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Compressive Sensing and Channel Coding: A Unified Perspective

Compressive Sensing and Channel Coding: A Unified Perspective

Compressive Sensing and Channel Coding: A Unified Perspective. Amir H. Banihashemi BCWS Centre, Director Dept. of Systems and Computer Engineering Carleton University Ottawa, Canada. Outline. Compressive Sensing Channel Coding

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1.5k views • 126 slides
Ron DeVore University of South Carolina Richard Baraniuk Rice University

Ron DeVore University of South Carolina Richard Baraniuk Rice University

Compressive Sensing Theory and Applications. Ron DeVore University of South Carolina Richard Baraniuk Rice University. Agenda. Introduction to Compressive Sensing (CS) [richb] motivation basic concepts CS Theoretical Foundation [ron] sparsity encoding and decoding

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1.22k views • 93 slides
To add

To add

To add. Relation with JL and JLCS Volkan for UMd:

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887 views • 76 slides
Richard Baraniuk Mark Davenport Marco Duarte Chinmay Hegde Rice University Michael Wakin

Richard Baraniuk Mark Davenport Marco Duarte Chinmay Hegde Rice University Michael Wakin

Manifold Models for Signal Acquisition, Compression and Processing. Richard Baraniuk Mark Davenport Marco Duarte Chinmay Hegde Rice University Michael Wakin Colorado School of Mines. Supported by NSF, ONR, ARO, AFOSR, DARPA, Texas Instruments. Digital Sensing Revolution.

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685 views • 54 slides
Richard Baraniuk Rice University dsp.rice/cs

Richard Baraniuk Rice University dsp.rice/cs

Compressive Signal Processing. Richard Baraniuk Rice University dsp.rice.edu/cs. Compressive Sensing (CS). When data is sparse/compressible, can directly acquire a condensed representation with no/little information loss Random projection will work. sparse signal. measurements.

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275 views • 12 slides

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