1 edition of Advanced Concepts in Adaptive Signal Processing found in the catalog.
This book presents a number of important, but not so well-known, topics that currently exist scattered in the research literature. Advanced Concepts in Adaptive Signal Processing is intended to serve practitioners who need to consider using advanced techniques that are not found documented in currently available books. This book can also be used as a text in advanced graduate level courses on special topics. As a work that integrates the major results of four recent PhD dissertations, this book represents many man-years of research efforts. The text is rich with illustrative computer-generated examples, many of which were designed to investigate unknown phenomena, rather than to demonstrate already known principles. Advanced Concepts in Adaptive Signal Processing is written for the reader who is familiar with the conventional field of captive signal processing. A fundamental course from one of the many referenced texts would provide sufficient background. Throughout the book, where it was deemed necessary, certain conventional topics are developed in more detail to provide essential background.
|Statement||by W. Kenneth Jenkins, Andrew W. Hull, Jeffrey C. Strait, Bernard A. Schnaufer, Xiaohui Li|
|Series||The Springer International Series in Engineering and Computer Science -- 365, International series in engineering and computer science -- 365.|
|Contributions||Hull, Andrew W., Strait, Jeffrey C., Schnaufer, Bernard A., Li, Xiaohui|
|LC Classifications||TK5102.9, TA1637-1638, TK7882.S65|
|The Physical Object|
|Format||[electronic resource] /|
|Pagination||1 online resource (xiii, 307 pages).|
|Number of Pages||307|
|ISBN 10||1461346592, 1441986588|
|ISBN 10||9781461346593, 9781441986580|
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The topics include: mathematical models for discrete-time signals, vector spaces, Hilbert spaces, Fourier analysis, time-frequency analysis, filters, signal classification and prediction, basic image processing, adaptive filters and neural nets. With time, we will cover advanced topics including wavelets, deep learning and compressed sensing. Signal processing is an electrical engineering subfield that focuses on analysing, modifying, and synthesizing signals such as sound, images, and scientific measurements. Signal processing techniques can be used to improve transmission, storage efficiency and subjective quality and to also emphasize or detect components of interest in a measured signal.
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Advanced Concepts in Adaptive Signal Processing (The Springer International Series in Engineering and Computer Science) th Edition by W. Kenneth Jenkins (Author), Andrew W. Hull (Author), Jeffrey C. Strait (Author), Bernard A. Schnaufer (Author), Xiaohui Li Cited by: Although adaptive filtering and adaptive array processing began with research and development efforts in the late 's and early 's, it was not until the publication of the pioneering books by Honig and Messerschmitt in and Widrow and Stearns in that the field of adaptive signal processing began to emerge as a distinct discipline in its own right.
Get this from a library. Advanced concepts in adaptive signal processing. [William Kenneth Jenkins;]. Advanced Concepts in Adaptive Signal Processing Advanced Concepts in Adaptive Signal Processing book intended to serve practitioners who need to consider using advanced techniques that are not found documented in currently available books.
This book can also be used as a text in advanced graduate. Although adaptive filtering and adaptive array processing began with evaluation and enchancment efforts in the late 's and early 's, it was not until the publication of the pioneering books by Honig and Messerschmitt in and Widrow and Stearns in that the sector of adaptive signal processing began to emerge as a particular.
Pris: kr. Häftad, Skickas inom vardagar. Köp Advanced Concepts in Adaptive Signal Processing av W Kenneth Jenkins, Andrew W Hull, Jeffrey C Strait, Bernard A Schnaufer, Xiaohui Li på Description: This book is an accessible guide to adaptive signal processing methods that equips the reader with advanced theoretical and practical tools for the study and development of circuit structures and provides robust algorithms relevant to a wide variety of application scenarios.
Examples include multimodal and multimedia communications, the biological and. For a graduate course the lecturer may choose to cover, in addition, the three chapters on adaptive control (Chapters 14–16), or Chapter 13 on control without velocity measurements and Chap to give a short introduction to adaptive Size: 3MB.
Adaptive Signal Processing is an invaluable tool for graduate students, researchers, and practitioners working in the areas of signal processing, communications, controls, radar, sonar, and biomedical engineering. Adaptive Polynomial Filters. Abstract.
Linear filtering techniques are known to be effective in a variety of applications in signal processing, telecommunications, and control.
However, in some applications where linear filters do not give satisfactory performance, nonlinear filtering techniques are able to provide better by: 2. This book is an accessible guide to adaptive signal processing methods that equips the reader with advanced theoretical and practical tools for the study and development of circuit structures and provides robust algorithms relevant to a wide variety of Brand: Springer International Publishing.
Adaptive Signal Processing book. Read reviews from world’s largest community for readers. A treatment of adaptive signal processing featuring frequent us /5. Advanced Digital Signal Processing Abdellatif Zaidi Digital signal processing system from above is reﬁned: Digital signal processor A/D D/A Sample-and-struction ﬁlter hold circuit Lowpass recon-lowpass ﬁlter Anti-aliasing Sample-and-hold circuit Sampling.
Diniz presents the basic concepts of adaptive signal processing and adaptive filtering in a concise and straightforward manner.
The main classes of adaptive filtering algorithms are presented in a unified framework, using clear notations that facilitate actual implementation.
Introductory, systematic treatment of the many interrelated aspects. Twenty-three contributions address the fundamentals, spectral estimation algorithms, image processing, land and ocean seismic data, telecommunications, 3-D object reconstructions.
Alk. paper. Annotation copyright Book News, Inc. About this book. Subband adaptive filtering is rapidly becoming one of the most effective techniques for reducing computational complexity and improving the convergence rate of algorithms in adaptive signal processing applications.
This book provides an introductory, yet extensive guide on the theory of various subband adaptive filtering techniques. Advanced Concepts in Adaptive Signal Processing is intended to serve practitioners who need to consider using advanced techniques that are not found documented in currently available books.
This book can also be used as a text in advanced graduate level courses on special topics. Abadi M and Moradiani F () Mean-square performance analysis of the family of selective partial update NLMS and affine projection adaptive filter algorithms in nonstationary environment, EURASIP Journal on Advances in Signal Processing.
Diniz presents updated text on the basic concepts of adaptive signal processing and adaptive filtering. He first introduces the main classes of adaptive filtering algorithms in a unified framework, using clear notations that facilitate actual implementation/5(6).
This chapter introduces concepts of digital signal processing (DSP) and reviews an overall picture of its applications. Illustrative application examples include digital noise filtering, signal frequency analysis, speech coding and compression, biomedical signal processing such as interference.
scaled VLSI systems, adaptive signal processing, multidimensional array processing, computer imaging, bio-inspired optimization algorithms for intelligent signal processing, and fault tolerant digital signal processing.
He co-authored the book Advanced Concepts in Adaptive Signal Processing, published by Kluwer in CCI, adaptive to current market conditions rather than using static parameters.
Specific methods are given for these indica- tors. Additionally, the general concepts presented for these can be extended to apply to any existing static indicator. Many of the Qgital signal processing techniques described in.Adaptive Signal Processing presents the next generation of algorithms that will produce these desired results, with an emphasis on important applications and theoretical advancements.
numerous examples to clarify concepts, and end-of-chapter problems to reinforce understanding of the materialContains contributions from acknowledged leaders.