000790015 000__ 04858cam\a2200529Ii\4500
000790015 001__ 790015
000790015 005__ 20230306143332.0
000790015 006__ m\\\\\o\\d\\\\\\\\
000790015 007__ cr\cn\nnnunnun
000790015 008__ 170622s2017\\\\sz\\\\\\ob\\\\101\0\eng\d
000790015 019__ $$a991787207
000790015 020__ $$a9783319547114$$q(electronic book)
000790015 020__ $$a3319547119$$q(electronic book)
000790015 020__ $$z9783319547107
000790015 020__ $$z3319547100
000790015 035__ $$aSP(OCoLC)ocn990777960
000790015 035__ $$aSP(OCoLC)990777960$$z(OCoLC)991787207
000790015 040__ $$aN$T$$beng$$erda$$epn$$cN$T$$dEBLCP$$dN$T$$dOCLCO$$dGW5XE$$dYDX$$dUAB
000790015 049__ $$aISEA
000790015 050_4 $$aQA403$$b.E928 2017 v. 5eb
000790015 08204 $$a515.2433$$223
000790015 24500 $$aExcursions in harmonic analysis.$$nVolume 5 :$$bthe February Fourier Talks at the Norbert Wiener Center /$$cRadu Balan, John J. Benedetto, Wojciech Czaja, Matthew Dellatorre, Kasso A. Okoudjou, editors.
000790015 24630 $$aFebruary Fourier Talks at the Norbert Wiener Center
000790015 264_1 $$aCham, Switzerland :$$bBirkhäuser, Springer,$$c[2017]
000790015 264_4 $$c©2017
000790015 300__ $$a1 online resource.
000790015 336__ $$atext$$btxt$$2rdacontent
000790015 337__ $$acomputer$$bc$$2rdamedia
000790015 338__ $$aonline resource$$bcr$$2rdacarrier
000790015 4901_ $$aApplied and numerical harmonic analysis
000790015 500__ $$aSelected conference papers.
000790015 504__ $$aIncludes bibliographical references and indexes.
000790015 5050_ $$aANHA Series Preface; Preface; The February Fourier Talks (FFT); The Norbert Wiener Center; The Structure of the Volumes; Acknowledgments; Contents; Part XVII Theoretical Harmonic Analysis; Time-Frequency Analysis and Representations of the Discrete Heisenberg Group; 1 Introduction; 2 Direct Integrals; 3 The Rational Case; 4 The Irrational Case; References; Fractional Differentiation: Leibniz Meets Hölder; 1 Introduction; 2 The counterexample; 3 The sharp Kato-Ponce inequalities and preliminaries; 4 The proof of the homogeneous inequality (7); 5 Final remarks; References
000790015 5058_ $$aWavelets and Graph C*-Algebras1 Introduction; 2 C-Algebras and Work by Bratteli and Jorgensen and Dutkay and Jorgensen on Representations of ON; 3 Marcolli-Paolucci Wavelets; 4 C*-Algebras Corresponding to Directed Graphs and Higher-Rank Graphs; 4.1 Directed Graphs, Higher-Rank Graphs, and C*-Algebras; 4.2 -Semibranching Function Systems and Representations of C*(); 5 Wavelets on L2(∞, M); 6 Traffic Analysis Wavelets on 2(0) for a Finite Strongly Connected k-Graph , and Wavelets from Spectral Graph Theory; 6.1 Wavelets for Spatial Traffic Analysis
000790015 5058_ $$a6.2 Wavelets on 2(0) Coming from Spectral Graph TheoryReferences; Part XVIII Image and Signal Processing; Precise State Tracking Using Three-Dimensional Edge Detection; 1 Introduction; 1.1 Previous Work in Tracking; 1.2 Previous Work in Edge Detection; 1.3 Outline and Contributions; 2 The Data; 3 3D Edge Detectors; 3.1 3D Canny Edge Detection; 3.2 3D Wavelet Edge Detection; 3.3 3D Shearlet Edge Detector; 3.4 3D Hybrid Wavelet and Shearlet Edge Detectors; 3.5 Performance of the Edge Detectors; 4 From Edge Detection to Tracking; 5 Experimental Results; 6 Conclusions; References
000790015 5058_ $$aApproaches for Characterizing Nonlinear Mixtures in Hyperspectral Imagery1 Introduction; 2 Methodology; 2.1 Fully Constrained Least Squares; 2.2 Proposed Method 1: Fully Constrained Least Squares (FCLS) Applied to Single Scattering Albedo Spectra; 2.3 Proposed Method 2: Generalized Kernel Fully Constrained Least Squares; 3 Description of Experiment; 4 Results; 5 Concluding Remarks; References; An Application of Spectral Regularization to Machine Learning and Cancer Classification; 1 Introduction; 1.1 Machine Learning; 1.2 Approach; 1.3 Prior Work; 1.4 Paper Contents; 2 Denoising Theorems
000790015 5058_ $$a2.1 Statements of Theorems2.1.1 Method 1: Local averaging on a graph; 2.1.2 Method 2: Support vector regression/regularization on a graph; 3 Application: Using Prior Information to Form Graphs; 3.1 Gene Expression; 4 Conclusion; References; Part XIX Quantization; Embedding-Based Representation of Signal Geometry; 1 Introduction; 1.1 Notation; 1.2 Outline; 2 Preserving Distances; 2.1 Randomized Linear Embeddings; 2.2 Embedding Map Design; 2.3 Distance-preserving properties of the map; 2.4 Learning the Embedding Map; 3 Preserving Inner Products, Angles, and Correlations
000790015 506__ $$aAccess limited to authorized users.
000790015 588__ $$aOnline resource; title from PDF title page (viewed June 26, 2017).
000790015 650_0 $$aHarmonic analysis$$vCongresses.
000790015 7001_ $$aBalan, Radu Victor,$$d1969-$$eeditor.
000790015 77608 $$iPrint version:$$z3319547100$$z9783319547107$$w(OCoLC)971339062
000790015 830_0 $$aApplied and numerical harmonic analysis.
000790015 852__ $$bebk
000790015 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-54711-4$$zOnline Access$$91397441.1
000790015 909CO $$ooai:library.usi.edu:790015$$pGLOBAL_SET
000790015 980__ $$aEBOOK
000790015 980__ $$aBIB
000790015 982__ $$aEbook
000790015 983__ $$aOnline
000790015 994__ $$a92$$bISE