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Adapted Compressed Sensing for Effective Hardware Implementations

A Design Flow for Signal-Level Optimization of Compressed Sensing Stages

  • Mauro Mangia
  • Fabio Pareschi
  • Valerio Cambareri
  • Riccardo Rovatti
  • Gianluca Setti

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Mauro Mangia, Fabio Pareschi, Valerio Cambareri, Riccardo Rovatti, Gianluca Setti
    Pages 1-28
  3. Mauro Mangia, Fabio Pareschi, Valerio Cambareri, Riccardo Rovatti, Gianluca Setti
    Pages 29-56
  4. Mauro Mangia, Fabio Pareschi, Valerio Cambareri, Riccardo Rovatti, Gianluca Setti
    Pages 57-82
  5. Mauro Mangia, Fabio Pareschi, Valerio Cambareri, Riccardo Rovatti, Gianluca Setti
    Pages 83-108
  6. Mauro Mangia, Fabio Pareschi, Valerio Cambareri, Riccardo Rovatti, Gianluca Setti
    Pages 109-137
  7. Mauro Mangia, Fabio Pareschi, Valerio Cambareri, Riccardo Rovatti, Gianluca Setti
    Pages 139-167
  8. Mauro Mangia, Fabio Pareschi, Valerio Cambareri, Riccardo Rovatti, Gianluca Setti
    Pages 169-210
  9. Mauro Mangia, Fabio Pareschi, Valerio Cambareri, Riccardo Rovatti, Gianluca Setti
    Pages 211-254
  10. Mauro Mangia, Fabio Pareschi, Valerio Cambareri, Riccardo Rovatti, Gianluca Setti
    Pages 255-319

About this book

Introduction

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..

Keywords

Compressed Sensing Compressive Sensing Compressed Sensing & Sparse Filtering Compressive Sensing for Wireless Networks Compressed sensing system design analog-to-information converter

Authors and affiliations

  • Mauro Mangia
    • 1
  • Fabio Pareschi
    • 2
  • Valerio Cambareri
    • 3
  • Riccardo Rovatti
    • 4
  • Gianluca Setti
    • 5
  1. 1.ARCESUniversità di BolognaBolognaItaly
  2. 2.ENDIFUniversità di FerraraFerraraItaly
  3. 3.ICTEAM/ELENUniversité Catholique de LouvainLouvain-la-NeuveBelgium
  4. 4.DEI, ARCESUniversità di BolognaBolognaItaly
  5. 5.ENDIFUniversità di FerraraFerraraItaly

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-61373-4
  • Copyright Information Springer International Publishing AG 2018
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-61372-7
  • Online ISBN 978-3-319-61373-4
  • Buy this book on publisher's site