SVD and signal processing III algorithms, architectures, and applications
Matrix Singular Value Decomposition (SVD) and its application to problems in signal processing is explored in this book. The papers discuss algorithms and implementation architectures for computing the SVD, as well as a variety of applications such as systems and signal modeling and detection. The p...
| Otros Autores: | , |
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Amsterdam ; New York :
Elsevier
1995.
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009755115606719 |
Tabla de Contenidos:
- Front Cover; SVD and Signal Processing, III: Algorithms, Architectures and Applications; Copyright Page; Preface; Contents; A short introduction to Beltrami's paper; On bilinear functions; PART 1: KEYNOTE PAPERS; Chapter 1. Implicitly restarted Arnoldi/Lanczos methods and large scale SVD applications; Chapter 2. Isospectral matrix flows for numerical analysis; Chapter 3. The Riemannian singular value decomposition; Chapter 4. Consistent signal reconstruction and convex coding; PART 2: ALGORITHMS AND THEORETICAL CONCEPTS; Chapter 5. The orthogonal qd-algorithm
- Chapter 6. Accurate singular value computation with the Jacobi methodChapter 7. Note on the accuracy of the eigensolution of matrices generated by finite elements; Chapter 8. Transpose-free Arnoldi iterations for approximating extremal singular values and vectors; Chapter 9. A Lanczos algorithm for computing the largest quotient singular values in regularization problems; Chapter 10. A QR-like SVD algorithm for a product/quotient of several matrices; Chapter 11. Approximating the PSVD and QSVD; Chapter 12. Bounds on singular values revealed by QR factorizations
- Chapter 13. A stable algorithm for downdating the ULV decompositionChapter 14. The importance of a good condition estimator in the URV and ULV algorithms; Chapter 15. L-ULV(A), a low-rank revealing ULV algorithm; Chapter 16. Fast algorithms for signal subspace fitting with Toeplitz matrices and applications to exponential data modeling; Chapter 17. A block Toeplitz look-ahead Schur algorithm; Chapter 18. The set of 2-by-3 matrix pencils - Kronecker structures and their transitions under perturbations - And versal deformations of matrix pencils
- Chapter 19. J-Unitary matrices for algebraic approximation and interpolation -The singular casePART 3: ARCHITECTURES AND REAL TIME IMPLEMENTATION; Chapter 20. Sphericalized SVD Updating for Subspace Tracking; Chapter 21. Real-time architectures for sphericalized SVD Updating; Chapter 22. Systolic Arrays for SVD Downdating; Chapter 23. Subspace separation by discretizations of double bracket flows; Chapter 24. A continuous time approach to the analysis and design of parallel algorithms for subspace tracking; Chapter 25. Stable Jacobi SVD updating by factorization of the orthogonal matrix
- Chapter 26. Transformational reasoning on time-adaptive Jacobi type algorithmsChapter 27. Adaptive direction-of-arrival estimation based on rank and subspace tracking; Chapter 28. Multiple subspace ULV algorithm and LMS tracking; PART 4: APPLICATIONS; Chapter 29. SVD-based analysis of image boundary distortion; Chapter 30. The SVD in image restoration; Chapter 31. Two dimensional zero error modeling for image compression; Chapter 32. Robust image processing for remote sensing data; Chapter 33. SVD for linear inverse problems; Chapter 34. Fitting of circles and ellipses, least squares solution
- Chapter 35. The use of SVD for the study of multivariate noise and vibration problems
