SGN-90206 International Doctoral Seminar in Signal Processing, 1 cr
Lessons
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Description:
This course is given by Fulbright Professor Bhaskar Rao.
Person responsible:
Jaakko Astola
Bhaskar Rao
Ioan Tabus
Target groups:
Electrical Engineering
Information Technology
International Students
Jatkotutkinto-opiskelijat
Signaalinkäsittely ja tietoliikennetekniikka
Assessment scale:
Numerical evaluation scale (1-5) will be used on the course
Additional information of course implementation:
In the recent decade, sparse representations have shown to be useful in many signal processing applications such as medical imaging, communications, among other applications. This course will provide an overview of the algorithms and available theoretical results in this emerging area. The course will cover the following topics: - Compressed Sensing, Sparse representations and the Sparse Signal Recovery (SSR) problem: an introduction to the SSR problem and an analysis of its properties and potential difficulties. - Matching Pursuit and recovery algorithms. The main basic matching pursuit algorithms, and regularization based algorithms will be discussed along with the performance guarantees. Recovery conditions based the dictionary coherence and the restricted isometry property will be discussed. - Bayesian Methods: Maximum Aposteriori (MAP) techniques will be discussed followed by hierarchical Bayesian methods such as Sparse Bayesian learning. - Extensions: Useful extensions such as block sparsity and the multiple measurements problem will be discussed. - Bibliographical pointers will be given for each type of problem and research topic.