|Lecturer||Boche, Holger and Walk, Philipp|
|Language||more on TUMOnline; German and English|
|Umfang||2 Lectures/ 1 Excercise|
"Compressive Sampling" (CS) or "Compressed Sensing" is a complete new research field developed rapidly over the last decade and gained a huge impact in almost every area of signal processing, communication, imaging and many other engineering fields.
The assumption in CS is that in a very large but finite system, which can be described by n parameters, only few, that is, k<<n of them determine the signals or states of the system. Such a system is called a k-sparse system.
It is known nowadays that for sparse systems there exists alternative measuring methods and algorithms which can reconstruct the sparse signals with fewer samples as needed by classical sampling methods. Hereby, the best known linear measuring methods are given by random measurements, which released a paradigm shift in sampling theory. To explain this compressive sampling methods we will introduce the student into geometry, approximation theory in finite dimension and convex optimization theory.
|Dates||Monday: 16:45 - 18:15 Lecture |
Monday: 18:15 - 19:00 Excercise
N4410 - Gebäude N4, 4. Etage, First Date: 06.10.2014