April 29, 2009 4pm-5pm, Shamrock Ballroom #261, UH Hilton
Abstract
One of the central tenets of signal processing and data acquisition is the Shannon/Nyquist sampling theory: the
number of samples needed to capture a signal is dictated by its bandwidth. This talk introduces a novel sampling
or sensing theory which goes against this conventional wisdom. This theory now known as Compressed Sensing or
Compressive Sampling?? allows the faithful recovery of signals and images from what appear to be highly
incomplete sets of data, i.e. from far fewer measurements or data bits than traditional methods use. We will
present the key ideas underlying this new sampling or sensing theory, and will survey some of the most important
results. We will emphasize the practicality and the broad applicability of this technique, and discuss what we
believe are far reaching implications; e.g. procedures for sensing and compressing data simultaneously and much
faster. Finally, there are already many ongoing efforts to build a new generation of sensing devices based on
compressed sensing and we will discuss remarkable recent progress in this area as well.
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Last modified: April 11 2016 - 18:14:43