arXiv subjects

Ramakant Kumar

Publications and source records attributed to Ramakant Kumar.

5 recordsLinked to original sources

arXiv2026

Smart Railway Obstruction Detection System using IoT and Computer Vision

Railway track intrusions pose a critical safety challenge for Indian Railways, encompassing wildlife incursions and deliberate malicious obstructions. The December 2025 collision in Assam, in which seven elephants were killed by the Rajdhani Express, underscores the urgency of effective real-time detection. Existing solutions such as the optical fiber-based Gajraj system suffer from prohibitive costs (\$1000/km) and high false alarm rates, limiting deployment to only 20 of India's 101 elephant corridors. This paper proposes NETRA, a cost-effective, internet-independent intrusion detection system deployed on Raspberry Pi Zero W and Raspberry Pi 4 edge platforms. NETRA employs probabilistic sensor fusion integrating a PIR motion sensor and an HC-SR04 ultrasonic distance sensor with a tunable threshold (tau_c = 0.65), enabling event-driven camera activation that reduces unnecessary visual processing by 52%. Upon confirmed intrusion, edge-AI classification using MobileNet-SSD (Pi Zero) or YOLOv5 ONNX (Pi 4) identifies threats including humans, large animals, and track obstructions. Confirmed threats are transmitted via LoRa (868 MHz) to alert the locomotive driver within 2.4 seconds end-to-end. Experimental evaluation across 113 motion events demonstrated 95% detection accuracy with zero false alarms through probabilistic fusion, compared to 85% for binary methods. Raspberry Pi 4 with YOLOv5 achieved 83.5% elephant F1-score, a 5.6x improvement over Pi Zero's heuristic approach (14.8%). LoRa communication achieved 100% packet delivery across 1-2 km in field trials. NETRA reduces deployment cost by 75% (\$247/km vs \$1000/km for Gajraj) while providing unified detection of both wildlife and obstruction threats.

cs.CV↗

arXiv2026

Privacy-Preserving Sensor-Based Human Activity Recognition for Low-Resource Healthcare Using Classical Machine Learning

Limited access to medical infrastructure forces elderly and vulnerable patients to rely on home-based care, often leading to neglect and poor adherence to therapeutic exercises such as yoga or physiotherapy. To address this gap, we propose a low-cost and automated human activity recognition (HAR) framework based on wearable inertial sensors and machine learning. Activity data, including walking, walking upstairs, walking downstairs, sitting, standing, and lying, were collected using accelerometer and gyroscope measurements. Four classical classifiers, Logistic Regression, Random Forest, Support Vector Machine (SVM), and k-Nearest Neighbors (k-NN), were evaluated and compared with the proposed Support Tensor Machine (STM). Experimental results show that SVM achieved an accuracy of 93.33 percent, while Logistic Regression, Random Forest, and k-NN achieved 91.11 percent. In contrast, STM significantly outperformed these models, achieving a test accuracy of 96.67 percent and the highest cross-validation accuracy of 98.50 percent. Unlike conventional methods, STM leverages tensor representations to preserve spatio-temporal motion dynamics, resulting in robust classification across diverse activities. The proposed framework demonstrates strong potential for remote healthcare, elderly assistance, child activity monitoring, yoga feedback, and smart home wellness, offering a scalable solution for low-resource and rural healthcare settings.

cs.LG↗

arXiv2024

Transforming Agriculture: Exploring Diverse Practices and Technological Innovations

Agriculture is a vital sector that significantly contributes to the economy and food security, particularly in regions like Varanasi, India. This paper explores various types of agriculture practiced in the area, including subsistence, commercial, intensive, extensive, industrial, organic, agroforestry, aquaculture, and urban agriculture. Each type presents unique challenges and opportunities, necessitating innovative approaches to enhance productivity and sustainability. To address these challenges, the integration of advanced technologies such as sensors and communication protocols is essential. Sensors can provide real-time data on soil health, moisture levels, and crop conditions, enabling farmers to make informed decisions. Communication technologies facilitate the seamless transfer of this data, allowing for timely interventions and optimized resource management. Moreover, programming techniques play a crucial role in developing applications that process and analyze agricultural data. By leveraging machine learning algorithms, farmers can gain insights into crop performance, predict yields, and implement precision agriculture practices. This paper highlights the significance of combining traditional agricultural practices with modern technologies to create a resilient agricultural ecosystem. The findings underscore the potential of integrating sensors, communication technologies, and programming in transforming agricultural practices in Varanasi. By fostering a data-driven approach, this research aims to contribute to sustainable farming, enhance food security, and improve the livelihoods of farmers in the region.

cs.DC↗

arXiv2024

Smart Space Environments: Key Challenges and Innovative Solutions

The integration of LoRaWAN (Long Range Wide Area Network) technology with both active and passive sensors presents a transformative opportunity for the development of smart home systems. This paper explores how active sensors, such as motion detectors and ultrasonic sensors, and passive sensors, including temperature and humidity sensors, work together to enhance connectivity and efficiency within diverse environments while addressing the challenges of modern living. By leveraging LoRaWAN long-range capabilities and low power consumption, the proposed framework enables effective data transmission from remote sensors, facilitating applications such as smart agriculture, environmental monitoring, and comprehensive home automation. Active sensors emit energy to detect changes in their surroundings, providing real-time data crucial for security and automation, while passive sensors capture ambient energy to monitor environmental conditions, ensuring resource efficiency and user comfort. The synergy between LoRaWAN and these various sensor types promotes innovation, contributing to a more responsive and sustainable living experience. Furthermore, this research highlights the adaptability of the proposed system, allowing for seamless integration of new devices and advanced functionalities. As the landscape of smart home technology continues to evolve, ongoing research in this area will yield advanced solutions tailored to user needs, ultimately paving the way for smarter, safer, and more efficient living environments.

cs.ET↗

arXiv2024

Integrating LoRaWAN with Mobile Ad-hoc Networks for Enhanced Campus Communication

The integration of Long Range Wide Area Network (LoRaWAN) with Mobile Ad-hoc Networks (MANETs) presents a promising solution for enhancing communication networks within campus environments. This paper explores the unique advantages of combining these two technologies, including scalability, energy efficiency, flexibility, and support for diverse applications. LoRaWAN low power consumption and extended range capabilities address the challenges of traditional communication methods, enabling reliable data transmission across various campus scenarios, such as emergency alerts, event coordination, and real-time monitoring. We also identify key challenges faced in this integrated architecture, including signal interference, data packet collisions, and energy management. By providing a comprehensive survey of existing techniques and solutions categorized by the network protocol stack layers, this study aims to inform future research and development efforts in creating robust, energy efficient communication systems tailored for modern educational institutions. Ultimately, the findings highlight the potential of LoRaWAN MANET architectures to transform campus communication into a more reliable, adaptable, and cost effective framework.

cs.NI↗