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UID:8@edgeaihub.co.uk
DTSTART;TZID=Europe/London:20251023T140000
DTEND;TZID=Europe/London:20251023T150000
DTSTAMP:20260814T103739Z
URL:https://edgeaihub.co.uk/events/national-edge-ai-hub-webinar-series-edg
 e-computing-for-rf-based-human-sensing/
SUMMARY:National Edge AI Hub Webinar Series: Edge Computing for RF-based Hu
 man Sensing
DESCRIPTION:\n\nJoin us for the next instalment of the National Edge AI Hub
 ’s webinar series\, featuring Shuokang Huang\, who will present his late
 st research in his talk "Edge Computing for RF-based Human Sensing." His t
 alk will explore how edge computing and machine learning can enhance devic
 e-free RF sensing in IoT systems by extracting meaningful features from co
 mplex\, interference-prone signals to improve detection accuracy and effic
 iency.\n\nShuokang Huang will delve into the technical foundations\, pract
 ical implementations\, and emerging use cases that are shaping the future 
 of human-centric edge AI systems.\n\nWhether you're a researcher\, enginee
 r\, or innovator in AI\, IoT\, or cyber-physical systems\, this webinar of
 fers valuable insights into one of the most exciting intersections of edge
  computing and human sensing.\n\nWhen: October 23rd\, 2025 at 2 PM BST\nWh
 ere: Zoom\, link will be provided to all who register.\n\n\n\nAbstract: In
  the Internet of Things (IoT)\, Radio Frequency (RF) sensing plays a cruci
 al role by detecting objects/events with RF signals. One of the latest RF 
 sensing approaches is device-free RF sensing which utilizes signals from e
 xisting edge devices (e.g.\, WiFi routers\, mobile phones\, smart watches)
 . These edge devices were initially designed for wireless communication bu
 t not for sensing\, so device-free RF sensing may fail to achieve satisfac
 tory detection accuracy due to radio frequency interference (RFI). To impr
 ove the accuracy of device-free RF sensing\, machine learning (ML) is a pr
 omising solution because ML can find approximately optimal solutions to th
 e complex objectives of RF sensing\, whose factors (e.g.\, frequency\, sig
 nal-to-noise ratio) are too complicated to express with explicit mathemati
 cal formulations. To this end\, we investigate edge computing to learn fro
 m the vast amounts of RF data (e.g.\, RF signals\, channel states) and the
 reby extract informative features from overlapping signals\, balancing sen
 sing performance and communication overhead. Overall\, our investigation n
 ot only poses new challenges for existing studies but also opens up new op
 portunities for future work.\n\nAbout the Speaker: Shuokang Huang is a Ph.
 D. student supervised by Prof. Julie A. McCann in AESE Group\, Department 
 of Computing\, Imperial College London. His research focuses on human sens
 ing with WiFi channel state information (CSI)\, device-free radio frequenc
 y (RF) sensing\, and applied machine learning on RF sensing. His previous 
 experiences involve semi-supervised adversarial learning\, federated learn
 ing\, etc. In 2021\, he received his M.Sc. degree in Computer Applied Tech
 nology from Peking University. In 2018\, he obtained his B.Eng. degree in 
 Electronic Information Science and Technology from Sun Yat-sen University.
CATEGORIES:Research Webinar,Webinar
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