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Introduction to statistical signal processing with applications by Mandyam D. Srinath, P.K. Rajasekaran, R. Viswanathan

Introduction to statistical signal processing with applications Mandyam D. Srinath, P.K. Rajasekaran, R. Viswanathan ebook
ISBN: 013125295X, 9780131252950
Format: djvu
Publisher: Prentice Hall
Page: 463

A range of important topics are covered in basic signal processing, model-based statistical signal processing and their applications. Theories and methods, it is also an ideal introduction to statistical signal processing in neuroscience. Elsevier Academic Press 30 Corporate Drive, Suite 400, Burlington, MA 01803, USA; 2005. This final volume of Kay's Next, he highlights specific algorithms that have “stood the test of time,” offers realistic examples from several key application areas, and introduces useful extensions. Bioelectrical Signal Processing in Cardiac and Neurological Applications. Digital Signal Processing, Principles, Algorithms, and Applications: John G. Introduction to Statistical Signal Processing. Remark: Condition (C1) is enforced as a simple way of introducing redundancy in the precoding process [7,26]. Kay shows how to convert theories of statistical signal processing estimation and detection into software algorithms that can be implemented on digital computers. In order to do so, we may consider the channel vector to be a deterministic unknown within the classical approach to statistical estimation or as a random vector by adopting the Bayesian viewpoint. This article is part of the series Signal Processing Methods for Diversity and Its Applications. In Fundamentals of Statistical Signal Processing, Volume III: Practical Algorithm Development, author Steven M.