Compressed Sensing & Sparse Filtering (Record no. 52266)
[ view plain ]
| 000 -LEADER | |
|---|---|
| fixed length control field | 04255nam a22005775i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-642-38398-4 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20200420220227.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 130913s2014 gw | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783642383984 |
| -- | 978-3-642-38398-4 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 621.382 |
| 245 10 - TITLE STATEMENT | |
| Title | Compressed Sensing & Sparse Filtering |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | XII, 502 p. 135 illus. |
| 490 1# - SERIES STATEMENT | |
| Series statement | Signals and Communication Technology, |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | 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 -- Nonnegative Tensor Decomposition -- 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. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | 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. . |
| 700 1# - AUTHOR 2 | |
| Author 2 | Carmi, Avishy Y. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Mihaylova, Lyudmila. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Godsill, Simon J. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | http://dx.doi.org/10.1007/978-3-642-38398-4 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Berlin, Heidelberg : |
| -- | Springer Berlin Heidelberg : |
| -- | Imprint: Springer, |
| -- | 2014. |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
| -- | c |
| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
| -- | cr |
| -- | rdacarrier |
| 347 ## - | |
| -- | text file |
| -- | |
| -- | rda |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Engineering. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Numerical analysis. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Algorithms. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Complexity, Computational. |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Engineering. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Signal, Image and Speech Processing. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Numeric Computing. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Mathematics of Algorithmic Complexity. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Complexity. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| -- | 1860-4862 |
| 912 ## - | |
| -- | ZDB-2-ENG |
No items available.