Experimental Validation of Ohm’s Law under Varying Temperature Conditions

A. S. M. Shamim Hasan — 2026-05-13

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

Ashley Thankam Ninan — 2026-05-29

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.

Research & Review: Electronics and Communication Engineering

Experimental Validation of Ohm’s Law under Varying Temperature Conditions

AI-enabled Wild Boar Intrusion Detection and Deterrent System