LEADER 03982nam 22006852 450 001 9910777078603321 005 20151005020622.0 010 $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 $a20100422d2002|||| 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 Tama?z Roska$b[electronic resource] 210 1$aCambridge :$cCambridge University Press,$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. 517 3 $aCellular Neural Networks & Visual Computing 606 $aNeural networks (Computer science) 615 0$aNeural networks (Computer science) 676 $a006.3/2 700 $aChua$b Leon O.$f1936-$0459925 702 $aRoska$b T. 801 0$bUkCbUP 801 1$bUkCbUP 906 $aBOOK 912 $a9910777078603321 996 $aCellular neural networks and visual computing$93836441 997 $aUNINA