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Compressed Sensing and its Applications

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Cover of 'Compressed Sensing and its Applications'

Table of Contents

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    Book Overview
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    Chapter 1 A Survey of Compressed Sensing
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    Chapter 2 Temporal Compressive Sensing for Video
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    Chapter 3 Compressed Sensing, Sparse Inversion, and Model Mismatch
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    Chapter 4 Recovering Structured Signals in Noise: Least-Squares Meets Compressed Sensing
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    Chapter 5 The Quest for Optimal Sampling: Computationally Efficient, Structure-Exploiting Measurements for Compressed Sensing
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    Chapter 6 Compressive Sensing in Acoustic Imaging
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    Chapter 7 Quantization and Compressive Sensing
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    Chapter 8 Compressive Gaussian Mixture Estimation
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    Chapter 9 Two Algorithms for Compressed Sensing of Sparse Tensors
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    Chapter 10 Sparse Model Uncertainties in Compressed Sensing with Application to Convolutions and Sporadic Communication
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    Chapter 11 Cosparsity in Compressed Sensing
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    Chapter 12 Structured Sparsity: Discrete and Convex Approaches
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    Chapter 13 Explicit Matrices with the Restricted Isometry Property: Breaking the Square-Root Bottleneck
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    Chapter 14 Tensor Completion in Hierarchical Tensor Representations
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    Chapter 15 Compressive Classification: Where Wireless Communications Meets Machine Learning
Attention for Chapter 4: Recovering Structured Signals in Noise: Least-Squares Meets Compressed Sensing
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Citations

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Chapter title
Recovering Structured Signals in Noise: Least-Squares Meets Compressed Sensing
Chapter number 4
Book title
Compressed Sensing and its Applications
Published by
Birkhäuser, Cham, January 2015
DOI 10.1007/978-3-319-16042-9_4
Book ISBNs
978-3-31-916041-2, 978-3-31-916042-9
Authors

Christos Thrampoulidis, Samet Oymak, Babak Hassibi

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 50%
Unknown 2 50%
Readers by discipline Count As %
Mathematics 1 25%
Engineering 1 25%
Unknown 2 50%

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