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DTSTART:20260329T020000
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DTSTAMP:20260305T201618Z
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DTSTART;TZID=Europe/London:20260325T163000
DTEND;TZID=Europe/London:20260325T173000
DESCRIPTION:The IEEE University of Strathclyde Student Branch is pleased to
  host Dr. Ghulam E. Mustafa Abro (Senior Member\, IEEE) for a special invi
 ted lecture on intelligent anomaly detection in autonomous UAV swarms.\n\n
 Unmanned Aerial Vehicle (UAV) swarms are increasingly used in applications
  such as surveillance\, disaster response\, environmental monitoring\, and
  autonomous logistics. However\, ensuring reliability and safety in large-
 scale multi-agent systems remains a significant challenge. Detecting abnor
 mal behaviour within distributed UAV networks is critical to maintaining r
 obust and secure operations.\n\nIn this talk\, Dr. Abro will present advan
 ced approaches for intelligent anomaly detection in UAV swarms\, leveragin
 g Graph Attention Networks (GAT) and RSSI signal analysis. The discussion 
 will explore how machine learning and network signal features can be combi
 ned to identify irregular behaviour patterns and enhance situational aware
 ness in autonomous aerial systems.\n\nParticipants will gain insight into 
 modern AI techniques applied to swarm intelligence\, distributed robotics\
 , and autonomous system monitoring.\n\nSpeaker(s): \, Dr. Abro\n\nVirtual:
  https://events.vtools.ieee.org/m/544911
LOCATION:Virtual: https://events.vtools.ieee.org/m/544911
ORGANIZER:david.emmanuel@strath.ac.uk
SEQUENCE:60
SUMMARY:Intelligent Anomaly Detection in UAV Swarms Using Graph Attention a
 nd RSSI Signals
URL;VALUE=URI:https://events.vtools.ieee.org/m/544911
X-ALT-DESC:Description: <br /><p data-start="763" data-end="973">The IEEE U
 niversity of Strathclyde Student Branch is pleased to host Dr. Ghulam E. M
 ustafa Abro (Senior Member\, IEEE) for a special invited lecture on intell
 igent anomaly detection in autonomous UAV swarms.</p>\n<p data-start="975"
  data-end="1369">Unmanned Aerial Vehicle (UAV) swarms are increasingly use
 d in applications such as surveillance\, disaster response\, environmental
  monitoring\, and autonomous logistics. However\, ensuring reliability and
  safety in large-scale multi-agent systems remains a significant challenge
 . Detecting abnormal behaviour within distributed UAV networks is critical
  to maintaining robust and secure operations.</p>\n<p data-start="1371" da
 ta-end="1753">In this talk\, Dr. Abro will present advanced approaches for
  intelligent anomaly detection in UAV swarms\, leveraging Graph Attention 
 Networks (GAT) and RSSI signal analysis. The discussion will explore how m
 achine learning and network signal features can be combined to identify ir
 regular behaviour patterns and enhance situational awareness in autonomous
  aerial systems.</p>\n<p data-start="1755" data-end="1898">Participants wi
 ll gain insight into modern AI techniques applied to swarm intelligence\, 
 distributed robotics\, and autonomous system monitoring.</p>
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