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Image of Compressed Sensing & Sparse Filtering

Electronic Resource

Compressed Sensing & Sparse Filtering

Carmi, Avishy Y. - Personal Name; Mihaylova, Lyudmila - Personal Name; Godsill, Simon J. - Personal Name;

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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.


Availability
Inventory Code Barcode Call Number Location Status
1408000361EB0000902621.3822 ComCentral Library (OPAC)Available
Detail Information
Series Title
Signals and Communication Technology
Call Number
621.3822 Com
Publisher
Berlin, Heidelberg : Springer Berlin, Heidelberg., 2013
Collation
XII, 502p.: Ill.
Language
English
ISBN/ISSN
978-3-642-38398-4
Classification
621.3822
Content Type
Ebook
Media Type
-
Carrier Type
online resource
Edition
1
Subject(s)
Signal processing--Digital techniques
Specific Detail Info
-
Statement of Responsibility
RTS
Other version/related

No other version available

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  • Compressed Sensing & Sparse Filtering
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