Finding relevant information in most natural signals is like looking for a needle in a haystack; as such most models of signals (e.g. those that model signals as members of a vector space) exhibit too many degrees of freedom. To overcome this challenge sparse models of signals have been introduced ranging from those that limit the number of nonzeroes in a vector to manifold models and low-rank matrices.
The Sparse Signal Processing Group (SSPG) at DML is focused on the application of sparse signal representations and compressed sensing theory to challenging signal processing tasks such as image categorization, remote sensing, brain decoding, image tracking, etc. Our current research is clustered into three main areas:
- Structured Sparse Representations
- Sparse Semi-Supervised Methods
DML research laboratory started research on this area in May 2011.
Sparse Signal Processing Group, DML CE, Sharif University of Technology, Tehran, Tehran, Iran Tel: +9821 6616 6683 Fax: +9821 66029163 E-mail: firstname.lastname@example.org
Last Update : 23 July 2013