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OverviewBecause of the wide use of adaptive filtering in digital signal processing and, because most of the modern electronic devices include some type of an adaptive filter, a text that brings forth the fundamentals of this field was necessary. The material and the principles presented in this book are easily accessible to engineers, scientists, and students who would like to learn the fundamentals of this field and have a background at the bachelor level. Adaptive Filtering Primer with MATLAB clearly explains the fundamentals of adaptive filtering supported by numerous examples and computer simulations. The authors introduce discrete-time signal processing, random variables and stochastic processes, the Wiener filter, properties of the error surface, the steepest descent method, and the least mean square (LMS) algorithm. They also supply many MATLAB functions and m-files along with computer experiments to illustrate how to apply the concepts to real-world problems. The book includes problems along with hints, suggestions, and solutions for solving them. An appendix on matrix computations completes the self-contained coverage. With applications across a wide range of areas, including radar, communications, control, medical instrumentation, and seismology, Adaptive Filtering Primer with MATLAB is an ideal companion for quick reference and a perfect, concise introduction to the field. Full Product DetailsAuthor: Alexander D. Poularikas (The University of Alabama, Huntsville, USA) , Zayed M. Ramadan (Al Ain University of Science and Technology,United Arab Emir)Publisher: Taylor & Francis Ltd Imprint: CRC Press Weight: 0.453kg ISBN: 9781138417939ISBN 10: 1138417939 Pages: 238 Publication Date: 27 July 2017 Audience: College/higher education , Tertiary & Higher Education Format: Hardback Publisher's Status: Active Availability: In Print ![]() This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us. Table of ContentsIntroduction. Discrete-Time Signal Processing. Random Variables, Sequences, and Stochastic Processes. Wiener Filters. Eigenvalues of Rx - Properties of the Error Surface. Newton and Steepest-Descent Method. The Least Mean-Square (LMS) Algorithm. Variations of LMS Algorithms. Least Squares and Recursive Least-Squares Signal Processing. Abbreviations. Bibliography. Appendix A: Matrix Analysis. Index.ReviewsAuthor InformationAlexander D. Poularikas, Zayed M. Ramadan Tab Content 6Author Website:Countries AvailableAll regions |