Unsupervised Adaptive Filtering, Blind Deconvolution (Hardcover)
Simon Haykin
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Venditore AbeBooks dal 29 giugno 2022
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Aggiungere al carrelloVenduto da CitiRetail, Stevenage, Regno Unito
Venditore AbeBooks dal 29 giugno 2022
Condizione: new
Quantità: 1 disponibili
Aggiungere al carrelloHardcover. A complete, one-stop reference on the state of the art of unsupervised adaptive filtering While unsupervised adaptive filtering has its roots in the 1960s, more recent advances in signal processing, information theory, imaging, and remote sensing have made this a hot area for research in several diverse fields. This book brings together cutting-edge information previously available only in disparate papers and articles, presenting a thorough and integrated treatment of the two major classes of algorithms used in the field, namely, blind signal separation and blind channel equalization algorithms. Divided into two volumes for ease of presentation, this important work shows how these algorithms, although developed independently, are closely related foundations of unsupervised adaptive filtering. Through contributions by the foremost experts on the subject, the book provides an up-to-date account of research findings, explains the underlying theory, and discusses potential applications in diverse fields. More than 100 illustrations as well as case studies, appendices, and references further enhance this excellent resource. Following coverage begun in Volume I: Blind Source Separation, this volume discusses: * The core of FSE-CMA behavior theory * Relationships between blind deconvolution and blind source separation * Blind separation of independent sources based on multiuser kurtosis optimization criteria Volume 2 presents current state of the art thinking in a single source. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Codice articolo 9780471379416
SIMON HAYKIN, PhD, is University Professor and Director of the Adaptive Systems Laboratory at McMaster University.
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