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R. Megalingam

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Conference Aug 2026

Design and Verilog Implementation of an Adaptive SEC-DED Based Fault-Tolerant DMA Controller with Dynamic Burst Optimization

In contemporary System on a Chip (SoC) and embedded designs, data transfer speed and reliability are crucial because data transfer should take place with minimal CPU intervention while preserving integrity. Traditional Direct Memory Access (DMA) controllers are primarily throughput-oriented and do not commonly incorpor...

S. Arun, R. Megalingam · 0 citations
Conference Aug 2026

Design of a 32-Bit RISC-V Processor with Dynamic Floating-Point ALU Switching

This project focuses on designing and simulating a 32-bit RISC-V processor that can switch dynamically between an Integer ALU (RV32I) and a Floating Point ALU (RV32F). The main aim of the work is to make the processor to perform both integer and floating-point operations using a single architecture, which eliminate the...

Abhijith Pananghat, Murugesan Hariharan, R. Megalingam · 0 citations
Conference Aug 2026

FPGA-Based Implementation and Simulation of a 3×3 Convolution Accelerator for Edge Detection

In an effort to mitigate processing delays and latency in the traditional edge detection in the vision based systems such as robotics and surveillance platforms, this paper attempts to introduce an FPGA-based 3×3 convolution accelerator. The proposed architecture employs a multiply-accumulate (MAC) unit and fixed-point...

Aysha Pareekutty, R. Megalingam · 0 citations
Conference Aug 2026

Adaptive Multi-Parameter Closed-Loop PWM Control Using Machine Learning

Pulse Width Modulation (PWM) is a popular control method used in embedded and power electronics systems to control voltage, speed, and power in applications like motor drives, converters, lighting systems, and Internet of Things (IoT) devices. The traditional PWM controllers tend to be implemented by fixed or rule-base...

Abhinav Chalil, Vivek Ashok Nair, R. Megalingam · 0 citations
Open access Jul 2026

An Interpretable and Edge Deployable Spatio-Temporal Trajectory Prediction for Autonomous Driving

A comprehensive Explainable AI (XAI) evaluation framework is introduced, including temporal sensitivity analysis, interaction-aware perturbation studies, spatial influence analysis, and gradient-based feature attribution methods that provide insights into how the model captures temporal motion dependencies, neighboring...

R. Megalingam, Naveen Prasaad Selvarajan, Pritty Vijay · 0 citations

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