'Sparse signal' diaporamas de présentation

Richard Baraniuk Rice University

Randomized Dimensionality Reduction with Applications to Signal Processing and Communications. Richard Baraniuk Rice University. The Digital Universe. Size: 281 billion gigabytes generated in 2007 digital bits > stars in the universe growing by a factor of 10 every 5 years

1.35k views • 100 slides

Richard Baraniuk Rice University Supported by NSF, ONR, DARPA TI Leadership University Program

1.01k views • 83 slides

Duarte Wakin Sarvotham Baraniuk Guo Shamai

Duarte Wakin Sarvotham Baraniuk Guo Shamai. Compressed Sensing meets Information Theory. Dror Baron drorb@ee.technion.ac.il www.ee.technion.ac.il/people/drorb. Technology Breakthroughs. Sensing, Computation, Communication fast, readily available, cheap

680 views • 51 slides

Duarte Wakin Sarvotham Baraniuk Guo Shamai

Duarte Wakin Sarvotham Baraniuk Guo Shamai. Compressed Sensing meets Information Theory. Dror Baron drorb@ee.technion.ac.il www.ee.technion.ac.il/people/drorb. Technology Breakthroughs. Sensing, Computation, Communication fast, readily available, cheap

514 views • 51 slides

Recovery of Clustered Sparse Signals from Compressive Measurements

Recovery of Clustered Sparse Signals from Compressive Measurements. Chinmay Hegde. Richard Baraniuk. Volkan Cevher volkan@rice.edu. Piotr Indyk. The Digital Universe. Size: ~300 billion gigabytes generated in 2007 digital bits > stars in the universe

792 views • 36 slides

Shriram Sarvotham Dror Baron Richard Baraniuk

Measurements and Bits: Compressed Sensing meets Information Theory. Shriram Sarvotham Dror Baron Richard Baraniuk. ECE Department Rice University dsp.rice.edu/cs. CS encoding. Replace samples by more general encoder based on a few linear projections (inner products)

498 views • 23 slides

Sparse Signal Reconstruction Using FOCUSS

689 views • 17 slides

Distilled Sensing: Selective Sampling for Sparse Signal Recovery

Distilled Sensing: Selective Sampling for Sparse Signal Recovery. by. Jarvis Haupt, Rui Castro, and Robert Nowak. (International Conference on Artificial Intelligence and Statistics, 2009). Presented by Lihan He ECE, Duke University April 16, 2009. Outline. Introduction

145 views • 14 slides

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.

275 views • 12 slides