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A comprehensive introduction to the mathematical principles and algorithms in statistical signal processing and modern neural networks.
This text is an expanded version of a graduate course on advanced signal processing at the Johns Hopkins University Whiting school program for professionals with students from electrical engineering, physics, computer and data science, and mathematics backgrounds. It covers the theory underlying applications in statistical signal processing including spectral estimation, linear prediction, adaptive filters, and optimal processing of uniform spatial arrays. Unique among books on the subject, it also includes a comprehensive introduction to modern neural networks with examples in time series prediction and image classification.
Coverage includes:
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Amir-Homayoon Najmi, Ph.D., was a Fulbright scholar at the Relativity Centre, University of Texas. He has published research in wide areas including quantum field theory in cosmological space-times, seismic inverse scattering, adaptive signal processing applied to electromagnetic waves and biosurveillance.
Todd Moon, Ph.D., is head of the Electrical and Computer Engineering Department at Utah State University. He has been published extensively on digital communications theory and signal processing. 
Najmi and Moon's book helps fill a long-standing gap in graduate-level textbooks on signal processing, which typically cover either classical methods only, or are more akin to research monographs and specialized to narrower topics. I have been looking for a more broad treatment that covers both the old and the new "under one roof," with sufficient mathematical rigor but without diving too deep into details that might discourage students along the way. This is the best book I have found that meets these goals it's both comprehensive and comprehensible, written at the right level for the graduate student population in my courses.
A. Lee Swindlehurst, Professor, Dept. of Electrical Engineering & Computer Science, 
Henry Samueli School of Engineering
"An excellent resource for all signal processing practitioners and students. The authors have successfully brought together in one place a diverse collection of essential signal analysis techniques that must be part of every engineer s tool set."
V John Mathews, Professor, School of Electrical Engineering & Computer Science, Oregon State University                                                                       
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Paperback. Condición: new. Paperback. Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product.A comprehensive introduction to the mathematical principles and algorithms in statistical signal processing and modern neural networks. This text is an expanded version of a graduate course on advanced signal processing at the Johns Hopkins University Whiting school program for professionals with students from electrical engineering, physics, computer and data science, and mathematics backgrounds. It covers the theory underlying applications in statistical signal processing including spectral estimation, linear prediction, adaptive filters, and optimal processing of uniform spatial arrays. Unique among books on the subject, it also includes a comprehensive introduction to modern neural networks with examples in time series prediction and image classification. Coverage includes:Mathematical structures of signal spaces and matrix factorizationslinear time-invariant systems and transformsLeast squares filtersRandom variables, estimation theory, and random processesSpectral estimation and autoregressive signal modelslinear prediction and adaptive filtersOptimal processing of linear arraysNeural networks "This is a core textbook on signal analysis and its underlying mathematics for upper undergraduate and graduate courses"-- Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9781260458930
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