29/07/2026
🗣️ 𝔸𝕀𝕠𝕋 𝕍𝕚𝕤𝕦𝕒𝕝 𝕊𝕖𝕟𝕤𝕚𝕟𝕘: 𝔸 𝕄𝕦𝕝𝕥𝕚-ℙ𝕒𝕣𝕒𝕕𝕚𝕘𝕞 ℙ𝕖𝕣𝕗𝕠𝕣𝕞𝕒𝕟𝕔𝕖 ℂ𝕠𝕞𝕡𝕒𝕣𝕚𝕤𝕠𝕟 💡
🌟Mr CHANDRA Kent Max, BSc (Hons) Computing
🌟Mr WANG Youkang Albert, BSc (Hons) Computer Science
🌟Mr PAZO RECIO Lucas, BSc (Hons) in Physics with a secondary major in AIDA
🌟Mr CHAR Cheuk Tung George, BSc (Hons) Scheme in Computing & AI
A team of interdisciplinary PolyU students—George, Kent, Lucas, and Albert—successfully transformed a standard 𝗔𝗜𝗼𝗧 𝗰𝗼𝘂𝗿𝘀𝗲𝘄𝗼𝗿𝗸 𝗮𝘀𝘀𝗶𝗴𝗻𝗺𝗲𝗻𝘁 into an award-winning research project. Their study focused on optimizing image classification for low-power IoT devices, exploring techniques like 𝗦𝗽𝗶𝗸𝗶𝗻𝗴 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀 (SNN) and CNNs. By implementing transfer learning, the team overcame significant data limitations, boosting classification accuracy from a modest 60% to over 90%.
Under the mentorship of Dr. Mohammed Aquil Mirza, their findings were published and presented at the IEEE ICNSC conference, where they received the prestigious 𝗕𝗲𝘀𝘁 𝗦𝘁𝘂𝗱𝗲𝗻𝘁 𝗣𝗮𝗽𝗲𝗿 𝗔𝘄𝗮𝗿𝗱. This project highlights the potential of energy-efficient AI in real-world applications like robotics and smart sensing, demonstrating how student collaboration and faculty support can turn academic challenges into 𝗶𝗺𝗽𝗮𝗰𝘁𝗳𝘂𝗹 industry contributions.
🎞️ Read the full story of the team at https://polyu.hk/zmupn
🔍 Explore more outstanding student work — award-winning papers, projects, and theses — on the PolyU OWS Portal: https://polyu.hk/sQngD