Research & Review: Electronics and Communication Engineering

Experimental Validation of Ohm’s Law under Varying Temperature Conditions

2026-05-13T08:31:27+00:00

This study presents a comprehensive experimental investigation into the validity and limitations of Ohm’s law under varying temperature conditions. While Ohm’s law assumes a constant resistance and linear voltage-current (V-I) relationship, practical conductive materials exhibit temperature-dependent resistance, leading to measurable deviations from ideal behavior. In this work, a controlled experimental framework was developed to analyze the impact of temperature on electrical resistance using a fixed metallic resistor over a temperature range of 25 to 75°C. Systematic V-I measurements were conducted at multiple temperature levels, with repeated trials to ensure reliability and minimize experimental uncertainty. The results demonstrate that the linear relationship between voltage and current is preserved under isothermal conditions; however, the slope of the V-I characteristics decreases with increasing temperature, indicating a rise in resistance. This behavior is further validated using the temperature-resistance model, confirming a positive temperature coefficient of resistance. To quantify non-ideal effects, percentage deviation from the reference resistance was calculated, revealing an increase from approximately 0% at 25°C to over 11.22% at 75°C. Additionally, slope analysis of the V-I curves highlights the inverse relationship between conductance and temperature, providing deeper insight into the underlying physical mechanisms. The experimental findings closely align with theoretical predictions, with deviations remaining within acceptable error margins. Unlike conventional demonstrations that neglect thermal effects, this study introduces a reproducible, low-cost methodology incorporating multi-temperature measurements, uncertainty analysis, and quantitative deviation assessment. The proposed approach not only enhances the practical understanding of Ohm’s Law but also provides a valuable framework for analyzing non-ideal electrical behavior in real-world applications, including power systems, electronic devices, and sensor technologies.

AI-enabled Wild Boar Intrusion Detection and Deterrent System

2026-05-29T10:35:21+00:00

Agriculture in forest-adjacent regions has long struggled with wildlife intrusions, and wild boar attacks are among the most destructive. Farmers often suffer serious crop losses, yet the tools available to them—electric fences, chemical repellents, or manual night patrols—demand continuous effort and still fall short in terms of reliability. This study presents an AI-driven intrusion detection and deterrent system specifically designed to tackle wild boar incursions in an automated, humane, and energy-efficient manner. The system employs a dual-sensor approach using passive infrared (PIR) and thermal sensors as the first line of motion detection. When a potential intrusion is triggered, a Raspberry Pi 4 running a custom-trained YOLOv8 model analyzes the captured frames to confirm the presence of a wild boar. Upon verified detection, an ultrasonic deterrent module emitting frequencies between 21–40 kHz activates to drive the animal away without physical harm. At the same time, real-time push notifications are sent to the farmer through “Boarex”—a custom Flutter mobile application—even when the app runs in the background. Every event is timestamped and logged, helping farmers track intrusion patterns and plan better protective strategies over time. Experimental testing demonstrated a detection precision of 94.2% and a recall of 91.6% for wild boars. The system processes video at 12–15 FPS with an inference delay of approximately 280 ms. False positives dropped by over 90% compared to conventional PIR-only setups, and power consumption in event-driven mode was around 2.1 W—roughly 67% less than continuous monitoring. The solar-powered design makes this solution viable even in remote, off-grid farmlands.

Smart Yoga Mat Integrated with AI: Revolutionizing Yoga Practice with Intelligent Technology

2026-06-15T09:41:26+00:00

Artificial Intelligence (AI) has opened up new possibilities for tracking one’s own fitness and health through wearable and Internet of Things devices. The Smart Yoga Mat described in this study has an embedded AI system that tracks, evaluates, and offers real-time feedback on yoga poses. To improve posture detection and correction accuracy, the technology makes use of pressure sensors, Inertial Measurement Units (IMUs), and AI-based pose estimate algorithms. According to hypothetical trial results, the technology offers individualized support and enhances user posture. While reducing the risk of injury, this technology has the potential to enhance the yoga experience at home. This paper presents a Smart Yoga Mat with AI that provides practitioners with corrected feedback based on real-time posture detection. The gadget tracks users’ weight distribution and touch points while they perform yoga poses using built-in pressure sensors in the mat. AI algorithms assess these sensor inputs to determine pose accuracy, spot deviations and offer tailored guidance through a companion smartphone application. Additionally, by incorporating biometric data, like heart rate, wearable technology enhances fitness tracking. The mat-based method is suitable for residential use in a range of lighting and climatic conditions because it offers a privacy-preserving substitute for camera-based systems. The system’s capacity to enhance posture, boost engagement, and promote regular practice has been demonstrated through testing with users of different skill levels. This invention creates an approachable and interactive method to promote safe, efficient yoga practice at home or in rehabilitation centers by fusing traditional yoga techniques with digital intelligence.

Quantum Entanglement as a Resource for Next-Generation Wireless Networks

2026-06-23T12:15:27+00:00

This review article investigates quantum entanglement and its use as a resource for next-generation wireless networks, including 6G and beyond. Quantum entanglement can be described as a uniquely quantum correlation between two particles, such that measurements performed on one particle are strongly correlated with those of the other, regardless of the distance between them. Current wireless networks handle large amounts of data and face several limitations, including persistent security challenges. Quantum entanglement offers a potential resource to overcome some of these limitations. It also forms the basis of technologies such as Quantum Key Distribution (QKD) for generating highly secure encryption keys and quantum teleportation for transmitting a quantum state from one location to another. These concepts are no longer purely theoretical and are now being explored through experimental demonstrations and practical implementations. However, several challenges remain, including decoherence, where interactions with the environment such as heat, vibration, and other sources of noise degrade or destroy entanglement, and hardware scalability, as current systems cannot efficiently support large numbers of qubits. This review article examines these opportunities and challenges, presenting the topic in a manner accessible to both specialist and non-specialist audiences.

Design and Implementation of Canteen Order Management System

2026-06-29T11:20:36+00:00

The increasing demand for fast and efficient food services in institutional canteens, such as those in colleges, universities, and corporate organizations, often results in long waiting queues, order-processing delays, and customer dissatisfaction during peak hours. Traditional manual ordering methods are time-consuming and can lead to errors in order handling and payment management. To address these challenges, this project presents the design and implementation of a smart canteen order management system that enables customers to place food orders via a user-friendly web application, eliminating the need to stand in physical queues. The proposed system is developed using the MERN Stack, comprising MongoDB, Express.js, React.js, and Node.js, providing a robust, scalable, and efficient platform for real-time order processing and data management. When a customer places an order, the system automatically generates a unique token number that serves as an order identifier. Customers can track the status of their orders and receive notifications when their food is prepared and ready for collection. The system also provides an administrative interface for canteen staff to manage menu items, monitor incoming orders, update order statuses, and maintain transaction records. By digitizing the ordering process, the proposed solution significantly reduces waiting times, improves operational efficiency, minimizes human errors, and enhances the overall customer experience. The system offers a practical and cost-effective approach for modernizing canteen services and supporting seamless food order management during high-demand periods.