𝐒𝐢𝐫𝐚𝐣𝐮𝐬 𝐒𝐚𝐥𝐞𝐡𝐢𝐧, Shakila Rahman, 𝐌𝐨𝐡𝐚𝐦𝐦𝐚𝐝 𝐍𝐮𝐫, 𝐀𝐡𝐦𝐚𝐝 𝐀𝐬𝐢𝐟, 𝐌𝐨𝐡𝐚𝐦𝐦𝐚𝐝 𝐁𝐢𝐧 𝐇𝐚𝐫𝐮𝐧, Jia Uddin,
Volume 20, Issue 4 (December (Special Issue on ADLEEE) 2024)
Abstract
Abnormal activity detection is crucial for video surveillance and security systems, aiming to identify behaviors that deviate from normal patterns and may indicate threats or incidents such as theft, vandalism, accidents, and aggression. Timely recognition of these activities enhances public safety across various environments, including transportation hubs, public spaces, workplaces, and homes. In this study, we focus on detecting violent and non-violent activities of humans using a YOLOv9-based deep learning model considering the above issues. A diverse dataset has been built of 9,341 images from various platforms, and then the dataset has been pre-processed, i.e., augmentation, resizing, and annotating. After pre-processing, the proposed model has been trained which demonstrated strong performance, achieving an F1 score of 95% during training for 150 epochs. It was also trained for 200 epochs, but early stopping was applied at 148 epochs as there was no significant improvement in the results. Finally, the results of the YOLOv9-based model have been analyzed with other baseline models (YOLOv5, YOLOv7, YOLOv8, and YOLOv10) and it performed better compared with others.
Shuvojit Kundu, Tuhel Ahmed, Jia Uddin,
Volume 21, Issue 1 (March 2025)
Abstract
This study aims to evaluate a cantilever beam type piezoelectric energy harvester operating on train-induced vibrations for powering Wireless Sensor Networks (WSNs) used in railway track monitoring systems. Harvester's behaviors under different conditions are simulated in MATLAB using the analytical model. Natural frequency, maximum deflection, and stress are calculated with greater precision using eigen frequency and stationary analysis using COMSOL Multiphysics. At a base excitation of 2 g and a resonant frequency of 4.38 Hz, the simulated results showed that the developed energy harvester prototype could generate up to 14 V of AC output voltage and 550 mW of output power. These findings highlight the promising potential of the proposed energy harvester for transforming train mechanical energy into electrical power. This energy harvester's viability and dependability for real-world applications in monitoring railway tracks are supported by developed analytical and simulation models.
