Ackshaya Varshini

Abstract

This paper presents the design and implementation of an IoT-based automatic disinfectant sprinkler system for indoor sanitization. The system features a ceiling-mounted nozzle assembly with 360-degree rotation capability, controlled remotely via a web-based interface. An embedded microcontroller manages spraying schedules and dispersion patterns, while real-time sensor feedback ensures optimal disinfectant distribution. The prototype was tested in enclosed rooms, demonstrating effective coverage and reduced manual intervention for post-pandemic sanitization needs.

Abstract

This study investigates the thermal performance and efficiency of solar photovoltaic panels through computational fluid dynamics simulation. The numerical model analyzes heat transfer characteristics, temperature distribution, and airflow patterns across the panel surface under varying environmental conditions. Results demonstrate how panel orientation, ambient temperature, and wind speed impact energy conversion efficiency. The findings provide design guidelines for optimizing solar panel installations to maximize power output while minimizing thermal degradation.

Abstract

This paper presents a novel concept design for a hybrid energy harvesting system that integrates solar photovoltaic panels with a vertical-axis wind turbine in a single compact unit. The design leverages the complementary nature of solar and wind energy to maintain consistent power generation across varying weather conditions. Structural analysis and preliminary CFD simulations validate the aerodynamic feasibility. The hybrid system targets small-scale, off-grid applications where space constraints demand multi-functional energy solutions.

Abstract

This research explores a self-powered cardiac pacemaker concept using triboelectric nanogenerator (TENG) technology to harvest biomechanical energy from the heart's own motion. The proposed device converts the periodic mechanical contraction of cardiac tissue into electrical energy sufficient to power pacing circuitry, eliminating the need for battery replacements. Material selection, output voltage characterization, and energy harvesting efficiency under simulated cardiac loading conditions are analyzed. The study demonstrates the feasibility of achieving energy autonomy for next-generation implantable medical devices.

Abstract

This paper investigates the correlation between pupil dilation patterns and emotional states, proposing a computer vision-based framework for real-time emotion recognition. Using image processing techniques to track pupillary response changes under controlled visual stimuli, the system classifies emotional states including surprise, fear, and engagement. A convolutional neural network model trained on the collected pupil dilation dataset achieves reliable classification accuracy. The approach offers a non-invasive, physiological complement to facial expression-based emotion detection systems.