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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
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TZID:Asia/Hong_Kong
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DTSTART:20230101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20240508T100000
DTEND;TZID=Asia/Hong_Kong:20240508T110000
DTSTAMP:20260512T175344
CREATED:20240429T062039Z
LAST-MODIFIED:20250114T063647Z
UID:18467-1715162400-1715166000@ece.hku.hk
SUMMARY:RPG Seminar – A New Adaptive Fading Instrumental Variable Pseudolinear Kalman Filter for 3D AOA Target Tracking
DESCRIPTION:Meeting ID: 990 0206 5927\nPassword: 585304 \nAbstract:\nThe instrumental variable pseudolinear Kalman filter (IV-PLKF) algorithm\, used for 3D angle-of-arrival (AOA) target tracking\, has been proven to be more robust to initialization errors\, with superior estimation performance and lower computational complexity compared to other state-of-the-art methods. However\, the IV-PLKF algorithm requires prior knowledge of the state and angle measurement noise information\, which is not available in practice. Improper selection of these values or mismatches due to time-varying changes can significantly impact the stability and estimation performance of the algorithm. To address this issue\, we propose a new adaptive fading (AF-) IV-PLKF algorithm that adaptively mitigates the possible scale mismatches in the state and measurement noise covariance matrices and the IV parameters. Simulation results demonstrate that the proposed algorithm outperforms the conventional IVPLKF under mismatched state and measurement noise covariance scenarios. Moreover\, the proposed method can even achieve comparable estimation performance to that of IV-PLKF with perfect knowledge of the noise information. \nSpeaker:\nMs. Mengxia HE\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nBiography of the speaker:\nMs. Mengxia HE received her B.Eng. degree from the University of Science and Technology Beijing in 2018 and her M.Eng. degree from the Beijing University of Posts and Telecommunications in 2021. She is currently pursuing the Ph.D. degree with the Department of Electrical and Electronic Engineering\, The University of Hong Kong\, Hong Kong. \nOrganizer:\nProf. S. C. CHAN \nAll are welcome.
URL:https://ece.hku.hk/events/20240508-1/
LOCATION:Online via Zoom
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20240508T103000
DTEND;TZID=Asia/Hong_Kong:20240508T113000
DTSTAMP:20260512T175344
CREATED:20240429T062517Z
LAST-MODIFIED:20250114T063611Z
UID:18468-1715164200-1715167800@ece.hku.hk
SUMMARY:RPG Seminar – Transformer-based Architectures for Automated Annotation in 3D Point Clouds
DESCRIPTION:Abstract\nManual annotation of 3D point clouds is notoriously labor-intensive\, prompting the need for automated solutions. Existing automated annotation methods\, however\, are typically complex and may neglect the crucial inter-object feature relationships that are informative for annotating challenging samples. In response\, we introduce two end-to-end Transformer-based models\, CAT and CAT++\, which are streamlined to serve as automated 3D-box labelers. These models leverage a minimal set of human annotations to produce precise 3D box annotations from 2D boxes. Our architecture employs a dual encoder strategy: a local intra-object encoder and a global inter-object encoder\, both utilizing self-attention mechanisms to process sequence and batch dimensions. The intra-object encoder captures point-level interactions within objects\, while the inter-object encoder discerns feature relationships across objects\, enhancing scene comprehension. The advanced CAT++ model incorporates a Hierarchical-interleaved encoding scheme and an implicit neural representation\, further refining the annotation process. Benchmarking experiments on the KITTI and nuScenes datasets demonstrate our models’ superior performance over current state-of-the-art methods\, particularly in annotating complex scenarios encompassing all hard samples. \nSpeaker\nMs. Xiaoyan QIAN\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nBiography of the speaker\nMs. Xiaoyan QIAN received the B.Eng. degree in Industrial Engineering from the Zhejiang University of Technology\, Zhejiang\, China. She is currently a Ph.D. candidate in the Department of Electrical and Electronic Engineering at the University of Hong Kong\, under the supervision of Dr. N Wong and Prof. SC Tan. Her current research interests mainly focus on 3D point clouds\, weakly supervised 3D object detection\, and auto-driving. \nOrganizer\nProf. N. WONG \nAll are welcome.
URL:https://ece.hku.hk/events/20240508-2/
LOCATION:Online via Zoom
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
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