Can machines sense without cameras or sensors? Computer vision allows machines to “see,” but their perception capabilities based on cameras are fundamentally limited to a specific field of view and good lighting conditions – they cannot see through any occlusions or in the dark. In this talk, I will introduce Wireless AI Perception that opens a new sense for machine perception to decipher the physical world, even in absolute darkness and through walls and obstacles. To achieve this, Wireless AI leverages ambient wireless signals for sensing and turns any Wi-Fi devices from a pure communication medium into a ubiquitous all-in-one sensing platform. We will first introduce the concepts, principles, and grand challenges of Wi-Fi sensing, and then share our unique solution of Wireless AI, which has been commercialized and deployed as real-world products, such as motion sensing, sleep monitoring, fall detection, indoor tracking, just to name a few. We foresee that Wi-Fi Sensing will enter billions of devices and millions of homes, and today is just the beginning of this revolution.
Dr Chenshu Wu is an Assistant Professor in the Department of Computer Science, The University of Hong Kong, where he leads the HKU AIoT Lab. His research focuses on wireless and mobile AIoT systems at the intersection of wireless sensing, ubiquitous computing, and the Internet of Things. He has published 3 books, 90+ papers in prestigious venues like SIGCOMM, NSDI, MobiCom, MobiSys, CHI, which won 5 best paper awards. He holds 60+ filed/granted US and international patents. His research has been deployed as award-winning products, which won the CES 2021 Best of Innovation Award among many others. He is a recipient of NSF China Excellent Young Scientists Fund (HK & Macau), NAM Healthy Longevity Catalyst Awards (Hong Kong), CCF Outstanding Doctoral Dissertation Award. More information at https://cswu.me
Clean water and clean air are vital for public health. This project focuses on developing high-efficiency and environmentally sustainable filters for removing harmful air/water pollutants. The team has developed novel architectures and functionalities for the filters to achieve high permeance, high removal efficiency, and excellent reusability.
