Universität Wien

250106 VO Compresses sensing (2012S)

5.00 ECTS (3.00 SWS), SPL 25 - Mathematik

Details

Language: English

Examination dates

Lecturers

Classes (iCal) - next class is marked with N

  • Thursday 01.03. 10:00 - 11:00 Seminarraum
  • Tuesday 06.03. 10:00 - 12:00 Seminarraum
  • Thursday 08.03. 10:00 - 11:00 Seminarraum
  • Tuesday 13.03. 10:00 - 12:00 Seminarraum
  • Thursday 15.03. 10:00 - 11:00 Seminarraum
  • Tuesday 20.03. 10:00 - 12:00 Seminarraum
  • Thursday 22.03. 10:00 - 11:00 Seminarraum
  • Tuesday 27.03. 10:00 - 12:00 Seminarraum
  • Thursday 29.03. 10:00 - 11:00 Seminarraum
  • Tuesday 17.04. 10:00 - 12:00 Seminarraum
  • Thursday 19.04. 10:00 - 11:00 Seminarraum
  • Tuesday 24.04. 10:00 - 12:00 Seminarraum
  • Thursday 26.04. 10:00 - 11:00 Seminarraum
  • Thursday 03.05. 10:00 - 11:00 Seminarraum
  • Tuesday 08.05. 10:00 - 12:00 Seminarraum
  • Thursday 10.05. 10:00 - 11:00 Seminarraum
  • Tuesday 15.05. 10:00 - 12:00 Seminarraum
  • Tuesday 22.05. 10:00 - 12:00 Seminarraum
  • Thursday 24.05. 10:00 - 11:00 Seminarraum
  • Thursday 31.05. 10:00 - 11:00 Seminarraum
  • Tuesday 05.06. 10:00 - 12:00 Seminarraum
  • Tuesday 12.06. 10:00 - 12:00 Seminarraum
  • Thursday 14.06. 10:00 - 11:00 Seminarraum
  • Tuesday 19.06. 10:00 - 12:00 Seminarraum
  • Thursday 21.06. 10:00 - 11:00 Seminarraum
  • Tuesday 26.06. 10:00 - 12:00 Seminarraum
  • Thursday 28.06. 10:00 - 11:00 Seminarraum

Information

Aims, contents and method of the course

Compressed sensing is a new mathematical theory of data acquisition and processing. The basic idea is that common data object possess a sparse representation is an appropriate basis. Therefore only fewer measurements than dictated by the dimension of the ambient vector space should be sufficient to completely describe an sparse vector (or signal or image). This idea goes against the conventional wisdom.
The theory of compressed sensing is due to E. Candes, T. Tao (Fields medal 2006), and D. Donoho. Compressed sensing is an interdisciplinary area of research between mathematics, data processing, and statistics. Many researchers believe that compressed will cause a revolution in the way we acquire, process, and store data (of signals, images, and high-dimensional structures).

Assessment and permitted materials

Oral exam

Minimum requirements and assessment criteria

The course will present the foundations of compressed sensing and the underlying mathematics. The deepest results in compressed sensing are connected and revive some fundamental problem in the local theory of Banach spaces as well as in probability theory.

Examination topics

Prerequisites: good knowledge of linear algebra and the basic of probability theory (expectation, random variable, density)
Additional material will be discussed during the lectures.

Reading list

Literatur: Ich werde mich auf eine Vorabdruck des Buches ``A
mathematical introduction to compressive sensing'' von Simon Foucart und
Holger Rauhut stuetzen.

Dazu kommen noch

``Introduction to compressed sensing'' von M. Davenport, M. Duarte,
Y. Eldar, G. Kutyniok (soll im Februar 2012) erscheinen

Michael Elad ``Sparse and redundant representations'' (Springer 2010)

sowie Originalliteratur.

Eine umfangreiche Liste von Literatur ist auf der
Compressed-Sensing-Webseite der Rice University zusammengestellt: http://dsp.rice.edu/cs

Association in the course directory

MAMV

Last modified: Mo 07.09.2020 15:40