LEADER 02658nam 2200577 450 001 9910788798703321 005 20220822065953.0 010 $a0-8218-8180-9 010 $a0-8218-4647-7 035 $a(CKB)3240000000070025 035 $a(EBL)3113255 035 $a(SSID)ssj0000629292 035 $a(PQKBManifestationID)11388858 035 $a(PQKBTitleCode)TC0000629292 035 $a(PQKBWorkID)10718794 035 $a(PQKB)11597033 035 $a(MiAaPQ)EBC3113255 035 $a(RPAM)15805975 035 $a(PPN)197108296 035 $a(EXLCZ)993240000000070025 100 $a20090707h20092009 uy| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aDiscrete groups and geometric structures $eWorkshop on Discrete Groups and Geometric Structures, with Applications III, May 26-30, 2008, Kortrijk, Belgium /$fKarel Dekimpe, Paul Igodt, Alain Valette, editors 210 1$aProvidence, Rhode Island :$cAmerican Mathematical Society,$d[2009] 210 4$dİ2009 215 $a1 online resource (162 p.) 225 1 $aContemporary mathematics,$v501$x0271-4132 300 $aDescription based upon print version of record. 320 $aIncludes bibliographical references. 327 $aContents -- Preface -- List of Participants -- Complex hyperbolic lattices -- Rank-one isometries of proper CAT(0)-spaces -- Trace polynomial for simple loops on the twice punctured torus -- Simplicial volume of products and fiber bundles -- Homology of Hantzsche-Wendt groups -- 1. Introduction -- 2. Facts and Preliminaries -- 3. Algorithm for Computing Homology -- 4. Example of Didicosm -- 5. Applications to Low Dimensions -- 6. Further Developments -- References -- Seifert fibred structure and rigidity on real Bott towers -- Exotic circles in groups of piecewise smooth circle homeomorphisms -- Groups generated by spine reflections admitting crooked fundamental domains. 410 0$aContemporary mathematics (American Mathematical Society).$v501$x0271-4132 606 $aDiscrete groups$vCongresses 606 $aGeometrical constructions$vCongresses 615 0$aDiscrete groups 615 0$aGeometrical constructions 676 $a512/.2 702 $aDekimpe$b Karel$f1967- 702 $aIgodt$b Paul$f1956- 702 $aValette$b Alain 712 12$aWorkshop on Discrete Groups and Geometric Structures, with Applications III 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910788798703321 996 $aDiscrete groups and geometric structures$9778617 997 $aUNINA LEADER 03086nam 22005653 450 001 9910816024503321 005 20250604153211.0 010 $a9781119681090 010 $a111968109X 010 $a9781119681113 010 $a1119681111 010 $a9781119681137 010 $a1119681138 035 $a(OCoLC)1132270858 035 $a(MiAaPQ)EBC5990184 035 $a(Au-PeEL)EBL5990184 035 $a(OCoLC)1130904568 035 $a(CKB)4940000000150528 035 $a(Perlego)1323962 035 $a(EXLCZ)994940000000150528 100 $a20250604d2019 uy 0 101 0 $aeng 135 $aurcn#---||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aMatrices and tensors in signal processing set$hVolume 1$iFrom algebraic structures to tensors /$fedited by Gerard Favier 205 $a1st ed. 210 1$aLondon :$cISTE :$cWiley,$d2019. 215 $a1 online resource 225 1 $aDigital signal and image processing series ;$vvolume 1 320 $aIncludes bibliographical references and index. 327 $aVolume 1.$tFrom algebraic structures to tensors. 330 8 $aNowadays, tensors play a central role for the representation, mining, analysis, and fusion of multidimensional, multimodal, and heterogeneous big data in numerous fields. This set on Matrices and Tensors in Signal Processing aims at giving a self-contained and comprehensive presentation of various concepts and methods, starting from fundamental algebraic structures to advanced tensor-based applications, including recently developed tensor models and efficient algorithms for dimensionality reduction and parameter estimation. Although its title suggests an orientation towards signal processing, the results presented in this set will also be of use to readers interested in other disciplines. This first book provides an introduction to matrices and tensors of higher-order based on the structures of vector space and tensor space. Some standard algebraic structures are first described, with a focus on the hilbertian approach for signal representation, and function approximation based on Fourier series and orthogonal polynomial series. Matrices and hypermatrices associated with linear, bilinear and multilinear maps are more particularly studied. Some basic results are presented for block matrices. The notions of decomposition, rank, eigenvalue, singular value, and unfolding of a tensor are introduced, by emphasizing similarities and differences between matrices and tensors of higher-order. 410 0$aDigital signal and image processing series ;$vvolume 1. 606 $aTensor algebra 606 $aMatrices 606 $aAlgebraic spaces 615 0$aTensor algebra. 615 0$aMatrices. 615 0$aAlgebraic spaces. 676 $a512.9 702 $aFavier$b Ge?rard 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910816024503321 996 $aMatrices and tensors in signal processing set$94098338 997 $aUNINA