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Vol 12, Issue 9 (September 2026) ELECTRONICS AND COMMUNICATION ENGINEERING

DESIGN AND IMPLEMENTATION OF AN FPGA BASED SMART TRAFFIC LIGHT CONTROL SYSTEM USING CAMERA BASED VEHICLE DETECTION

Traffic Congestion Is A Major Challenge In Rapidly Growing Urban Areas, While Conventional Fixed-time Traffic Signals Cannot Respond Effectively To Changing Traffic Density. This Paper Presents A Camera-based Smart Traffic Light Control System That C... Traffic Congestion Is A Major Challenge In Rapidly Growing Urban Areas, While Conventional Fixed-time Traffic Signals Cannot Respond Effectively To Changing Traffic Density. This Paper Presents A Camera-based Smart Traffic Light Control System That Combines Vehicle Detection With Field-programmable Gate Array (FPGA) Based Real-time Signal Control. A Camera Is Used To Acquire Traffic Images Or Video, And An Image-processing Stage Determines The Approximate Vehicle Density In Each Traffic Direction. The Extracted Traffic Information Is Supplied To An FPGA Controller, Where A Finite-state Machine (FSM) Generates Adaptive Red, Yellow And Green Signal Sequences. The Proposed Architecture Is Intended To Reduce Unnecessary Waiting Time By Allocating Comparatively Longer Green Intervals To Approaches Having Higher Traffic Density. The FPGA Implementation Is Targeted Toward A Compact And Low-cost Platform Such As The Sipeed Tang Nano 9K. The Paper Describes The System Architecture, Image-processing Flow, FPGA Control Logic, Hardware Interfacing And An Experimental Evaluation Framework. The Final Measured Accuracy, Latency And Signal-timing Results Are To Be Inserted After Completion Of The Hardware Experiments Read more Read less

Authors: SURYA R | Dr. SIVASANKAR P | Dr.KULANTHAIVEL G

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

Discrete Sliding-Mode Velocity Estimation And Filter-Independent PID Tuning For Gimbal BLDC Drives

Velocity Loops On Low-cost Brushless Drives Are Al-most Always Closed On A Filtered Finite Difference Of A Magnetic Encoder Angle, And Are Almost Always Tuned By Watching That Same Filtered Signal. This Paper Reports A Hardware Study In Which The Pra... Velocity Loops On Low-cost Brushless Drives Are Al-most Always Closed On A Filtered Finite Difference Of A Magnetic Encoder Angle, And Are Almost Always Tuned By Watching That Same Filtered Signal. This Paper Reports A Hardware Study In Which The Practice Is Examined From Both Ends. Four Velocity Estimators — An Unfiltered Finite Difference, A First-order Low-pass Filter, A Two-state Extended Kalman Filter And A Two-state Sliding-mode Observer — Are Evaluated On One 7 Pole-pair Gim-bal Motor Driven By Field-oriented Control On An STM32G431 Microcontroller, All Four Consuming The Same Encoder Sample At The Same Instant. A Discrete Limit Cycle In The Sliding-mode Observer Is First Diagnosed And Removed: The Switching Action Had Been Applied As A Per-sample Increment Rather Than As A Rate Integrated Over The Sample Interval, Which Placed The In-layer Loop Gain Above The Discrete Stability Bound And Produced A 244 RPM Oscillation With The Rotor Held Stationary. With The Update Law Corrected, The Observer Becomes The Least Noisy Of The Four Estimators At Every Speed Tested, With 2.3–5.2 Less Noise Than The Raw Difference And 1.4–1.9 Less Thanthe Kalman Filter. The Corrected Estimator Is Then Used As An Independent Yardstick For A Replicated Velocity-PID Study Of 9 Gain Configurations Over 27 Runs. Scored On The Controller’s Own Feedback Signal, Raising The Feedback Filter Constant From 10 Ms To 50 Ms Appears To Cut Speed Ripple By 14 ; Scored On The Shaft It Cuts It By 1.8 . At A Proportional Gain Of 1.0 The Feedback Signal Reports A Standard Deviation Of 63 RPM While The Shaft Is Steady At 4.8 RPM And Simply Sits 26 RPM Below Setpoint. We Conclude That Such Drives Must Be Tuned Against An Estimate That Does Not Share The Control Filter, And Give A Procedure That Does So. Read more Read less

Authors: Mannar Mannan V | R. Suja Mani Malar

Vol 12, Issue 9 (September 2026) ELECTRICAL AND ELECTRONICS ENGINEERING

Efficiency Optimization Of Interleaved Boost Converters Via Dynamic Phase Management And PI Control

This Paper Presents The Design, Hardware Realization, And Experimental Validation Of A Two-phase Interleaved Boost Converter (IBC) With Dynamic (adaptive) Phase Management For Low-voltage Photovoltaic (PV) Energy Harvesting. The Converter Interfaces... This Paper Presents The Design, Hardware Realization, And Experimental Validation Of A Two-phase Interleaved Boost Converter (IBC) With Dynamic (adaptive) Phase Management For Low-voltage Photovoltaic (PV) Energy Harvesting. The Converter Interfaces An 18 V PV Source With A Regulated 48 V DC Bus And Tracks The Array’s Maximum Power Point Using A Perturb And Observe (P&O) Algorithm. A Classical Proportional-Integral (PI) Controller Closes The Output-voltage Loop, While An Adap-tive Phase Shedding (APS) Scheme Dynamically Disables The Sec-ond Phase At Reduced Input Voltage To Minimize Switching Losses Under Light-load Conditions. A 20 W Hardware Prototype Was Constructed Using An ESP32 Microcontroller, A TC427 Dual Gate Driver, INA240 Current-sense Amplifiers, And Precision Voltage-divider Feedback Networks. Experimental Results Confirm That The PI-controlled Prototype Regulates The 48 V Bus With A Peak-to-peak Output Ripple Of 2.4 V (5.34% Of Output) Under Nominal Two-phase Operation At 18 V Input, And Successfully Transitions To Single-phase Operation With A Peak-to-peak Ripple Of 9.6 V (25.49%) When The Input Drops To 12 V And The Second Phase Is Shed. The Results Demonstrate That A Two-phase Interleaved Topology, Combined With Dynamic Phase Management And A Well-tuned PI Loop, Provides An Efficient, Low-complexity And Practically Realizable Solution For Regulated Step-up Conversion In Distributed Solar Energy Harvesting Systems. Read more Read less

Authors: SHAJINA N | Dr. G. A. Rathy

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

Real-Time Speech Emotion Recognition Using Lightweight Convolutional Neural Network

Speech Emotion Recognition (SER) Allows Machines To Understand Human Emotional States From Their Speech, Enabling Applications In Human-computer Interaction, Call Center Analytics, And Mental Health Monitoring. Traditional Deep CNNs Achieve High Accu... Speech Emotion Recognition (SER) Allows Machines To Understand Human Emotional States From Their Speech, Enabling Applications In Human-computer Interaction, Call Center Analytics, And Mental Health Monitoring. Traditional Deep CNNs Achieve High Accuracy; However, They Are Costly When It Comes To Computation, Which Means They Cannot Be Used In Real Time On Edge Devices With Limited Computational Power. This Paper Proposes A Lightweight CNN-based Architecture For Real-time SER Using Log-Mel Spectrograms As Input Representations. The Proposed Model Incorporates Conv2D Layers, Batch Normalization, Max Pooling, And Global Average Pooling To Learn Discriminative Time-frequency Features While Reducing Model Complexity And Inference Overhead. Unlike The Existing Artificial Neural Network (ANN) Baseline, Which Was Trained Using Only The RAVDESS Dataset, The Proposed CNN Is Trained Using A Combined RAVDESS And TESS Dataset Covering Eight Emotion Classes: Neutral, Calm, Happy, Sad, Angry, Fearful, Disgust, And Surprised. Experimental Evaluation Shows That TheproposedLightweightCNNattained75.70%testaccuracywithaweightedF1-scoreof0.78, Compared To The ANN Baseline Model’s 54.86% Accuracyand0.55F1-score. Additionally, Compared To The ANN Baseline, The Proposed Lightweight CNN Architecture Demonstrated Faster Real-time Performance Using Microphone Input While Providing Confidences Cores For Each Emotion Detected. Overall, The Proposed Lightweight CNN Model Provides Faster Inference With Good Accuracy For SER Applications. Read more Read less

Authors: Natchathiram K | Prof. Dr. G. Kulanthaivel | Prof. Dr. P. Sivasankar

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

Dual-Loop PI–Hysteresis Control Of A Single-Phase Bridgeless AFE Rectifier For Power-Factor-Corrected CC–CV Battery Charging

Conventional Diode- And Thyristor-based Rectifiers Draw Non-sinusoidal Current, Producing Poor Power Factor And Harmonic Distortion Exceeding IEEE-519 Limits. This Paper Presents The Design, Modelling, And Closed-loop Simulation Of A Single-phase Tot... Conventional Diode- And Thyristor-based Rectifiers Draw Non-sinusoidal Current, Producing Poor Power Factor And Harmonic Distortion Exceeding IEEE-519 Limits. This Paper Presents The Design, Modelling, And Closed-loop Simulation Of A Single-phase Totem-pole Bridgeless Active Front-End (AFE) AC–DC Converter That Performs Power-factor Correction While Charging A Battery Through A Constant Current–Constant Voltage (CC–CV) Profile, Automatically Selected From Battery State Of Charge (SOC). A Proportional–Integral (PI) Outer Loop Regulates Current Below 80% SOC And Voltage Above It, While An Inner Hysteresis Current Controller Shapes The Source Current To A Synchronised Sinusoidal Reference. Simulated In MATLAB/Simulink For A 100 W, Single-phase Charger For 22.2 V, 5 Ah Lithium-ion Battery, The Converter Achieves A Power Factor Above 0.9 And A Source-current THD Below 5%, Confirming An Effective, Low-complexity Means Of Combining Power-factor Correction With Regulated CC–CV Battery Charging. Read more Read less

Authors: Jeyakrishnan V | Dr.G.A.Rathy