation
Rohit Kumar Verma
DOI: 10.17148/IJIREEICE.2026.14204
Abstract: This paper presents a load flow analysis of a 69-bus radial distribution system using MATLAB programming. Reactive power compensation is implemented through switched shunt capacitors to improve the voltage profile and system performance. The proposed approach determines suitable capacitor placement for effective reactive power injection at selected load buses. The developed MATLAB program calculates voltage magnitude and angle at each bus and identifies buses operating outside permissible voltage limits. Simulation results show that the installation of switched shunt capacitors significantly improves voltage regulation and brings the bus voltages within the specified range. The method is effective for enhancing voltage stability and maintaining a near-flat voltage profile in radial distribution networks.
Keywords: Radial distribution network, 69-bus test system, forward–backward sweep method, Reactive power injection, Voltage stability, Capacitor placement, MATLAB.
Keywords: Radial distribution network, 69-bus test system, forward–backward sweep method, Reactive power injection, Voltage stability, Capacitor placement, MATLAB.
Abstract
Power Quality Improvement Techniques in Electronic Load Controllers for Standalone Micro-Hydro Systems
Rohit Kumar Verma
DOI: 10.17148/IJIREEICE.2026.14205
Abstract: Standalone micro-hydropower systems are widely deployed in remote regions where extending the main electrical grid is not economically feasible. Maintaining stable voltage and frequency under varying load conditions remains a key challenge in such isolated systems. Electronic Load Controllers (ELCs) are commonly used to regulate generator speed by diverting surplus electrical energy to ballast loads. Although ELCs successfully stabilize frequency, they introduce power quality concerns such as harmonic distortion and power factor degradation due to nonlinear switching devices. This paper reviews the main power quality improvement techniques used in Electronic Load Controllers for standalone micro-hydro systems. Various converter topologies and control approaches are discussed, and their performance characteristics are compared. Key challenges and possible improvement areas are also highlighted.
Keywords: Electronic Load Controller, Micro-hydro power plant, Total Harmonic Distortion, Power Quality, PWM Control.
Keywords: Electronic Load Controller, Micro-hydro power plant, Total Harmonic Distortion, Power Quality, PWM Control.
Abstract
A Spirometer-like System, Providing Indication About Lung Health, Using Air flow Sensor, FPGAs and VHDL
Dr Evangelos I. Dimitriadis, Leonidas Dimitriadis
DOI: 10.17148/IJIREEICE.2026.14206
Abstract: A spirometer-like system, based on MD0550 air flow sensor, FPGAs and VHDL, is presented here. The sys- tem is capable of acting as an indicator for lung health by measuring forced expiratory air flow of a human, versus time and subsequently calculate and display both air flow values and the ratio FEV1/FVC of Forced Expiratory Volume in first second (FEV1) to Forced Vital Capacity (FVC). The above ratio is critical for providing useful indication about human lung health and its values must be equal or higher to 0.7 for a healthy person. Our system except from display- ing air flow and ratio values, also uses a LED system for providing information about low, medium or high air flow values, with limits set by user, thus lighting on blue, green or red external LEDs, respectively. Half left or right of board LEDs also light up for low or high air flow values, respectively. In case that FEV1/FVC ratio values are below 0.7 then buzzer sounds and white external LED is ON, while yellow LED lights up in case that FEV1/FVC ratio values are equal or higher than the critical value of 0.7. The system uses DE10-Lite FPGA board, is cheap to manufacture, easy to use and can be also combined with IoT systems, allowing it to share valuable health information of a human, with its doctor.
Keywords: MD0550 air flow sensor, air flow monitoring, lung health, FEV1/FVC ratio, FPGA, VHDL, Buzzer, LEDs.
Keywords: MD0550 air flow sensor, air flow monitoring, lung health, FEV1/FVC ratio, FPGA, VHDL, Buzzer, LEDs.
Abstract
Design and Implementation IOT Based Energy Monitoring & Controlling System
Dnyaneshwari Pundlik Borkute, Dhiraj Mohan Mothankar, Rohit Ashok Vaidhya, Shekhar Digambar Kawale, Sahil Purushottam Tale, Prof. Rajendra Bhombe, Mr. Harshal Makde
DOI: 10.17148/IJIREEICE.2026.14207
Abstract: To design and implement an IoT-based system for real-time monitoring and control of electrical energy consumption to improve efficiency, reduce wastage, and enable remote management. The aim of this project is to develop an Internet of Things (IoT) based Energy Monitoring System to reduce energy waste and cutting down energy cost. Therefore, it needs a system which provide efficient energy consumption management. For the starting of designing an IoT-based Energy Monitoring System, different sensor and calculated AC measurement methods were studied. The development of this system is to integrate each of the sensor, energy measuring device and IoT system into one complete module.
The device that transfers the data or information from the energy monitoring device to the cloud storage is the ESP8266 Wi-Fi module. The output is the energy consumption that has been used according to the real-time data measurement. Lastly, the data that is transmitted to the cloud can be monitored through the mobile application Thing View. In conclusion, this system is necessary because it can control and manage energy consumption to avoid wastage and promote energy conservation.
The device that transfers the data or information from the energy monitoring device to the cloud storage is the ESP8266 Wi-Fi module. The output is the energy consumption that has been used according to the real-time data measurement. Lastly, the data that is transmitted to the cloud can be monitored through the mobile application Thing View. In conclusion, this system is necessary because it can control and manage energy consumption to avoid wastage and promote energy conservation.
Abstract
GaN-on-SiC Heterostructures for High-Power RF Applications
Ramsundar. R, Dr. K.G. Padmasine, Bharathi.P
DOI: 10.17148/IJIREEICE.2026.14208
Abstract: GaN-on-SiC heterostructures are emerging as key materials for high-power and high-frequency electronic applications due to their superior electrical and thermal properties. This paper presents a detailed study of the material characteristics, device behavior, and performance advantages of GaN grown on SiC substrates. Comparative analysis with conventional silicon technology demonstrates improved efficiency, reduced power loss, enhanced thermal stability, and higher operating frequency capability. The results confirm the suitability of GaN-on-SiC devices for next- generation RF systems, electric vehicles, renewable energy converters, and aerospace electronics.
Keywords: GaN-on-SiC Heterostructures, High-Power RF Devices, Thermal Stability and Power Density, Wide Bandgap Semiconductors
Keywords: GaN-on-SiC Heterostructures, High-Power RF Devices, Thermal Stability and Power Density, Wide Bandgap Semiconductors
Abstract
REAL-TIME MULTI-SENSOR FUSION FRAMEWORK FOR EARLY FAULT DETECTION IN ELECTRIC VEHICLE LITHIUM-ION BATTERIES
Gokul. C, Dr. S. Rathinavel
DOI: 10.17148/IJIREEICE.2026.14209
Abstract: The power storage component that mainly powers electric vehicles (EVs) is lithium batteries due to their high performance, comparative durability, and excellent operational efficiency. However, there are chances of battery failures such as over temperature, over voltage, internal short circuit, gas leakage, and swelling, which can cause serious battery failures. Generally, Battery Management Systems (BMS) methods often use fewer sensing parameters, thus limiting the detection capabilities of battery faults. In this article, a real-time multi sensor fusion system will be proposed as an early warning system of lithium battery faults in electric vehicles. The multi sensor fault detection will be implemented using a rule-based algorithm, integrating voltage sensors, current sensors, temperature sensors, gas sensors, pressure sensors, and strain sensors. The developed system will be verified using Proteus IS software.
Keywords: Lithium-Ion Battery, Multi-Sensor Fusion, Thermal Runaway, Real-Time Monitoring.
Keywords: Lithium-Ion Battery, Multi-Sensor Fusion, Thermal Runaway, Real-Time Monitoring.
Abstract
QUALITY ANALYSIS OF FABRICS USING LABVIEW
Reshma M Menon, Dr. K.G. Padmasine, Bharathi P
DOI: 10.17148/IJIREEICE.2026.14210
Abstract: Fabric defect detection is one of the major stages in the cloth manufacturing sector. Defects such as broken yarns, stains, and holes reduce the quality and value of the produced fabric. Traditionally, these defects are inspected manually, which is time-consuming and leads to operator fatigue over long periods of work. This work presents a machine learning–based fabric defect detection system implemented in LabVIEW for identifying surface defects in commonly used textile materials such as cotton and polyester with plain surfaces. The proposed approach uses extracted texture and statistical features from fabric images, which are classified using a trained machine learning model to distinguish between normal and defective regions. Compared to manual inspection methods, the proposed system provides improved accuracy, consistency, and inspection speed. While machine learning-based approaches require labelled training data and an initial training phase, they offer better robustness to variations in fabric texture and illumination. The automated inspection process reduces human effort, minimizes subjectivity, and improves consistency, making it a practical, cost- effective, and scalable solution for real-time industrial textile quality assessment.
Keywords: Fabric defect detection, support vector machine, texture analysis, industrial automation, quality control
Keywords: Fabric defect detection, support vector machine, texture analysis, industrial automation, quality control
Abstract
AN INTELLIGENT TRANSPORTATION SAFETY SYSTEM USING REAL-TIME IMAGE ANALYTICS ON EMBEDDED HARDWARE
M.MOULEESWARAN, Dr. S. RATHINAVEL
DOI: 10.17148/IJIREEICE.2026.14211
Abstract: Road crashes often happen because of visibility fast-moving vehicles and the absence of early alert mechanisms in vital road areas like tight bends and junctions. This project suggests an accident avoidance system utilizing real-time image analysis with a microcontroller development board. A camera module is employed to record live road scenes and track vehicle flow. The gathered information is analysed to identify and categorize vehicle types such, as cars, buses and trucks.The processed data is examined by a microcontroller equipped with edge AI features to enable precise decision-making. Upon detecting a vechicle the system shows the vehicle type, switches on a red traffic light and issues a voice alert to caution drivers and pedestrians, about incoming vehicles. In the absence of a detected vehicle the display and voice alert stay inactive. A green light is turned on to signal a safe situation. This proposed system improves road safety by delivering alerts and visual cues thereby lowering the risk of accidents. Its affordable time functioning and straightforward implementation render it appropriate for use in locations susceptible, to accidents.
Keywords: Image Processing, Vehicle Detection, Embedded System, Microcontroller, Camera Module, Accident Prevention
Keywords: Image Processing, Vehicle Detection, Embedded System, Microcontroller, Camera Module, Accident Prevention
Abstract
A SURVEY FOOTBALL INJURIES ACCORDING TO PLAYING POSITION AMONG ELITE FOOTBALL PLAYERS
Kuljeet Singh, Sinku Kumar Singh
DOI: 10.17148/IJIREEICE.2026.14212
Abstract: Football is one of the most popular and physically demanding sports in the world, and the risk of injury among players remains a major concern for sports scientists and coaches. The purpose of the present study was to conduct a survey to examine the prevalence of injuries among elite football players according to their playing positions. A total of 1000 elite football players aged between 14 and 30 years participated in the study. The participants were categorized into four age groups: 14–17 years, 18–21 years, 22–25 years, and 26–30 years. Data were collected using a self-developed football injury questionnaire adapted from Singh (2012). The study employed a descriptive retrospective research design, and the collected data were analyzed using descriptive statistics such as percentages with the help of SPSS software.
The findings of the study revealed that injuries were more prevalent among defenders (35.33%) followed by offenders/forwards (28.00%), midfielders (20.19%), and goalkeepers (16.33%). Similar patterns were observed across all age groups, where defenders and offenders experienced a higher percentage of injuries compared to midfielders and goalkeepers. The higher injury rates among defenders and offenders may be attributed to frequent physical contact, tackling, and high-intensity movements during match play. The study highlights the importance of position-specific training, injury prevention strategies, and proper conditioning programs to reduce the risk of injuries among football players.
Keywords: Football injuries, playing position, elite football players, injury prevalence, sports injury survey, injury risk factors
The findings of the study revealed that injuries were more prevalent among defenders (35.33%) followed by offenders/forwards (28.00%), midfielders (20.19%), and goalkeepers (16.33%). Similar patterns were observed across all age groups, where defenders and offenders experienced a higher percentage of injuries compared to midfielders and goalkeepers. The higher injury rates among defenders and offenders may be attributed to frequent physical contact, tackling, and high-intensity movements during match play. The study highlights the importance of position-specific training, injury prevention strategies, and proper conditioning programs to reduce the risk of injuries among football players.
Keywords: Football injuries, playing position, elite football players, injury prevalence, sports injury survey, injury risk factors
Abstract
Effect of Weight Training on Speed Performance of Handball Players
Dilip Dattataryarao Bhadke
DOI: 10.17148/IJIREEICE.2026.14213
Abstract: The purpose of the present study was to examine the effect of weight training on speed performance among handball players. A total of 100 male handball players aged between 14–19 years were selected and divided into two groups: an experimental group (n=50) and a control group (n=50). The experimental group underwent a structured 12- week weight training program, while the control group continued their routine activities without any specialized training. Speed performance was measured using the 50-yard dash test, conducted before and after the training period. Descriptive statistics such as mean and standard deviation were calculated, and Analysis of Covariance (ANCOVA) was applied to determine the significance of differences at the 0.05 level.
The results revealed that the experimental group showed improvement in speed performance, as the mean time decreased from 8.12 seconds in the pre-test to 7.20 seconds in the post-test. The control group showed minimal change, with mean values of 8.09 and 8.01 seconds respectively. However, ANCOVA results indicated that the improvement in speed was not statistically significant.
The study concludes that while weight training may contribute to improvements in speed, the effect was not significant within the duration of the study. It is recommended that weight training be combined with speed-specific exercises for better results.
Keywords: Weight Training, Speed, Handball, Physical Fitness, 50 Yard Dash
The results revealed that the experimental group showed improvement in speed performance, as the mean time decreased from 8.12 seconds in the pre-test to 7.20 seconds in the post-test. The control group showed minimal change, with mean values of 8.09 and 8.01 seconds respectively. However, ANCOVA results indicated that the improvement in speed was not statistically significant.
The study concludes that while weight training may contribute to improvements in speed, the effect was not significant within the duration of the study. It is recommended that weight training be combined with speed-specific exercises for better results.
Keywords: Weight Training, Speed, Handball, Physical Fitness, 50 Yard Dash