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SUMMARY:Generalized Sampling and Infinite Dimensional Compressed  Sensing
DTSTART:20110622T151500
DTSTAMP:20260429T125423Z
UID:c1a71be56de004bdeef2a9bf7d37be6d16d96fba95d6bb244ceb7171
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Anders Christian Hansen\, University of Cambridge\nI wil
 l discuss a generalization of the Shannon Sampling Theorem that allows for
  reconstruction of signals in arbitrary bases. Not only can one reconstruc
 t in arbitrary bases\, but this can also be done in a completely stable wa
 y. When extra information is available\, such as sparsity or compressibili
 ty of the signal in a particular bases\, one may reduce the number of samp
 les dramatically. This is done via Compressed Sensing techniques\, however
 \, the usual finite-dimensional framework is not sufficient. To overcome t
 his obstacle I'll introduce the concept of Infinite Dimensional Compressed
  Sensing. Prof. Hansen's homepage
LOCATION:BC 01 https://plan.epfl.ch/?room==BC%2001
STATUS:CONFIRMED
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