(Skills) Introduction to Compressive Sensing: From Theory to Applications / (Skills) Best papers Lent 2010

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• Wei Chen and others
• Monday 08 March 2010, 14:00-15:00
• SS03, William Gates Building.

If you have a question about this talk, please contact Andrew Rice.

Introduction to Compressive Sensing: From Theory to Applications, Wei Chen

The conventional approach to sampling signals or images follows the celebrated Shannon sampling theorem: the sampling rate must be at least twice the maximum frequency present in the signal (the so-called Nyquist rate). In fact, this principle underlies nearly all signal acquisition protocols used in consumer audio and visual electronics, medical imaging devices, radio receivers, and so on. In the field of data conversion, for example, standard analog-to-digital converter (ADC) technology implements the usual quantized Shannon representation: the signal is uniformly sampled at or above the Nyquist rate. Compressive sensing (CS) is a novel sensing/sampling paradigm that goes against the common wisdom in data acquisition. The CS theory asserts that one can recover certain signals and images from far fewer samples or measurements than traditional methods use.

Skills: Best Papers Michaelmas 2009

Each member of the group will submit an entry for the best paper they have read last term. We will have very a brief presentation on as many as we can fit in to 30 minutes.

This talk is part of the Computer Laboratory Digital Technology Group (DTG) Meetings series.

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