BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系 - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系
X-ORIGINAL-URL:https://ece.hku.hk
X-WR-CALDESC:Events for Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Asia/Hong_Kong
BEGIN:STANDARD
TZOFFSETFROM:+0800
TZOFFSETTO:+0800
TZNAME:HKT
DTSTART:20240101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250410T163000
DTEND;TZID=Asia/Hong_Kong:20250410T173000
DTSTAMP:20260512T022506
CREATED:20250410T071105Z
LAST-MODIFIED:20250410T071105Z
UID:111090-1744302600-1744306200@ece.hku.hk
SUMMARY:Semantic-Relevance Based Sensor Selection for Edge-AI Empowered Sensing Systems
DESCRIPTION:Abstract\nThe sixth-generation (6G) mobile network is envisioned to incorporate sensing and edge artificial intelligence (AI) as two key functions. Their natural convergence leads to the emergence of Integrated Sensing and Edge AI (ISEA)\, a novel paradigm enabling real-time  acquisition and understanding of sensory information at the network edge. However\, ISEA faces a communication bottleneck due to the large number of sensors and the high dimensionality of sensory features. Traditional approaches to communication-efficient ISEA lack awareness of semantic relevance\, i.e.\, the level of relevance between sensor observations and the downstream task. In this seminar\, I will introduce a novel framework for semantic-relevance-aware sensor selection to achieve optimal end-to-end (E2E) task performance under heterogeneous sensor relevance and channel states. E2E sensing accuracy analysis is provided to characterize the sensing task performance in terms of selected sensors’ relevance scores and channel states. Building on the results\, the sensor-selection problem for accuracy maximization is formulated as an integer program and solved through a tight approximation of the objective. The optimal solution exhibits a priority-based structure\, which ranks sensors based on a priority indicator combining relevance scores and channel states and selects top-ranked sensors. Experimental results on both synthetic and real datasets show substantial accuracy gain achieved by the proposed selection scheme compared to existing benchmarks. \nSpeaker\nLIU Zhiyan\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nZhiyan Liu received the B.Eng. degree from the Dept. of Electronic Engineering\, Tsinghua University\, Beijing\, in 2021. He is currently working towards the Ph.D. degree with Dept. of Electrical and Electronic Engineering\, The University of Hong Kong (HKU)\, Hong Kong. His research interests include edge intelligence and distributed sensing in 6G wireless networks. \nOrganiser\nProf. Kaibin Huang\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong\n\nAll are welcome!
URL:https://ece.hku.hk/events/20250410-1/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
END:VEVENT
END:VCALENDAR