Compressed sensing & sparse filtering [electronic resource] / Avishy Y. Carmi, Lyudmila S. Mihaylova, Simon J. Godsill, editors.
Carmi, Avishy Y., editor of compilation.; Mihaylova, Lyudmila, editor of compilation.; Godsill, Simon J., 1965-, editor of compilation.
2013
TK5102.9
Available Online

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Title Compressed sensing & sparse filtering [electronic resource] / Avishy Y. Carmi, Lyudmila S. Mihaylova, Simon J. Godsill, editors.
ISBN 9783642383984 electronic book
364238398X electronic book
9783642383977
DOI https://doi.org/10.1007/978-3-642-38398-4
Published Heidelberg : Springer, [2013?]
Copyright ©2014
Language English
Description 1 online resource.
Item Number 10.1007/978-3-642-38398-4 doi
Call Number TK5102.9
Dewey Decimal Classification 621.382/2
Summary This book is aimed at presenting concepts, methods and algorithms ableto cope with undersampled and limited data. One such trend that recently gained popularity and to some extent revolutionised signal processing is compressed sensing. Compressed sensing builds upon the observation that many signals in nature are nearly sparse (or compressible, as they are normally referred to) in some domain, and consequently they can be reconstructed to within high accuracy from far fewer observations than traditionally held to be necessary. Apart from compressed sensing this book contains other related approaches. Each methodology has its own formalities for dealing with such problems. As an example, in the Bayesian approach, sparseness promoting priors such as Laplace and Cauchy are normally used for penalising improbable model variables, thus promoting low complexity solutions. Compressed sensing techniques and homotopy-type solutions, such as the LASSO, utilise l1-norm penalties for obtaining sparse solutions using fewer observations than conventionally needed. The book emphasizes on the role of sparsity as a machinery for promoting low complexity representations and likewise its connections to variable selection and dimensionality reduction in various engineering problems. This book is intended for researchers, academics and practitioners with interest in various aspects and applications of sparse signal processing.
Access Note Access limited to authorized users.
Source of Description Description based on print version record.
Added Author Carmi, Avishy Y., editor of compilation.
Mihaylova, Lyudmila, editor of compilation.
Godsill, Simon J., 1965-, editor of compilation.
Series Signals and communication technology.
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Record Appears in Online Resources > Ebooks
All Resources
Introduction to Compressed Sensing and Sparse Filtering
The Geometry of Compressed Sensing
Sparse Signal Recovery with Exponential-Family Noise
Nuclear Norm Optimization and its Application to Observation Model Specification
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Sub-Nyquist Sampling and Compressed Sensing in Cognitive Radio Networks
Sparse Nonlinear MIMO Filtering and Identification
Optimization Viewpoint on Kalman Smoothing with Applications to Robust and Sparse Estimation
Compressive System Identification
Distributed Approximation and Tracking using Selective Gossip
Recursive Reconstruction of Sparse Signal Sequences
Estimation of Time-Varying Sparse Signals in Sensor Networks
Sparsity and Compressed Sensing in Mono-static and Multi-static Radar Imaging
Structured Sparse Bayesian Modelling for Audio Restoration
Sparse Representations for Speech Recognition.

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