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Compressed sensing: some theory, algorithms and applications

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A new way of thinking about signal acquisition has recently emerged called compressed sensing. The basic philosophy is to try to sample close to some measure of information rate of the signal rather than the traditional Shannon/Nyquist rate without recourse to adaptive sampling. This talk will review some of the underlying theory that explains why under certain circumstances this is possible. It will then look at some of the practical algorithmic solutions to the signal approximation problem that have been developed. I will conclude with a couple of specific example applications that we are looking at in Edinburgh.

This talk is part of the Signal Processing and Communications Lab Seminars series.

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