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Vol 12, Issue 9 (September 2026) Engineering

Experimental Investigation On The Strength And Durability Properties Of M30 Concrete By Partially Replacing Cement With Eggshell Powder

This Study Investigates Egg Shell Powder (ESP) As A Partial Replacement For Ordinary Portland Cement In M30 Grade Concrete, With Emphasis On Fresh Behaviour, Mechanical Strength And Durability. Concrete Mixes Were Prepared With 0%, 5%, 10%, 15%, 20%,... This Study Investigates Egg Shell Powder (ESP) As A Partial Replacement For Ordinary Portland Cement In M30 Grade Concrete, With Emphasis On Fresh Behaviour, Mechanical Strength And Durability. Concrete Mixes Were Prepared With 0%, 5%, 10%, 15%, 20%, 25% And 30% ESP Replacement By Mass Of Cement. The Control Mix Was Proportioned In Accordance With IS 10262:2019 Using OPC 53 Grade Cement, A Water-cement Ratio Of 0.42, 158 Kg/m³ Of Water, 648.9 Kg/m³ Of Fine Aggregate And 1275.6 Kg/m³ Of Coarse Aggregate. Workability Was Evaluated By The Slump Test; Compressive And Flexural Strengths Were Measured At 3, 7 And 28 Days; And 28-day Durability Was Assessed By Water Absorption And Rapid Chloride Permeability Test (RCPT). The 5% ESP Mixture Showed The Best Overall Performance. Its 28-day Compressive Strength Was 36.40 N/mm², Approximately 12% Above The Control Value Of 32.50 N/mm², While Its Flexural Strength Reached 5.10 N/mm², A 13.3% Increase Over The Control. Water Absorption Decreased From 2.85% To 2.45%, And RCPT Charge Decreased From 3850 To 3420 Coulombs. At 10% ESP, Strength Remained Above The Control, Whereas Replacement Beyond 10% Caused Progressive Losses In Strength And Durability. The Results Identify 5% ESP As The Optimum Replacement Level For The Investigated M30 Concrete And Demonstrate The Potential Of Controlled Eggshell-waste Utilization In Sustainable Concrete Production. Read more Read less

Authors: Akshay Laxman Kamthe | Prof. Abhijeet Undre | Dr. Atul Pujari

Vol 12, Issue 9 (September 2026) POWER ELECTRONICS AND DRIVES

MPPT Solar Street Light With IoT-Enabled Vision-Assisted Adaptive Luminous Control

Conventional Solar Street Lighting Systems Generally Operate With Fixed Illumination Levels And Non-optimal Power Extraction, Resulting In Inefficient Utilization Of Harvested Solar Energy. This Paper Presents The Modelling, Simulation And Analysis O... Conventional Solar Street Lighting Systems Generally Operate With Fixed Illumination Levels And Non-optimal Power Extraction, Resulting In Inefficient Utilization Of Harvested Solar Energy. This Paper Presents The Modelling, Simulation And Analysis Of A Maximum Power Point Tracking (MPPT) Based Solar Street Lighting System Integrated With An Internet Of Things (IoT)-enabled, Vision-assisted Adaptive Luminous Control Strategy. A Perturb And Observe (P&O) MPPT Algorithm, Implemented On A DsPIC30F2010 Digital Signal Controller, Regulates The Duty Cycle Of A SEPIC DC–DC Converter To Extract Maximum Power From A 50 W Monocrystalline Photovoltaic (PV) Module During The Day. At Night, The Same Converter Is Reconfigured Through A Relay-based Changeover To Drive The LED Load With A Duty Cycle Jointly Determined By The Day's Harvested Energy, Expressed As An Energy Sufficiency Ratio, And Real-time Road Activity Sensed By An ESP32-CAM Vision Module, Ensuring That Continuous 12-hour Illumination Is Guaranteed Irrespective Of Solar Variability While Never Compromising Visibility For Detected Pedestrians Or Vehicles. The Complete System, Including The Single-diode PV Model, The Fourth-order SEPIC Converter, The P&O Control Law And The Two-layer Adaptive-brightness Controller, Is Modelled And Simulated In MATLAB/Simulink. Simulation Results Show Stable Maximum-power-point Operation With An MPPT Tracking Efficiency Of 99.83%, A Cold-start Convergence Time Of Approximately 50 Ms, An Estimated Converter Efficiency Of Approximately 90%, And An Average Night-time Energy Saving Of 58–61% Relative To Fixed Full-brightness Operation. A Day-level Energy Balance Under A 5 Peak-sun-hour Irradiance Profile And A 12 V/18 Ah Battery Confirms A Positive Surplus Of +49.1 Wh Under Full-brightness, Full-night (12 H) Operation And +121.1 Wh Under Adaptive Dimming, Providing Resilience Against Consecutive Overcast Days. The Results Confirm That The Proposed Architecture Provides An Efficient, Low-cost, Scalable And Intelligent Solution For Adaptive Solar Street Lighting Suitable For Rural And Semi-urban Deployment, And Establish A Validated Simulation Baseline For Subsequent Hardware Realization. Read more Read less

Authors: VIJAY A J | G. A. Rathy

Vol 12, Issue 9 (September 2026) Electronics And Communication Engineering

IoT Based Smart Public Transport Framework For Location Tracking And Seat Availability Monitoring

Public Transportation Systems Face Significant Challenges In Providing Real-time Information About Bus Location And Seat Availability To Passengers, Resulting In Long Waiting Times, Overcrowding, And Inefficient Utilization Of Transport Resources. Th... Public Transportation Systems Face Significant Challenges In Providing Real-time Information About Bus Location And Seat Availability To Passengers, Resulting In Long Waiting Times, Overcrowding, And Inefficient Utilization Of Transport Resources. This Paper Presents An IoT-Based Smart Public Transport Framework That Integrates Embedded Systems, Wireless Communication, GPS Technology, And Cloud-based Monitoring To Address These Challenges. The Proposed System Consists Of Two Main Units: The Bus Unit And The Bus Stop Unit. The Bus Unit Employs An ESP32 Microcontroller As The Central Controller, Utilizing Ultrasonic Sensors For Passenger Counting At Entry And Exit Points, Infrared (IR) Sensors For Seat Occupancy Detection, And Servo Motors For Automatic Door Control. A 433 MHz RF Communication System With HT12E/HT12D Encoder-decoder Modules Enables Wireless Transmission Of Passenger And Seat Information From The Bus Unit To The Bus Stop Unit. The System Incorporates GPS-based Location Tracking And Wi-Fi Connectivity For Transmitting Real-time Data To An IoT Cloud Platform, Enabling Remote Monitoring Through A Web-based Dashboard. The Design Was Implemented Using The ESP32 Development Board With Sensors And RF Modules. Experimental Results Demonstrate Accurate Passenger Counting, Reliable Seat Occupancy Detection, Stable RF Communication, And Effective IoT-based Remote Monitoring. Total System Power Consumption Was Measured At Different Sparsity Conditions Representing Passenger Occupancy Levels. The Proposed Framework Provides A Cost-effective, Scalable Solution For Improving Passenger Convenience, Reducing Waiting Uncertainty, And Supporting Smart City Transportation Initiatives. Read more Read less

Authors: Kalaivanan K | Dr. P. Sivasankar | Dr. G. Kulanthaivel

Vol 12, Issue 9 (September 2026) ELECTRONICS ENGINEERING

AI-Driven Edge-Based Smart Parking System With NFC Authentication For Parking Slot Recommendation And Access Time Prediction

Efficient Parking Management Requires Intelligent, Real-time Decision-making To Minimize Search Time And Improve Space Utilization. This Paper Presents An AI-driven, Edge-based Smart Parking System Built Around A Comparative Evaluation Of Random Fore... Efficient Parking Management Requires Intelligent, Real-time Decision-making To Minimize Search Time And Improve Space Utilization. This Paper Presents An AI-driven, Edge-based Smart Parking System Built Around A Comparative Evaluation Of Random Forest And Neural Network Models For Parking Slot Recommendation And Parking Access-time Prediction, In Which The Neural Network Model Was Selected For Its Higher Accuracy, Consistency, And Smaller Model Size. The Selected Neural Network Models Were Independently Trained As Two TinyML Regression Models In Edge Impulse (test_slot And Test_time), Deployed In Float32 Form, And Merged Into A Single Inference Library (test7time_inferencing_v2.h) Running On An ESP32-WROOM-DA. The Implemented System Performs NFC-based User Authentication Using A PN532 Reader/writer, IR-sensor-based Parking-slot Status Detection Across A 3-lane, 9-slot Layout, Lane-wise Congestion Calculation, On-device Slot Recommendation Based On The Lowest Suitability Score, User Confirmation Through A 2.8-inch SPI TFT Touch Display, On-device Access-time Prediction, Servo-actuated Barrier Control, And RTC DS1307 With Logger.py-based Real-hardware Data Logging. Both TinyML Models Achieved Strong Test-set Performance, With 97.26% Accuracy For Slot Recommendation And 98.68% For Access-time Prediction, While Maintaining Approximately 3 Ms Inference Latency And RAM Usage Below 1.5 KB On The ESP32. Real-time Behavioural Testing Confirmed That The Deployed Models Respond Correctly To Changes In Slot Status, Congestion, And Distance, Dynamically Generating Slot Scores And Access-time Predictions, And Real-hardware Testing Confirmed Successful Integration Of NFC Authentication, IR-based Slot Sensing, TinyML Inference, User Interaction, Barrier Control, RTC Timestamping, And CSV-based Data Logging. The Results Demonstrate That The Selected Neural Network Models Can Be Deployed And Operated As A Real-time, Authenticated, Edge-based Smart Parking System Without Dependence On Cloud-based Processing. Read more Read less

Authors: NITHYA M | Dr. P. Sivasankar | Dr. G. Kulanthaivel

Vol 12, Issue 9 (September 2026) Information Technology

A Review On Machine Learning And Deep Learning Models For Forecasting Renewable Energy Production

The Transition Towards Renewable Energy Is No Longer Just An Option But A Necessity In Addressing The Growing Challenges Of Climate Change, Environmental Degradation, And Energy Security. Fossil Fuels, Which Currently Dominate Global Energy Consumpti... The Transition Towards Renewable Energy Is No Longer Just An Option But A Necessity In Addressing The Growing Challenges Of Climate Change, Environmental Degradation, And Energy Security. Fossil Fuels, Which Currently Dominate Global Energy Consumption, Are Finite And Contribute Significantly To Greenhouse Gas Emissions. Shifting To Renewable Energy Sources Such As Solar, Wind, Hydropower, And Biomass Offers A Sustainable Path Forward, Benefiting Both The Environment And Global Economies. Despite Its Benefits, The Migration To Renewable Energy Faces Challenges, Including The Need For Substantial Initial Investments, Technological Limitations, And Resistance From Entrenched Fossil Fuel Industries. Governments, Private Sectors, And International Organizations Must Work Together To Create Policies And Incentives That Encourage Renewable Energy Adoption. Public Awareness Campaigns, Subsidies, And Research Funding Are Critical In Overcoming These Barriers. To Develop A Sustainable Renewable Energy Infrastructure, We Need To Forecast The Amount Of Renewable Energy Production In Future And Meet Energy Demands In Future Through Renewable Sources. This Paper Presents A Comprehensive Review Of Statistical Models For Forecasting Renewable Energy Along With Associated Challenges That The Sector Faces. This Review Would Enable Future Research In Forecasting The Patterns In A Particular Renewable Energy Source. Read more Read less

Authors: Rakesh Madhukar | Prof. Kamlendra Verma