BEGIN:VCALENDAR
VERSION:2.0
PRODID:IEEE vTools.Events//EN
CALSCALE:GREGORIAN
BEGIN:VTIMEZONE
TZID:America/Los_Angeles
BEGIN:DAYLIGHT
DTSTART:20250309T030000
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3
TZNAME:PDT
END:DAYLIGHT
BEGIN:STANDARD
DTSTART:20241103T010000
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11
TZNAME:PST
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20241113T051219Z
UID:61B00FBC-7F22-4FC3-81EA-56E3C7AB0B6F
DTSTART;TZID=America/Los_Angeles:20241112T173000
DTEND;TZID=America/Los_Angeles:20241112T183000
DESCRIPTION:Small Unmanned Aerial Systems (sUAS) must be monitored closely 
 to identify\, diagnose\, and potentially mitigate flight problems as they 
 arise. During the flight\, the vast amounts of multivariate time series da
 ta typically generated by sUAS flight controllers can be complex to unders
 tand and analyze. While formal product documentation often provides exampl
 e data plots with diagnostic suggestions\, the sheer diversity of attribut
 es\, critical thresholds\, and complex data interactions can be overwhelmi
 ng to non-experts who subsequently seek help from discussion forums to int
 erpret their data logs. Solutions based on deep learning or heuristics can
  be used to detect anomalies in different time-series data attributes. How
 ever\, understanding and mitigating the root cause of flight problems base
 d upon the combination of multiple detected data anomalies requires signif
 icant domain expertise. To address these challenges\, this talk will prese
 nt approaches that leverage deep learning and heuristic methods to detect 
 anomalies in flight data\, both for real-time and post-flight analysis. Ad
 ditionally\, it will explore how the combination of multiple detected anom
 alies can be utilized to diagnose the root cause of the flight issue. The 
 solutions proposed aim to simplify the anomaly detection process while pro
 viding a more systematic approach for diagnosing complex flight behaviors.
 \n\nCo-sponsored by: Media Partner: Open Research Institute (ORI)\n\nSpeak
 er(s): Md Nafee Al Islam\n\nAgenda: \n- Invited talk from Assistant Prof. 
 Md Nafee Al Islam \, from the University of San Diego.\n- Q/A Session\n\nV
 irtual: https://events.vtools.ieee.org/m/442925
LOCATION:Virtual: https://events.vtools.ieee.org/m/442925
ORGANIZER:upalmahbub@yahoo.com
SEQUENCE:27
SUMMARY:Detection and Diagnosis of Flight Anomalies in Small Unmanned Aeria
 l Systems
URL;VALUE=URI:https://events.vtools.ieee.org/m/442925
X-ALT-DESC:Description: <br /><div>Small Unmanned Aerial Systems (sUAS) mus
 t be monitored closely to identify\, diagnose\, and potentially mitigate f
 light problems as they arise. During the flight\, the vast amounts of mult
 ivariate time series data typically generated by sUAS flight controllers c
 an be complex to understand and analyze. While formal product documentatio
 n often provides example data plots with diagnostic suggestions\, the shee
 r diversity of attributes\, critical thresholds\, and complex data interac
 tions can be overwhelming to non-experts who subsequently seek help from d
 iscussion forums to interpret their data logs. Solutions based on deep lea
 rning or heuristics can be used to detect anomalies in different time-seri
 es data attributes. However\, understanding and mitigating the root cause 
 of flight problems based upon the combination of multiple detected data an
 omalies requires significant domain expertise. To address these challenges
 \, this talk will present approaches that leverage deep learning and heuri
 stic methods to detect anomalies in flight data\, both for real-time and p
 ost-flight analysis. Additionally\, it will explore how the combination of
  multiple detected anomalies can be utilized to diagnose the root cause of
  the flight issue. The solutions proposed aim to simplify the anomaly dete
 ction process while providing a more systematic approach for diagnosing co
 mplex flight behaviors.&nbsp\;</div><br /><br />Agenda: <br /><ul>\n<li>In
 vited talk from Assistant Prof. Md Nafee Al Islam \, from the University o
 f San Diego.&nbsp\;</li>\n<li>Q/A Session</li>\n</ul>
END:VEVENT
END:VCALENDAR