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This Author published in this journals
All Journal Journal of Computer Science Advancements Journal of Selvicoltura Asean Research of Scientia Naturalis Journal of Social Entrepreneurship and Creative Technology Journal of Multidisciplinary Sustainability Asean
Lucas Wong
Singapore Management University
Agriculture, Biological Sciences & Forestry Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Library & Information Science Physics Other

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Journal : journal of computer science advancements

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IMAGE PROCESSING AND COMPUTER VISION TECHNIQUES FOR AUTOMATED SMART SURVEILLANCE SYSTEMS Zainal Syahlan; Sofia Lim; Lucas Wong
Journal of Computer Science Advancements Vol. 3 No. 6 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i6.3323

Abstract

The rapid development of urbanization and security concerns has prompted the integration of automated smart surveillance systems to enhance public safety and operational efficiency. Traditional surveillance methods often rely on human monitoring, which is prone to errors and inefficiencies. Image processing and computer vision techniques provide a solution by automating object detection, tracking, and anomaly recognition. This study aims to investigate advanced image processing and computer vision techniques for improving the performance of automated smart surveillance systems. A hybrid approach combining convolutional neural networks (CNNs), attention mechanisms, and edge computing is proposed to enhance both detection accuracy and real-time processing speed. The research employed experimental design, utilizing a dataset of 12,000 annotated image frames and 85 hours of video footage from diverse environmental conditions. Performance metrics such as precision, recall, mean average precision (mAP), and processing speed were measured. Results demonstrate that the proposed model outperforms traditional CNN models, achieving higher detection accuracy and faster processing speed. The study concludes that integrating edge computing with adaptive image processing and attention-based neural networks significantly improves automated surveillance system performance in real-world settings. These findings offer valuable insights for the development of scalable and efficient smart surveillance technologies.
GOODBYE LATENCY: WHY FUTURE MEDICAL DEVICES NEED ARTIFICIAL BRAINS Megan Koh; Marcus Tan; Lucas Wong
Journal of Computer Science Advancements Vol. 3 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i4.3332

Abstract

The transition of medical technology from passive monitoring to autonomous, closed-loop intervention is critically impeded by the latency and power inefficiencies of traditional Von Neumann computing architectures. This study investigates the efficacy of neuromorphic hardware as a solution, aiming to validate a bio-inspired architecture capable of sub-millisecond decision-making for life-critical applications. Employing a rigorous hardware-in-the-loop simulation framework, we benchmarked a custom Spiking Neural Network (SNN) against industry-standard microcontrollers, utilizing large-scale cardiac and neurological datasets to evaluate inference speed, energy consumption, and signal fidelity. Quantitative results reveal that the neuromorphic system achieved a 94% reduction in end-to-end latency and a thirty-eight-fold improvement in energy efficiency compared to the digital baseline. The event-driven architecture successfully maintained 96.4% diagnostic accuracy while operating within a negligible thermal envelope suitable for implantation. These findings definitively establish that mimicking biological asynchronous processing eliminates fatal temporal delays, validating neuromorphic “artificial brains” as the essential technological foundation for the next generation of responsive, privacy-secure, and energy-autonomous medical implants.
Co-Authors Achmad Agus Salim Anna Dara Ava Lee Dodi Setiawan Riatmaja Ethan Tan Ingrid Olsson Johannes Muller Marcus Tan Megan Koh Megan Koh Pemy Chiristiaan Ryan Teo Seo Jiwon Sofia Lim Zainal Syahlan Ziad Khalil