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VERSION:2.0
PRODID:-//Memento EPFL//
BEGIN:VEVENT
SUMMARY:Sparse Signal Processing
DTSTART:20120907T140000
DTSTAMP:20260406T185300Z
UID:291cf60b71f537dcb83b71c1cfd96a538d5ded3002f16c135f3ba773
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. F. Marvasti\, Sharif University of Technology\nThe Biome
 dial Imaging Group (BIG) invites Prof Marvasti: Classical sampling theorem
  states that by using an anti-aliased low-pass filter at the Nyquist rate\
 , one can transmit and retrieve the filtered signal. This approach\, which
  has been used for decades in signal processing\, is not good for high qua
 lity speech\, image and video signals where the actual signals are not low
 -pass but rather sparse. The traditional sampling theorems do not work for
  sparse signals. Modern approach\, developed by statisticians at Stanford 
 (Donoho and Candes)\, give some lower bounds for the minimum sampling rate
  such that a sparse signal can be retrieved with high probability. However
 \, their approach\, using a sampling matrix called compressive matrix\, ha
 s certain drawbacks: Compressive matrices require the knowledge of all the
  samples\, which defeats the whole purpose of compressive sampling! Moreov
 er\, for real signals\, one does not need a compressive matrix and we shal
 l show in this seminar that random sampling performs as good as or better 
 than compressive sampling. In addition\, we show that greedy methods such 
 as Orthogonal Matching Pursuit (OMP) are too complex with inferior perform
 ance compared to IMAT and other iterative methods. Furthermore\, we shall 
 compare IMAT to OMP and other reconstruction methods in terms of complexit
 y and show the advantages of IMAT. Various applications such as image and 
 speech recovery from random or block losses\, salt & pepper noise\, OFDM c
 hannel estimation\, MRI\, and finally spectral estimation will be discusse
 d and simulated.
LOCATION:BM 5202 https://plan.epfl.ch/?room==BM%205202
STATUS:CONFIRMED
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