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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 publication presents a number of keynote papers, highlighting recent developments in the field, namely large scale SVD applications, isospectral matrix flows, Riemannian SVD and consistent signal reconstruction. It also features a translation of a historical paper by Eugenio Beltrami, containing on

Signal processing --- Decomposition (Mathematics) --- Digital techniques --- Congresses --- -Signal processing --- -Academic collection --- #TELE:SISTA --- 519.6 --- 681.3*G13 --- Processing, Signal --- Information measurement --- Signal theory (Telecommunication) --- Mathematics --- Probabilities --- -Congresses --- Computational mathematics. Numerical analysis. Computer programming --- Numerical linear algebra: conditioning; determinants; Eigenvalues; error analysis; linear systems; matrix inversion; pseudoinverses; sparse and very largesystems --- 519.6 Computational mathematics. Numerical analysis. Computer programming --- 681.3*G13 Numerical linear algebra: conditioning; determinants; Eigenvalues; error analysis; linear systems; matrix inversion; pseudoinverses; sparse and very largesystems --- Academic collection --- Digital techniques&delete& --- Decomposition (Mathematics) - Congresses. --- Signal processing - Digital techniques - Congresses --- Decomposition (Mathematics) - Congresses --- Signals --- Processing