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Adapted compressed sensing for effec...

Mangia, Mauro.

Adapted compressed sensing for effective hardware implementationsa design flow for signal-level optimization of compressed sensing stages /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Adapted compressed sensing for effective hardware implementationsby Mauro Mangia ... [et al.].
Reminder of title:	a design flow for signal-level optimization of compressed sensing stages /
other author:	Mangia, Mauro.
Published:	Cham :Springer International Publishing :2018.
Description:	xiv,319 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Compressed sensing (Telecommunication)
Online resource:	http://dx.doi.org/10.1007/978-3-319-61373-4
ISBN:	9783319613734$q(electronic bk.)

Adapted compressed sensing for effective hardware implementationsa design flow for signal-level optimization of compressed sensing stages /
Adapted compressed sensing for effective hardware implementationsa design flow for signal-level optimization of compressed sensing stages /[electronic resource] :by Mauro Mangia ... [et al.]. - Cham :Springer International Publishing :2018. - xiv,319 p. :ill., digital ;24 cm.

Chapter 1. Introduction to Compressed Sensing: Fundamentals and Guarantees -- Chapter 2.How (Well) Compressed Sensing Works in Practice -- Chapter 3. From Universal to Adapted Acquisition: Rake that Signal! -- Chapter 4.The Rakeness Problem with Implementation and Complexity Constraints -- Chapter 5.Generating Raking Matrices: a Fascinating Second-Order Problem -- Chapter 6.Architectures for Compressed Sensing -- Chapter 7.Analog-to-information Conversion -- Chapter 8.Low-complexity Biosignal Compression using Compressed Sensing -- Chapter 9.Security at the analog-to-information interface using Compressed Sensing.

This book describes algorithmic methods and hardware implementations that aim to help realize the promise of Compressed Sensing (CS), namely the ability to reconstruct high-dimensional signals from a properly chosen low-dimensional "portrait". The authors describe a design flow and some low-resource physical realizations of sensing systems based on CS. They highlight the pros and cons of several design choices from a pragmatic point of view, and show how a lightweight and mild but effective form of adaptation to the target signals can be the key to consistent resource saving. The basic principle of the devised design flow can be applied to almost any CS-based sensing system, including analog-to-information converters, and has been proven to fit an extremely diverse set of applications. Many practical aspects required to put a CS-based sensing system to work are also addressed, including saturation, quantization, and leakage phenomena.

ISBN: 9783319613734$q(electronic bk.)

Standard No.: 10.1007/978-3-319-61373-4doiSubjects--Topical Terms:

769143
Compressed sensing (Telecommunication)

LC Class. No.: TK5102.9

Dewey Class. No.: 621.3822

Adapted compressed sensing for effective hardware implementationsa design flow for signal-level optimization of compressed sensing stages /
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245 0 0 $a Adapted compressed sensing for effective hardware implementations $h [electronic resource] : $b a design flow for signal-level optimization of compressed sensing stages / $c by Mauro Mangia ... [et al.].
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505 0 $a Chapter 1. Introduction to Compressed Sensing: Fundamentals and Guarantees -- Chapter 2.How (Well) Compressed Sensing Works in Practice -- Chapter 3. From Universal to Adapted Acquisition: Rake that Signal! -- Chapter 4.The Rakeness Problem with Implementation and Complexity Constraints -- Chapter 5.Generating Raking Matrices: a Fascinating Second-Order Problem -- Chapter 6.Architectures for Compressed Sensing -- Chapter 7.Analog-to-information Conversion -- Chapter 8.Low-complexity Biosignal Compression using Compressed Sensing -- Chapter 9.Security at the analog-to-information interface using Compressed Sensing.
520 $a This book describes algorithmic methods and hardware implementations that aim to help realize the promise of Compressed Sensing (CS), namely the ability to reconstruct high-dimensional signals from a properly chosen low-dimensional "portrait". The authors describe a design flow and some low-resource physical realizations of sensing systems based on CS. They highlight the pros and cons of several design choices from a pragmatic point of view, and show how a lightweight and mild but effective form of adaptation to the target signals can be the key to consistent resource saving. The basic principle of the devised design flow can be applied to almost any CS-based sensing system, including analog-to-information converters, and has been proven to fit an extremely diverse set of applications. Many practical aspects required to put a CS-based sensing system to work are also addressed, including saturation, quantization, and leakage phenomena.
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