Compressive Sampling ()

Vortragende/r (Mitwirkende/r)
Umfang3 SWS
SemesterWintersemester 2020/21
Stellung in StudienplänenSiehe TUMonline
TermineSiehe TUMonline




At the end of the lecture, participants will have an understanding of the basic mathematical methods and reasonings behind the ideas of Compressive Sampling. Additionally the participants will develop an intuition for suitability of CS regarding possible applications.


The aim of the lecture is to introduce the topic of "Compressive Sampling" to students with an engineering background. In the past few years CS has become an important tool in signal processing applications. The main observation behind CS is that usually natural signals exhibit sparse representations which means that only few components of the signal carries information. In CS this sparsity is exploited and the signal can be reconstructed from far fewer measurements then its ambient dimension.

Inhaltliche Voraussetzungen

Linear Algebra, Systemtheory, Signal representation in Time- and Frequency domain, mathematical interest

Lehr- und Lernmethoden


Studien-, Prüfungsleistung

Homeworks, project and oral exam

Empfohlene Literatur

S. Foucart, H. Rauhut "A mathematical introduction to Compressed Sensing" , Eldar, Y. & Kutyniok, G. "Compressed Sensing: Theory and Applications", R.Baraniuk, E. Candes, Romberg, Davenport - Lecture Notes and Tutorials (on M. Elad "Sparse and Redundant Representations"