000			03880nam a2200445 i 4500
710	2		_aSpringerLink (Online service) _0http://id.loc.gov/authorities/names/no2005046756 _1http://viaf.org/viaf/274647764/ _9106996
999			_c102441 _d102441 _x1
001			102441
003			ES-MaUEC
005			20230102113042.0
006			a||||fo|||| 00| 0
007			cr nn 008mamaa
008			170714s2018 gw | s |||| 0|eng d
020			_a9783319613734
024	7		_a10.1007/978-3-319-61373-4 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTK5102.9 _b2018 EB
100	1		_aMangia, Mauro _eautor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _9673586
245	1	0	_aAdapted Compressed Sensing for Effective Hardware Implementations : _bA Design Flow for Signal-Level Optimization of Compressed Sensing Stages _cby Mauro Mangia, Fabio Pareschi, Valerio Cambareri, Riccardo Rovatti, Gianluca Setti.
264		1	_aCham _bSpringer International Publishing _c2018
300			_a1 recurso en línea (XIV, 319 páginas) _b180 ilustraciones, 142 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aEngineering (Springer-11647)
505	0		_aChapter 1. Introduction to Compressed Sensing: Fundamentals and Guarantees -- Chapter 2.How (Well) Compressed Sensing Works in Practice -- Chapter 3. From Universal to Adapted Acquisition: Rake that Signal! -- Chapter 4.The Rakeness Problem with Implementation and Complexity Constraints -- Chapter 5.Generating Raking Matrices: a Fascinating Second-Order Problem -- Chapter 6.Architectures for Compressed Sensing -- Chapter 7.Analog-to-information Conversion -- Chapter 8.Low-complexity Biosignal Compression using Compressed Sensing -- Chapter 9.Security at the analog-to-information interface using Compressed Sensing.
520	3		_aThis 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.
988			_aEBSPRINGER_2018
650		7	_2embne _9150608 _aProceso de señales
700	1		_aPareschi, Fabio _eautor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _9673587
700	1		_aCambareri, Valerio _eautor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _9673588
700	1		_aRovatti, Riccardo _eautor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _0http://id.loc.gov/authorities/names/n98095546 _1http://viaf.org/viaf/5156225/ _9673589
700	1		_aSetti, Gianluca _eautor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _0http://id.loc.gov/authorities/names/n00010648 _1http://viaf.org/viaf/7612755/ _9673590
776	0	8	_iEdición impresa: _z9783319613727
776	0	8	_iEdición impresa: _z9783319613741
776	0	8	_iEdición impresa: _z9783319870656
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-61373-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b05/2020 _dz _ek _zSI