Compressed Sensing and its Applications MATHEON Workshop 2013 /

Compressed Sensing and its Applications MATHEON Workshop 2013 /

Since publication of the initial papers in 2006, compressed sensing has captured the imagination of the international signal processing community, and the mathematical foundations are nowadays quite well understood. Parallel to the progress in mathematics, the potential applications of compressed se...

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Bibliographic Details
Published in:Springer eBooks
Corporate Author: SpringerLink (Online service)
Other Authors: Boche, Holger (Editor), Calderbank, Robert (Editor), Kutyniok, Gitta (Editor), Vybíral, Jan (Editor)
Format: eBook
Language:English
Published: Cham : Springer International Publishing : Imprint: Birkhäuser, 2015.
Series:Applied and Numerical Harmonic Analysis,
Subjects:
mathematics.
Coding theory.
Matrix theory.
Algebra.
Information theory.
Computer mathematics.
Numerical analysis.
Mathematics.
Information and Communication, Circuits.
Signal, Image and Speech Processing.
Coding and Information Theory.
Numerical Analysis.
Linear and Multilinear Algebras, Matrix Theory.
Computational Science and Engineering.
Online Access:http://dx.doi.org/10.1007/978-3-319-16042-9
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Table of Contents:
  • Survey on Compressed Sensing
  • Temporal compressive sensing for video
  • Compressed Sensing, Sparse Inversion, and Model Mismatch
  • Recovering Structured Signals in Noise: Least-Squares Meets Compressed Sensing
  • The Quest for Optimal Sampling: Computationally Efficient, Structure-exploiting Measurements for Compressed Sensing
  • Compressive Sensing in Acoustic Imaging
  • Quantization and Compressive Sensing
  • Compressive Gaussian Mixture Estimation
  • Two Algorithms for Compressed Sensing of Sparse Tensors
  • Sparse Model Uncertainties in Compressed Sensing with Application to Convolutions and Sporadic Communication
  • Cosparsity in Compressed Sensing
  • Structured Sparsity: Discrete and Convex Approaches
  • Explicit Matrices with the Restricted Isometry Property: Breaking the Square-Root Bottleneck
  • Tensor Completion in Hierarchical Tensor Representations
  • Compressive Classification: Where Wireless Communications Meets Machine Learning.

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