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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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