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A Method For Generating Two Dimensional Adaptive Filtering Algorithms Utilizing Reduced Or No Apriori Information
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Book Synopsis A Method for Generating Two-dimensional Adaptive Filtering Algorithms Utilizing Reduced Or No Apriori Information by : J. P. Violette
Download or read book A Method for Generating Two-dimensional Adaptive Filtering Algorithms Utilizing Reduced Or No Apriori Information written by J. P. Violette and published by . This book was released on 1999 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author :Paulo Sergio Ramirez Diniz Publisher :Springer Science & Business Media ISBN 13 :9781402071256 Total Pages :594 pages Book Rating :4.0/5 (712 download)
Book Synopsis Adaptive Filtering by : Paulo Sergio Ramirez Diniz
Download or read book Adaptive Filtering written by Paulo Sergio Ramirez Diniz and published by Springer Science & Business Media. This book was released on 2002 with total page 594 pages. Available in PDF, EPUB and Kindle. Book excerpt: Adaptive Filtering: Algorithms and Practical Implementation, Second Edition, presents a concise overview of adaptive filtering, covering as many algorithms as possible in a unified form that avoids repetition and simplifies notation. It is suitable as a textbook for senior undergraduate or first-year graduate courses in adaptive signal processing and adaptive filters. The philosophy of the presentation is to expose the material with a solid theoretical foundation, to concentrate on algorithms that really work in a finite-precision implementation, and to provide easy access to working algorithms. Hence, practicing engineers and scientists will also find the book to be an excellent reference. This second edition contains a substantial amount of new material: -Two new chapters on nonlinear and subband adaptive filtering; -Linearly constrained Weiner filters and LMS algorithms; -LMS algorithm behavior in fast adaptation; -Affine projection algorithms; -Derivation smoothing; -MATLAB codes for algorithms. An instructor's manual, a set of master transparencies, and the MATLAB codes for all of the algorithms described in the text are also available. Useful to both professional researchers and students, the text includes 185 problems; over 38 examples, and over 130 illustrations. It is of primary interest to those working in signal processing, communications, and circuits and systems. It will also be of interest to those working in power systems, networks, learning systems, and intelligent systems.
Book Synopsis Adaptive Filtering by : Paulo S. R. Diniz
Download or read book Adaptive Filtering written by Paulo S. R. Diniz and published by Springer Science & Business Media. This book was released on 2012-08-14 with total page 664 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the fourth edition of Adaptive Filtering: Algorithms and Practical Implementation, author Paulo S.R. 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. The main algorithms are described in tables, which are detailed enough to allow the reader to verify the covered concepts. Many examples address problems drawn from actual applications. New material to this edition includes: Analytical and simulation examples in Chapters 4, 5, 6 and 10 Appendix E, which summarizes the analysis of set-membership algorithm Updated problems and references Providing a concise background on adaptive filtering, this book covers the family of LMS, affine projection, RLS and data-selective set-membership algorithms as well as nonlinear, sub-band, blind, IIR adaptive filtering, and more. Several problems are included at the end of chapters, and some of these problems address applications. A user-friendly MATLAB package is provided where the reader can easily solve new problems and test algorithms in a quick manner. Additionally, the book provides easy access to working algorithms for practicing engineers.
Book Synopsis Adaptive Algorithms for Two Dimensional Filtering by : Steven L. Wilstrup
Download or read book Adaptive Algorithms for Two Dimensional Filtering written by Steven L. Wilstrup and published by . This book was released on 1988 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis, an adaptive two dimensional least mean squares (LMS) algorithm and a recursive least squares (RLS) algorithm are developed from the one dimensional algorithms. Design of the two dimensional LMS and RLS algorithms are studied for accuracy based on the results of a two dimensional system identification model which was used in testing the algorithms. Application of the two algorithms is demonstrated through computer simulation in which the adaptive filters are employed in a noise canceler and an adaptive line enhancer and applied to an image processing problem. Keywords: Wiener filters, One dimensional. (KR).
Book Synopsis Partial Update Least-Square Adaptive Filtering by : Bei Xie
Download or read book Partial Update Least-Square Adaptive Filtering written by Bei Xie and published by Springer. This book was released on 2014-05-19 with total page 105 pages. Available in PDF, EPUB and Kindle. Book excerpt: Adaptive filters play an important role in the fields related to digital signal processing and communication, such as system identification, noise cancellation, channel equalization, and beamforming. In practical applications, the computational complexity of an adaptive filter is an important consideration. The Least Mean Square (LMS) algorithm is widely used because of its low computational complexity ($O(N)$) and simplicity in implementation. The least squares algorithms, such as Recursive Least Squares (RLS), Conjugate Gradient (CG), and Euclidean Direction Search (EDS), can converge faster and have lower steady-state mean square error (MSE) than LMS. However, their high computational complexity ($O(N^2)$) makes them unsuitable for many real-time applications. A well-known approach to controlling computational complexity is applying partial update (PU) method to adaptive filters. A partial update method can reduce the adaptive algorithm complexity by updating part of the weight vector instead of the entire vector or by updating part of the time. In the literature, there are only a few analyses of these partial update adaptive filter algorithms. Most analyses are based on partial update LMS and its variants. Only a few papers have addressed partial update RLS and Affine Projection (AP). Therefore, analyses for PU least-squares adaptive filter algorithms are necessary and meaningful. This monograph mostly focuses on the analyses of the partial update least-squares adaptive filter algorithms. Basic partial update methods are applied to adaptive filter algorithms including Least Squares CMA (LSCMA), EDS, and CG. The PU methods are also applied to CMA1-2 and NCMA to compare with the performance of the LSCMA. Mathematical derivation and performance analysis are provided including convergence condition, steady-state mean and mean-square performance for a time-invariant system. The steady-state mean and mean-square performance are also presented for a time-varying system. Computational complexity is calculated for each adaptive filter algorithm. Numerical examples are shown to compare the computational complexity of the PU adaptive filters with the full-update filters. Computer simulation examples, including system identification and channel equalization, are used to demonstrate the mathematical analysis and show the performance of PU adaptive filter algorithms. They also show the convergence performance of PU adaptive filters. The performance is compared between the original adaptive filter algorithms and different partial-update methods. The performance is also compared among similar PU least-squares adaptive filter algorithms, such as PU RLS, PU CG, and PU EDS. In addition to the generic applications of system identification and channel equalization, two special applications of using partial update adaptive filters are also presented. One application uses PU adaptive filters to detect Global System for Mobile Communication (GSM) signals in a local GSM system using the Open Base Transceiver Station (OpenBTS) and Asterisk Private Branch Exchange (PBX). The other application uses PU adaptive filters to do image compression in a system combining hyperspectral image compression and classification.
Book Synopsis A Rapid Introduction to Adaptive Filtering by : Leonardo Rey Vega
Download or read book A Rapid Introduction to Adaptive Filtering written by Leonardo Rey Vega and published by Springer Science & Business Media. This book was released on 2012-08-07 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, the authors provide insights into the basics of adaptive filtering, which are particularly useful for students taking their first steps into this field. They start by studying the problem of minimum mean-square-error filtering, i.e., Wiener filtering. Then, they analyze iterative methods for solving the optimization problem, e.g., the Method of Steepest Descent. By proposing stochastic approximations, several basic adaptive algorithms are derived, including Least Mean Squares (LMS), Normalized Least Mean Squares (NLMS) and Sign-error algorithms. The authors provide a general framework to study the stability and steady-state performance of these algorithms. The affine Projection Algorithm (APA) which provides faster convergence at the expense of computational complexity (although fast implementations can be used) is also presented. In addition, the Least Squares (LS) method and its recursive version (RLS), including fast implementations are discussed. The book closes with the discussion of several topics of interest in the adaptive filtering field.
Book Synopsis Low-complexity Adaptive Filtering Algorithms Based on the Minimum L [infinity]-norm Method by : Abhishek Tandon
Download or read book Low-complexity Adaptive Filtering Algorithms Based on the Minimum L [infinity]-norm Method written by Abhishek Tandon and published by . This book was released on 2004 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The complexity of an adaptive filtering algorithm is proportional to the tap length of the filter and hence, may become computationally prohibitive for applications requiring a long filter tap. In this thesis, we provide a framework for developing low-complexity adaptive filter algorithms by utilizing the concept of partial-updating along with the technique of finding the gradient vector in the hyperplane based on the L ∞ -norm criterion. The resulting algorithm should have low-complexity not only because of the updating of only a subset of the filter coefficients at each time step, but also from the fact that updating a filter coefficient using the algorithm based on L ∞ -norm requires less number of operations compared to the L 2 -norm algorithm. Two specific coefficient selection techniques, namely the sequential and M -Max coefficient selection techniques, are considered in this thesis. Statistical analyses of these two algorithms are carried out to derive the evolution equations for the mean and mean-square of the filter coefficient misalignment as well as to obtain stability bounds on the step-size of the two algorithms. Further, these analyses are used to show that the algorithm employing the M -Max coefficient selection technique can achieve a convergence rate that is closest to the full update algorithm. As a consequence, even though there are various ways of selecting a subset of the filter coefficients, the study of the other techniques becomes redundant. Simulations are carried out to validate the results obtained from the statistical analyses of the algorithms. The concept of developing algorithms based on the partial-updating and L ∞ -norm is extended to proportionate adaptive filtering. Finally, the performance of the proposed adaptive filtering algorithms as well as that of the existing ones is studied in echo cancellation.
Book Synopsis Introduction to Adaptive Filters by : Simon S. Haykin
Download or read book Introduction to Adaptive Filters written by Simon S. Haykin and published by . This book was released on 1984 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Adaptive Filtering Methods by : Petrus C. Sommen
Download or read book Adaptive Filtering Methods written by Petrus C. Sommen and published by . This book was released on 1992 with total page 167 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis American Doctoral Dissertations by :
Download or read book American Doctoral Dissertations written by and published by . This book was released on 1998 with total page 784 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Adaptive Filters by : Behrouz Farhang-Boroujeny
Download or read book Adaptive Filters written by Behrouz Farhang-Boroujeny and published by John Wiley & Sons. This book was released on 2013-04-02 with total page 800 pages. Available in PDF, EPUB and Kindle. Book excerpt: This second edition of Adaptive Filters: Theory and Applications has been updated throughout to reflect the latest developments in this field; notably an increased coverage given to the practical applications of the theory to illustrate the much broader range of adaptive filters applications developed in recent years. The book offers an easy to understand approach to the theory and application of adaptive filters by clearly illustrating how the theory explained in the early chapters of the book is modified for the various applications discussed in detail in later chapters. This integrated approach makes the book a valuable resource for graduate students; and the inclusion of more advanced applications including antenna arrays and wireless communications makes it a suitable technical reference for engineers, practitioners and researchers. Key features: • Offers a thorough treatment of the theory of adaptive signal processing; incorporating new material on transform domain, frequency domain, subband adaptive filters, acoustic echo cancellation and active noise control. • Provides an in-depth study of applications which now includes extensive coverage of OFDM, MIMO and smart antennas. • Contains exercises and computer simulation problems at the end of each chapter. • Includes a new companion website hosting MATLAB® simulation programs which complement the theoretical analyses, enabling the reader to gain an in-depth understanding of the behaviours and properties of the various adaptive algorithms.
Book Synopsis Kernel Adaptive Filtering by : Weifeng Liu
Download or read book Kernel Adaptive Filtering written by Weifeng Liu and published by Wiley. This book was released on 2010-02-18 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: Online learning from a signal processing perspective There is increased interest in kernel learning algorithms in neural networks and a growing need for nonlinear adaptive algorithms in advanced signal processing, communications, and controls. Kernel Adaptive Filtering is the first book to present a comprehensive, unifying introduction to online learning algorithms in reproducing kernel Hilbert spaces. Based on research being conducted in the Computational Neuro-Engineering Laboratory at the University of Florida and in the Cognitive Systems Laboratory at McMaster University, Ontario, Canada, this unique resource elevates the adaptive filtering theory to a new level, presenting a new design methodology of nonlinear adaptive filters. Covers the kernel least mean squares algorithm, kernel affine projection algorithms, the kernel recursive least squares algorithm, the theory of Gaussian process regression, and the extended kernel recursive least squares algorithm Presents a powerful model-selection method called maximum marginal likelihood Addresses the principal bottleneck of kernel adaptive filters—their growing structure Features twelve computer-oriented experiments to reinforce the concepts, with MATLAB codes downloadable from the authors' Web site Concludes each chapter with a summary of the state of the art and potential future directions for original research Kernel Adaptive Filtering is ideal for engineers, computer scientists, and graduate students interested in nonlinear adaptive systems for online applications (applications where the data stream arrives one sample at a time and incremental optimal solutions are desirable). It is also a useful guide for those who look for nonlinear adaptive filtering methodologies to solve practical problems.
Book Synopsis Reduced-complexity Adaptive Filtering Technqiues for Communications Applications by : Reza Arablouei
Download or read book Reduced-complexity Adaptive Filtering Technqiues for Communications Applications written by Reza Arablouei and published by . This book was released on 2013 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis A Constrained Two-dimensional Adaptive Digital Filter with Reduced Computational Complexity by : Richard Philip Faust
Download or read book A Constrained Two-dimensional Adaptive Digital Filter with Reduced Computational Complexity written by Richard Philip Faust and published by . This book was released on 1988 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Adaptive Filtering and Change Detection by : Fredrik Gustafsson
Download or read book Adaptive Filtering and Change Detection written by Fredrik Gustafsson and published by John Wiley & Sons. This book was released on 2000-10-03 with total page 520 pages. Available in PDF, EPUB and Kindle. Book excerpt: Adaptive filtering is a branch of digital signal processing which enables the selective enhancement of desired elements of a signal and the reduction of undesired elements. Change detection is another kind of adaptive filtering for non-stationary signals, and is the basic tool in fault detection and diagnosis. This text takes the unique approach that change detection is a natural extension of adaptive filtering, and the broad coverage encompasses both the mathematical tools needed for adaptive filtering and change detection and the applications of the technology. Real engineering applications covered include aircraft, automotive, communication systems, signal processing and automatic control problems. The unique integration of both theory and practical applications makes this book a valuable resource combining information otherwise only available in separate sources Comprehensive coverage includes many examples and case studies to illustrate the ideas and show what can be achieved Uniquely integrates applications to airborne, automotive and communications systems with the essential mathematical tools Accompanying Matlab toolbox available on the web illustrating the main ideas and enabling the reader to do simulations using all the figures and numerical examples featured This text would prove to be an essential reference for postgraduates and researchers studying digital signal processing as well as practising digital signal processing engineers.
Book Synopsis Two-dimensional Transform Domain Adaptive Filtering by : Mark Nicholas Howard
Download or read book Two-dimensional Transform Domain Adaptive Filtering written by Mark Nicholas Howard and published by . This book was released on 1993 with total page 180 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Electrical & Electronics Abstracts by :
Download or read book Electrical & Electronics Abstracts written by and published by . This book was released on 1997 with total page 1948 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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