LEADER 04266nam  22006375  450 
001 9910299901603321
005 20200706010332.0
010   $a3-319-61373-1
024 7 $a10.1007/978-3-319-61373-4
035   $a(CKB)4340000000062783
035   $a(DE-He213)978-3-319-61373-4
035   $a(MiAaPQ)EBC4915508
035   $a(PPN)203671767
035   $a(EXLCZ)994340000000062783
100   $a20170714d2018      u|         0
101 0 $aeng
135   $aurnn#008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aAdapted Compressed Sensing for Effective Hardware Implementations $eA Design Flow for Signal-Level Optimization of Compressed Sensing Stages  /$fby Mauro Mangia, Fabio Pareschi, Valerio Cambareri, Riccardo Rovatti, Gianluca Setti
205   $a1st ed. 2018.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018.
215   $a1 online resource (XIV, 319 p. 180 illus., 142 illus. in color.)
311   $a3-319-61372-3 
320   $aIncludes bibliographical references at the end of each chapters.
327   $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.
330   $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.
606   $aElectronic circuits
606   $aSignal processing
606   $aImage processing
606   $aSpeech processing systems
606   $aElectronics
606   $aMicroelectronics
606   $aCircuits and Systems$3https://scigraph.springernature.com/ontologies/product-market-codes/T24068
606   $aSignal, Image and Speech Processing$3https://scigraph.springernature.com/ontologies/product-market-codes/T24051
606   $aElectronics and Microelectronics, Instrumentation$3https://scigraph.springernature.com/ontologies/product-market-codes/T24027
615  0$aElectronic circuits.
615  0$aSignal processing.
615  0$aImage processing.
615  0$aSpeech processing systems.
615  0$aElectronics.
615  0$aMicroelectronics.
615 14$aCircuits and Systems.
615 24$aSignal, Image and Speech Processing.
615 24$aElectronics and Microelectronics, Instrumentation.
676   $a621.3815
700   $aMangia$b Mauro$4aut$4http://id.loc.gov/vocabulary/relators/aut$01062650
702   $aPareschi$b Fabio$4aut$4http://id.loc.gov/vocabulary/relators/aut
702   $aCambareri$b Valerio$4aut$4http://id.loc.gov/vocabulary/relators/aut
702   $aRovatti$b Riccardo$4aut$4http://id.loc.gov/vocabulary/relators/aut
702   $aSetti$b Gianluca$4aut$4http://id.loc.gov/vocabulary/relators/aut
906   $aBOOK
912   $a9910299901603321
996   $aAdapted Compressed Sensing for Effective Hardware Implementations$92527395
997   $aUNINA