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DTSTART:19700329T030000
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DTSTART:19701025T040000
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CREATED:20230430T132718Z
LAST-MODIFIED:20230430T132718Z
DTSTAMP:20260719T224129Z
UID:1784490089@tuc.gr
SUMMARY:Ομιλία κας Αγγελικής Ξενάκη "Array S
 ignal Processing and Machine Learnin
 g Methods for Applications in Underw
 ater Acoustics"
LOCATION:
DESCRIPTION:https://www.ece.tuc.gr/el/katalogos-
 ekdiloseon?tx_tucevents2_tuceventsdi
 splay%5Baction%5D=show&tx_tucevents2
 _tuceventsdisplay%5Bcontroller%5D=Ev
 ent&tx_tucevents2_tuceventsdisplay%5
 Bevent%5D=6157&cHash=b024844f97a7083
 fd658c5f6dcd51726\nAbstract\n Array 
 signal processing aims to infer info
 rmation about a signal from spatio-t
 emporal measurements of the associat
 ed wavefield on an array of sensors.
  Combining the outputs of the sensor
 s on an array enhances the signal ov
 er noise and attributes directionali
 ty to the system, allowing to charac
 terize not only the spectral content
  of the recorded wavefield but also 
 the number and locations of the sour
 ces that produce it. Therefore, arra
 y signal processing is an active res
 earch area in diverse fields, e.g., 
 in radar, seismic, and acoustic imag
 ing. In acoustics, array signal proc
 essing is used for visualization of 
 sound fields, identification and loc
 alization of sound sources, or acous
 tic imaging of objects that scatter 
 sound. Machine learning methods allo
 w parameter inference of increasingl
 y complex systems by capitalizing on
  large amount of data. Hence, data-d
 riven inference with machine learnin
 g methods has gained popularity over
  traditional model-based methods as 
 it requires no particular assumption
 s or decisions about the underlying 
 physics. This presentation will cove
 r some model-based and data-driven m
 ethods for sonar signal processing. 
 Synthetic aperture sonar processing,
  sparse reconstruction and represent
 ation learning for high-resolution a
 coustic imaging will be discussed in
  more detail, highlighting the exper
 imental results and the scientific c
 ontributions.\n About the speaker\n 
 Angeliki Xenaki received the Diploma
  degree in electrical engineering an
 d computer science from the National
  Technical University of Athens, Gre
 ece, in 2007, and the M.Sc. and Ph.D
 . degrees in acoustics from the Tech
 nical University of Denmark (DTU), i
 n 2010 and 2015, respectively.  From
  2012 to 2014 she was a visiting res
 earcher at the Scripps Institution o
 f Oceanography, University of Califo
 rnia San Diego and from 2015 to 2016
  she was a postdoctoral researcher a
 t DTU. From 2016 to 2018 she was a r
 esearch scientist at GN Hearing A/S,
  Denmark, specializing in array sign
 al processing for hearing devices. I
 n 2018, she joined the Centre for Ma
 ritime Research and Experimentation,
  Italy, as a Scientist working in th
 e field of synthetic aperture sonar.
  Her research interests include sign
 al processing, statistical modeling 
 and machine learning.\n
STATUS:CONFIRMED
ORGANIZER;RSVP=FALSE;CN=TUC;CUTYPE=TUC:mailto:webmaster@tuc.gr
DTSTART:20230504T110000
DTEND:20230504T120000
TRANSP:OPAQUE
CLASS:DEFAULT
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