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, sampling of complex networks, etc. Our current research is focused on the following projects: