BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Europe/London BEGIN:DAYLIGHT DTSTART:20230326T020000 TZOFFSETFROM:+0000 TZOFFSETTO:+0100 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZNAME:BST END:DAYLIGHT BEGIN:STANDARD DTSTART:20231029T010000 TZOFFSETFROM:+0100 TZOFFSETTO:+0000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZNAME:GMT END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20230621T100302Z UID:6B43A10F-22AF-4A90-AF19-50C66969B127 DTSTART;TZID=Europe/London:20230522T070000 DTEND;TZID=Europe/London:20230522T083000 DESCRIPTION:Dr Julien Le Kernec - UoG\n\nBiography:\n\nJulien Le Kernec rec eived his B.Eng. and M.Eng. degrees in electronic engineering from the Cor k Institute of Technology\, Ireland\, respectively\, in 2004 and 2006\, re spectively\, his Ph.D. degree in electronic engineering from University Pi erre and Marie Curie\, France\, in 2011\, and his "Habilitation a Diriger des Recherches" from University Cergy-Pontoise\, France\, in 2022. He held a post-doctoral position with the Kuang-Chi Institute of Advanced Technol ogy\, Shenzhen\, China\, from 2011 to 2012. He was a Lecturer with the Dep artment of Electrical and Electronic Engineering\, The University of Notti ngham Ningbo China\, from 2012 to 2016. He is currently a Senior Lecturer with the School of Engineering in the Autonomous Systems & Connectivity Gr oup\, University of Glasgow. He is also an adjunct associate professor in University Cergy-Pontoise in France in the ETIS (Information and Signal Pr ocessing group). His research interests include radar system design\, soft ware-defined radio/radar\, signal processing\, and health applications.\n\ nPresentation Title: Radar sensing in assisted living: an overview\n\nAbst ract: I will discuss the place of radar for assisted living. First\, the c ontext of assisted living and the urgency to address the problem will be d escribed. The second part will give an overview of existing sensing modali ties for assisted living and explains why radar is an upcoming preferred m odality to address this issue. The third section presents developments in machine learning that help improve performances in classification\, especi ally with deep learning with a reflection on lessons learned from it. Fina lly\, I'll conclude with open challenges and future developments.\n\nProf Qian He – UESTC\n\nBiography: Qian He received her Ph.D. in signal and i nformation processing with honor in 2010 from University of Electronic Sci ence and Technology of China (UESTC)\, China\, and has been a full Profess or at UESTC since 2015. She was a Visiting Scholar and Postdoctoral Resear ch Associate with Electrical and Computer Engineering Department\, Lehigh University\, USA. Her research interests include statistical signal proces sing\, artificial intelligence\, and their applications in radar\, communi cation\, and medical/healthcare systems.\n\nProf. He was on the Editorial Boards of IEEE Signal Processing Letters\, Journal of Radars\, Journal of Communications and Information Sciences\, Advances in Energy and Power Eng ineering\, and was a member of the IEEE Signal Processing Society (SPS) Se nsor Array and Multichannel technical committee\, a member of the IEEE SPS Promoting Diversity in Signal Processing organizing committee\, and the C hair of the IEEE SPS Chengdu Chapter. She is a member of Sigma Xi\, senior member of IEEE\, senior member of the Chinese Institute of Electronics\, member of the IEEE SPS Young Professionals Committee and the Chair of the Engagement and Career Training Subcommittee\, and a member of the SPS Sign al Processing Theory and Methods technical committee.\n\nPresentation titl e: Quantization-based Collaborative MIMO Radar and Communications\n\nAbstr act: For a co-existing radar and communications (CERC) system with possibl y widely dispersed multiple-input and multiple-output (MIMO) antennas\, th rough collaboration between subsystems\, a hybrid-active-passive radar and a radar-aided communications manifest. We focus on radar target parameter estimation for the collaborative CERC. To reduce the communication burden \, local radar measurements are quantized before being sent to the fusion center (FC). Two quantization-based estimation strategies are introduced\, one quantizes the received signals locally to send to the FC\, while the other preprocesses the received signals and quantizes the preprocessor out put locally to send to the FC. Utilizing the quantized measurements collec ted from all receivers\, the FC makes the final estimation of the paramete rs of interest. The maximum likelihood estimators and Cramer-Rao bounds (C RBs) are derived for the target parameter estimation\, the impact of quant ization bits and collaboration are analyzed\, and when to use which strate gy is discussed.\n\nCo-sponsored by: University of Glasgow \n\nBldg: resea rch building B\, UESTC\, Qingshuihe\, No.2006\, Xiyuan Ave\, West Hi-Tech Zone\,\, Chengdu\, Sichuan\, China\, 611731\, Virtual: https://events.vtoo ls.ieee.org/m/364834 LOCATION:Bldg: research building B\, UESTC\, Qingshuihe\, No.2006\, Xiyuan Ave\, West Hi-Tech Zone\,\, Chengdu\, Sichuan\, China\, 611731\, Virtual: https://events.vtools.ieee.org/m/364834 ORGANIZER:julien.lekernec@glasgow.ac.uk SEQUENCE:7 SUMMARY:Dr Julien Le Kernec (UoG) - Radar in assisted living: an overview - Prof Qian He (UESTC) - Quantization-based Collaborative MIMO Radar and Co mmunications URL;VALUE=URI:https://events.vtools.ieee.org/m/364834 X-ALT-DESC:Description: <br /><p><strong><u>Dr Julien Le Kernec - UoG</u></ strong></p>\n<p><strong><u>Biography</u></strong>:</p>\n<p>Julien Le Kerne c received his B.Eng. and M.Eng. degrees in electronic engineering from th e Cork Institute of Technology\, Ireland\, respectively\, in 2004 and 2006 \, respectively\, his Ph.D. degree in electronic engineering from Universi ty Pierre and Marie Curie\, France\, in 2011\, and his "Habilitation a Dir iger des Recherches" from University Cergy-Pontoise\, France\, in 2022. He held a post-doctoral position with the Kuang-Chi Institute of Advanced Te chnology\, Shenzhen\, China\, from 2011 to 2012. He was a Lecturer with th e Department of Electrical and Electronic Engineering\, The University of Nottingham Ningbo China\, from 2012 to 2016. He is currently a Senior Lect urer with the School of Engineering in the Autonomous Systems &amp\; Conne ctivity Group\, University of Glasgow. He is also an adjunct associate pro fessor in University Cergy-Pontoise in France in the ETIS (Information and Signal Processing group). His research interests include radar system des ign\, software-defined radio/radar\, signal processing\, and health applic ations.</p>\n<p><strong>&nbsp\;</strong></p>\n<p><strong><u>Presentation T itle</u></strong>: Radar sensing in assisted living: an overview</p>\n<p>< strong><u>Abstract:</u></strong> I will discuss the place of radar for ass isted living. First\, the context of assisted living and the urgency to ad dress the problem will be described. The second part will give an overview of existing sensing modalities for assisted living and explains why radar is an upcoming preferred modality to address this issue. The third sectio n presents developments in machine learning that help improve performances in classification\, especially with deep learning with a reflection on le ssons learned from it. Finally\, I'll conclude with open challenges and fu ture developments.</p>\n<p>Prof Qian He &ndash\; UESTC</p>\n<p>Biography: Qian He received her Ph.D. in signal and information processing with honor in 2010 from University of Electronic Science and Technology of China (UE STC)\, China\, and has been a full Professor at UESTC since 2015.&nbsp\; S he was a Visiting Scholar and Postdoctoral Research Associate with Electri cal and Computer Engineering Department\, Lehigh University\, USA. Her res earch interests include statistical signal processing\, artificial intelli gence\, and their applications in radar\, communication\, and medical/heal thcare systems.&nbsp\;<br /><br />Prof. He was on the Editorial Boards of IEEE Signal Processing Letters\, Journal of Radars\, Journal of Communicat ions and Information Sciences\, Advances in Energy and Power Engineering\, and was a member of the IEEE Signal Processing Society (SPS) Sensor Array and Multichannel technical committee\, a member of the IEEE SPS Promoting Diversity in Signal Processing organizing committee\, and the Chair of th e IEEE SPS Chengdu Chapter. She is a member of Sigma Xi\, senior member of IEEE\, senior member of the Chinese Institute of Electronics\, member of the IEEE SPS Young Professionals Committee and the Chair of the Engagement and Career Training Subcommittee\, and a member of the SPS Signal Process ing Theory and Methods technical committee.<br /><br />Presentation title: Quantization-based Collaborative MIMO Radar and Communications<br /><br / >Abstract: For a co-existing radar and communications (CERC) system with p ossibly widely dispersed multiple-input and multiple-output (MIMO) antenna s\, through collaboration between subsystems\, a hybrid-active-passive rad ar and a radar-aided communications manifest. We focus on radar target par ameter estimation for the collaborative CERC. To reduce the communication burden\, local radar measurements are quantized before being sent to the f usion center (FC). Two quantization-based estimation strategies are introd uced\, one quantizes the received signals locally to send to the FC\, whil e the other preprocesses the received signals and quantizes the preprocess or output locally to send to the FC. Utilizing the quantized measurements collected from all receivers\, the FC makes the final estimation of the pa rameters of interest. The maximum likelihood estimators and Cramer-Rao bou nds (CRBs) are derived for the target parameter estimation\, the impact of quantization bits and collaboration are analyzed\, and when to use which strategy is discussed.</p> END:VEVENT END:VCALENDAR