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VERSION:2.0
PRODID:IEEE vTools.Events//EN
CALSCALE:GREGORIAN
BEGIN:VTIMEZONE
TZID:Asia/Shanghai
BEGIN:STANDARD
DTSTART:19910915T010000
TZOFFSETFROM:+0900
TZOFFSETTO:+0800
TZNAME:CST
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BEGIN:VEVENT
DTSTAMP:20251224T024256Z
UID:A050F049-95AE-48EB-8FCA-AE6408158B91
DTSTART;TZID=Asia/Shanghai:20250421T150000
DTEND;TZID=Asia/Shanghai:20250421T163000
DESCRIPTION:Abstract: As Internet-of-Vehicles (IoV) systems continue to evo
 lve\, video transmission plays a crucial role in applications such as auto
 nomous driving. Unlike static images\, video data provides richer and more
  continuous information\, making it essential for real-time\, dynamic deci
 sion-making in IoV systems. However\, delivering high-quality video over w
 ireless networks in real-time presents significant challenges\, including 
 bandwidth constraints\, high latency\, and degraded system performance. Th
 is talk introduces a novel semantic communication system for video streami
 ng that leverages generative AI to significantly reduce data transmission 
 while ensuring receiver requirements are met. Experimental results from a 
 prototype implementation demonstrate that this approach outperforms existi
 ng methods\, delivering substantial improvements in efficiency and perform
 ance.\n\nSpeaker(s): Celimgue Wu\n\nRoom: Room 224\, Meeting Room No. 2\, 
 Bldg: Pan Zhonglai Building\, School of Electronic Science and Engineering
 \, Nanjing University\, Nanjing\, Jiangsu\, China
LOCATION:Room: Room 224\, Meeting Room No. 2\, Bldg: Pan Zhonglai Building\
 , School of Electronic Science and Engineering\, Nanjing University\, Nanj
 ing\, Jiangsu\, China
ORGANIZER:junzheng@seu.edu.cn
SEQUENCE:22
SUMMARY:IEEE VTS NANJING CHAPTER DISTINGUISHED LECTURE BY PROF. Celimgue WU
URL;VALUE=URI:https://events.vtools.ieee.org/m/480895
X-ALT-DESC:Description: <br /><p style="margin: 0cm\; margin-bottom: .0001p
 t\; text-align: justify\; text-justify: inter-ideograph\; line-height: 110
 %\; layout-grid-mode: char\;"><strong style="mso-bidi-font-weight: normal\
 ;"><span lang="EN-US" style="font-family: 'Arial'\,'sans-serif'\; color: #
 a50021\;">Abstract:</span></strong><span lang="EN-US" style="font-family: 
 'Calibri'\,'sans-serif'\; mso-bidi-font-family: Times-Roman\; color: navy\
 ;"> </span><span lang="EN-US" style="font-size: 14.0pt\; line-height: 110%
 \; font-family: 'Calibri'\,'sans-serif'\; mso-bidi-font-family: Times-Roma
 n\; color: navy\; mso-font-kerning: 1.0pt\;">As Internet-of-Vehicles (IoV)
  systems continue to evolve\, video transmission plays a crucial role in a
 pplications such as autonomous driving. Unlike static images\, video data 
 provides richer and more continuous information\, making it essential for 
 real-time\, dynamic decision-making in IoV systems. However\, delivering h
 igh-quality video over wireless networks in real-time presents significant
  challenges\, including bandwidth constraints\, high latency\, and degrade
 d system performance. This talk introduces a novel semantic communication 
 system for video streaming that leverages generative AI to significantly r
 educe data transmission while ensuring receiver requirements are met. Expe
 rimental results from a prototype implementation demonstrate that this app
 roach outperforms existing methods\, delivering substantial improvements i
 n efficiency and performance.</span></p>
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