arXiv subjects

Nishant Vasantkumar Hegde

Publications and source records attributed to Nishant Vasantkumar Hegde.

2 recordsLinked to original sources

arXiv2025

eFPE: Design, Implementation, and Evaluation of a Lightweight Format-Preserving Encryption Algorithm for Embedded Systems

Resource-constrained embedded systems demand secure yet lightweight data protection, particularly when data formats must be preserved. This paper introduces eFPE (Enhanced Format-Preserving Encryption), an 8-round Feistel cipher featuring a "novel lightweight Pseudorandom Function (PRF)" specifically designed for this domain. The PRF, architected with an efficient two-iteration structure of AES-inspired operations (byte-substitution, keyed XOR, and byte-rotation), underpins eFPE's ability to directly encrypt even-length decimal strings without padding or complex conversions, while aiming for IND-CCA2 security under standard assumptions. Implemented and evaluated on an ARM7TDMI LPC2148 microcontroller using Keil μVision 4, eFPE demonstrates the efficacy of its targeted design: a total firmware Read-Only Memory (ROM) footprint of 4.73 kB and Random Access Memory (RAM) usage of 1.34 kB. The core eFPE algorithm module itself is notably compact, requiring only 3.55 kB ROM and 116 B RAM. These characteristics make eFPE a distinct and highly suitable solution for applications like financial terminals, medical sensors, and industrial IoT devices where data format integrity, minimal resource footprint, and low operational latency are paramount.

cs.CR↗

arXiv2025

A Novel AI-Driven System for Real-Time Detection of Mirror Absence, Helmet Non-Compliance, and License Plates Using YOLOv8 and OCR

Road safety is a critical global concern, with manual enforcement of helmet laws and vehicle safety standards (e.g., rear-view mirror presence) being resource-intensive and inconsistent. This paper presents an AI-powered system to automate traffic violation detection, significantly enhancing enforcement efficiency and road safety. The system leverages YOLOv8 for robust object detection and EasyOCR for license plate recognition. Trained on a custom dataset of annotated images (augmented for diversity), it identifies helmet non-compliance, the absence of rear-view mirrors on motorcycles, an innovative contribution to automated checks, and extracts vehicle registration numbers. A Streamlit-based interface facilitates real-time monitoring and violation logging. Advanced image preprocessing enhances license plate recognition, particularly under challenging conditions. Based on evaluation results, the model achieves an overall precision of 0.9147, a recall of 0.886, and a mean Average Precision (mAP@50) of 0.843. The mAP@50 95 of 0.503 further indicates strong detection capability under stricter IoU thresholds. This work demonstrates a practical and effective solution for automated traffic rule enforcement, with considerations for real-world deployment discussed.

cs.CV↗