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Description
This book provides readers with a thorough exposition of adaptive filter algorithms applicable for nonlinear systems, kernel-based systems and for complex and quaternionic data. The authors describe in detail the algorithms and methods used for adaptive filtering in these circumstances. They present an introduction to linear adaptive filters, adaptive filters for complex data, and kernel adaptive filters. They also discuss new algorithms for using Kernel-based adaptive algorithms for complex or quaternionic data. Introduction.- Linear and Kernel Filter Theory.- Complex Data for Adaptive Filters.- The Complex Kernel Least Mean Square Algorithm.- Learning with Quaternions.- Information Theoretic Learning.- The Complex Kernel Affine Projection Algorithm.- Extending Complex Kernel Filters with Widely Linear Estimation.- Convergence Analysis of Complex Kernel LMS Algorithm.- The Quaternion Kernel LMS Algorithm.- The Quaternion Kernel Maximum Correntropy Algorithm.- Summary and Future Work.
Thomas K. Paul has worked in signal communications since 1996. Most recently, he has worked at ICR Inc. and Northrop Grumman Corp, in support of government defense contracts. Prior to that, at Ralink Technology, he worked on development of a cost-reduced version of the 802.11b WLAN chipset. And earlier, at PC-TEL Inc., he worked on the maintenance and improvement of their V.92 software modem product. He also holds a U.S. Patent regarding dynamic block processing in soft-ware modems. He earned the B.A.Sc. degree at the University of Toronto, Canada in 1996, and M.S.E.E. and Ph.D. degrees at Santa Clara University, Santa Clara, California in 2007 and 2013, respectively. His research interests are in communica-tions system design and digital signal processing.
Tokunbo Ogunfunmi received the B.S. (first class honors) degree from Obafemi Awolowo University, Nigeria, and the M.S. and Ph.D. degrees from Stanford University, Stanford, California, all in Electrical Engineering. He is currently a Professor of Electrical and Computer Engineering and Director of the Information Processing and Machine Learning Research Laboratory at Santa Clara University (SCU), Santa Clara, California. From 2010-2014, he served as the Associate Dean for Research and Faculty Development for the SCU School of Engineering. At SCU, he teaches a variety of courses in circuits, systems, signal processing and related areas. His current research interests include machine learning, deep learning, speech and multimedia (audio, video) compression, digital and adaptive signal processing and applications and nonlinear signal processing. He has published three books and over 250 refereed journal and conference papers in these areas.
