Adapted Compressed Sensing for Effective Hardware Implementations
A Design Flow for Signal-Level Optimization of Compressed Sensing Stages
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Beschreibung
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..
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.
Discusses the theory as necessary, but either in parallel with extremely simple examples or with reference to the performances that can be actually obtained by CS-based sensing systems Describes a design flow that can be applied to almost any signal class, and that makes CS competitive with respect to alternative options Addresses both analog and digital implementations, clarifying with which conventional systems they should be matched and what are the respective benefits. Includes supplementary material: sn.pub/extras
Autor*in
Mauro Mangia
Themen in »Adapted Compressed Sensing for Effective Hardware Implementations«
Compressed Sensing Compressive Sensing Compressed Sensing & Sparse Filtering Compressive Sensing for Wireless Networks Compressed sensing system design analog-to-information converter