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Keywords: Solar Energy, Railway Track Fault Detection, Arduino UNO, Embedded System, Real-Time Monitoring, GSM & GPS Communication.
VOLUME 14, ISSUE 5, MAY 2026
A Novel System for Monitoring and Controlling Industrial Engine Operation, using Vibration, Magnetic Field, Humidity and Temperature Sensors, Implemented with FPGAs and VHDL
Leonidas Dimitriadis, Dr Evangelos I. Dimitriadis
Automated Transmission Line Monitoring and Fault Detection Using Arduino B. Suresh Reddy
Asst Professor, Dept. of Electrical & Electronics Engineering
Recipe Generation from Food Images Using Deep Learning
FALGUNI KAMBLE, SAKSHI MILMILE, BHAGYASHREE SONTAKE, SHIVGOPAL GHOTI, Prof. SURAJ BANKAR, PROF BHAGYASHREE KALE
IoT and SCADA Technology in Power Systems
Badal Vishwakarma, Saish Gaikwad, Rajitha T.B., Lovely Gaur
DC Fast Charging and its Impact on Battery Life
Om Walunj, Aryan Sanaf, I. Srilakshmi, Kavita Sawant
Urban Oasis: Automated Indoor Farming for a Greener Future
Dr. P. G. Kaushik, Ms. Raksha Rathod, Mr. Prathmesh Chute, Ms. Harsha Dongre, Mr. Vedant Ragenwar, Mr. Harsh Nimkar, Ms. Rutika Tikhe, Mr. Lenit Shende
RAILWAY TRACK CRACK DETECTION & OBSTACLE DETECTION SYSTEM Ch. Pravalika
Asst. Professor, Dept. of Electrical & Electronics Engineering
SamVaad: A Scalable Full-Stack Web Application Using React, Spring Boot, and PostgreSQL
Madhuri Jadhav, Pratik Darade, Sahil Shinde, Saurabh Nirmal, Kshitij Shinde
Design and Implementation of an Automatic Solar Tracking System
Ch. Pravalika, M. Vidisha, B. Sushma, E. Devender, B. Ephiram, Asst. Professor, Dept. of Electrical & Electronics Engineering
Two-Phase Interleaved Boost Converter with MPPT Control for Solar PV System
Dr. GVSSN Srirama Sarma, Dasari Vikas, Beesam Shravani, ANSS Varshit
OPTIMAL MULTI-PERIOD ELECTRICITY MARKET CLEARING USING LINEAR PROGRAMMING: A SOCIAL WELFARE MAXIMIZATION APPROACH
Sri K. Naresh and Dr. G.N.Srinivas
IOT BASED AUTOMATIC POWER FACTOR CORRECTION SYSTEM USING RASPBERRY PI
Mr. M.Srinivas, P.Nikhil Kumar, CH.Aravind, P.Satya Narayana, CH.Shashank
RFID based intelligent Ambulance monitoring and Traffic control system with pulse and temperature observation
Dr.M.SARITHA, BOMMA SAITEJA, CHILUKA CHARAN, MANGA SANTOSH
SOLAR BASED RAILWAY TRACK FAULT DETECTION SYSTEM
M.V Subramanayam, Indugula Jayadeva, Dommata Manasa, Nandala Ajay kumar
Wireless Communication Technologies for Connected Electric Vehicles: A Review of 5G, IoT and OFDM-Based V2X Systems Toward 6G Mobility
Senate Judith Makobane
Hardware-Oriented Quantum Communication Systems for Practical Quantum Key Distribution: Architectures, Challenges, and Implementations
Chirag Thakur, Raj Kumar Saini, Amita Verma, Ravi Kant, Simon Nyithpuou Chiman, Chirag Bharmaik, Senate Judith Makobane, Nitesh Kumar Vashisht
Smart Monitoring and Control Framework for Landscape-Integrated Renewable Energy Power Systems-A Review
Simon Nyithpuou Chiman
Advances in VLSI and Embedded Systems: FPGA-Based Design, Low-Power Microcontrollers, and Nanowire Growth Technologies A-Review
Nitesh Kumar Vashisht
DIGITAL RELAY PROTECTION OF GENERATOR TRANSFORMER USING MICROCONTROLLER
Dr.A. Gowthaman, Dr. M. Mohammadha Hussaini, M. Sandhiya, K. Monisha, C. Subhasri
IOT BASED TRANSFORMER PARAMETERS MONITORING SYSTEM
ALLWIN W NESHWAR V, HARISH KUMAR K, PRABUKARTHICK P, RAGUL V, Dr. A. GOWTHAMAN. ME., Ph. D
Automatic Street Lighting with Smart Zebra Crossing System
Arjun S. Pawar, Aarya S. Kadam, Sayali A. Jadhav, Manasi S. Sutar, Prof. Ms. P. H. Phatak
Load Flow and Fault Studies for Efficient Power Management of Steel Plant
Ananya C, Dr. E. Latha Mercy
Comprehensive Review of Image Compression Techniques in Digital Image Processing
Manasvi S. Mogal, Urvashi M. Borse, Sunita N. Deore
Centralized Energy Management System Using Ethernet Communication for Cement Industry
Dr.Chitra S, Archana Prasadini G S
Analysis of Motor Acceleration Performance and Harmonics for a Data Center
Sebastin Clinton. V, Dr. P. Maruthupandi
Dynamic Motor Starting and Harmonic Analysis for a Chemical Manufacturing Industry
Jeevananthan J, Dr. V. Prasanna Moorthy
PROTECTION-ORIENTED POWER SYSTEM STUDIES FOR THE ELECTRICAL NETWORK
Ananya C*, Dr. E. Latha Mercy
AI-Based Smart Energy Monitoring and Consumption Optimization System with Appliance-Level Control
DR. P. GOVINDASAMY, DR. M. MOHAMMADHA HUSSAINI, M. DHARANI
IJIREEICE.2026.14530-PLC
PLC BASED STROKE CYCLING TIME IN, COMMERCIAL VEHICLE USING LVDT, POSITION SENSOR, Dr. P. Govindasamy+, Dr. M. Mohammadha Hussaini, M. Kiruba, K. Monisha, R. V. Surya, G. M. Swathika
Real Time Face Recognition Based Electronic Voting Machine Using Raspberry Pi
Mr. Pandharinath Baban Bichkule, Ms. Waghmode K.H
Occupational Burnout, Emotional Exhaustion and Professional Resilience among Secondary Education Teachers in Greece: A Comprehensive Quantitative Analysis
Dr. Konstantinos N. Domouchtsis, Dr. Apostolos Ch. Klonis, Computer Science, Physicist – Theologian, M. Sc. Environmental Physic, M.A. Bible Databases, PhD in Knowledge
Structural Analysis and Strategic Management of Secondary Education in Greece: Evaluating Centralization and Institutional Autonomy
Dr. Konstantinos N. Domouchtsis, Dr. Apostolos Ch. Klonis, Computer Science, Physicist – Theologian, M. Sc. Environmental Physic, M.A. Bible Databases, PhD in Knowledge Mining, Postdoctoral in Knowledge Mining
IoT Enabled Tele ECG Monitoring System Using Rasberry Pi and Cloud Analytics
Ms. Neeta Sanjay Zagade, Mr. Shrikant S. Salave
SMART PATROL ROBOT USING IOT AND DUAL MICROCONTROLLER ARCHITECTURE FOR REAL-TIME SURVEILLANCE
Shreya Aadpad, Sakshi Mahamuni, Asmita Khamgal, P. A. Patil, Dr. P. N. Shinde
PV Powered Charging Station for E-Vehicle
Dr N. Narmadhai, Lavanya J
Dual-Battery Reconfigurable Converter-Assisted Vehicle-to-Grid System for Adaptive Microgrid Frequency Stabilization: A Simulation Study
K. Deepak, P. Pedda Reddy, DR.K. Chithambaraiah Setty, M. Shiva Kumar
Attention-Augmented Recurrent Deep Q-Network with Adaptive CLLC Resonant Converter for Intelligent Multi-Source EV Fast Charging and Hybrid Thermoelectric-PCM Thermal Management: A Comprehensive Simulation Study
G. Mahesh, M. Shiva Kumar, Dr K. Chithambaraiah Setty, P. Pedda Reddy
FOPID-Controlled Hybrid Transformerless PV Inverter for Single-Phase Grid Integration with Improved Leakage Current Suppression and Reactive Power Support
P. Ramaiah, Syed Saheb, Dr. K. Chithambaraiah Setty
Intelligent superconducting DC–DC double-boost converter for ultra-fast EV charging, featuring liquid hydrogen cooling and AI-based PWM Control
S. Dhanush, Undyal Amear Qurashi, DR.K. Chithambaraiah Setty, P. Pedda Reddy
Improved Power Stability in Solar-Powered Multi-Port EV Charging Systems via FCS-MPC-Driven Virtual Synchronous Generator Control
D.Vinay, DR.K. Chithambaraiah Setty, P. Pedda Reddy, M. Shiva Kumar
AN INTELLIGENT SEPSIS PREDICTION AND PATIENT MONITORING SYSTEM USING MACHINE LEARNING
Dr. T. Amalraj Victoire, V. Swetha
Fractional-Order Super-Twisting Sliding Mode Control of a Solar PV-Integrated Three-Phase EV Charging System with Adaptive Sparse Kernel Filtering for Seamless Grid Integration
M. Maheswari, E. Ravi Teja, DR.K. Chithambaraiah Setty
Design and Development of Magnetic levitation Vehicle
Mrs. Anusha M K, Mrs. Sushmitha K R, Bhumika P P, Pavan Kumar K Y, Varun D S, Bhagyashree
IoT-Based Real-Time Industrial Machine Health Monitoring and Automated Control System Utilizing ESP32 Microcontroller
Dr.S.Karthigailakshmi, M Balaji, G Bhuvaneshwaran, P Aswin Balaji
AI Based Job and Internship Recommendation System (Jobify)
Arti Jaibhai, Geetesh Nasare, Soham Daundkar, Srujan Pagar, Sanket Jadhav
A New Multi-Output Dc-Dc Converter for Electric Vehicle Application
K. Rakesh, Mr. Syed Saheb, Dr. K. Chithambaraiah Setty
A Review of Modern Antenna Design Approaches for 5G, 6G, IoT, and Satellite Applications
Karthigai Lakshmi S, Arun Kumar K S, Yuvasri S, Santhiya S, Sudha Sree T
IoT Based Solar and Piezoelectric Energy Generation System
Dr. Sunil Kumar C, Amulya K M, Harshitha R, Janhavi B G, Keerthana H S
The Role of Decision Making in attaining Sustainability in Industrial and Manufacturing Fields: PCIA Based Approach
Somnath Singroul*, Chhatrapal Dewangan, Atul Kumar Sahu, Nitin Kumar Sahu
Smart Solar Plant Optimization Using MPPT Controller and IoT Alert System
Ms. Komal M. Avhad, Mr. Somnath S. Hadpe, Dr. Sridhar S. Khule, Mr. Bhagwan S. Bodke
Multi-Agent Deep Reinforcement Learning- Based Autonomous Control of Grid-Forming Inverters in Renewable-Dominated Power Systems
Prof. Suyog Sangharatna Dhoke, Mahesh P. Ingle, Shruti S. Burande, Gunjan R. Lakde
Smart Laser Repellent Technology for Sustainable Farming
Akash K, Srividya C N, Dr. Manoj Kumar S B, Yogeshwari D, Sinchana A P
Portable Solar & Wind Laptop & Mobile Charging System
Prof. P. K. Digge, Sneha Babulal Gaikwad, Priyanka Sanjay Bhadange, Vaishnavi Vishwas Kale, Arpita Subhash Inamdar
Sign Language Translator
Swetha B, Halima Sadiya, Mangala F K, Sudarshan M K, T E Jyothi Theertha
PRISM-X ->3D PRINTED PERSONALIZED REHAB INTELLIGENT SMART MOTION EXOSKELETON
Apoorva H M, Naveen B, Bhanu Prakash, Prabhavathi, Greeshma C
Design and Development of an ESP32-Based Autonomous Office Logistics Robot
A. Ramachandiran, M. Ashwin, M. Dharun, S. Rithick, M. Shyam
LPG Leakage Detection and Automated Prevention System Using Arduino
A. Ramachandiran, A. Adheeba Banu, K. Dhevasena, T. Pavithara, S. Sneha
IOT-BASED AUTOMATED SOLAR PANEL CLEANING SYSTEM USING SMARTPHONES
Mr. Ajith M S, Dr. Naveen B, Mr. Kiran M S, Prabhavathi K, Ms. Megha M R
AUTOMATIC FIRE EXTINGUISHING SYSTEM
Dr. Dilip Kumar Patel, Abhijeet Srivastava, Gaurav Kushwaha, Monu Rathaur, Utkarsh Srivastava
A Container-Orchestrated Framework for Enterprise Asset Lifecycle Management with Automated Continuous Delivery on the Cloud
ATYAM S L N S R GAYATRI SINDHU, Dr. CHIRAPARAPU SRINIVASA RAO*
A Cloud-Native Customer Relationship Management Platform on AWS Fargate with Automated DevOps Workflow Orchestration
Iragavarupu Sravan Sai Kumar, A.N. Rama Mani*
A Deep Learning Framework for Deepfake Detection and Digital Media Protection with Explainable Forensic Verdicts and Provenance Watermarking
MULLAGIRI MARY SAROJA, KARRI LAKSHAMANA REDDY*
A Highly Available Cloud-Based Real Estate Marketplace Using Elastic Load Balancing and Multi-Zone Redundancy
YANDRA.VAISHNAVI and K. LAKSHMI SAI SRI*
A Multi-Tenant SaaS Framework for Hostel Management Deployed on AWS Elastic Beanstalk with Elastic Auto-Scaling
KETHA DURGA, B.N. SRINIVASA GUPTA*
An Adaptive AI-Driven Framework for Personalized Study Scheduling and Exam Preparation Using Locally Hosted Large Language Models
Saride.Balu, P. Sreenivasa Reddy*
Abstract
A Novel System for Monitoring and Controlling Industrial Engine Operation, using Vibration, Magnetic Field, Humidity and Temperature Sensors, Implemented with FPGAs and VHDL
Leonidas Dimitriadis, Dr Evangelos I. Dimitriadis
DOI: 10.17148/IJIREEICE.2026.14501
Abstract: A novel system which uses FPGAs and VHDL for monitoring and controlling industrial engine operation, is presented here. The system monitors and controls four basic parameters exerting decisive effect on engine operation, using corresponding sensors. Engine’s vibration, spontaneous fluctuations of magnetic field, humidity and temperature, are simultaneously monitored and controlled. Each sensor acts as input for DE10-Lite FPGA board used here and its values are presented in seven-segment displays. Every sensor is accompanied by indicator LEDs, of which green one indicates that input values are below critical set value limit, while red indicates that the input values of the correspond- ing parameter are greater than or equal to the critical upper value set by the programmer. Our system also uses three additional LEDs controlling the overall engine operation, from which the green one indicates that all sensor input val- ues are within acceptable limits, while red one shows that at least one parameter’s input values are above set limits, meaning that there is a danger for the engine because it is not operating in the appropriate way. The third and yellow LED is activated when the engine overcomes time set period of operation, thus our system implements an additional control and protection of the industrial engine. Both FPGA’s board LEDs and buzzer are activated only if vibration input values are above upper set limit. Our system is cheap to manufacture, easy to use and can be also combined with IoT and AI systems, allowing it to share and process valuable information of all factory’s engines. It also provides the flexibility of setting upper sensor input value limits, depending on monitored engines, thus making it useful in a variety of applications.
Keywords: Industrial engine monitoring, sensors, vibration, magnetic field, humidity, temperature, FPGA, VHDL, Buzzer, LEDs.
Keywords: Industrial engine monitoring, sensors, vibration, magnetic field, humidity, temperature, FPGA, VHDL, Buzzer, LEDs.
Abstract
Automated Transmission Line Monitoring and Fault Detection Using Arduino B. Suresh Reddy
Asst Professor, Dept. of Electrical & Electronics Engineering
DOI: 10.17148/IJIREEICE.2026.14502
Abstract: The reliability of electrical power systems is crucial for industrial and domestic applications, yet they are highly vulnerable to faults such as short circuits, overloads, and transformer malfunctions. Identifying faults quickly and accurately reduces downtime, prevents accidents, and improves maintenance efficiency. This project proposes an IoT-based system using Arduino to monitor transmission lines and transformers, detect faults, and estimate the distance of the fault location. The system collects real-time data from sensors, processes it through Arduino, and transmits the fault status to an IoT platform, enabling remote monitoring and timely corrective action.
Keywords: Electrical Power Systems, Arduino Uno, Transformer Malfunctions, Iot Platform, Remote Monitoring.
Keywords: Electrical Power Systems, Arduino Uno, Transformer Malfunctions, Iot Platform, Remote Monitoring.
Abstract
Recipe Generation from Food Images Using Deep Learning
FALGUNI KAMBLE, SAKSHI MILMILE, BHAGYASHREE SONTAKE, SHIVGOPAL GHOTI, Prof. SURAJ BANKAR, PROF BHAGYASHREE KALE
DOI: 10.17148/IJIREEICE.2026.14503
Abstract: The advancement of artificial intelligence in computer vision and natural language processing has enabled innovative applications such as automatic recipe generation from food images. This paper presents a deep learning- based system that analyzes food images to predict ingredients and generate step-by-step cooking instructions. The proposed system integrates Convolutional Neural Networks (CNNs) for extracting visual features and Natural Language Processing (NLP) models such as Long Short-Term Memory (LSTM) or Transformers for generating recipes. The system aims to assist users in identifying unknown dishes and preparing them efficiently. Experimental observations indicate that the model produces contextually relevant and grammatically correct recipes. This approach has potential applications in smart kitchens, food blogging, and diet planning systems.
Keywords: Food Image Recognition, Recipe Generation, Deep Learning, CNN, NLP, Image Captioning
Keywords: Food Image Recognition, Recipe Generation, Deep Learning, CNN, NLP, Image Captioning
Abstract
IoT and SCADA Technology in Power Systems
Badal Vishwakarma, Saish Gaikwad, Rajitha T.B., Lovely Gaur
DOI: 10.17148/IJIREEICE.2026.14504
Abstract: The rapid advancement of digital technologies has transformed traditional power systems into smart grids. IoT and SCADA play a key role by enabling real-time monitoring, automated control, and efficient decision-making. IoT uses connected sensors to collect data, while SCADA provides centralized control and visualization. Together, they improve power system efficiency, reliability, and support sustainable energy management.
Keywords: Internet of Things (IoT), SCADA Systems, Smart Grid, Power System Automation, Real-Time Monitoring, Remote Control Systems, Data Acquisition, Wireless Sensors, Energy Management, Predictive Maintenance, Grid Modernization
Keywords: Internet of Things (IoT), SCADA Systems, Smart Grid, Power System Automation, Real-Time Monitoring, Remote Control Systems, Data Acquisition, Wireless Sensors, Energy Management, Predictive Maintenance, Grid Modernization
Abstract
DC Fast Charging and its Impact on Battery Life
Om Walunj, Aryan Sanaf, I. Srilakshmi, Kavita Sawant
DOI: 10.17148/IJIREEICE.2026.14505
Abstract: DC fast charging is an advanced method that charges batteries quickly by supplying direct current (DC) directly to the battery, bypassing the onboard charger. It is widely used in electric vehicles, smartphones, and modern electronics to reduce charging time.
Keywords: DC Fast Charging (DCFC), Battery Life, Lithium-ion Battery, Electric Vehicles, Charging Speed, Direct Current (dc), AC charging.
Keywords: DC Fast Charging (DCFC), Battery Life, Lithium-ion Battery, Electric Vehicles, Charging Speed, Direct Current (dc), AC charging.
Abstract
Urban Oasis: Automated Indoor Farming for a Greener Future
Dr. P. G. Kaushik, Ms. Raksha Rathod, Mr. Prathmesh Chute, Ms. Harsha Dongre, Mr. Vedant Ragenwar, Mr. Harsh Nimkar, Ms. Rutika Tikhe, Mr. Lenit Shende
DOI: 10.17148/IJIREEICE.2026.14506
Abstract: With the growing demand for sustainable and space-efficient farming methods, hydroponics has emerged as a promising alternative to traditional soil-based agriculture. It allows plants to grow faster and healthier by providing nutrients directly through water solutions, reducing the need for large farmland and excessive water usage. The Smart Hydroponic System is designed to automate and optimize plant growth in a controlled environment using Arduino-based monitoring and control. The system integrates sensors such as DHT11, water level, TDS, and turbidity sensors to track temperature, humidity, nutrient levels, and solution quality. An air pump ensures root oxygenation, while a flexible lighting setup supports plant growth. Voice alerts and scheduled music playback via the APR33A3 module enhance interactivity, with real-time data displayed on a 16×2 LCD. This compact 2-foot model demonstrates an efficient, low- cost solution for sustainable, soil-less cultivation.
Keywords: Smart Agriculture, Remote Monitoring, Sustainable Farming.
Keywords: Smart Agriculture, Remote Monitoring, Sustainable Farming.
Abstract
Design of an Off-Board Electric Vehicle Charging System Powered by Solar PV Array
DR. B. Veeru
DOI: 10.17148/IJIREEICE.2026.14507
Abstract: In the past decade, the automobiles ect or has experienced significant growth due to the advancement of electric vehicles (EVs). The battery charging system is crucial for the progress of EVs. Charging EV batteries from the grid increases the demand on the load. Consequently, this study proposes photovoltaic (PV) array-based off-board EV battery charging system. Regardless of solar irradiance levels, the EV battery must be charged consistently, which is accomplished by incorporating a backup battery bank alongside the PV array. By utilizing as epic converter and a three- phase bi directional DC–DC converter, the proposed system can charge the EV battery during both sunny and non-sunny periods. During peak sunlight hours, the backup battery charges in conjunction with the EV battery, while during non- sunny hours, the backup battery facilitates the charging of the EV battery. The proposed charging system has been simulated using Simulink with in the MATLAB software, and an experimental prototype has been constructed and tested in the laboratory, with the results presented in this study.
Abstract
RAILWAY TRACK CRACK DETECTION & OBSTACLE DETECTION SYSTEM Ch. Pravalika
Asst. Professor, Dept. of Electrical & Electronics Engineering
DOI: 10.17148/IJIREEICE.2026.14508
Abstract: The Indian Railways has one of the largest railway networks in the world, crises- crossing over 1,15,000 km in distance, all over India. However, with regard to reliability and passenger safety Indian Railways is not up to global standards. Among other factors, cracks developed on the rails due to absence of timely detection and the associated maintenance pose serious questions on the security of operation of rail transport. A recent study revealed that over 25% of the track length is in need of replacement due to the development of cracks on it. Manual detection of tracks is cumbersome and not fully effective owing to much time consumption and requirement of skilled technicians. This project work is aimed towards addressing the issue by developing an automatic railway track crack detection system.
This work introduces a project that aims in designing robust railway crack detection scheme (RCODS) using IR SENSOR assembly system which avoids the train accidents by detecting the cracks on railway tracks.And also capable of alerting the authorities in the form of SMS messages along with location by using GPS and WiFi modules. The system also includes distance measuring sensor which displays the track deviation distance between the railway tracks.
Keywords: Node MCU ESP8266, MotorDriverL239D, IR Sensor, GPS.
This work introduces a project that aims in designing robust railway crack detection scheme (RCODS) using IR SENSOR assembly system which avoids the train accidents by detecting the cracks on railway tracks.And also capable of alerting the authorities in the form of SMS messages along with location by using GPS and WiFi modules. The system also includes distance measuring sensor which displays the track deviation distance between the railway tracks.
Keywords: Node MCU ESP8266, MotorDriverL239D, IR Sensor, GPS.
Abstract
SamVaad: A Scalable Full-Stack Web Application Using React, Spring Boot, and PostgreSQL
Madhuri Jadhav, Pratik Darade, Sahil Shinde, Saurabh Nirmal, Kshitij Shinde
DOI: 10.17148/IJIREEICE.2026.14509
Abstract: These days people want web applications that're scalable, secure and work well. Full-stack development is a way to build software solutions by combining modern frontend technologies with robust backend systems. This paper talks about SamVaad, a full-stack web application built using React.js, Vite, Tailwind CSS Spring Boot and PostgreSQL. The frontend of SamVaad is fast and easy to use while the backend provides APIs, authentication and business logic. PostgreSQL is used to manage data in a way and JPA is used to map objects to relational data. The system follows a client-server architecture that makes it easy to maintain, scale and perform well. This project shows how modern web technologies can be used to create real-world applications with user interaction and efficient data handling.
Keywords: Full-Stack Development, React.js, Spring Boot, PostgreSQL, REST API, JPA, Authentication, Web Application
Keywords: Full-Stack Development, React.js, Spring Boot, PostgreSQL, REST API, JPA, Authentication, Web Application
Abstract
Design and Implementation of an Automatic Solar Tracking System
Ch. Pravalika, M. Vidisha, B. Sushma, E. Devender, B. Ephiram, Asst. Professor, Dept. of Electrical & Electronics Engineering
DOI: 10.17148/IJIREEICE.2026.14510
Abstract: In modern-day technology the largest hassle World is dealing with is strength juncture and we endorse that fossil fuels are to be had in very limited amount. Also there overuse in final 30-forty years has reduced them further. So, now to fulfill our strength needs the most effective choice we're left is to make use of the Renewable wealth of strength this is to be had in abundance. There are diverse wealth of renewable strength like wind, solar and geothermal however maximum price powerful amongst them is Solar Energy. Solar strength cannot most effective meet our current strength needs however also can offer us smooth and reasonably-priced strength. Solar Panels as soon as set up can deliver strength for numerous years while not having any preservation price. Solar photovoltaic structures are such structures which might be used for harnessing sun strength however we because the earth is rotating across the solar because of which sun strength in current Solar panels is to be had most effective for a confined time all through the day. To triumph over this hassle sun trackers are used. Authors on this have a look at have attempted to discover the opportunity of sun trackers and their price effectiveness in sun photovoltaic.
Abstract
Two-Phase Interleaved Boost Converter with MPPT Control for Solar PV System
Dr. GVSSN Srirama Sarma, Dasari Vikas, Beesam Shravani, ANSS Varshit
DOI: 10.17148/IJIREEICE.2026.14511
Abstract: This paper presents the development of a solar photovoltaic energy conversion system using a two-phase interleaved boost converter integrated with a maximum power point tracking control strategy. Due to environmental variations, the output of a solar panel is non-linear and fluctuating, which reduces system efficiency if not properly controlled. To address this issue, a perturb and observe-based MPPT algorithm is implemented using a microcontroller to dynamically adjust the duty cycle of the converter. The proposed converter employs two parallel boost stages operating with a 180-degree phase shift, which significantly reduces input current ripple and output voltage fluctuations. The system continuously monitors voltages using sensing circuits and provides real-time feedback. The boosted output is stored in a battery and used to drive a DC load. Experimental prototype level results indicate improved performance in terms of voltage stability, ripple reduction, and power extraction efficiency.
Keywords: Solar PV, MPPT, Interleaved Boost Converter, PWM, Ripple Reduction, Arduino, Power Electronics, Perturb and Observe (P&O), Voltage Boosting, Battery Charging, Phase Shift Control.
Keywords: Solar PV, MPPT, Interleaved Boost Converter, PWM, Ripple Reduction, Arduino, Power Electronics, Perturb and Observe (P&O), Voltage Boosting, Battery Charging, Phase Shift Control.
Abstract
OPTIMAL MULTI-PERIOD ELECTRICITY MARKET CLEARING USING LINEAR PROGRAMMING: A SOCIAL WELFARE MAXIMIZATION APPROACH
Sri K. Naresh and Dr. G.N.Srinivas
DOI: 10.17148/IJIREEICE.2026.14512
Abstract: This paper presents an optimal electricity market clearing framework based on linear programming (LP), applied to a real-time, multi-period Indian power exchange market. The system operator maximises social welfare by dispatching five generating units against seven demand entities across six trading periods using IEX 2025–26 average data. The dual variable of the energy-balance constraint directly yields the market clearing price (MCP) for each period. A market price forecasting module based on linear regression, using engineered supply–demand features, is integrated to predict clearing prices without re-solving the optimisation, achieving a mean absolute percentage error (MAPE) of 2.47%. Simulation results confirm that the LP clears the market at prices ranging from 5,900 to 8,500/MWh with a total social welfare of 26.87 Million, and the forecasting module provides economically consistent predictions suitable for realtime operator decision support.
Keywords: electricity market clearing, linear programming, social welfare maximisation, market clearing price, price forecasting, Indian power exchange, IEX, day-ahead market.
Keywords: electricity market clearing, linear programming, social welfare maximisation, market clearing price, price forecasting, Indian power exchange, IEX, day-ahead market.
Abstract
IOT BASED AUTOMATIC POWER FACTOR CORRECTION SYSTEM USING RASPBERRY PI
Mr. M.Srinivas, P.Nikhil Kumar, CH.Aravind, P.Satya Narayana, CH.Shashank
DOI: 10.17148/IJIREEICE.2026.14513
Abstract: This paper presents the design and implementation of an IoT-based Automatic Power Factor Correction (APFC) system using a Raspberry Pi Pico W microcontroller. In modern electrical networks, inductive loads such as motors and transformers cause a lagging power factor, leading to increased line losses, voltage instability, and penalty charges from utilities. To address these challenges, the proposed system continuously monitors voltage, current, and phase difference using CT, PT, and Zero Crossing Detector circuits. Based on real-time measurements, the controller dynamically switches capacitor banks through relay modules to compensate for reactive power and improve the power factor close to unity.
The system integrates IoT connectivity via the ThingSpeak platform, enabling remote monitoring, data logging, and performance analysis. An LCD display and LED indicators provide local feedback, while cloud-based dashboards enhance transparency and predictive maintenance. Experimental prototype results demonstrate significant improvement in power factor (from 0.70 to 0.97), reduction in energy losses, and enhanced system reliability. The design is compact, cost-effective, and scalable, making it suitable for both industrial and domestic applications.
Keywords: Automatic Power Factor Correction (APFC), IoT, Raspberry Pi Pico W, Current Transformer (CT), Potential Transformer (PT), Zero Crossing Detector, Relay Control, Capacitor Bank, ThingSpeak, Energy Efficiency, Smart Grid.
The system integrates IoT connectivity via the ThingSpeak platform, enabling remote monitoring, data logging, and performance analysis. An LCD display and LED indicators provide local feedback, while cloud-based dashboards enhance transparency and predictive maintenance. Experimental prototype results demonstrate significant improvement in power factor (from 0.70 to 0.97), reduction in energy losses, and enhanced system reliability. The design is compact, cost-effective, and scalable, making it suitable for both industrial and domestic applications.
Keywords: Automatic Power Factor Correction (APFC), IoT, Raspberry Pi Pico W, Current Transformer (CT), Potential Transformer (PT), Zero Crossing Detector, Relay Control, Capacitor Bank, ThingSpeak, Energy Efficiency, Smart Grid.
Abstract
RFID based intelligent Ambulance monitoring and Traffic control system with pulse and temperature observation
Dr.M.SARITHA, BOMMA SAITEJA, CHILUKA CHARAN, MANGA SANTOSH
DOI: 10.17148/IJIREEICE.2026.14514
Abstract: Traffic congestion in urban areas often delays ambulances during medical emergencies, which can lead to serious consequences for patients. To overcome this problem, an intelligent traffic control system is required to provide priority to emergency vehicles. This paper proposes an RFID-based ambulance monitoring and traffic control system that helps ambulances pass through traffic signals without delay. In this system, ambulances are equipped with RFID tags, and RFID readers are installed at traffic intersections. When an ambulance approaches the signal, the RFID reader detects the tag and sends the information to the Arduino microcontroller, which automatically changes the signal to green for that route. This allows the ambulance to move quickly through the intersection. The system also includes patient health monitoring using pulse and temperature sensors inside the ambulance. These health parameters are transmitted wirelessly to the hospital using Zigbee communication, enabling doctors to prepare for treatment before the ambulance arrives. This system helps reduce ambulance delay, improve emergency response time, and enhance patient care through smart traffic management.
Keywords: RFID reader, RFID tag, Zigbee transmitter module, Zigbee receiver module ,wireless serial port communication module, temperature sensor, pulse sensor, Traffic control , Ambulance monitoring.
Keywords: RFID reader, RFID tag, Zigbee transmitter module, Zigbee receiver module ,wireless serial port communication module, temperature sensor, pulse sensor, Traffic control , Ambulance monitoring.
Abstract
SOLAR BASED RAILWAY TRACK FAULT DETECTION SYSTEM
M.V Subramanayam, Indugula Jayadeva, Dommata Manasa, Nandala Ajay kumar
DOI: 10.17148/IJIREEICE.2026.14515
Keywords: Solar Energy, Railway Track Fault Detection, Arduino UNO, Embedded System, Real-Time Monitoring, GSM & GPS Communication.
Abstract
Wireless Communication Technologies for Connected Electric Vehicles: A Review of 5G, IoT and OFDM-Based V2X Systems Toward 6G Mobility
Senate Judith Makobane
DOI: 10.17148/IJIREEICE.2026.14516
Abstract: Electric vehicles have become increasingly common in recent years, and electricity consumption patterns have significantly changed, which has greatly transformed the communication systems, specifically within the transport sector. Connected Electric Vehicles (CEVs) rely heavily on the steady , accurate transmission of data to enable safety, operate intelligently , and make smart decisions fast. This study covers the communication technologies that feed into this ecosystem – 5G networks, IoT integration, Orthogonal Frequency Division Multiplexing (OFDM)-based V2X systems. Rather than approaching these technologies as standalone innovations, this review analyses their interactions in real-world vehicular applications, where practical constraints on mobility, interference, and heterogeneous network environments are encountered. The approach goes so far as to consider newer 6G networks, including AI networking approaches and more advanced integrated communication architectures. Finally, the paper addresses the challenges and gaps for further research leading to an end-to-end response in delivering connected electric mobility system deployment into a secure environment.
Keywords: Connected Electric Vehicles, V2X(Vehicle to Everything) Communication, OFDM, 5G Networks, IoT, 6G Mobility
Keywords: Connected Electric Vehicles, V2X(Vehicle to Everything) Communication, OFDM, 5G Networks, IoT, 6G Mobility
Abstract
Hardware-Oriented Quantum Communication Systems for Practical Quantum Key Distribution: Architectures, Challenges, and Implementations
Chirag Thakur, Raj Kumar Saini, Amita Verma, Ravi Kant, Simon Nyithpuou Chiman, Chirag Bharmaik, Senate Judith Makobane, Nitesh Kumar Vashisht
DOI: 10.17148/IJIREEICE.2026.14517
Abstract: While classical encryption algorithms like RSA and ECC are based on mathematical complexity and are susceptible to attacks by quantum computers in the future, Quantum Key Distribution offers an information-theoretically secure protocol based on the laws of quantum physics. From its conception to today, Quantum Key Distribution technology has developed from a mere theory to practical implementations of quantum communication systems that implement Quantum Key Distribution through quantum states of photons.
This paper provides an overview of the existing Quantum Key Distribution technology, paying particular attention to the hardware aspects of these systems, including their building blocks, like photon sources, modulators, quantum channels, and single-photon detectors, along with implementation issues, including loss, noise, scalability, and overall complexity. Emphasis is paid to recent developments in integrated photonics and photonic integrated circuits.
In addition to conventional Quantum Key Distribution systems, this paper covers advanced Quantum Key Distribution protocols, such as Continuous-Variable Quantum Key Distribution, Measurement-Device-Independent Quantum Key Distribution and Twin-Field Quantum Key Distribution, as well as the related security threats, including side-channel and detector-related attacks. Moreover, this paper looks at how Quantum Key Distribution is evolving to quantum networks and how it can be integrated with post-quantum cryptography.
Finally, this paper addresses some of the emerging topics, including quantum internet and intelligent optimization of Quantum Key Distribution systems.
Keywords: Quantum Key Distribution, Quantum Cryptography, Measurement-Device-Independent Quantum Key Distribution, Twin-Field Quantum Key Distribution.
This paper provides an overview of the existing Quantum Key Distribution technology, paying particular attention to the hardware aspects of these systems, including their building blocks, like photon sources, modulators, quantum channels, and single-photon detectors, along with implementation issues, including loss, noise, scalability, and overall complexity. Emphasis is paid to recent developments in integrated photonics and photonic integrated circuits.
In addition to conventional Quantum Key Distribution systems, this paper covers advanced Quantum Key Distribution protocols, such as Continuous-Variable Quantum Key Distribution, Measurement-Device-Independent Quantum Key Distribution and Twin-Field Quantum Key Distribution, as well as the related security threats, including side-channel and detector-related attacks. Moreover, this paper looks at how Quantum Key Distribution is evolving to quantum networks and how it can be integrated with post-quantum cryptography.
Finally, this paper addresses some of the emerging topics, including quantum internet and intelligent optimization of Quantum Key Distribution systems.
Keywords: Quantum Key Distribution, Quantum Cryptography, Measurement-Device-Independent Quantum Key Distribution, Twin-Field Quantum Key Distribution.
Abstract
Smart Monitoring and Control Framework for Landscape-Integrated Renewable Energy Power Systems-A Review
Simon Nyithpuou Chiman
DOI: 10.17148/IJIREEICE.2026.14518
Abstract: Smart renewable energy systems are no longer installed only as separate technical equipment; they are increasingly built into roofs, facades, farms, public spaces, and community networks. This review discusses how monitoring systems can support such landscape-integrated generations. It draws together work on IoT sensing, embedded controllers, communication links, edge-cloud platforms, energy management, BIPV, agrivoltaics, digital twins, predictive maintenance, and cybersecurity. Most studies show that present systems can record electrical and environmental data reliably and can provide dashboards or alarms. The weaker point is that many designs still ignore the setting in which the system operates. Shading, access, appearance, crop response, user confidence, and maintenance routes all influence long- term performance. The review therefore proposes a context-aware monitoring framework built around dependable sensing, edge processing, flexible communication, secure data handling, user-specific dashboards, and gradual lifecycle intelligence.
Keywords: Renewables; Internet of Things; Building-Integrated Photovoltaics; Agrivoltaics; Cybersecurity
Keywords: Renewables; Internet of Things; Building-Integrated Photovoltaics; Agrivoltaics; Cybersecurity
Abstract
Advances in VLSI and Embedded Systems: FPGA-Based Design, Low-Power Microcontrollers, and Nanowire Growth Technologies A-Review
Nitesh Kumar Vashisht
DOI: 10.17148/IJIREEICE.2026.14519
Abstract: Very-large-scale integration (VLSI) and embedded systems are moving toward heterogeneous designs where speed, energy efficiency, flexibility, and device innovation must work together. This review discusses three important areas: FPGA-based system design, low-power microcontrollers, and semiconductor nanowire growth. FPGAs are valuable for rapid prototyping, parallel processing, hardware acceleration, and hardware/software co-design. Low-power microcontrollers support battery-operated sensing, wireless embedded platforms, TinyML, and always-on edge intelligence. Nanowire growth offers possibilities for future transistor channels, sensitive sensors, and post-planar nanoelectronics devices. The review shows that these topics are often studied separately, although future embedded platforms will need closer cooperation between reconfigurable hardware, efficient control units, and emerging nanoscale devices. FPGAs can provide strong acceleration but may consume more power. Microcontrollers are energy efficient but limited in memory and throughput. Nanowire devices are promising but still face integration and manufacturing challenges. The paper identifies the need for cross-layer co-design methods that connect architecture, firmware, security, power modelling, and device technology for practical future VLSI systems in IoT, healthcare, instrumentation, and edge computing.
Keywords: VLSI; FPGA-Based Design; Low-Power Microcontrollers; Nanowire Electronics
Keywords: VLSI; FPGA-Based Design; Low-Power Microcontrollers; Nanowire Electronics
Abstract
DIGITAL RELAY PROTECTION OF GENERATOR TRANSFORMER USING MICROCONTROLLER
Dr.A. Gowthaman, Dr. M. Mohammadha Hussaini, M. Sandhiya, K. Monisha, C. Subhasri
DOI: 10.17148/IJIREEICE.2026.14520
Abstract: This paper presents a simple and efficient digital relay protection system designed for generator transformers using a microcontroller. Generator transformers play a key role in power systems and are continuously exposed to electrical faults such as overcurrent, short circuit, and earth faults. Traditional protection methods are often slow and require manual supervision. To overcome these issues, a microcontroller-based system is developed to monitor electrical parameters and detect faults quickly.
The proposed system uses sensors to measure current and voltage values, which are processed by the microcontroller. When abnormal conditions are detected, the relay is activated automatically to isolate the faulty section and protect the equipment. The system is designed to be fast, reliable, and easy to implement. Experimental results show that the system responds quickly and improves the safety of the transformer. This method can be further enhanced by integrating advanced technologies for smart monitoring.
Keywords: Digital Relay, Generator Transformer, Microcontroller, Fault Detection, Protection System
The proposed system uses sensors to measure current and voltage values, which are processed by the microcontroller. When abnormal conditions are detected, the relay is activated automatically to isolate the faulty section and protect the equipment. The system is designed to be fast, reliable, and easy to implement. Experimental results show that the system responds quickly and improves the safety of the transformer. This method can be further enhanced by integrating advanced technologies for smart monitoring.
Keywords: Digital Relay, Generator Transformer, Microcontroller, Fault Detection, Protection System
Abstract
IOT BASED TRANSFORMER PARAMETERS MONITORING SYSTEM
ALLWIN W NESHWAR V, HARISH KUMAR K, PRABUKARTHICK P, RAGUL V, Dr. A. GOWTHAMAN. ME., Ph. D
DOI: 10.17148/IJIREEICE.2026.14521
Abstract: Transformers play a major role in electrical power transmission and distribution systems. Continuous monitoring of transformer parameters is essential to ensure reliable operation and prevent unexpected failures. Traditional transformer monitoring methods mainly depend on manual inspection and periodic maintenance, which are time- consuming and less efficient. To overcome these limitations, an IoT Based Transformer Parameters Monitoring System is proposed.
The proposed system continuously monitors important transformer parameters such as temperature and oil level using suitable sensors. The sensor data is processed using the NodeMCU microcontroller and transmitted to the cloud platform through Wi-Fi communication. The system also includes a fault detection mechanism that identifies abnormal conditions such as overheating and low oil level. Whenever the monitored parameters exceed predefined threshold values, alerts are generated automatically.
The monitored data can be accessed remotely through the IoT platform, enabling real-time transformer monitoring from any location. The proposed system is simple, reliable, cost-effective, and easy to implement. It reduces manual maintenance effort, improves transformer safety, and enhances operational efficiency.
Keywords: IoT, Transformer Monitoring, NodeMCU, Temperature Sensor, Oil Level Monitoring, Fault Detection.
The proposed system continuously monitors important transformer parameters such as temperature and oil level using suitable sensors. The sensor data is processed using the NodeMCU microcontroller and transmitted to the cloud platform through Wi-Fi communication. The system also includes a fault detection mechanism that identifies abnormal conditions such as overheating and low oil level. Whenever the monitored parameters exceed predefined threshold values, alerts are generated automatically.
The monitored data can be accessed remotely through the IoT platform, enabling real-time transformer monitoring from any location. The proposed system is simple, reliable, cost-effective, and easy to implement. It reduces manual maintenance effort, improves transformer safety, and enhances operational efficiency.
Keywords: IoT, Transformer Monitoring, NodeMCU, Temperature Sensor, Oil Level Monitoring, Fault Detection.
Abstract
Automatic Street Lighting with Smart Zebra Crossing System
Arjun S. Pawar, Aarya S. Kadam, Sayali A. Jadhav, Manasi S. Sutar, Prof. Ms. P. H. Phatak
DOI: 10.17148/IJIREEICE.2026.14522
Abstract: This study presents the design and implementation of an intelligent street lighting system integrated with a smart zebra crossing mechanism aimed at improving pedestrian safety and reducing energy consumption. Conventional lighting systems operate continuously without considering environmental conditions, resulting in unnecessary power usage. In the proposed system, a Light Dependent Resistor (LDR) is used to monitor ambient light intensity, while Infrared (IR) or ultrasonic sensors are employed to detect pedestrian presence. A microcontroller processes these inputs and controls street lights and warning indicators accordingly. When a pedestrian is detected near the crossing, visual alerts are activated to warn approaching vehicles. The system operates only when required, thereby conserving energy and enhancing safety. “The proposed solution is cost-effective and suitable for modern smart city applications.’’
Keywords: Automatic street lighting, smart zebra crossing, LDR sensor, pedestrian detection, energy efficiency, Arduino.
Keywords: Automatic street lighting, smart zebra crossing, LDR sensor, pedestrian detection, energy efficiency, Arduino.
Abstract
Load Flow and Fault Studies for Efficient Power Management of Steel Plant
Ananya C, Dr. E. Latha Mercy
DOI: 10.17148/IJIREEICE.2026.14523
Abstract: This project presents the load flow and fault studies of the Steel Plant using ETAP (version 19.0.1) software to evaluate the systems steady state performance and reliability. The plant receives power from the Substation (220/33 kV). Two transformers are connected to this bus where 25 MVA, 220/33 kV unit feeding Zone-I and 60 MVA, 220/33 kV unit feeding Zone-II. The load flow simulation results indicate that all bus voltages remain within ±10% of their nominal values, confirming a balanced load distribution and stable operation under normal conditions. In the fault studies, both single line-to-ground (L-G) and three-phase faults are simulated at various bus locations to analyze the systems response under abnormal conditions. The resulting fault currents, bus voltages are examined, particularly during the sub-transient period. These results help verify the adequacy of circuit breaker ratings and relay settings, ensuring safe and reliable fault clearance.
Keywords: ETAP, Load flow analysis, Short-circuit analysis, Power system protection, Industrial power systems.
Keywords: ETAP, Load flow analysis, Short-circuit analysis, Power system protection, Industrial power systems.
Abstract
Comprehensive Review of Image Compression Techniques in Digital Image Processing
Manasvi S. Mogal, Urvashi M. Borse, Sunita N. Deore
DOI: 10.17148/IJIREEICE.2026.14524
Abstract: Digital Image Processing (DIP) has become an essential technology in modern communication, healthcare, multimedia systems, remote sensing, surveillance, and scientific research. The rapid growth of high-resolution image data has increased the demand for efficient image storage and transmission methods. Image compression techniques play a major role in reducing image size by eliminating redundant information while maintaining acceptable image quality. This review paper presents a comprehensive study of different image compression methods used in digital image processing. The paper discusses both lossless and lossy compression techniques, including JPEG, PNG, Huffman Coding, Run Length Encoding (RLE), Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), Singular Value Decomposition (SVD), fractal compression, and modern deep learning-based approaches. A comparative analysis of compression ratio and image quality is also presented. Furthermore, recent developments in artificial intelligence and hybrid compression models are reviewed to understand future trends in image compression systems.
Keywords: Digital Image Processing, Image Compression, Lossless Compression, Lossy Compression, DCT, DWT, JPEG, JPEG2000, Deep Learning.
Keywords: Digital Image Processing, Image Compression, Lossless Compression, Lossy Compression, DCT, DWT, JPEG, JPEG2000, Deep Learning.
Abstract
Centralized Energy Management System Using Ethernet Communication for Cement Industry
Dr.Chitra S, Archana Prasadini G S
DOI: 10.17148/IJIREEICE.2026.14525
Abstract: This paper presents the design and implementation of a centralized energy monitoring system for industrial applications. The system enables real-time monitoring of electrical parameters such as voltage, current, power, and energy consumption from multiple energy meters. These meters are interconnected using RS-485 communication, which provides reliable data transmission over long distances. The Modbus protocol is used to ensure structured and accurate data exchange between devices. A data converter is employed to convert Modbus RTU communication into Modbus TCP format, allowing integration with Ethernet networks. The converted data is transmitted to a central server, which acts as the main unit for data collection, processing, and storage. The system continuously updates electrical parameters, enabling real-time observation and analysis of system performance. Time-stamped data storage allows detailed evaluation of energy usage over a period of time. The proposed system reduces the need for manual data collection and minimizes human errors, thereby improving monitoring efficiency. It also provides better visibility of system behaviour and supports informed decision-making. The communication network ensures stable and uninterrupted data transfer under continuous operation. The system is flexible and can be expanded by adding more devices as required. Overall, the proposed system offers a cost-effective and scalable solution for centralized energy monitoring and enhances efficient utilization of electrical energy in industrial environments
Keywords: Energy Monitoring, RS-485, Modbus TCP, Ethernet Communication, Industrial Automation, Real-Time Monitoring
Keywords: Energy Monitoring, RS-485, Modbus TCP, Ethernet Communication, Industrial Automation, Real-Time Monitoring
Abstract
Analysis of Motor Acceleration Performance and Harmonics for a Data Center
Sebastin Clinton. V, Dr. P. Maruthupandi
DOI: 10.17148/IJIREEICE.2026.14526
Abstract: Data centers are highly sensitive electrical infrastructures that require reliable power supply, voltage stability, and high-power quality due to the presence of critical IT loads, HVAC systems, Uninterruptible Power Supplies (UPS), and Variable Frequency Drives (VFDs). The increasing penetration of non-linear loads introduces harmonic distortions, while large motor starting operations can cause significant voltage dips and affect system stability. This paper presents the modeling and analysis of a 13.8 kV/0.4 kV data center electrical network using ETAP software. The system includes medium-voltage ring main units (RMUs), distribution transformers, low-voltage networks, and critical/non-critical loads. Motor acceleration studies were performed to evaluate starting current, acceleration time, voltage drop, and motor terminal performance for major induction motors under starting conditions. In addition, harmonic analysis was carried out to assess voltage and current distortion levels caused by VFD-driven HVAC motors and UPS systems, with compliance verification based on IEEE 519 standards. The study results demonstrate the impact of motor starting on bus voltage profiles and identify harmonic levels across different buses in the system. The analysis confirms system operational reliability under normal conditions and provides recommendations for improving power quality and ensuring stable operation of data center electrical infrastructure.
Keywords: Load Flow Analysis, Harmonics Analysis, Motor Acceleration, ETAP, Industrial Power Systems, IEC 60909, IEEE 519.
Keywords: Load Flow Analysis, Harmonics Analysis, Motor Acceleration, ETAP, Industrial Power Systems, IEC 60909, IEEE 519.
Abstract
Dynamic Motor Starting and Harmonic Analysis for a Chemical Manufacturing Industry
Jeevananthan J, Dr. V. Prasanna Moorthy
DOI: 10.17148/IJIREEICE.2026.14527
Abstract: For manufacturing industries like chemical manufacturing industries, maintaining a stable and continuous power supply is very important because power quality issues can affect continuous industrial processes. Starting large motors can lead to voltage dips and usage of non-linear loads can lead to power quality issues. Hence, in this project, motor starting and harmonic analysis are carried out for the chemical industry using ETAP under various operating scenarios. As a result, it checks whether the voltage dip during motor starting and the THD at the PCC.
Keywords: Voltage dip, Motor Acceleration, Harmonics, Power quality issues.
Keywords: Voltage dip, Motor Acceleration, Harmonics, Power quality issues.
Abstract
PROTECTION-ORIENTED POWER SYSTEM STUDIES FOR THE ELECTRICAL NETWORK
Ananya C*, Dr. E. Latha Mercy
DOI: 10.17148/IJIREEICE.2026.14528
Abstract: This thesis presents the power system analysis of an electrical network using ETAP software to evaluate steady- state performance, fault levels and protection coordination. Adaptive Newton–Raphson techniques are applied to assess the reliability, safety and operational effectiveness of the system. The electrical network is modelled in ETAP to analyze voltage profiles, fault current levels and relay coordination. Load flow analysis confirms that bus voltages remain within permissible limits and equipment operates within rated capacities. Short circuit analysis determines fault current levels for equipment verification, while protection coordination studies ensure selective operation of relays and circuit breakers with proper primary and backup protection. The study concludes that the electrical network operates reliably and efficiently under normal operating conditions. Further studies such as motor acceleration and transient stability analysis can also be performed to evaluate the dynamic performance and stability of the system.
Keywords: Power System Analysis, Protection Coordination and ETAP Modelling.
Keywords: Power System Analysis, Protection Coordination and ETAP Modelling.
Abstract
AI-Based Smart Energy Monitoring and Consumption Optimization System with Appliance-Level Control
DR. P. GOVINDASAMY, DR. M. MOHAMMADHA HUSSAINI, M. DHARANI
DOI: 10.17148/IJIREEICE.2026.14529
Abstract: The rapid growth of electricity demand in residential and commercial sectors, combined with the increasing penetration of renewable energy sources, necessitates intelligent, real-time energy management solutions. This paper presents an AI-Based Smart Energy Monitoring and Consumption Optimization System with Appliance-Level Control, which integrates Internet of Things (IoT) sensing infrastructure, Non-Intrusive Load Monitoring (NILM) and reinforcement learning-based optimization to achieve granular control over individual household appliances. The proposed system architecture comprises five functional layers: a sensing layer using smart meters and IoT-connected plugs, a NILM disaggregation engine based on LSTM and Transformer models, an ML-based forecasting module, a Multi-Objective Deep Q-Network (MO-DQN) optimization agent and an IoT actuator control layer with a mobile user interface. Experimental evaluations demonstrate appliance-level energy classification accuracy exceeding 95%, a forecasting accuracy of 99.7% for hourly consumption using LSTM and electricity cost reductions of up to 18% through demand response integration. The system further supports dynamic peak clipping, valley filling and load shifting strategies under real-time pricing signals. This work contributes a scalable, privacy-aware and user-adaptive framework for next-generation smart home energy management
Keywords: Smart Energy Monitoring, Appliance-Level Control, Non-Intrusive Load Monitoring (NILM), Reinforcement Learning, Home Energy Management System (HEMS), IoT, Demand Response, Deep Q-Network, LSTM, Smart Grid.
Keywords: Smart Energy Monitoring, Appliance-Level Control, Non-Intrusive Load Monitoring (NILM), Reinforcement Learning, Home Energy Management System (HEMS), IoT, Demand Response, Deep Q-Network, LSTM, Smart Grid.
Abstract
IJIREEICE.2026.14530-PLC
PLC BASED STROKE CYCLING TIME IN, COMMERCIAL VEHICLE USING LVDT, POSITION SENSOR, Dr. P. Govindasamy+, Dr. M. Mohammadha Hussaini, M. Kiruba, K. Monisha, R. V. Surya, G. M. Swathika
DOI: 10.17148/IJIREEICE.2026.14530
Abstract: This paper presents the design and implementation of a PLC-based automated system for controlling 10 stroke cycling operations in commercial vehicle testing using an LVDT position sensor. An Omron PLC serves as the central controller while a Linear Variable Differential Transformer (LVDT) provides precise real-time displacement feedback through a closed-loop control mechanism. The system governs the forward and reverse motion of a pneumatic actuator, with an integrated counter ensuring exactly ten stroke cycles before automatic shutdown. Testing confirmed high accuracy, repeatability, and reliability, establishing the system's suitability for industrial automotive testing applications.
Keywords: PLC, LVDT, Stroke Cycling, Pneumatic Actuator, Closed-Loop Control, Industrial Automation, Commercial Vehicle Testing, Ladder Logic, Signal Conditioning.
Keywords: PLC, LVDT, Stroke Cycling, Pneumatic Actuator, Closed-Loop Control, Industrial Automation, Commercial Vehicle Testing, Ladder Logic, Signal Conditioning.
Abstract
Real Time Face Recognition Based Electronic Voting Machine Using Raspberry Pi
Mr. Pandharinath Baban Bichkule, Ms. Waghmode K.H
DOI: 10.17148/IJIREEICE.2026.14531
Abstract: With the introduction of electronic voting systems, the efficiency, transparency, and accuracy of the voting process can be significantly improved. However, the conventional Electronic Voting Machines (EVMs) involve a manual verification of the voter's identity. This makes the system highly susceptible to voter impersonation and duplicate voting. This paper presents a real-time face recognition-based electronic voting system using a Raspberry Pi to improve the accuracy of the voter verification process. In this paper, the authors have designed a face recognition- based electronic voting system that includes a raspberry pi camera to acquire the voter's face images, a face detection system that uses the Haar Cascade Classifier algorithm, and a face recognition system that uses the Local Binary Pattern Histogram (LBPH) algorithm. After successful verification of the voter's identity, the voter will be allowed to cast their vote through a touchscreen interface. The system was able to attain a recognition accuracy of up to 92% under bright lighting conditions, as well as 90% under normal indoor lighting, with an average authentication time of approximately 1.3 seconds. Functional testing was equally successful, with the system being able to prevent duplicate voting attempts by 100%, as well as store the votes in the database. This shows that the proposed system is effective for use in institutional voting scenarios
Keyword: Electronic Voting Machines, Local Binary Pattern Histogram, Face Recognition, Raspberry Pi
Keyword: Electronic Voting Machines, Local Binary Pattern Histogram, Face Recognition, Raspberry Pi
Abstract
Occupational Burnout, Emotional Exhaustion and Professional Resilience among Secondary Education Teachers in Greece: A Comprehensive Quantitative Analysis
Dr. Konstantinos N. Domouchtsis, Dr. Apostolos Ch. Klonis, Computer Science, Physicist – Theologian, M. Sc. Environmental Physic, M.A. Bible Databases, PhD in Knowledge
DOI: 10.17148/IJIREEICE.2026.14532
Abstract: This research paper provides an in-depth analysis of occupational burnout among secondary education teachers in Greece. Amidst a landscape of continuous educational reforms and the lingering effects of the socio- economic crisis, educators face a complex array of psychological pressures. Using a quantitative approach and the Maslach Burnout Inventory (MBI) as a primary tool, this study explores the levels of emotional exhaustion, depersonalization, and personal accomplishment among a sample of Greek educators. The findings reveal a significant correlation between administrative workload and emotional fatigue, while also highlighting the role of professional resilience. This study aims to provide a baseline for future psychological interventions in the Greek public school system.
Keywords: Occupational Burnout, Secondary Education, Greece, Emotional Exhaustion, Teacher Resilience, MBI Scale.
Keywords: Occupational Burnout, Secondary Education, Greece, Emotional Exhaustion, Teacher Resilience, MBI Scale.
Abstract
Structural Analysis and Strategic Management of Secondary Education in Greece: Evaluating Centralization and Institutional Autonomy
Dr. Konstantinos N. Domouchtsis, Dr. Apostolos Ch. Klonis, Computer Science, Physicist – Theologian, M. Sc. Environmental Physic, M.A. Bible Databases, PhD in Knowledge Mining, Postdoctoral in Knowledge Mining
DOI: 10.17148/IJIREEICE.2026.14533
Abstract: This study provides an extensive examination of the administrative architecture of the Greek secondary education system. Characterized by a rigid, centralized structure, the system’s efficiency is often challenged by bureaucratic complexities and a lack of local decision-making power. This research analyzes the hierarchical levels of management, from the Ministry of Education to the Regional Directorates and individual School Units. By integrating quantitative data regarding administrative workload and qualitative assessments of leadership efficacy, the paper highlights the systemic barriers to modernization. The results suggest that while centralized control ensures national uniformity, it inadvertently restricts school-based innovation and responsive management. Strategic recommendations for a shift towards a more decentralized, pedagogical leadership model are discussed.
Keywords: Educational Administration, Secondary Education, Greece, Bureaucracy, Decentralization, School Leadership, Public Policy.
Keywords: Educational Administration, Secondary Education, Greece, Bureaucracy, Decentralization, School Leadership, Public Policy.
Abstract
IoT Enabled Tele ECG Monitoring System Using Rasberry Pi and Cloud Analytics
Ms. Neeta Sanjay Zagade, Mr. Shrikant S. Salave
DOI: 10.17148/IJIREEICE.2026.14534
Abstract: Continuous monitoring of cardiac activities plays a vital role in the early detection of cardiovascular diseases. This paper aims to present a tele-ECG monitoring system enabled through the Internet of Things (IoT) technology. The system will be implemented through the usage of a Raspberry Pi device and the ThingSpeak cloud-based analytics platform. The proposed tele-ECG monitoring system will include the usage of an ECG sensor module that will be integrated with a Raspberry Pi device to acquire ECG signals from the patient. The ECG signals will be transmitted to the cloud-based platform through the IoT communication protocol. The ECG signals will be stored in the cloud-based platform. Doctors can track the ECG signals to understand the cardiac conditions of the patient. The experiment was carried out through the usage of ECG signals from various test subjects. In addition to this, the system successfully transmitted the ECG signals to the cloud with an average data transmission delay of 1.5-2 seconds. Moreover, the system successfully monitored the accuracy of the signals with an accuracy of 94-96% compared to the reference ECG readings. Based on the above results, it can be concluded that the proposed system can be effectively used for the implementation of a cost-effective system for the monitoring of cardiac signals.
Keywords: ECG System, Raspberry Pi, Internet of Things, ThingSpeak, Cloud Analytics
Keywords: ECG System, Raspberry Pi, Internet of Things, ThingSpeak, Cloud Analytics
Abstract
SMART PATROL ROBOT USING IOT AND DUAL MICROCONTROLLER ARCHITECTURE FOR REAL-TIME SURVEILLANCE
Shreya Aadpad, Sakshi Mahamuni, Asmita Khamgal, P. A. Patil, Dr. P. N. Shinde
DOI: 10.17148/IJIREEICE.2026.14535
Abstract: Security and surveillance systems play a vital role in residential, industrial, and public environments. However, manual monitoring requires continuous human effort and may not always be efficient. To address this issue, a smart patrol robot is proposed for real-time surveillance and remote monitoring. The system utilizes dual microcontrollers, namely ESP32 Dev Module and ESP8266, integrated with a camera module, sensors, and a motor driver. The ESP32 Dev Module is responsible for motor control and robot movement, while the ESP8266 handles sensor data acquisition and wireless communication. The robot can be controlled wirelessly via Wi-Fi, enabling users to monitor the surroundings remotely through a mobile application. The camera module provides live video streaming for effective surveillance. The proposed system is compact, cost-effective, and easy to implement. It enhances security monitoring, reduces human effort, and provides flexible mobility for surveillance applications in homes, offices, industries, and restricted areas.
Keywords: ESP32, ESP8266, IoT, Smart Patrol Robot, Real-Time Surveillance, Flutter Application.
Keywords: ESP32, ESP8266, IoT, Smart Patrol Robot, Real-Time Surveillance, Flutter Application.
Abstract
PV Powered Charging Station for E-Vehicle
Dr N. Narmadhai, Lavanya J
DOI: 10.17148/IJIREEICE.2026.14536
Abstract: The rapid growth of electric vehicles has increased the demand for efficient and sustainable charging infrastructure across the world. In recent years, solar photovoltaic powered charging stations have emerged as an effective solution to reduce grid dependency and carbon emissions associated with conventional transportation systems. Most developed countries primarily focus on advanced charging infrastructure for passenger electric vehicles with high battery capacity and fast charging capability. The increasing adoption of electric mobility has encouraged the development of renewable energy integrated charging stations across various regions. This paper presents a detailed literature survey on electric vehicle charging infrastructure, battery specifications, charging standards, renewable energy integration, and region wise charging practices followed in Europe, Japan, China, Turkey, and India. The study also compares charging levels, battery voltage ranges, connector standards, charging power ratings, and charging duration for both two-wheeler and four-wheeler electric vehicles. Various published research works related to photovoltaic integrated charging stations, smart charging systems, and battery energy storage technologies are reviewed and analyzed. From the survey, it is identified that electric two wheelers require economical, low voltage, and decentralized charging infrastructure suitable for urban and semi urban regions. This review helps researchers understand the present development of renewable energy based charging infrastructure and highlights future directions for sustainable electric mobility and smart charging applications.
Keywords: Electric Vehicle, Solar Photovoltaic, Charging Infrastructure, Electric Two-Wheeler, AC Charging, Renewable Energy, Battery Energy Storage.
Keywords: Electric Vehicle, Solar Photovoltaic, Charging Infrastructure, Electric Two-Wheeler, AC Charging, Renewable Energy, Battery Energy Storage.
Abstract
Dual-Battery Reconfigurable Converter-Assisted Vehicle-to-Grid System for Adaptive Microgrid Frequency Stabilization: A Simulation Study
K. Deepak, P. Pedda Reddy, DR.K. Chithambaraiah Setty, M. Shiva Kumar
DOI: 10.17148/IJIREEICE.2026.14537
Abstract: This paper presents a unified MATLAB/Simulink co-simulation framework integrating a reconfigurable dual- battery contactor-based converter (CBC) architecture with a Vehicle-to-Grid (V2G) energy management layer within a renewable-integrated community microgrid. Unlike conventional V2G investigations that model each EV as a fixed- voltage equivalent, the proposed CBC enables real-time series/parallel reconfiguration of two 36 V battery packs to deliver up to 72 V effective bus voltage during severe under-frequency events, while an integrated voltage-balancing stage suppresses inter-pack circulating currents by 93.7%. A Fractional-Order Super-Twisting Sliding Mode Controller (FO-STSMC, α = 0.85) governs the frequency regulation outer loop with Lyapunov-guaranteed finite-time convergence, and a cascaded Multi-Variable Compensating (MVC) notch filter maintains grid-current total harmonic distortion (THD) below 2.8%. A state-of-charge (SoC)-stratified dispatch algorithm coordinates 100 EVs across five behavioural profiles over a 24-hour simulation. Across 30 Monte Carlo scenarios with randomised fleet SoC spread, load variability, and renewable irradiance, the proposed system demonstrates a 47.7% frequency nadir improvement and 55.7% recovery time reduction versus the no-V2G baseline under worst-case disturbance conditions, with zero traction SoC violations and 18.7% daily diesel fuel saving.
Keywords: Contactor-based converter; dual-battery EV; fractional-order super-twisting SMC; microgrid frequency regulation; multi-variable compensating filter; SoC management; V2G aggregator; vehicle-to-grid.
Keywords: Contactor-based converter; dual-battery EV; fractional-order super-twisting SMC; microgrid frequency regulation; multi-variable compensating filter; SoC management; V2G aggregator; vehicle-to-grid.
Abstract
Attention-Augmented Recurrent Deep Q-Network with Adaptive CLLC Resonant Converter for Intelligent Multi-Source EV Fast Charging and Hybrid Thermoelectric-PCM Thermal Management: A Comprehensive Simulation Study
G. Mahesh, M. Shiva Kumar, Dr K. Chithambaraiah Setty, P. Pedda Reddy
DOI: 10.17148/IJIREEICE.2026.14538
Abstract: This paper presents a simulation-based investigation of an Attention-Augmented Recurrent Deep Q-Network (A-RDQN) for energy management, executed on an Adaptive Capacitive-LLC (ACLLC) bidirectional resonant converter within an isolated renewable DC microgrid. This microgrid can power three fast-charging stations for electric vehicles (EVs) at the same time. The A-RDQN uses a four-head self-attention mechanism in a bidirectional LSTM (BiLSTM) encoder to model long-term temporal patterns in the variable profiles of photovoltaic (PV) and wind energy generation. This lets it look ahead 12 seconds, which purely reactive controllers can't do. A cascaded Hybrid Thermoelectric Cooler- Phase Change Material (HTC-PCM) system takes care of battery thermal safety. It uses a shared reward function and a two-layer thermal prediction sub-network to optimize the current and coolant flow rate of the Peltier module at the same time. In four different dynamic scenarios, MATLAB R2024a/Simulink-PLECS co-simulations show that the A-RDQN can restore the battery's state of charge (SoC) from 20% to 92% in just 710 seconds. It also keeps the peak cell temperature at 41 °C, limits total harmonic distortion (THD) to 0.68%, and achieves a bus voltage settling time of 0.019 seconds. It does better than FCS-MPC and ANN-based controllers in every metric that was tested. The ACLLC's secondary switched-capacitor bank also makes sure that Zero Voltage Switching (ZVS) happens across the 60-115% rated-load range, keeping the peak conversion efficiency above 96.8%.
Keywords: Adaptive CLLC resonant converter, attention mechanism, battery thermal management, deep reinforcement learning, dueling DQN, EV fast charging, hybrid thermoelectric-PCM, LSTM, model predictive control, renewable DC microgrid, and V2G.
Keywords: Adaptive CLLC resonant converter, attention mechanism, battery thermal management, deep reinforcement learning, dueling DQN, EV fast charging, hybrid thermoelectric-PCM, LSTM, model predictive control, renewable DC microgrid, and V2G.
Abstract
FOPID-Controlled Hybrid Transformerless PV Inverter for Single-Phase Grid Integration with Improved Leakage Current Suppression and Reactive Power Support
P. Ramaiah, Syed Saheb, Dr. K. Chithambaraiah Setty
DOI: 10.17148/IJIREEICE.2026.14539
Abstract: This study uses simulations to test a better control method for a nine-switch hybrid transformerless single- phase photovoltaic (PV) inverter that is meant to work with the grid. The proposed inverter design puts together DC-side decoupling, AC-side isolation, and middle voltage clamping into one structure that works together. In all scenarios, this configuration preserves the common-mode voltage at half of the DC-link voltage. Because of this, the leakage current is substantially lower, with a maximum value of 5 mA, which is around 11 to 22 times lower than what is seen in many well-known transformerless inverter topologies. A four-layer hierarchical control structure is employed to make both the dynamic response and the steady-state accuracy better. At the primary level, finite control set model predictive control (FCS-MPC) makes sure that the grid current is tracked accurately. A fractional-order super-twisting sliding mode controller (FO-STSMC) with a fractional order of 0.87 makes this even stronger. It is incredibly stable even when things go wrong or the system is unsure. A two-stage multi-variable compensating notch filter in a cascade is used to get rid of harmonics. It gets rid of dominant harmonics at 100 Hz and 150 Hz by more than 44 dB. A fractional-order PID (FOPID) controller also acts as the outer supervisory layer and allows you adjust the system with five different parameters. Simulation experiments run in MATLAB/Simulink show that the system works very well, with a grid current total harmonic distortion (THD) of 0.30%, a quick transient response, and the ability to inject reactive power effectively. The system is also compliant with IEEE 1547-2018 and EN 50549, which means it is ready for deployment in the real world.
Keywords: Fractional-order PID, fractional-order super-twisting sliding mode control, leakage current suppression, model predictive control, multi-variable compensating filter, reactive power injection, transformerless PV inverter, VtCM consistency.
Keywords: Fractional-order PID, fractional-order super-twisting sliding mode control, leakage current suppression, model predictive control, multi-variable compensating filter, reactive power injection, transformerless PV inverter, VtCM consistency.
Abstract
Intelligent superconducting DC–DC double-boost converter for ultra-fast EV charging, featuring liquid hydrogen cooling and AI-based PWM Control
S. Dhanush, Undyal Amear Qurashi, DR.K. Chithambaraiah Setty, P. Pedda Reddy
DOI: 10.17148/IJIREEICE.2026.14540
Abstract: The global push for electric road transport requires a major change in charging infrastructure, especially in terms of throughput, conversion efficiency, and operational scalability. This paper formulates, mathematically delineates, and empirically substantiates an Artificially Intelligent Superconducting DC–DC Double-Boost Converter (AI-SBC) designed for rapid battery electric vehicle (BEV) charging. The converter uses high-temperature superconducting (HTS) Bi₂Sr₂Ca₂Cu₃Oₓ (Bi2223) and magnesium diboride (MgB₂) windings as inductive elements that don't lose energy. Each winding is kept at cryogenic temperature by a closed-loop liquid hydrogen (LH₂) thermosiphon. A new fuzzy-logic duty- cycle modulator with an embedded Ripple-Frequency Optimizer (RFO) changes the switching frequency in real time to reduce inductor current ripple while also controlling CC and CV charging modes. State-space averaging makes a small- signal model that is easy to use and from which stability margins and frequency-domain characteristics can be found. When compared to a copper-coil baseline in MATLAB/Simulink studies, both types of superconductors show that the MgB₂ configuration has an electrical conversion efficiency of 95.8% at 15 kW, which is 10–17 percentage points better than the copper reference. The Bi2223 design gets 93.2% in the same conditions. Through smart frequency modulation, the total harmonic distortion of the inductor current drops by 34% compared to the fixed-frequency baseline. The time it takes to recover from a 50% load step goes from 95 ms with a standard PI regulator to 38 ms with the proposed FLC. The architecture is a scalable, net-zero-aligned way for next-generation ultra-fast EV charging to go from 15 kW to several hundred kilowatts.
Keywords: Battery electric vehicle (BEV) charging, Bi2223 superconducting coil, DC–DC double-boost converter, fuzzy logic control, liquid hydrogen cooling, MgB₂ superconductor, superconducting boost converter (SBC), and ultra- fast charging.
Keywords: Battery electric vehicle (BEV) charging, Bi2223 superconducting coil, DC–DC double-boost converter, fuzzy logic control, liquid hydrogen cooling, MgB₂ superconductor, superconducting boost converter (SBC), and ultra- fast charging.
Abstract
Improved Power Stability in Solar-Powered Multi-Port EV Charging Systems via FCS-MPC-Driven Virtual Synchronous Generator Control
D.Vinay, DR.K. Chithambaraiah Setty, P. Pedda Reddy, M. Shiva Kumar
DOI: 10.17148/IJIREEICE.2026.14541
Abstract: This study introduces an innovative control architecture for a three-port, 5 kW solar-integrated bidirectional electric vehicle charging platform, referred to as FCS-MPC-VSG-FO. A Finite Control Set Model Predictive Controller (FCS-MPC) is combined with a Virtual Synchronous Generator (VSG) emulation layer. This is made even stronger by a Fractional-Order Super-Twisting Sliding Mode Controller (FO-STSMC, α = 0.85) and a Multi-Variable Compensating (MVC) notch filter. The unified architecture fixes problems with poor transient recovery, grid-frequency susceptibility, and poor current quality when parameters are not known. The VSG layer acts like virtual inertia with Jvsg = 0.12 kg·m² and damping Dv = 18 N·m·s/rad. It keeps the frequency in check without any extra hardware. The PV boost stage is controlled by an Adaptive Variable-Step P&O (AVSP&O) algorithm that converges to the MPP in less than 15 ms and has an efficiency of 99.1%. Nine Lyapunov energy-balance requirements ensure stability in a closed loop. The findings from MATLAB/Simulink show that the grid current THD is less than 2.1%, the DC-bus variation is within ±1.4%, and the overall weighted efficiency is 96.8%. Comparative benchmarking against seven PI-based techniques shows that this one is better at transient responsiveness, harmonic rejection, and providing extra grid services.
Keywords: Adaptive MPPT, bidirectional EV charger, FCS-MPC, fractional-order super-twisting SMC, virtual synchronous generator, Lyapunov stability, harmonic filter, and photovoltaic integration.
Keywords: Adaptive MPPT, bidirectional EV charger, FCS-MPC, fractional-order super-twisting SMC, virtual synchronous generator, Lyapunov stability, harmonic filter, and photovoltaic integration.
Abstract
AN INTELLIGENT SEPSIS PREDICTION AND PATIENT MONITORING SYSTEM USING MACHINE LEARNING
Dr. T. Amalraj Victoire, V. Swetha
DOI: 10.17148/IJIREEICE.2026.14542
Abstract: In critical care settings, early detection of medical emergencies plays a crucial role. Traditional methods often rely on healthcare professionals manually observing patients, which can lead to the oversight of early signs of declining health. This study introduces a real-time monitoring system that utilizes machine learning to analyze essential physiological data and identify potential health risks before they deteriorate. The system focuses on patients with sepsis and monitors key vital signs, including heart rate, oxygen saturation, body temperature, respiratory rate, systolic blood pressure, and diastolic blood pressure. We employed various machine learning algorithms, including Logistic Regression, Decision Tree, and Random Forest, to effectively assess the risk of sepsis. Careful data preprocessing and feature selection significantly enhanced the performance of the models. Among the models tested, the Random Forest classifier achieved the highest accuracy. Additionally, we integrated the trained model into an easy-to-use dashboard built on Streamlit, enabling real-time patient monitoring, anomaly detection, and comprehensive risk analysis. This system is designed to assist healthcare professionals in promptly addressing hidden medical emergencies, thereby improving clinical decision-making. Furthermore, SHapley Additive Explanations (SHAP) analysis was used to explain the model predictions, enhancing transparency and trust.
Keywords: Machine Learning, Sepsis Prediction, Healthcare Monitoring, Random Forest, Data Analytics, Streamlit Dashboard
Keywords: Machine Learning, Sepsis Prediction, Healthcare Monitoring, Random Forest, Data Analytics, Streamlit Dashboard
Abstract
Fractional-Order Super-Twisting Sliding Mode Control of a Solar PV-Integrated Three-Phase EV Charging System with Adaptive Sparse Kernel Filtering for Seamless Grid Integration
M. Maheswari, E. Ravi Teja, DR.K. Chithambaraiah Setty
DOI: 10.17148/IJIREEICE.2026.14543
Abstract: This research presents a three-phase solar photovoltaic grid-interfaced electric vehicle battery management system (3Ph-SPV-GEVBC) regulated by two synergistic control algorithms. The main control architecture uses a Fractional-Order Super-Twisting Sliding Mode Controller (FO-STSMC) and an Adaptive Gain Nonlinear Extended State Disturbance Observer (AGNESDO) to keep the DC-link voltage and EV battery current in check when power flows in both directions. The secondary framework uses an Adaptive Sparse Kernel Maximum Versoria Criterion (ASKMVC) filter to get three-phase fundamental load-current references from a grid that isn't working properly. The FOSTSMC gets rid of the high-frequency chattering that is common in first-order sliding mode implementations, but it still converges in a finite amount of time in both grid-to-vehicle (G2V) and vehicle-to-grid (V2G) modes. AGNESDO is better than other fixed-gain observers like LIDO, SOSMDO, and LESDO at limiting DC-link transient undershoot and speeding up recovery after a disturbance. The ASK-MVC filter combines a Versoria-kernel loss function with bias-compensated sparsity regularization. This makes it faster to converge and have a lower mean-square error than synchronous reference frame (SRF), LMS, NLMS, NMCC, and SABCAF techniques when there is non-Gaussian impulsive noise, which is common in real-world distribution feeders. The Modified Variable Step-Size Incremental Conductance (MVSI-InC) system allows for maximum power point tracking that is stable even when there is partial shade. The hardware layout has two levels: a DC-DC boost converter for connecting to solar systems and a threephase two-level voltage source converter (VSC) that also works as a DSTATCOM. This setup connects a 5 kW SPV array, a 7.2 kWh LiFePO₄ battery pack, and a 415 V three-phase utility grid. Comprehensive MATLAB/Simulink validation across four operating scenarios, along with hardware prototype experiments, confirm a grid current total harmonic distortion (THD) of 2.41% in V2G mode and 2.87% in G2V mode, both of which meet IEEE 519-2022 limits. The system also runs with a unity power factor and a DC-link undershoot below 3.2% under AGNESDO guidance.
Keywords: Adaptive filtering, battery energy storage, disturbance observer, electric vehicle, FO-STSMC, grid integration, maximum power point tracking, power quality, sliding mode control, solar photovoltaic, ASK-MVC filter, V2G/G2V operation.
Keywords: Adaptive filtering, battery energy storage, disturbance observer, electric vehicle, FO-STSMC, grid integration, maximum power point tracking, power quality, sliding mode control, solar photovoltaic, ASK-MVC filter, V2G/G2V operation.
Abstract
Design and Development of Magnetic levitation Vehicle
Mrs. Anusha M K, Mrs. Sushmitha K R, Bhumika P P, Pavan Kumar K Y, Varun D S, Bhagyashree
DOI: 10.17148/IJIREEICE.2026.14544
Abstract: This project presents the design and development of a magnetic levitation (maglev) vehicle system that demonstrates contactless floating and electromagnetic propulsion using low-cost, accessible materials. The track consists of a 1 to 1.5 feet long linear arrangement of neodymium magnets embedded in a wooden or plastic base, providing a strong magnetic field for levitation. The vehicle is constructed from lightweight foam board, with four neodymium magnets attached to its underside arranged in a configuration that creates repulsive force against the track magnets, causing the vehicle to float approximately 2-5 mm above the track surface. The vehicle carries a NodeMCU microcontroller (ESP8266-based), a 4.2V lithium-ion battery (single cell), two electronic speed controllers (ESCs), and two sets of copper armature coils. The NodeMCU hosts a direct web page that users connect to via Wi-Fi, eliminating the need for an external router or internet connection. From this web page, the user sends control commands (forward, stop, reverse) to the NodeMCU. Upon receiving a forward command, the NodeMCU activates the coils, which are placed strategically above the track. When energized, these copper armature coils generate an electromagnetic field that interacts with the permanent magnets on the vehicle, producing a Lorentz force that propels the vehicle along the track. Two ESCs independently control the two coil sets, allowing for directional control and speed variation. The system is powered entirely by the onboard 4.2V Li-ion battery, which supplies the NodeMCU, ESCs, and coils. This project provides an educational, low-cost, and visually engaging demonstration of maglev principles, electromagnetic propulsion, and IoT- based web control.
Abstract
IoT-Based Real-Time Industrial Machine Health Monitoring and Automated Control System Utilizing ESP32 Microcontroller
Dr.S.Karthigailakshmi, M Balaji, G Bhuvaneshwaran, P Aswin Balaji
DOI: 10.17148/IJIREEICE.2026.14545
Abstract: Contemporary industrial environments demand continuous and intelligent supervision of operational equipment to ensure uninterrupted productivity, personnel safety, and optimized resource utilization. Conventional maintenance paradigms, which rely predominantly on periodic manual inspection, have proven inadequate for the dynamic and high-throughput demands of modern manufacturing facilities. The emergence of Internet of Things (IoT) technology has catalyzed a fundamental transformation in industrial monitoring by enabling autonomous, wireless, and real-time acquisition of critical machine parameters. This paper presents the design, development, and experimental validation of an IoT-based Industrial Machine Health Monitoring and Automated Control System constructed around the ESP32 microcontroller. The proposed architecture integrates a heterogeneous array of sensors—encompassing temperature (DHT11), vibration, smoke/gas, voltage, and current transducers—to facilitate holistic and multi- dimensional assessment of machine operational health. Sensor data is continuously acquired, digitally processed, and securely transmitted to the Blynk cloud platform via Wi-Fi, enabling remote visualization and analytics through mobile and web interfaces. A hierarchical alert and protection framework is embedded within the system, capable of triggering auditory alarms, dispatching cloud-based notifications, and executing automatic relay-controlled disconnection of machine power circuits upon detection of anomalous operating conditions. Experimental results substantiate the system's capability to deliver accurate, low-latency monitoring alongside dependable protective responses across diverse fault scenarios. The proposed solution offers a scalable, cost-effective, and energy-efficient alternative to conventional industrial monitoring approaches, with direct applicability in predictive maintenance, fault diagnostics, and smart factory integration.
Keywords: Internet of Things (IoT), ESP32 Microcontroller, Industrial Machine Health Monitoring, Predictive Maintenance, Blynk Cloud Platform, Vibration Analysis, Temperature Monitoring, Remote Condition Monitoring, Industrial Automation, Smart Factory.
Keywords: Internet of Things (IoT), ESP32 Microcontroller, Industrial Machine Health Monitoring, Predictive Maintenance, Blynk Cloud Platform, Vibration Analysis, Temperature Monitoring, Remote Condition Monitoring, Industrial Automation, Smart Factory.
Abstract
AI Based Job and Internship Recommendation System (Jobify)
Arti Jaibhai, Geetesh Nasare, Soham Daundkar, Srujan Pagar, Sanket Jadhav
DOI: 10.17148/IJIREEICE.2026.14546
Abstract: Jobify is a job and internship recommendation system that makes looking for a job simple for people. When people use websites they usually search by keywords or use filters and they get a lot of results that are not very helpful. So people spend a lot of time looking at options without really knowing which ones are a good fit for them. Jobify tries to understand what people are looking for in a practical way by looking at what is in their resume. It uses computer techniques to find important information like skills, education and work experience. Then it compares this information to what the job requires and suggests jobs that're a good match for the person not just because of a few keywords.
Jobify does more than just suggest jobs. It also helps people understand where they are now. It shows them what skills they are missing and what companies are looking for. It tells them what they need to do to get better. So Jobify is not just good for looking for jobs it is also good for getting ready for jobs. Overall Jobify will make looking for a job easier. Suggest better jobs for you. It will do this by using a bit of information in a way that helps you and it will make it easier for you to move forward at your own pace. Jobify is a job and internship recommendation system that will save you time and effort. It will help you find a job that's a good fit for you and it will make the whole process easier. You will be able to use Jobify to look for jobs and to get ready for jobs. It will make a big difference. The Jobify system is designed to make things easy for you. It will help you achieve your goals. You can use Jobify to look for jobs. You can also use it to get ready for jobs and it will be very helpful. Jobify is a tool, for anyone who is looking for a job and it will make the process easier and more simple.
Keywords: Job Recommendation System, Natural Language Processing, Machine Learning, Resume Parsing, Cosine Similarity, Skill Gap Analysis, AI Chatbot
Jobify does more than just suggest jobs. It also helps people understand where they are now. It shows them what skills they are missing and what companies are looking for. It tells them what they need to do to get better. So Jobify is not just good for looking for jobs it is also good for getting ready for jobs. Overall Jobify will make looking for a job easier. Suggest better jobs for you. It will do this by using a bit of information in a way that helps you and it will make it easier for you to move forward at your own pace. Jobify is a job and internship recommendation system that will save you time and effort. It will help you find a job that's a good fit for you and it will make the whole process easier. You will be able to use Jobify to look for jobs and to get ready for jobs. It will make a big difference. The Jobify system is designed to make things easy for you. It will help you achieve your goals. You can use Jobify to look for jobs. You can also use it to get ready for jobs and it will be very helpful. Jobify is a tool, for anyone who is looking for a job and it will make the process easier and more simple.
Keywords: Job Recommendation System, Natural Language Processing, Machine Learning, Resume Parsing, Cosine Similarity, Skill Gap Analysis, AI Chatbot
Abstract
A New Multi-Output Dc-Dc Converter for Electric Vehicle Application
K. Rakesh, Mr. Syed Saheb, Dr. K. Chithambaraiah Setty
DOI: 10.17148/IJIREEICE.2026.14547
Abstract: Multiport converters play a significant role in portable electronic and electric vehicle (EV) applications. In literature, different configurations of single-input multi-output (SIMO) converters are presented. Most of the SIMO converters generate the outputs with operating constraints on the duty ratio and charging of inductors. The cross- regulation problem is still a challenge in SIMO converters design. A SIMO topology is proposed in this study to overcome the limitations mentioned earlier. It can generate three different output voltages without constraint on the duty cycle and inductor currents. Cross regulation problems do not exist in the proposed topology, so the load voltage is not affected by the variation of output current. The loads are isolated from each other during control. In the laboratory, a 200 W prototype circuit is developed; simulation and experimental results are validated.
Keywords: Multiport converters, single input multi output converters, electric vehicles, DC-DC converter, cross- regulation.
Keywords: Multiport converters, single input multi output converters, electric vehicles, DC-DC converter, cross- regulation.
Abstract
A Review of Modern Antenna Design Approaches for 5G, 6G, IoT, and Satellite Applications
Karthigai Lakshmi S, Arun Kumar K S, Yuvasri S, Santhiya S, Sudha Sree T
DOI: 10.17148/IJIREEICE.2026.14548
Abstract: Modern antenna design has advanced with the integration of machine learning, innovative materials, and new fabrication techniques. This paper reviews various antenna design approaches for next-generation systems such as 5G, 6G, IoT, and satellite communication. It covers structures like patch, fractal, flexible, and array antennas, along with machine learning-based optimization methods. The study highlights improvements in performance parameters such as gain, bandwidth, and efficiency, while reducing design complexity. Overall, this review provides insights into current developments and future directions in antenna design.
Keywords: Antenna Design, Machine Learning, 5G, 6G, IoT, Satellite Communication, Fractal Antenna.
Keywords: Antenna Design, Machine Learning, 5G, 6G, IoT, Satellite Communication, Fractal Antenna.
Abstract
IoT Based Solar and Piezoelectric Energy Generation System
Dr. Sunil Kumar C, Amulya K M, Harshitha R, Janhavi B G, Keerthana H S
DOI: 10.17148/IJIREEICE.2026.14549
Abstract: This project presents a Hybrid Energy Harvesting System that generates electrical energy using both a solar panel and piezoelectric discs for continuous power generation under different weather conditions. The 12V solar panel harvests solar energy during sunny conditions, while piezoelectric discs convert raindrop impact into electrical energy during rainfall. The AC output from the piezoelectric discs is converted into DC using bridge rectifiers and combined with the solar output to charge a 3-cell lithium-ion battery through a buck converter. An ESP32 microcontroller monitors the battery voltage using a voltage sensor and uploads real-time data to the ThingSpeak cloud platform for remote monitoring and analysis. The system also includes a secondary buck converter to power the ESP32 and a 12V LED bulb as a load. This project provides a low-cost, eco-friendly, and IoT-based solution for efficient renewable energy harvesting and smart energy monitoring.
Abstract
The Role of Decision Making in attaining Sustainability in Industrial and Manufacturing Fields: PCIA Based Approach
Somnath Singroul*, Chhatrapal Dewangan, Atul Kumar Sahu, Nitin Kumar Sahu
DOI: 10.17148/IJIREEICE.2026.14550
Abstract: In this study, decision making tools in the form of Plan, Control, Improve, Act (PCIA) based approach is discussed for attaining sustainability in diverse application fields related to Industrial and manufacturing filed. The developed techniques can help decision makers in attaining appropriate insights and decision tactics. This study examines how decision-making frameworks based on PCIA can support sustainability objectives across diverse industrial sectors. Modern industries face complex sustainability challenges high emissions, resource depletion, waste generation and social impacts. Iterative management cycles and data-driven decision tools help organizations navigate these challenges. We outline a PCIA methodology where Plan entails setting sustainability goals and strategies, Control involves continuous monitoring of key indicators, Improve focuses on process optimization and Act implements corrective measures and institutional learning.
This PCIA approach is applied to case studies in beverage, oil, cement, steel, furniture manufacturing and academic institutions. Beverage sector, planning may target renewable energy and packaging reduction, control uses sensor data for waste and water use and improvement implements circular-economy practices. In the cement and steel industries, PCIA guides low-carbon material choices and process innovation as stakeholders evaluate trade-offs via Life cycle assessment (LCA) and multi-criteria decision analysis. The study demonstrates that combining lean principles and digital tools within PCIA yields better decisions for environmental performance. Our findings suggest that a structured PCIA cycle integrating planning, monitoring, continuous improvement and decisive action can help managers systematically align industrial operations with sustainability targets. Ultimately, embedding PCIA fosters transparency, accountability and continuous learning in organizational sustainability strategy, advancing long-term resilience and competitiveness.
Keywords: Decision making, Industrial and manufacturing, sustainability, Life cycle assessment, critical thinking.
This PCIA approach is applied to case studies in beverage, oil, cement, steel, furniture manufacturing and academic institutions. Beverage sector, planning may target renewable energy and packaging reduction, control uses sensor data for waste and water use and improvement implements circular-economy practices. In the cement and steel industries, PCIA guides low-carbon material choices and process innovation as stakeholders evaluate trade-offs via Life cycle assessment (LCA) and multi-criteria decision analysis. The study demonstrates that combining lean principles and digital tools within PCIA yields better decisions for environmental performance. Our findings suggest that a structured PCIA cycle integrating planning, monitoring, continuous improvement and decisive action can help managers systematically align industrial operations with sustainability targets. Ultimately, embedding PCIA fosters transparency, accountability and continuous learning in organizational sustainability strategy, advancing long-term resilience and competitiveness.
Keywords: Decision making, Industrial and manufacturing, sustainability, Life cycle assessment, critical thinking.
Abstract
Smart Solar Plant Optimization Using MPPT Controller and IoT Alert System
Ms. Komal M. Avhad, Mr. Somnath S. Hadpe, Dr. Sridhar S. Khule, Mr. Bhagwan S. Bodke
DOI: 10.17148/IJIREEICE.2026.14551
Abstract: This paper presents a smart solar monitoring and predictive maintenance system that improves the efficiency and reliability of photovoltaic systems using IoT and sensor integration with an MPPT charge controller. The system is based on an Atmega328 microcontroller and uses sensors to measure voltage, current, sunlight intensity, dust levels, and vibrations. Real-time data is transmitted through an ESP8266 Wi-Fi module to the ThingSpeak cloud for remote analysis. By comparing actual power output with expected values, the system detects performance issues such as shading, dust accumulation, or faults. It also predicts cleaning needs and identifies potential physical damage or disturbances. The system is powered by a solar PV panel integrated with and Maximum Power Point Tracking (MPPT)-based charge controller and battery backup to ensure reliable and uninterrupted operation. The implemented MPPT controller continuously tracks the maximum power point of the solar panel to maximize energy extraction and improve charging efficiency. The proposed solution minimizes manual maintenance requirements while providing an efficient, economical, and scalable approach for intelligent solar energy management.
Keywords: Solar Panel Monitoring, IoT-based System, Predictive Maintenance, ThingSpeak Cloud, MPPT Charge Controller, Renewable Energy, Real-time Data, and Remote Monitoring.
Keywords: Solar Panel Monitoring, IoT-based System, Predictive Maintenance, ThingSpeak Cloud, MPPT Charge Controller, Renewable Energy, Real-time Data, and Remote Monitoring.
Abstract
Multi-Agent Deep Reinforcement Learning- Based Autonomous Control of Grid-Forming Inverters in Renewable-Dominated Power Systems
Prof. Suyog Sangharatna Dhoke, Mahesh P. Ingle, Shruti S. Burande, Gunjan R. Lakde
DOI: 10.17148/IJIREEICE.2026.14552
Abstract: The rapid transition towards renewable energy sources such as solar and wind power has significantly transformed modern power systems. However, the large-scale integration of these intermittent and stochastic energy sources introduces severe challenges related to grid stability, frequency regulation, voltage control, and power quality. Traditional power systems rely on synchronous generators to provide inertia and maintain system stability, but with increasing penetration of inverter-based renewable sources, this inherent stability is gradually decreasing.
Grid-Forming Inverters (GFIs) have emerged as a promising solution to address these challenges by emulating the behavior of conventional synchronous machines. These inverters are capable of regulating voltage and frequency while supporting grid stability under dynamic conditions. However, conventional control strategies such as droop control, PI/PID controllers, and model-based techniques exhibit limitations in handling highly nonlinear, uncertain, and time- varying operating conditions.
In this context, Artificial Intelligence (AI), particularly Deep Reinforcement Learning (DRL), provides a powerful framework for developing adaptive and intelligent control strategies. This paper proposes a Multi-Agent Deep Reinforcement Learning (MADRL)-based autonomous control approach for grid-forming inverters in renewable- dominated power systems. In the proposed framework, each inverter operates as an independent intelligent agent that learns optimal control actions through continuous interaction with the environment.
The multi-agent structure enables decentralized control, coordination among multiple inverters, and scalability for large power systems. The proposed approach enhances voltage stability, frequency regulation, power sharing, and fault tolerance under varying operating conditions. Simulation results demonstrate that the MADRL-based control significantly outperforms conventional methods in terms of dynamic response, stability margins, and overall system efficiency.
Keywords: Grid-Forming Inverter, Deep Reinforcement Learning, Multi-Agent Systems, Smart Grid, Renewable Energy Integration, Autonomous Control, Power System Stability.
Grid-Forming Inverters (GFIs) have emerged as a promising solution to address these challenges by emulating the behavior of conventional synchronous machines. These inverters are capable of regulating voltage and frequency while supporting grid stability under dynamic conditions. However, conventional control strategies such as droop control, PI/PID controllers, and model-based techniques exhibit limitations in handling highly nonlinear, uncertain, and time- varying operating conditions.
In this context, Artificial Intelligence (AI), particularly Deep Reinforcement Learning (DRL), provides a powerful framework for developing adaptive and intelligent control strategies. This paper proposes a Multi-Agent Deep Reinforcement Learning (MADRL)-based autonomous control approach for grid-forming inverters in renewable- dominated power systems. In the proposed framework, each inverter operates as an independent intelligent agent that learns optimal control actions through continuous interaction with the environment.
The multi-agent structure enables decentralized control, coordination among multiple inverters, and scalability for large power systems. The proposed approach enhances voltage stability, frequency regulation, power sharing, and fault tolerance under varying operating conditions. Simulation results demonstrate that the MADRL-based control significantly outperforms conventional methods in terms of dynamic response, stability margins, and overall system efficiency.
Keywords: Grid-Forming Inverter, Deep Reinforcement Learning, Multi-Agent Systems, Smart Grid, Renewable Energy Integration, Autonomous Control, Power System Stability.
Abstract
AI BASED ONLINE QUIZ EVALUATOR
T. Amalraj Victoire, Vanmathe V
DOI: 10.17148/IJIREEICE.2026.14553
Abstract: Artificial intelligence (AI) has immensely influenced the field of education through automation, customization, and decision-making capabilities. Some of its important usages are online exam evaluation, which helps increase efficiency and improve accuracy. This research paper attempts to describe an AI-enabled online test examiner that helps automate test administration, test answering, and feedback. ML algorithms along with NLP techniques will be incorporated into this AI-powered system to conduct tests and analyze test answers in a timely manner. This AI system will automatically assess student test answers, saving time and effort, avoiding any kind of human error or bias in marking tests. Such a scalable solution will be able to efficiently manage several users at once. Besides being highly effective in the learning process, this tool will make the learning experience better by allowing for immediate feedback to be generated in order to help students realize what topics they should work on more.
Keywords: Artificial Intelligence, Online Quiz, Machine Learning, Natural Language Processing, Automated Evaluation
Keywords: Artificial Intelligence, Online Quiz, Machine Learning, Natural Language Processing, Automated Evaluation
Abstract
Smart Laser Repellent Technology for Sustainable Farming
Akash K, Srividya C N, Dr. Manoj Kumar S B, Yogeshwari D, Sinchana A P
DOI: 10.17148/IJIREEICE.2026.14554
Abstract: Agriculture is one of the most important sectors supporting the economy and food production worldwide. However, crop damage caused by birds, wild animals, and intruders significantly reduces agricultural productivity and farmer income. Traditional crop protection methods such as fencing, scarecrows, and manual monitoring are inefficient, labor-intensive, and environmentally harmful. This paper proposes an IoT-based Smart Laser Repellent System for Sustainable Farming that automatically detects and repels birds and animals using laser technology and intelligent monitoring systems. The proposed system uses PIR sensors, ESP32 microcontroller, laser modules, and servo motors to detect movement and activate a rotating laser beam for repelling intrusions without causing physical harm. IoT connectivity enables real-time monitoring and mobile notifications to farmers. The system provides an eco- friendly, cost-effective, energy-efficient, and sustainable solution for modern smart agriculture applications. Experimental analysis demonstrates improved crop protection efficiency, reduced labor dependency, and minimized environmental impact compared to traditional farming protection methods.
Keywords: Smart Farming, Sustainable Agriculture, IoT, Laser Repellent System, ESP32, Crop Protection, Precision Agriculture, Wildlife Detection.
Keywords: Smart Farming, Sustainable Agriculture, IoT, Laser Repellent System, ESP32, Crop Protection, Precision Agriculture, Wildlife Detection.
Abstract
Portable Solar & Wind Laptop & Mobile Charging System
Prof. P. K. Digge, Sneha Babulal Gaikwad, Priyanka Sanjay Bhadange, Vaishnavi Vishwas Kale, Arpita Subhash Inamdar
DOI: 10.17148/IJIREEICE.2026.14555
Abstract: The Portable Solar & Wind Laptop & Mobile Charging System is a hybrid renewable-energy-based project designed to provide reliable and sustainable off-grid power for charging electronic devices such as laptops, mobile phones, tablets, and USB gadgets. The system combines solar and wind energy sources to generate electricity and store it in a rechargeable battery for continuous operation. Two 12V solar panels are used as the primary energy source, while a small wind turbine acts as a secondary power source during low sunlight conditions. The generated energy is regulated using a charge controller and stored in a 12V battery, ensuring stable and efficient power management. The system is capable of supplying both AC and DC outputs. A 100W inverter circuit with a step-up transformer is used to provide AC power for laptop charging, while regulated USB modules supply safe 5V DC output for mobile devices and other USBpowered equipment. An Arduino Nano-based intelligent control system enhances the functionality of the project by providing password-protected access, automatic charging control, and user-configurable charging duration. A 16×4 LCD display shows real-time system information such as battery voltage, charging status, and active power source. The charging station also includes relay-based automatic output cutoff and buzzer alerts for safety and efficient energy utilization. Due to its portable design and renewable energy integration, the system is suitable for rural areas, outdoor activities, emergency applications, and eco-friendly portable power solutions.
Keywords: Hybrid Renewable Energy System, 2. Solar Energy, 3. Wind Energy, 4. Portable Charging Station, 5.
Keywords: Hybrid Renewable Energy System, 2. Solar Energy, 3. Wind Energy, 4. Portable Charging Station, 5.
Abstract
Sign Language Translator
Swetha B, Halima Sadiya, Mangala F K, Sudarshan M K, T E Jyothi Theertha
DOI: 10.17148/IJIREEICE.2026.14556
Abstract: The Sign Language Translator Glove is an assistive wearable system designed to bridge the communication gap between speech- and hearing-impaired individuals and the general public. The system uses six flex sensors, three placed on each hand, to measure finger-bending patterns. These analog signals are converted into numerical values through an Arduino Mega microcontroller. Each sensor reading is compared against predefined thresholds to map finger states to specific gestures. The Arduino processes these patterns and displays the recognized gesture on an OLED screen. When a valid gesture is confirmed, the system outputs the corresponding text and audio message, enabling real-time gesture-to-text and speech translation.
Keywords: Sign Language Translator, Flex Sensors, Arduino Mega, Gesture Recognition, Assistive Technology.
Keywords: Sign Language Translator, Flex Sensors, Arduino Mega, Gesture Recognition, Assistive Technology.
Abstract
PRISM-X ->3D PRINTED PERSONALIZED REHAB INTELLIGENT SMART MOTION EXOSKELETON
Apoorva H M, Naveen B, Bhanu Prakash, Prabhavathi, Greeshma C
DOI: 10.17148/IJIREEICE.2026.14557
Abstract: Modern rehabilitation systems often struggle to provide individualized therapy that adapts to the unique recovery patterns of patients with motor impairments. To address this limitation, this paper presents PRISM-X, a smart rehabilitation exoskeleton designed using a combination of artificial intelligence, biosignal acquisition, and additive manufacturing. The system captures real-time physiological signals such as electromyography (EMG), motion data, and applied force, enabling accurate interpretation of user intent.
Unlike conventional rehabilitation devices, the proposed system dynamically adjusts its level of assistance based on continuous learning from patient-specific data. The use of 3D printing enables the development of a lightweight and customizable structure tailored to individual anatomical requirements. A closed-loop feedback mechanism ensures precise motion assistance while maintaining safety and comfort. The integration of intelligent control algorithms with wearable robotics enhances therapy effectiveness, reduces reliance on manual supervision, and supports remote monitoring capabilities. The proposed approach demonstrates improved adaptability, user engagement, and rehabilitation efficiency, making it a promising solution for next-generation assistive healthcare technologies.
Keywords: Exoskeleton, Rehabilitation Engineering, Electromyography (EMG), Artificial Intelligence, Wearable Robotics, 3D Printing, Human–Machine Interaction, Assistive Technology.
Unlike conventional rehabilitation devices, the proposed system dynamically adjusts its level of assistance based on continuous learning from patient-specific data. The use of 3D printing enables the development of a lightweight and customizable structure tailored to individual anatomical requirements. A closed-loop feedback mechanism ensures precise motion assistance while maintaining safety and comfort. The integration of intelligent control algorithms with wearable robotics enhances therapy effectiveness, reduces reliance on manual supervision, and supports remote monitoring capabilities. The proposed approach demonstrates improved adaptability, user engagement, and rehabilitation efficiency, making it a promising solution for next-generation assistive healthcare technologies.
Keywords: Exoskeleton, Rehabilitation Engineering, Electromyography (EMG), Artificial Intelligence, Wearable Robotics, 3D Printing, Human–Machine Interaction, Assistive Technology.
Abstract
Design and Development of an ESP32-Based Autonomous Office Logistics Robot
A. Ramachandiran, M. Ashwin, M. Dharun, S. Rithick, M. Shyam
DOI: 10.17148/IJIREEICE.2026.14558
Abstract: This paper presents the design, development, and implementation of a cost-effective, autonomous indoor mobile robotic platform developed for intra-office logistics and material handling. Built around the high-performance ESP32 microcontroller, the system integrates infrared (IR) reflectance sensors to perform real-time path tracking over a predefined high-contrast indoor track. To ensure operational safety and collision avoidance within a dynamic workplace environment, a front-mounted IR obstacle detection sensor is paired with an active audible buzzer alert framework. The robot features dual-core processing capabilities and establishes stable Wi-Fi connectivity to integrate with the Blynk IoT cloud platform, allowing seamless transitioning between autonomous line-following mode and remote manual override control via a web or mobile application dashboard. Power regulation is managed through an efficient LM2596 step- down buck converter to supply steady voltage levels from a rechargeable lithium-ion battery package. Experimental evaluation demonstrates robust line detection, stable torque control via an L298N motor driver, and reliable hazard mitigation, showcasing a highly viable solution for modern automated office workflows.
Keywords: Autonomous Mobile Robot, ESP32 Microcontroller, Office Automation, Path Tracking, Internet of Things (IoT), Obstacle Avoidance.
Keywords: Autonomous Mobile Robot, ESP32 Microcontroller, Office Automation, Path Tracking, Internet of Things (IoT), Obstacle Avoidance.
Abstract
LPG Leakage Detection and Automated Prevention System Using Arduino
A. Ramachandiran, A. Adheeba Banu, K. Dhevasena, T. Pavithara, S. Sneha
DOI: 10.17148/IJIREEICE.2026.14559
Abstract: Liquefied Petroleum Gas (LPG) is widely used in domestic and industrial environments because of its high energy efficiency and affordability. However, accidental gas leakages present severe safety hazards including catastrophic fires, explosions, and suffocation. This paper presents the design and implementation of a compact, low-cost LPG leakage detection system equipped with an automatic regulator cut-off mechanism and real-time status monitoring. The proposed system employs an MQ-2 gas sensor for continuous detection of LPG concentration, an Arduino Uno microcontroller for processing sensor data, color-coded LED indicators for immediate visual status alerts, and a micro-servo motor mechanism that physically rotates the gas regulator knob to the OFF position when concentrations exceed a safe threshold. Real-time messages are provided through an integrated OLED module. The system operates reliably using a 5V supply and demonstrates quick response during testing by switching to alert mode and activating the cut-off mechanism within seconds of gas exposure, effectively removing human intervention from primary accident prevention.
Keywords: LPG leakage detection, Arduino Uno, MQ-2 gas sensor, automated safety mechanism, micro-servo motor, OLED display module, embedded systems.
Keywords: LPG leakage detection, Arduino Uno, MQ-2 gas sensor, automated safety mechanism, micro-servo motor, OLED display module, embedded systems.
Abstract
IOT-BASED AUTOMATED SOLAR PANEL CLEANING SYSTEM USING SMARTPHONES
Mr. Ajith M S, Dr. Naveen B, Mr. Kiran M S, Prabhavathi K, Ms. Megha M R
DOI: 10.17148/IJIREEICE.2026.14560
Abstract: The efficiency of solar photovoltaic (PV) panels is heavily influenced by their cleanliness and can be hampered by a loss of energy production by up to 30% due to dust and environmental debris. Traditional means of keeping PV panels clean are costly and impractical to use on large installations due to time and manpower needed. In this paper, we explore the design and development of an internet of things (IoT) based system capable of remotely controlled automated cleaning of solar panels through smartphones. The project utilizes a ESP32microcontroller alongside dust sensors and light dependent resistors (LDR). Depending on predetermined limits, the system automatically initiates a cleaning process involving a brush and water sprayed from a solenoid valve connected to the microcontroller. Internet connectivity provides a means to monitor the process in real time. Limit switches have been installed in order to control movement and avoid any damage to the equipment. The method is energy and cost effective, and is thus suitable both for household and industrial installations.
Keywords: Solar Panel Cleaning, Internet of Things (IoT), ESP32, Dust Sensor, LDR Sensor, Automated Cleaning System, Smart Monitoring, Renewable Energy, Motorized Brush, Water Spray System.
Keywords: Solar Panel Cleaning, Internet of Things (IoT), ESP32, Dust Sensor, LDR Sensor, Automated Cleaning System, Smart Monitoring, Renewable Energy, Motorized Brush, Water Spray System.
Abstract
AUTOMATIC FIRE EXTINGUISHING SYSTEM
Dr. Dilip Kumar Patel, Abhijeet Srivastava, Gaurav Kushwaha, Monu Rathaur, Utkarsh Srivastava
DOI: 10.17148/IJIREEICE.2026.14561
Abstract: Fire accidents in public places such as malls, offices, and hospitals pose serious threats to life and property. Conventional fire safety systems often result in delayed response and excessive water usage. To address these issues, this project presents an IoT-Based Smart Fire Extinguishing System using Node MCU. The system uses sensors to detect fire or abnormal temperature conditions and enables real-time decision-making through the Node MCU microcontroller. Upon detection, a relay activates the water pump while a servo motor directs water only toward the affected area, reducing unnecessary water wastage. The integration of IoT technology improves monitoring and ensures quick response during emergencies. The developed prototype demonstrates efficient fire detection, rapid suppression, and reliable operation, making it suitable for public and commercial environments as a cost-effective and automated fire safety solution.
Keywords: ESP8266, Node MCU, IoT.
Keywords: ESP8266, Node MCU, IoT.
Abstract
A Container-Orchestrated Framework for Enterprise Asset Lifecycle Management with Automated Continuous Delivery on the Cloud
ATYAM S L N S R GAYATRI SINDHU, Dr. CHIRAPARAPU SRINIVASA RAO*
DOI: 10.17148/IJIREEICE.2026.14562
Abstract: Modern enterprises manage extensive portfolios of physical and digital assets whose value, condition, and compliance status evolve continuously from acquisition through retirement. Traditional management systems are frequently built as monolithic applications hosted on fixed infrastructure, which limits scalability, complicates release cycles, and impedes timely visibility into asset state across geographically distributed operations. This study proposes a container-orchestrated framework that administers the complete lifecycle of enterprise assets while embedding automated continuous integration and delivery within its operational fabric. The methodology decomposes the system into independently deployable microservices executed on a managed container-orchestration service, coordinated through an application load balancer and supported by relational persistence, object storage, and an in-memory cache. A managed delivery pipeline automatically builds, tests, packages, and deploys service revisions, thereby shortening release latency and reducing manual intervention. The business logic is implemented in Python, while a Node.js service layer handles asynchronous client interaction. An experimental evaluation conducted under progressively increasing concurrent load demonstrates that the proposed framework sustains an average response time of approximately 198 milliseconds at moderate load and a throughput near 940 requests per second, substantially exceeding a monolithic baseline whose latency deteriorates sharply beyond four hundred concurrent users. The framework attains a scaling efficiency of about 89 percent, reduces mean deployment time to roughly four minutes, and lowers fault-recovery time by an order of magnitude. The principal contributions comprise an integrated lifecycle-management architecture, a pipeline-driven deployment model, and an empirical scalability assessment validating container orchestration for enterprise asset administration.
Keywords: Asset lifecycle management, container orchestration, microservices, continuous delivery, DevOps automation, cloud computing, scalability, enterprise systems.
Keywords: Asset lifecycle management, container orchestration, microservices, continuous delivery, DevOps automation, cloud computing, scalability, enterprise systems.
Abstract
A Cloud-Native Customer Relationship Management Platform on AWS Fargate with Automated DevOps Workflow Orchestration
Iragavarupu Sravan Sai Kumar, A.N. Rama Mani*
DOI: 10.17148/IJIREEICE.2026.14563
Abstract: Customer relationship management systems are central to modern sales and service operations, yet many remain bound to monolithic, server-bound deployments that scale poorly, cost more than necessary during idle periods, and are slow and risky to update. This paper presents the design and evaluation of a cloud-native customer relationship management platform that decomposes core business capabilities into containerized microservices executed on a serverless container runtime, removing the burden of provisioning and managing virtual machines. The backend services are implemented in Java and exposed through a load-balanced gateway, while a Node.js client delivers an interactive console for managing customers, leads, deals, and activities. Persistent records reside in a managed relational database, frequently accessed data is cached in memory, attachments are stored in object storage, and asynchronous events propagate through a managed messaging bus. The complete software lifecycle—source control, build, image creation, and deployment—is automated through a managed delivery pipeline driven by container images, and runtime telemetry feeds automatic horizontal scaling. Experimental evaluation under synthetic concurrency shows that the platform sustains a 95th-percentile response time of approximately 128 ms at 1000 concurrent users, where a monolithic baseline on a fixed virtual machine exceeds 760 ms, and that automated delivery reduced deployment lead time from roughly 80 minutes to under 10 while lowering steady-state cost. The contributions are an integrated serverless-container reference architecture for customer relationship management, a reproducible DevOps automation strategy, and a quantitative comparison demonstrating the operational and economic benefits of the proposed approach.
Keywords: Cloud-native computing; Customer relationship management; AWS Fargate; Microservices; DevOps; Continuous delivery; Containerization; Auto-scaling
Keywords: Cloud-native computing; Customer relationship management; AWS Fargate; Microservices; DevOps; Continuous delivery; Containerization; Auto-scaling
Abstract
A Deep Learning Framework for Deepfake Detection and Digital Media Protection with Explainable Forensic Verdicts and Provenance Watermarking
MULLAGIRI MARY SAROJA, KARRI LAKSHAMANA REDDY*
DOI: 10.17148/IJIREEICE.2026.14564
Abstract: The rapid democratization of generative synthesis tools has made hyper-realistic manipulated images and videos, commonly termed deepfakes, trivially easy to produce and disseminate, posing serious threats to identity, reputation, journalism, and public trust. Conventional manual verification cannot scale to the volume and sophistication of such content, and many automated detectors offer a binary label without interpretable evidence or any downstream protection of authentic media. This paper presents a deep-learning framework that not only classifies digital media as authentic or manipulated but also produces explainable forensic verdicts and applies provenance safeguards to genuine content. The proposed system fuses spatial convolutional features with frequency-domain and temporal-inconsistency cues, generates region-level manipulation heatmaps for interpretability, and embeds an invisible watermark together with a logged provenance hash for verified media. A Python back end implements model inference and forensic analysis, while a Node.js layer delivers an analyst-facing dashboard. Evaluated against handcrafted-feature and single-stream convolutional baselines, the framework attained approximately 94% accuracy and an area under the ROC curve of 0.96, with balanced precision and recall. The principal contributions are a multi-cue detection pipeline that improves robustness over single-stream models, an explainability component that surfaces where manipulation is suspected, and an integrated protection mechanism that links detection to media authentication.
Keywords: Deepfake detection; digital media forensics; convolutional neural networks; explainable AI; image and video authentication; watermarking; media provenance.
Keywords: Deepfake detection; digital media forensics; convolutional neural networks; explainable AI; image and video authentication; watermarking; media provenance.
Abstract
A Highly Available Cloud-Based Real Estate Marketplace Using Elastic Load Balancing and Multi-Zone Redundancy
YANDRA.VAISHNAVI and K. LAKSHMI SAI SRI*
DOI: 10.17148/IJIREEICE.2026.14565
Abstract: Online property marketplaces have transformed how real estate is discovered, listed, and transacted, yet the platforms that host them must withstand highly variable traffic, sustain rich media catalogues, and remain continuously available, since downtime during peak demand directly translates into lost opportunities. Conventional single-server deployments are vulnerable to overload and represent a single point of failure, while naive scaling strategies inflate cost without guaranteeing resilience. This paper presents a cloud-based real estate marketplace engineered for high availability through elastic load balancing and redundant, multi-zone deployment. The platform combines a Java service backend with a Node.js web client and distributes incoming requests across automatically scaled application instances spanning two availability zones, with a managed relational database replicated for failover and an object store and cache supporting media-rich listings. Health-checked load balancing reroutes traffic away from unhealthy instances, while auto-scaling adjusts capacity to demand. Experimental evaluation under simulated load demonstrated an average response time of 110 milliseconds at one thousand concurrent users and graceful degradation up to ten thousand, alongside a 99.98% availability score, substantially outperforming a single-server baseline whose latency and failure rate rose sharply. The principal contributions of this work are a resilient, load-balanced, multi-zone marketplace architecture, an integrated failover and auto-scaling strategy that sustains continuous service, and an empirical demonstration of superior availability, scalability, and throughput relative to conventional deployments.
Keywords: Real estate marketplace, high availability, elastic load balancing, cloud computing, auto-scaling, fault tolerance, multi-zone redundancy, web application.
Keywords: Real estate marketplace, high availability, elastic load balancing, cloud computing, auto-scaling, fault tolerance, multi-zone redundancy, web application.
Abstract
A Multi-Tenant SaaS Framework for Hostel Management Deployed on AWS Elastic Beanstalk with Elastic Auto-Scaling
KETHA DURGA, B.N. SRINIVASA GUPTA*
DOI: 10.17148/IJIREEICE.2026.14566
Abstract: The administration of residential accommodation in educational institutions and private establishments remains heavily dependent on manual registers and isolated desktop tools, an approach that scales poorly and offers little resilience. The emergence of cloud-native Software-as-a-Service (SaaS) delivery, combined with managed deployment platforms, presents an opportunity to consolidate such administration into a single elastic service shared across many independent organizations. This paper proposes a multi-tenant SaaS framework for hostel management that isolates the data of each tenant within a shared infrastructure while provisioning, scaling, and monitoring are delegated to a managed cloud platform. The system adopts a shared-database, shared-schema tenancy model with a tenant identifier propagated through every request, enforced by an authentication and role-based access-control layer. The application couples a Node.js and React presentation layer with a Python service tier and a relational data store, and is deployed through AWS Elastic Beanstalk behind a load balancer with horizontal auto-scaling. Experimental evaluation under synthetic concurrent load demonstrated that the auto-scaled deployment sustained an average response time below 460 ms at 1,200 concurrent users, where a single-instance baseline degraded beyond 3,600 ms, and peak throughput rose from 320 to 1,480 requests per second. The principal contributions are a pragmatic tenant-isolation design, an automated elastic deployment pipeline, and an empirical characterization of scalability and cost-efficiency for institutional accommodation management.
Keywords: Multi-Tenancy, Software-as-a-Service, Cloud Computing, AWS Elastic Beanstalk, Auto-Scaling, Hostel Management, Role-Based Access Control, Web Application
Keywords: Multi-Tenancy, Software-as-a-Service, Cloud Computing, AWS Elastic Beanstalk, Auto-Scaling, Hostel Management, Role-Based Access Control, Web Application
Abstract
An Adaptive AI-Driven Framework for Personalized Study Scheduling and Exam Preparation Using Locally Hosted Large Language Models
Saride.Balu, P. Sreenivasa Reddy*
DOI: 10.17148/IJIREEICE.2026.14567
Abstract: The exponential growth of academic content and the heterogeneity of learner aptitudes have rendered fixed, one-size-fits-all study planning increasingly inadequate for contemporary students. This paper presents an adaptive, artificial-intelligence-driven framework that automatically constructs personalized study schedules and exam- preparation pathways by reasoning over individual learner profiles, topic difficulty, and proximity to assessment deadlines. The proposed system couples a Python-based scheduling engine with a Node.js presentation layer and integrates a locally hosted large language model served through Ollama, thereby preserving data privacy and eliminating recurring cloud-inference costs. A feedback-aware profiler continuously revises the learner model from quiz outcomes and study-session telemetry, enabling spaced, priority-weighted re-planning. The framework was evaluated against static, rule-based, and cloud-LLM baselines using schedule adherence, mastery progression, recommendation relevance, and inference latency as metrics. Experimental observations indicate that the proposed approach attained approximately 88% schedule adherence and a 26-percentage-point gain in average mastery over an eight-week horizon relative to a static baseline, while sustaining acceptable local-inference latency. The principal contributions are a privacy-preserving on-device intelligence layer, an adaptive re-scheduling algorithm that fuses forgetting-curve and difficulty signals, and an integrated assessment loop that closes the gap between planning and measured learning outcomes.
Keywords: Personalized learning; adaptive scheduling; large language models; Ollama; educational technology; spaced repetition; on-device inference; intelligent tutoring systems.
Keywords: Personalized learning; adaptive scheduling; large language models; Ollama; educational technology; spaced repetition; on-device inference; intelligent tutoring systems.