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Digital signal processing with Matla...

Giron-Sierra, Jose Maria.

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Digital signal processing with Matlab examples.. Volume 3,. Model-based actions and sparse representation

Record Type:	Electronic resources : Monograph/item
Title/Author:	Digital signal processing with Matlab examples./ by Jose Maria Giron-Sierra.
remainder title:	Model-based actions and sparse representation
Author:	Giron-Sierra, Jose Maria.
Published:	Singapore :Springer Singapore : : 2017.,
Description:	xvi, 431 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Part VI- Model-based Actions: Filtering, Prediction, Smoothing -- Kalman Filter, Particle Filter and other Bayesian Filters -- Part VII Sparse Representation. Compressed Sensing -- Sparse Representations -- Appendices -- Selected Topics of Mathematical Optimization -- Long Programs.
Contained By:	Springer eBooks
Subject:	Signal processing - Digital techniques -
Online resource:	http://dx.doi.org/10.1007/978-981-10-2540-2
ISBN:	9789811025402

Digital signal processing with Matlab examples.. Volume 3,. Model-based actions and sparse representation
Giron-Sierra, Jose Maria.

Digital signal processing with Matlab examples.Volume 3,Model-based actions and sparse representation[electronic resource] /Model-based actions and sparse representationby Jose Maria Giron-Sierra. - Singapore :Springer Singapore :2017. - xvi, 431 p. :ill. (some col.), digital ;24 cm. - Signals and communication technology,1860-4862. - Signals and communication technology..

Part VI- Model-based Actions: Filtering, Prediction, Smoothing -- Kalman Filter, Particle Filter and other Bayesian Filters -- Part VII Sparse Representation. Compressed Sensing -- Sparse Representations -- Appendices -- Selected Topics of Mathematical Optimization -- Long Programs.

This is the third volume in a trilogy on modern Signal Processing. The three books provide a concise exposition of signal processing topics, and a guide to support individual practical exploration based on MATLAB programs. This book includes MATLAB codes to illustrate each of the main steps of the theory, offering a self-contained guide suitable for independent study. The code is embedded in the text, helping readers to put into practice the ideas and methods discussed. The book primarily focuses on filter banks, wavelets, and images. While the Fourier transform is adequate for periodic signals, wavelets are more suitable for other cases, such as short-duration signals: bursts, spikes, tweets, lung sounds, etc. Both Fourier and wavelet transforms decompose signals into components. Further, both are also invertible, so the original signals can be recovered from their components. Compressed sensing has emerged as a promising idea. One of the intended applications is networked devices or sensors, which are now becoming a reality; accordingly, this topic is also addressed. A selection of experiments that demonstrate image denoising applications are also included. In the interest of reader-friendliness, the longer programs have been grouped in an appendix; further, a second appendix on optimization has been added to supplement the content of the last chapter.

ISBN: 9789811025402

Standard No.: 10.1007/978-981-10-2540-2doiSubjects--Uniform Titles:

MATLAB.
Subjects--Topical Terms:

595704
Signal processing
--Digital techniques

LC Class. No.: TK5102.9

Dewey Class. No.: 621.3822

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505 0 $a Part VI- Model-based Actions: Filtering, Prediction, Smoothing -- Kalman Filter, Particle Filter and other Bayesian Filters -- Part VII Sparse Representation. Compressed Sensing -- Sparse Representations -- Appendices -- Selected Topics of Mathematical Optimization -- Long Programs.
520 $a This is the third volume in a trilogy on modern Signal Processing. The three books provide a concise exposition of signal processing topics, and a guide to support individual practical exploration based on MATLAB programs. This book includes MATLAB codes to illustrate each of the main steps of the theory, offering a self-contained guide suitable for independent study. The code is embedded in the text, helping readers to put into practice the ideas and methods discussed. The book primarily focuses on filter banks, wavelets, and images. While the Fourier transform is adequate for periodic signals, wavelets are more suitable for other cases, such as short-duration signals: bursts, spikes, tweets, lung sounds, etc. Both Fourier and wavelet transforms decompose signals into components. Further, both are also invertible, so the original signals can be recovered from their components. Compressed sensing has emerged as a promising idea. One of the intended applications is networked devices or sensors, which are now becoming a reality; accordingly, this topic is also addressed. A selection of experiments that demonstrate image denoising applications are also included. In the interest of reader-friendliness, the longer programs have been grouped in an appendix; further, a second appendix on optimization has been added to supplement the content of the last chapter.
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