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Title Compressed sensing & sparse filtering / Avishy Y. Carmi, Lyudmila S. Mihaylova, Simon J. Godsill, editors
Published Heidelberg : Springer, [2013?]
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Description 1 online resource
Series Signals and communication technology
Signals and communication technology.
Contents Introduction to Compressed Sensing and Sparse Filtering / Avishy Y. Carmi, Lyudmila S. Mihaylova and Simon J. Godsill -- The Geometry of Compressed Sensing / Thomas Blumensath -- Sparse Signal Recovery with Exponential-Family Noise / Irina Rish and Genady Grabarnik -- Nuclear Norm Optimization and Its Application to Observation Model Specification / Ning Hao, Lior Horesh and Misha Kilmer -- Nonnegative Tensor Decomposition / N. Hao, L. Horesh and M.E. Kilmer -- Sub-Nyquist Sampling and Compressed Sensing in Cognitive Radio Networks / Hongjian Sun, Arumugam Nallanathan and Jing Jiang -- Sparse Nonlinear MIMO Filtering and Identification / G. Mileounis and N. Kalouptsidis -- Optimization Viewpoint on Kalman Smoothing with Applications to Robust and Sparse Estimation / Aleksandr Y. Aravkin, James V. Burke and Gianluigi Pillonetto -- Compressive System Identification / Avishy Y. Carmi -- Distributed Approximation and Tracking Using Selective Gossip / Deniz Üstebay, Rui Castro, Mark Coates and Michael Rabbat -- Recursive Reconstruction of Sparse Signal Sequences / Namrata Vaswani and Wei Lu -- Estimation of Time-Varying Sparse Signals in Sensor Networks / Manohar Shamaiah and Haris Vikalo -- Sparsity and Compressed Sensing in Mono-Static and Multi-Static Radar Imaging / Ivana Stojanović, Müjdat Çetin and W. Clem Karl -- Structured Sparse Bayesian Modelling for Audio Restoration / James Murphy and Simon Godsill -- Sparse Representations for Speech Recognition / Tara N. Sainath, Dimitri Kanevsky, David Nahamoo, Bhuvana Ramabhadran and Stephen Wright
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
Notes Print version record
Subject Signal processing -- Digital techniques.
Form Electronic book
Author Carmi, Avishy Y., editor
Mihaylova, Lyudmila, editor
Godsill, Simon J., 1965- editor
ISBN 3642383971
364238398X (electronic bk.)
9783642383984 (electronic bk.)
Other Titles Compressed sensing and sparse filtering