LEADER 03965nam 22006974a 450 001 9910826759503321 005 20200520144314.0 010 $a1-107-11746-1 010 $a0-511-04051-2 010 $a1-280-42067-7 010 $a9786610420674 010 $a0-511-17694-5 010 $a0-511-15782-7 010 $a0-511-32984-9 010 $a0-511-75449-3 010 $a1-60119-735-7 010 $a0-511-04825-4 035 $a(CKB)1000000000001282 035 $a(EBL)201564 035 $a(OCoLC)475915400 035 $a(SSID)ssj0000071541 035 $a(PQKBManifestationID)11107099 035 $a(PQKBTitleCode)TC0000071541 035 $a(PQKBWorkID)10091153 035 $a(PQKB)10262786 035 $a(UkCbUP)CR9780511754494 035 $a(Au-PeEL)EBL201564 035 $a(CaPaEBR)ebr10019064 035 $a(CaONFJC)MIL42067 035 $a(MiAaPQ)EBC201564 035 $a(PPN)261362976 035 $a(EXLCZ)991000000000001282 100 $a20010302d2002 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aCellular neural networks and visual computing $efoundation and applications /$fLeon O. Chua and Tamaz Roska 205 $a1st ed. 210 $aCambridge, UK ;$aNew York, NY $cCambridge University Press$d2002 215 $a1 online resource (xi, 396 pages) $cdigital, PDF file(s) 300 $aTitle from publisher's bibliographic system (viewed on 05 Oct 2015). 311 $a0-521-01863-3 311 $a0-521-65247-2 320 $aIncludes bibliographical references (p. 348-360) and index. 327 $aCover; Half-title; Title; Copyright; Dedication; Contents; Acknowledgements; 1 Introduction; 2 Notation, definitions, and mathematical foundation; 3 Characteristics and analysis of simple CNN templates; 4 Simulation of the CNN dynamics; 5 Binary CNN characterization via Boolean functions; 6 Uncoupled CNNs: unified theoryand applications; 7 Introduction to the CNN Universal Machine; 8 Back to basics: Nonlinear dynamics and complete stability; 9 The CNN Universal Machine (CNN-UM); 10 Template design tools; 11 CNNs for linear image processing; 12 Coupled CNN with linear synaptic weights 327 $a13 Uncoupled standard CNNs with nonlinear synaptic weights14 Standard CNNs with delayed synaptic weights and motion analysis; 15 Visual microprocessors ... analog and digital VLSI implementation of the CNN Universal Machine; 16 CNN models in the visual pathwayand the Bionic EyeZ?; Notes; Bibliography; Exercises; Appendices; Index 330 $aCellular Nonlinear/neural Network (CNN) technology is both a revolutionary concept and an experimentally proven new computing paradigm. Analogic cellular computers based on CNNs are set to change the way analog signals are processed and are paving the way to an analog computing industry. This unique undergraduate level textbook includes many examples and exercises, including CNN simulator and development software accessible via the Internet. It is an ideal introduction to CNNs and analogic cellular computing for students, researchers and engineers from a wide range of disciplines. Although its prime focus is on visual computing, the concepts and techniques described in the book will be of great interest to those working in other areas of research including modeling of biological, chemical and physical processes. Leon Chua, co-inventor of the CNN, and Tama?s Roska are both highly respected pioneers in the field. 606 $aNeural networks (Computer science) 615 0$aNeural networks (Computer science) 676 $a006.3/2 700 $aChua$b Leon O.$f1936-$0459925 701 $aRoska$b T$01444880 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910826759503321 996 $aCellular neural networks and visual computing$94184646 997 $aUNINA