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 LEADER 02087nam 2200457z- 450 001 9910347050603321 005 20210211 010 $a1000042280 035 $a(CKB)4920000000102010 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/54922 035 $a(oapen)doab54922 035 $a(EXLCZ)994920000000102010 100 $a20202102d2014 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aNumerically Efficient Gradient Crystal Plasticity with a Grain Boundary Yield Criterion and Dislocation-based Work-Hardening 210 $cKIT Scientific Publishing$d2014 215 $a1 online resource (XIII, 260 p. p.) 225 1 $aSchriftenreihe Kontinuumsmechanik im Maschinenbau / Karlsruher Institut für Technologie, Institut für Technische Mechanik - Bereich Kontinuumsmechanik 311 08$a3-7315-0245-3 330 $aThis book is a contribution to the further development of gradient plasticity. Several open questions are addressed, where the efficient numerical implementation is particularly focused on. Thebook inspects an equivalent plastic strain gradient plasticity theory and a grain boundary yield model. Experiments can successfully be reproduced. The hardening model is based on dislocation densities evolving according to partial differential equations taking into account dislocation transport. 606 $aTechnology: general issues$2bicssc 610 $acrystals 610 $aDislocations 610 $aGro?ßeneffekt 610 $ahardening 610 $aKristalle 610 $aplasticity 610 $aPlastizita?t 610 $asize effect 610 $aVerfestigung 610 $aVersetzungen 615 7$aTechnology: general issues 700 $aWulfinghoff$b Stephan$4auth$01296126 906 $aBOOK 912 $a9910347050603321 996 $aNumerically Efficient Gradient Crystal Plasticity with a Grain Boundary Yield Criterion and Dislocation-based Work-Hardening$93023785 997 $aUNINA