Corelab Seminar

Themis Gouleakis (NTUA)
Compressive sensing (and applications)

Compressive sensing is a new sampling/data acquisition theory based on the discovery that one can exploit sparsity or compressibility when acquiring signals of general interest, and that one can design nonadaptive sampling techniques that condense the information in a compressible signal into a small amount of data. Interestingly, this may be changing the way engineers think about signal acquisition in areas ranging from analog-to-digital conversion, digital optics, magnetic resonance imaging, and seismics.

This talk will introduce fundamental conceptual ideas underlying this new sampling or sensing theory. Also, connections to well-known results from mathematics such as the Johnson-Lindenstrauss lemma will be addressed. There are already many ongoing efforts to build a new generation of sensing devices based on compressive sensing, and I will address remarkable recent progress in this area as well.