TY - BOOK ID - 15994790 TI - Adapted Compressed Sensing for Effective Hardware Implementations : A Design Flow for Signal-Level Optimization of Compressed Sensing Stages AU - Mangia, Mauro. AU - Pareschi, Fabio. AU - Cambareri, Valerio. AU - Rovatti, Riccardo. AU - Setti, Gianluca. PY - 2018 SN - 3319613731 3319613723 9783319613727 PB - Cham : Springer International Publishing : Imprint: Springer, DB - UniCat KW - Engineering. KW - Electronics. KW - Microelectronics. KW - Electronic circuits. KW - Circuits and Systems. KW - Signal, Image and Speech Processing. KW - Electronics and Microelectronics, Instrumentation. KW - Compressed sensing (Telecommunication) KW - Compressive sensing (Telecommunication) KW - Sensing, Compressed (Telecommunication) KW - Sparse sampling (Telecommunication) KW - Signal processing KW - Systems engineering. KW - Electrical engineering KW - Physical sciences KW - Engineering systems KW - System engineering KW - Engineering KW - Industrial engineering KW - System analysis KW - Design and construction KW - Signal processing. KW - Image processing. KW - Speech processing systems. KW - Microminiature electronic equipment KW - Microminiaturization (Electronics) KW - Electronics KW - Microtechnology KW - Semiconductors KW - Miniature electronic equipment KW - Computational linguistics KW - Electronic systems KW - Information theory KW - Modulation theory KW - Oral communication KW - Speech KW - Telecommunication KW - Singing voice synthesizers KW - Pictorial data processing KW - Picture processing KW - Processing, Image KW - Imaging systems KW - Optical data processing KW - Processing, Signal KW - Information measurement KW - Signal theory (Telecommunication) KW - Electron-tube circuits KW - Electric circuits KW - Electron tubes UR - https://www.unicat.be/uniCat?func=search&query=sysid:15994790 AB - 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. ER -