LEADER 03631nam 2200637Ia 450 001 9910453843603321 005 20200520144314.0 010 $a1-281-78197-5 010 $a9786611781972 010 $a1-84642-767-3 035 $a(CKB)1000000000552847 035 $a(EBL)350314 035 $a(OCoLC)476168596 035 $a(SSID)ssj0000100261 035 $a(PQKBManifestationID)11113555 035 $a(PQKBTitleCode)TC0000100261 035 $a(PQKBWorkID)10020357 035 $a(PQKB)11771425 035 $a(MiAaPQ)EBC350314 035 $a(Au-PeEL)EBL350314 035 $a(CaPaEBR)ebr10251502 035 $a(CaONFJC)MIL178197 035 $a(EXLCZ)991000000000552847 100 $a20070828d2008 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aAgeing, disability, and spirituality$b[electronic resource] $eaddressing the challenge of disability in later life /$fedited by Elizabeth MacKinlay 210 $aLondon ;$aPhiladelphia $cJessica Kingsley Publishers$d2008 215 $a1 online resource (275 p.) 300 $aDescription based upon print version of record. 311 $a1-84310-584-5 320 $aIncludes bibliographical references. 327 $afront cover; Ageing, Disability and Spirituality: Addressing the Challenge of Disability in Later Life; Contents; ACKNOWLEDGEMENTS; PREFACE; Chapter 1 Introduction: Ageing, Disability and Spirituality; Chapter 2 Remembering the Person: Theological Reflections on God, Personhood and Dementia; Chapter 3 Ethics, Ageing and Disability; Chapter 4 New and Old Challenges of Ageing: Disabilities, Spirituality and Pastoral Responses; Chapter 5 The Particular Needs of Older People with Intellectual Disabilities and Their Carers: A Perspective from the Experience of L'Arche 327 $aChapter 6 Better Dead than Disabled? When Ethics and Disability Meet: A Narrative of Ageing, Loss and ExclusionChapter 7 Disabled or Enabled: Ethical and Theological Issues for Dementia Care; Chapter 8 On Relationships Not Things: Exploring Disability and Spirituality; Chapter 9 Scriptural Reminiscence and Narrative Gerontology: Jacob's Wrestling with the Unknown (Genesis 32); Chapter 10 Tracing Rainbows through the Rain: Addressing the 330 $aThis collection examines theological and ethical issues of ageing, disability and spirituality, with an emphasis on how ageing affects people who have mental health and developmental disabilities. The book presents ways of moving towards more effective relationships between carers and older people with disabilities; ways in which to connect compassionately and beneficially with the person's spiritual dimension. The contributors highlight the importance of recognizing the personhood of all people regardless of age and of disability, whatever form it takes. They identify factors inherent in pers 606 $aOlder people$xReligious life 606 $aChurch work with older people 606 $aPeople with disabilities$xReligious life 606 $aChurch work with people with disabilities 608 $aElectronic books. 615 0$aOlder people$xReligious life. 615 0$aChurch work with older people. 615 0$aPeople with disabilities$xReligious life. 615 0$aChurch work with people with disabilities. 676 $a362.6 701 $aMacKinlay$b Elizabeth$f1940-$0887894 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910453843603321 996 $aAgeing, disability, and spirituality$92282770 997 $aUNINA LEADER 02376nam 2200529 450 001 9910797879203321 005 20191120190034.0 010 $a1-4985-3073-7 035 $a(CKB)3710000000497940 035 $a(EBL)4086557 035 $a(SSID)ssj0001571224 035 $a(PQKBManifestationID)16218791 035 $a(PQKBTitleCode)TC0001571224 035 $a(PQKBWorkID)14800972 035 $a(PQKB)11504205 035 $a(MiAaPQ)EBC4086557 035 $a(EXLCZ)993710000000497940 100 $a20150918h20162016 uy| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aSexual myths of modernity $esadism, masochism, and historical teleology /$fAlison M. Moore 210 1$aLanham :$cLexington Books,$d[2016] 210 4$dİ2016 215 $a1 online resource (289 p.) 300 $aDescription based upon print version of record. 311 $a0-7391-3077-3 320 $aIncludes bibliographical references and index. 327 $aIntroduction -- Perversion, gender, and nature in nineteenth-century visions of pleasure, violence, and civilization -- Psychoanalytic sexual teleology -- Civilized perversions in interwar europe -- Critical myths of nazi perversion : sadism, homosexuality, enlightenment, and barbarism -- The polarizing myth of "real" sadists and masochists -- Fantasies of the "sadiconazista" -- Nazi sexual pathology in historiography -- Genocidal pleasures -- Bibliography -- About the author. 330 $aThis ambitious and wide-ranging study of late-nineteenth- and twentieth-century culture and thought transverses texts of evolutionary biology, psychiatry, psychoanalysis, political propaganda, fiction, historiography of Nazism, and scholarship on comparative genocide to analyze the notion that mass violence is sexually motivated. 606 $aSadomasochism$xHistory 606 $aPolitical violence$xHistory 606 $aTeleology$xHistory 615 0$aSadomasochism$xHistory. 615 0$aPolitical violence$xHistory. 615 0$aTeleology$xHistory. 676 $a306.77/5 700 $aMoore$b Alison M.$01481754 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910797879203321 996 $aSexual myths of modernity$93698879 997 $aUNINA LEADER 11488nam 2200541 450 001 9910830508903321 005 20231110223951.0 010 $a1-119-68778-0 010 $a1-119-68775-6 010 $a1-119-68777-2 035 $a(MiAaPQ)EBC7134083 035 $a(Au-PeEL)EBL7134083 035 $a(CKB)25301856200041 035 $a(EXLCZ)9925301856200041 100 $a20230320d2023 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aAdvances in hyperspectral image processing techniques /$fedited by Chein-I Chang 210 1$aHoboken, New Jersey :$cWiley :$cIEEE Press,$d[2023] 210 4$dİ2023 215 $a1 online resource (611 pages) 225 1 $aIEEE Press 311 08$aPrint version: Chang, Chein-I Advances in Hyperspectral Image Processing Techniques Newark : John Wiley & Sons, Incorporated,c2022 9781119687764 320 $aIncludes bibliographical references and index. 327 $aCover -- Title Page -- Copyright Page -- Contents -- Editor Biography -- List of Contributors -- Preface -- Part I General Theory -- Chapter 1 Introduction: Two Fundamental Principles Behind Hyperspectral Imaging -- 1.1 Introduction -- 1.2 Why Is Hyperspectral Imaging? -- 1.3 Two Principles for Hyperspectral Imaging -- 1.3.1 Pigeon-Hole Principle -- 1.3.2 Orthogonality Principle -- 1.4 What Are the Issues of Hyperspectral Imaging? -- 1.5 Determination of p by Virtual Dimensionality via Pigeon-Hole Principle -- 1.6 Order Determination of Low Rank and Sparse Matrices by Virtual Dimensionality via Pigeon-Hole Principle -- 1.7 Band Selection by Pigeon-Hole Principle -- 1.8 Band Selection by a Hyperspectral Band Channel via Pigeon-Hole Principle -- 1.9 Band Sampling via Pigeon-Hole Principle -- 1.10 Spectral Unmixing via Orthogonality Principle -- 1.11 Target Detection by Orthogonality Principle -- 1.11.1 ATGP -- 1.11.1.1 Automatic Target Generation Process (ATGP) -- 1.11.2 Constrained Energy Minimization (CEM) -- 1.12 Anomaly Detection by Orthogonality Principle -- 1.13 Endmember Finding by Orthogonality Principle -- 1.13.1 Pixel Purity Index (PPI) -- 1.13.2 Vertex Component Analysis (VCA) -- 1.13.3 Simplex Growing Algorithm (SGA) -- 1.14 Low Rank and Sparse Representation by OSP via Orthogonality Principle -- 1.15 Hyperspectral Classification -- 1.15.1 Hyperspectral Mixed Pixel Classification (HMPC) -- 1.15.2 Number of Sampled Bands Lower Than Number of Classes -- 1.15.3 Potential and Promise of Band Sampling in HMPC -- 1.16 Conclusion -- References -- Chapter 2 Overview of Hyperspectral Imaging Remote Sensing from Satellites -- 2.1 Hyperspectral Imaging Remote Sensing from Airplanes to Satellites -- 2.1.1 History of Development of Airborne Hyperspectral Imagers. 327 $a2.1.2 Early Development of Spaceborne Hyperspectral Imagers -- 2.2 Development of Spaceborne Hyperspectral Imagers in the Last Two Decades -- 2.2.1 Survey of Spaceborne Hyperspectral Imagers -- Acronyms List -- 2.2.2 Brief Description of Spaceborne Hyperspectral Imagers -- 2.2.2.1 Visible Imagers and Spectrographic Imagers (UVISI) Onboard the MSX Satellite -- 2.2.2.2 HyperSpectral Imager (HSI) for the LEWIS Mission -- 2.2.2.3 MODIS Onboard Terra and Aqua Satellites -- 2.2.2.4 Hyperion Onboard NASA's EO-1 Satellite -- 2.2.2.5 CHRIS Onboard ESA's PROBA Satellite -- 2.2.2.6 MERIS Onboard ESA's ENVISAT Satellite -- 2.2.2.7 VIRTIS for ESA's Rosetta, Venus-Express, and NASA-Dawn Planetary Missions -- 2.2.2.8 CRISM Aboard Mars Reconnaissance Orbiter -- 2.2.2.9 Moon Mineralogy Mapper for Mapping Lunar Surface -- 2.2.2.10 Fourier Transform Hyperspectral Imager Onboard Chinese Environment Satellite -- 2.2.2.11 HySI Onboard Indian Mini Satellite-1 -- 2.2.2.12 ARTEMIS Onboard TacSat-3 -- 2.2.2.13 HICO Onboard the International Space Station -- 2.2.2.14 Visible and Near-infrared Imaging Spectrometer Aboard Chang'E 3 Spacecraft -- 2.2.2.15 Ocean and Land Color Imager (OLCI) on Sentinel-3A -- 2.2.2.16 Miniature High-Resolution Imaging Spectrometer on GHGSat-D -- 2.2.2.17 Aalto-1 Spectral Imager .(AaSI) on a 3U Nanosatellite -- 2.2.2.18 DLR Earth Sensing Imaging Spectrometer on the International Space Station -- 2.2.2.19 HyperScout Hyperspectral Camera on ESA's Nanosatellite GomX-4B -- 2.2.2.20 Advanced Hyperspectral Imager (AHSI) on Chinese Gaofen-5 Satellite -- 2.2.2.21 Italian Hyperspectral Satellite PRISMA -- 2.2.2.22 Hyperspectral Imager Suite Onboard the International Space Station -- 2.2.2.23 German Spaceborne Hyperspectral Imager EnMAP -- 2.2.2.24 ESA's Moons and Jupiter Imaging Spectrometer (MAJIS) -- 2.3 Conclusion. 327 $aReferences -- Chapter 3 Efficient Hardware Implementation for Hyperspectral Anomaly and Target Detection -- 3.1 Introduction -- 3.2 Hyperspectral Anomaly and Target Detection -- 3.2.1 DPBS-CEM -- 3.2.2 DBN-RXD -- 3.2.3 Fast-ATGP -- 3.2.4 Fast-MGD -- 3.3 Model-Based Design -- 3.3.1 What is Model-Based Design? -- 3.3.2 FPGA Development Based on MBD -- 3.3.3 Examples of IP Design Based on HLS -- 3.3.3.1 Efficient Off-Chip Storage Access IP -- 3.3.3.2 Parallel Matrix Multiplication IP -- 3.3.3.3 Matrix Dot-Product-Plus IP -- 3.3.3.4 Erosion/Dilation IP -- 3.4 System Integration Framework Design -- 3.4.1 Efficient FPGA Implementation -- 3.4.1.1 FPGA Implementation of DPBS-CEM -- 3.4.1.2 FPGA Implementation of DBN-RXD -- 3.4.1.3 FPGA Implementation of Fast-ATGP -- 3.4.1.4 FPGA Implementation of Fast-MGD -- 3.5 Experiments and Discussions -- 3.5.1 Hyperspectral Image Data Set -- 3.5.1.1 TE1 Data Set -- 3.5.1.2 HyMap Data Set -- 3.5.1.3 Airport-Beach-Urban. .(ABU) Data Set -- 3.5.1.4 Cuprite Data Set -- 3.5.1.5 San Diego Data Set -- 3.5.1.6 HYDICE Data Set -- 3.5.2 Experiments of DPBS-CEM -- 3.5.2.1 Detection Accuracy -- 3.5.2.2 Acceleration Performance -- 3.5.3 Experiments of DBN-RXD -- 3.5.3.1 Detection Accuracy -- 3.5.3.2 Acceleration Performance -- 3.5.4 Experiments of Fast-ATGP -- 3.5.4.1 Detection Accuracy -- 3.5.4.2 Results for the AVIRIS Cuprite Scene -- 3.5.5 Experiments of Fast-MGD -- 3.5.5.1 Detection Accuracy -- 3.5.5.2 Performance Evaluation -- 3.6 Conclusion -- References -- Part II Band Selection for Hyperspectral Imaging -- Chapter 4 Constrained Band Selection for Hyperspectral Imaging -- 4.1 Introduction -- 4.2 Constrained BS -- 4.2.1 Band Vector-Constrained BS -- 4.2.1.1 Band Correlation Minimization (BCM) -- 4.2.1.2 Band Dependence Minimization (BDM). 327 $a4.2.1.3 Band Correlation Constraint (BCC) -- 4.2.1.4 Band Dependence Constraint (BDC) -- 4.2.2 Band Image-Constrained BS -- 4.3 BCBS Experiments -- 4.3.1 HYDICE Data -- 4.3.1.1 Target Detection -- 4.3.1.2 Unsupervised Mixed Pixel Classification -- 4.3.2 AVIRIS Cuprite Data -- 4.4 Target-Constrained BS -- 4.4.1 Target-Constrained Band Prioritization -- 4.4.1.1 Single Band Minimum Variance Band Prioritization by TCBS -- 4.4.1.2 Leave-One-Out Maximum Variance Band Prioritization by TCBS -- 4.4.2 Constrained-Target Band Selection -- 4.4.2.1 Sequential Feed-Forward TCBS -- 4.4.2.2 Sequential Backward TCBS -- 4.5 TCBS Experiments -- 4.6 Conclusion -- References -- Chapter 5 Band Subset Selection for Hyperspectral Imaging -- 5.1 Introduction -- 5.2 Simultaneous Multiple Band Selection -- 5.3 Search Strategies for BSS -- 5.3.1 Sequential Band Subset Selection -- 5.3.2 Successive Band Subset Selection -- 5.4 Channel Capacity BSS -- 5.5 Multiple Band-Constrained Band Subset Selection -- 5.5.1 Constrained BSS (CBSS) -- 5.5.2 Search Algorithms for CBSS -- 5.5.2.1 Sequential CBSS (SQ CBSS) -- 5.5.2.2 Successive CBSS (SC CBSS) -- 5.6 Application-Specified BSS (AS-BSS) -- 5.6.1 Application to Hyperspectral Classification -- 5.6.2 LCMV Criterion for BSS -- 5.6.3 LCMV-BSS Algorithms -- 5.6.3.1 SQ LCMV-CBSS -- 5.6.3.2 SC LCMV-CBSS -- 5.7 Experiments -- 5.7.1 MBC-BSS -- 5.7.2 MTC-BSS -- 5.7.2.1 Purdue Indiana Indian Pines Scene -- 5.7.2.2 Salinas -- 5.7.2.3 ROSIS Data -- 5.8 Conclusion -- References -- Chapter 6 Progressive Band Selection Processing for Hyperspectral Image Classification -- 6.1 Introduction -- 6.2 Measures of Class Classification Priority -- 6.3 p-Ary Huffman Coding Tree Construction -- 6.4 Iterative LCMV -- 6.4.1 Linearly Constrained Minimum Variance (LCMV). 327 $a6.4.2 Iterative Linearly Constrained Minimum Variance (ILCMV) -- 6.5 Class Signature Constrained Band Prioritization-Based Band Selection -- 6.6 Progressive Band Selection -- 6.7 Classification Measures -- 6.8 Real Images to be Used for Experiments -- 6.8.1 Purdue Indiana Indian Pines -- 6.8.2 Salinas -- 6.8.3 ROSIS Data -- 6.9 Experiments -- 6.9.1 Purdue Indiana Indian Pines -- 6.9.2 Salinas -- 6.9.3 University of Pavia -- 6.10 Conclusion -- References -- Part III Compressive Sensing for Hyperspectral Imaging -- Chapter 7 Restricted Entropy and Spectrum Properties for Hyperspectral Imaging -- 7.1 Introduction -- 7.2 Compressive Sensing Review -- 7.3 Restricted Entropy Property -- 7.4 Restricted Spectrum Property -- 7.5 REP and RSP Hyperspectral Measures -- 7.6 Experiments -- 7.7 Conclusion -- References -- Chapter 8 Endmember Finding in Compressively Sensed Band Domain -- 8.1 Introduction -- 8.2 Compressive Hyperspectral Band Sensing -- 8.2.1 Compressive Sensing Framework -- 8.2.2 Compressive Sensing of Hyperspectral Bands -- 8.2.3 Universality Model -- 8.3 Simplex Volume Calculation -- 8.3.1 Simplex Volume via Singular Value Decomposition -- 8.3.2 Simplex Volume via Matrix Determinant -- 8.4 Restricted Simplex Volume Property -- 8.5 Two Sequential Algorithms for p-FINDR -- 8.5.1 SeQuential p-FINDR (SQ p-FINDR) -- 8.5.2 SuCcessive p-FINDR (SC p-FINDR) -- 8.5.3 SQ p-FINDR and SC p-FINDR in CSBD -- 8.6 Experiments -- 8.6.1 Experimental Setup -- 8.6.2 Algorithm Analysis on Experimental Data -- 8.7 Experimental Results and Discussions -- 8.7.1 SQ p-FINDR Experimental Result Analysis -- 8.7.2 SC p-FINDR Experimental Result Analysis -- 8.8 Conclusion -- References -- Chapter 9 Hyperspectral Image Classification in Compressively Sensed Band Domain -- 9.1 Introduction -- 9.2 Compressive Sensing Review. 327 $a9.2.1 Compressive Sensing Framework. 330 $a"This book is derived from recent development of hyperspectral imaging (HSI) techniques in the field. Many new ideas have been explored and have led in various new directions in the past a few years. The book's content is based on the expertise of invited scholars and is categorized into six parts. Part I describes hyperspectral data unmixing (4 chapters). Part II spans topics from hyperspectral target detection to hyperspectral image classification (8 chapters). Part III explains band selection for HSI (4 chapters). Part IV covers compressive sensing for HSI (2 chapters). Part V pertains to fusion for HSI (5 chapters)."--$cProvided by publisher. 410 0$aIEEE Press 606 $aHyperspectral imaging 606 $aImage processing 615 0$aHyperspectral imaging. 615 0$aImage processing. 676 $a771 702 $aChang$b Chein-I 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910830508903321 996 $aAdvances in hyperspectral image processing techniques$93965976 997 $aUNINA