Compressive Sampling ()
| Vortragende/r (Mitwirkende/r) | |
|---|---|
| Nummer | 0000000989 |
| Art | Vorlesung mit integrierten Übungen |
| Umfang | 3 SWS |
| Semester | Wintersemester 2022/23 |
| Unterrichtssprache | Deutsch |
| Stellung in Studienplänen | Siehe TUMonline |
| Termine | Siehe TUMonline |
Termine
-
(Keine Termine gefunden)
Teilnahmekriterien
Lernziele
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.
Beschreibung
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
Tafel
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 http://dsp.rice.edu/cs)
M. Elad "Sparse and Redundant Representations"
M. Elad "Sparse and Redundant Representations"