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Compressive Sensing, strukturierte Zufallsmatritzen und Anwendungen

date
24.10.2018 
time
05:00 PM - 06:00 PM 
speaker
Prof. Dr. Holger Rauhut 
affiliation
RWTH Aachen 
part of series
TUD Dresden Mathematics Seminar 
language
de 
main topic
Mathematics: general
host
Prof. Dr. Oliver Sander 
abstract

Compressive Sensing originates in mathematical signal processing and
predicts that sparse (or compressible) signals can be reconstructed from a
small number of linear measurements - far fewer than previously believed to
be possible. Efficient reconstruction methods including convex optimization
approaches are available. Remarkably, all provably optimal measurement
processes known so far are modeled by random matrices. While many
standard results are formulated for (sub-)Gaussian random matrices, practical
applications require more structure in the measurement process, which leads
to the study of structured random matices. Examples arise from random
sampling of Fourier expansionsor from subsampled random convolutions.
Applications of compressive sensing include a variety of signal processing
tasks such as magnetic resonance imaging, radar imaging, astronomical data
processing, sparse statistical modeling. More recently, compressive sensing
has also been used for the numerical solution of high-dimensional parametric
partial differential equations (uncertainty quantification).
The talk will give an introduction to compressive sensing, report on some
techniques for the analysis of structured random matrices and outline
applications in signal processing and numerical analysis.

 

Last update: 19.10.2018 15:19.

venue 

TUD Willers-Bau (B 321) 
Zellescher Weg 12-14
01069 Dresden
homepage
https://navigator.tu-dresden.de/etplan/wil/00 

organizer 

TUD Mathematik
Willersbau, Zellescher Weg 12-14
01069 Dresden
telefon
49-351-463 33376 
homepage
http://tu-dresden.de/mathematik 
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