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Adapted Compressed Sensing For Effective Hardware Implementations A Design Flow For Signallevel Optimization Of Compressed Sensing Stages Cambareri Valerio Mangia Mauro Pareschi Fabio Rovatti Riccardo Setti Gianluca

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Adapted Compressed Sensing For Effective Hardware Implementations A Design Flow For Signallevel Optimization Of Compressed Sensing Stages Cambareri Valerio Mangia Mauro Pareschi Fabio Rovatti Riccardo Setti Gianluca
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Adapted Compressed Sensing For Effective Hardware Implementations A Design Flow For Signallevel Optimization Of Compressed Sensing Stages Cambareri Valerio Mangia Mauro Pareschi Fabio Rovatti Riccardo Setti Gianluca instant download after payment.

Publisher: Springer
File Extension: PDF
File size: 11.3 MB
Pages: 329
Author: Cambareri Valerio; Mangia Mauro; Pareschi Fabio; Rovatti Riccardo; Setti Gianluca
ISBN: 9783319613727, 9783319613734, 3319613723, 3319613731
Language: English
Year: 2018

Product desciption

Adapted Compressed Sensing For Effective Hardware Implementations A Design Flow For Signallevel Optimization Of Compressed Sensing Stages Cambareri Valerio Mangia Mauro Pareschi Fabio Rovatti Riccardo Setti Gianluca by Cambareri Valerio; Mangia Mauro; Pareschi Fabio; Rovatti Riccardo; Setti Gianluca 9783319613727, 9783319613734, 3319613723, 3319613731 instant download after payment.

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.
Abstract: 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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