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UID:20260621T185923EDT-5459DULkmb@132.216.98.100
DTSTAMP:20260621T225923Z
DESCRIPTION:The Montreal Chapter of the IEEE Signal Processing Society\, in
  collaboration with STARaCom\n\n\n	Speaker: Mr. Wesley L. Passos\, Federal 
 University of Rio de Janeiro (COPPE/UFRJ).\n	\n	Abstract: The mosquito Aedes
  aegypti is the transmitter of several diseases\, such as dengue\, zika\, 
 and chikungunya. The current best way to combat these diseases is to contr
 ol and eliminate potential mosquito breeding grounds. The Aedes aegypti re
 produces in clean and stagnant water. So\, any containers that store water
  are possible breeding grounds. According to current sanitary regulations\
 , health agents must visit properties to search for and eliminate potentia
 l mosquito breeding grounds. However\, this approach presents many limitat
 ions\, including temporal or frequency constraints\, safety concerns\, and
  costs. Satellite imagery is not considered a viable alternative due to li
 mited spatial and time resolutions besides its elevated costs. Using unman
 ned aerial vehicles (UAVs) or drones presents several advantages over trad
 itional methods\, including increased safety for auditors\, higher spatial
  and temporal resolution\, and minimal operational costs. Therefore\, in t
 his presentation\, we show the idea of using images and videos captured by
  a drone to support local health agents in locating potential hazardous si
 tes and using machine learning and computer vision techniques to aid the s
 pecialist in localizing relevant mosquito foci.\n\nBio: Wesley Lobato Pass
 os was born in Rio de Janeiro\, Brazil\, in 1991. He received the BSc degr
 ee in Control and Automation Engineering from the Federal Center for Techn
 ological Education (CEFET/RJ)\, Rio de Janeiro\, Brazil\, in 2016\, with 1
 4 months at the University of Georgia (UGA)\, USA (2015) and two months at
  the Illinois Institute of Technology (IIT)\, USA (2015). He received a Ma
 ster’s degree in Electrical Engineering from the Federal University of Rio
  de Janeiro (COPPE/UFRJ) in 2019. He is pursuing a Doctoral degree in Elec
 trical Engineering at the Federal University of Rio de Janeiro (COPPE/UFRJ
 ). He won the Google Latin America Research Award in 2019 and 2020 for the
  project on detecting Aedes aegypti mosquito outbreaks using computer visi
 on and machine learning techniques. He has participated in several researc
 h and development projects on signal processing and machine learning\, esp
 ecially for oil and gas industry applications. He has experience with aeri
 al images\, image and video processing\, computer vision\, and machine lea
 rning.\n
DTSTART:20230316T203000Z
DTEND:20230316T213000Z
LOCATION:MC 603\, McConnell Engineering Building\, CA\, QC\, Montreal\, H3A
  0E9\, 3480 rue University
SUMMARY:Deep-Learning Detection of Aedes aegypti Breeding Grounds Based on 
 Drone Images
URL:/cim/channels/event/deep-learning-detection-aedes-
 aegypti-breeding-grounds-based-drone-images-351767
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