Adaptive Signal Models
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Adaptive Signal Models
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Author : Michael M. Goodwin
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-09-10
Adaptive Signal Models written by Michael M. Goodwin and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-09-10 with Technology & Engineering categories.
Adaptive Signal Models: Theory, Algorithms and Audio Applications presents methods for deriving mathematical models of natural signals. The introduction covers the fundamentals of analysis-synthesis systems and signal representations. Some of the topics in the introduction include perfect and near-perfect reconstruction, the distinction between parametric and nonparametric methods, the role of compaction in signal modeling, basic and overcomplete signal expansions, and time-frequency resolution issues. These topics arise throughout the book as do a number of other topics such as filter banks and multiresolution. The second chapter gives a detailed development of the sinusoidal model as a parametric extension of the short-time Fourier transform. This leads to multiresolution sinusoidal modeling techniques in Chapter Three, where wavelet-like approaches are merged with the sinusoidal model to yield improved models. In Chapter Four, the analysis-synthesis residual is considered; for realistic synthesis, the residual must be separately modeled after coherent components (such as sinusoids) are removed. The residual modeling approach is based on psychoacoustically motivated nonuniform filter banks. Chapter Five deals with pitch-synchronous versions of both the wavelet and the Fourier transform; these allow for compact models of pseudo-periodic signals. Chapter Six discusses recent algorithms for deriving signal representations based on time-frequency atoms; primarily, the matching pursuit algorithm is reviewed and extended. The signal models discussed in the book are compact, adaptive, parametric, time-frequency representations that are useful for analysis, coding, modification, and synthesis of natural signals such as audio. The models are all interpreted as methods for decomposing a signal in terms of fundamental time-frequency atoms; these interpretations, as well as the adaptive and parametric natures of the models,serve to link the various methods dealt with in the text. Adaptive Signal Models: Theory, Algorithms and Audio Applications serves as an excellent reference for researchers of signal processing and may be used as a text for advanced courses on the topic.
Adaptive Nonlinear System Identification
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Author : Tokunbo Ogunfunmi
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-09-05
Adaptive Nonlinear System Identification written by Tokunbo Ogunfunmi and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-09-05 with Science categories.
Focuses on System Identification applications of the adaptive methods presented. but which can also be applied to other applications of adaptive nonlinear processes. Covers recent research results in the area of adaptive nonlinear system identification from the authors and other researchers in the field.
Statistical And Adaptive Signal Processing
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Author : Dimitris G. Manolakis
language : en
Publisher: McGraw-Hill Science, Engineering & Mathematics
Release Date : 2000
Statistical And Adaptive Signal Processing written by Dimitris G. Manolakis and has been published by McGraw-Hill Science, Engineering & Mathematics this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000 with Technology & Engineering categories.
The goal of this volume is to provide a unified, practical and complete introduction to spectral estimation, signal modelling and adaptive filtering. It includes computer-based experiments to illustrate important concepts.
Fundamentals Of Adaptive Signal Processing
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Author : Aurelio Uncini
language : en
Publisher: Springer
Release Date : 2014-12-30
Fundamentals Of Adaptive Signal Processing written by Aurelio Uncini and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-12-30 with Technology & Engineering categories.
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 biomedical fields, economic models, environmental sciences, acoustics, telecommunications, remote sensing, monitoring and in general, the modeling and prediction of complex physical phenomena. The reader will learn not only how to design and implement the algorithms but also how to evaluate their performance for specific applications utilizing the tools provided. While using a simple mathematical language, the employed approach is very rigorous. The text will be of value both for research purposes and for courses of study.
Adaptive Signal Processing
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Author : Widrow
language : en
Publisher: Pearson Education India
Release Date : 2016
Adaptive Signal Processing written by Widrow and has been published by Pearson Education India this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
Adaptive Signal Processing
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Author : Jacob Benesty
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-09
Adaptive Signal Processing written by Jacob Benesty and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-09 with Technology & Engineering categories.
By adaptive signal processing, we mean, in general, adaptive ?ltering.In- known environments where we need to model, identify, or track time-varying channels, adaptive ?ltering has been proven to be an e?ective and powerful tool. As a result, this tool is now in use in many di?erent ?elds. Since the invention, by Widrow and Ho? in 1959, of one of the ?rst ad- tive ?lters, the so-called least-mean-square, many applications appeared to have the potential to use this fundamental concept. While the number of - plications (using adaptive algorithms) has been (and keeps) ?ourishing with time, thanks to several successes, the need for more sophisticated adaptive algorithms became obvious as real-world problems are more complex and more demanding. Even though the theory of adaptive ?ltering is already a well-established topic in signal processing, new and improved concepts are discovered every year by researchers. Some of these recent approaches are discussed in this book. The goal of this book is to provide, for the ?rst time, a reference to the hottest real-world applications where adaptive ?ltering techniques play an important role. To do so, we invited top researchers in di?erent ?elds to c- tribute chapters addressing their speci?c topic of study. Thousands of pages wouldprobablynotbe enoughto describeallthe practicalapplicationsutil- ing adaptive algorithms. Therefore, we limited the topics to some important applications in acoustics, speech, wireless, and networking, where research is still very active and open.
Adaptive Signal Processing
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Author : Tülay Adali
language : en
Publisher: John Wiley & Sons
Release Date : 2010-06-25
Adaptive Signal Processing written by Tülay Adali and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-06-25 with Science categories.
Leading experts present the latest research results in adaptive signal processing Recent developments in signal processing have made it clear that significant performance gains can be achieved beyond those achievable using standard adaptive filtering approaches. Adaptive Signal Processing presents the next generation of algorithms that will produce these desired results, with an emphasis on important applications and theoretical advancements. This highly unique resource brings together leading authorities in the field writing on the key topics of significance, each at the cutting edge of its own area of specialty. It begins by addressing the problem of optimization in the complex domain, fully developing a framework that enables taking full advantage of the power of complex-valued processing. Then, the challenges of multichannel processing of complex-valued signals are explored. This comprehensive volume goes on to cover Turbo processing, tracking in the subspace domain, nonlinear sequential state estimation, and speech-bandwidth extension. Examines the seven most important topics in adaptive filtering that will define the next-generation adaptive filtering solutions Introduces the powerful adaptive signal processing methods developed within the last ten years to account for the characteristics of real-life data: non-Gaussianity, non-circularity, non-stationarity, and non-linearity Features self-contained chapters, numerous examples to clarify concepts, and end-of-chapter problems to reinforce understanding of the material Contains contributions from acknowledged leaders in the field 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 Learning Methods For Nonlinear System Modeling
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Author : Danilo Comminiello
language : en
Publisher: Butterworth-Heinemann
Release Date : 2018-07-05
Adaptive Learning Methods For Nonlinear System Modeling written by Danilo Comminiello and has been published by Butterworth-Heinemann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-05 with Technology & Engineering categories.
Adaptive Learning Methods for Nonlinear System Modeling presents some of the recent advances on adaptive algorithms and machine learning methods designed for nonlinear system modeling and identification. Real-life problems always entail a certain degree of nonlinearity, which makes linear models a non-optimal choice. This book mainly focuses on those methodologies for nonlinear modeling that involve any adaptive learning approaches to process data coming from an unknown nonlinear system. By learning from available data, such methods aim at estimating the nonlinearity introduced by the unknown system. In particular, the methods presented in this book are based on online learning approaches, which process the data example-by-example and allow to model even complex nonlinearities, e.g., showing time-varying and dynamic behaviors. Possible fields of applications of such algorithms includes distributed sensor networks, wireless communications, channel identification, predictive maintenance, wind prediction, network security, vehicular networks, active noise control, information forensics and security, tracking control in mobile robots, power systems, and nonlinear modeling in big data, among many others. This book serves as a crucial resource for researchers, PhD and post-graduate students working in the areas of machine learning, signal processing, adaptive filtering, nonlinear control, system identification, cooperative systems, computational intelligence. This book may be also of interest to the industry market and practitioners working with a wide variety of nonlinear systems. Presents the key trends and future perspectives in the field of nonlinear signal processing and adaptive learning. Introduces novel solutions and improvements over the state-of-the-art methods in the very exciting area of online and adaptive nonlinear identification. Helps readers understand important methods that are effective in nonlinear system modelling, suggesting the right methodology to address particular issues.
Complex Valued Nonlinear Adaptive Filters
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Author : Danilo P. Mandic
language : en
Publisher: John Wiley & Sons
Release Date : 2009-04-20
Complex Valued Nonlinear Adaptive Filters written by Danilo P. Mandic and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-04-20 with Science categories.
This book was written in response to the growing demand for a text that provides a unified treatment of linear and nonlinear complex valued adaptive filters, and methods for the processing of general complex signals (circular and noncircular). It brings together adaptive filtering algorithms for feedforward (transversal) and feedback architectures and the recent developments in the statistics of complex variable, under the powerful frameworks of CR (Wirtinger) calculus and augmented complex statistics. This offers a number of theoretical performance gains, which is illustrated on both stochastic gradient algorithms, such as the augmented complex least mean square (ACLMS), and those based on Kalman filters. This work is supported by a number of simulations using synthetic and real world data, including the noncircular and intermittent radar and wind signals.
Adaptive Processing Of Brain Signals
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Author : Saeid Sanei
language : en
Publisher: John Wiley & Sons
Release Date : 2013-05-28
Adaptive Processing Of Brain Signals written by Saeid Sanei and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-05-28 with Technology & Engineering categories.
In this book, the field of adaptive learning and processing is extended to arguably one of its most important contexts which is the understanding and analysis of brain signals. No attempt is made to comment on physiological aspects of brain activity; instead, signal processing methods are developed and used to assist clinical findings. Recent developments in detection, estimation and separation of diagnostic cues from different modality neuroimaging systems are discussed. These include constrained nonlinear signal processing techniques which incorporate sparsity, nonstationarity, multimodal data, and multiway techniques. Key features: Covers advanced and adaptive signal processing techniques for the processing of electroencephalography (EEG) and magneto-encephalography (MEG) signals, and their correlation to the corresponding functional magnetic resonance imaging (fMRI) Provides advanced tools for the detection, monitoring, separation, localising and understanding of functional, anatomical, and physiological abnormalities of the brain Puts a major emphasis on brain dynamics and how this can be evaluated for the assessment of brain activity in various states such as for brain-computer interfacing emotions and mental fatigue analysis Focuses on multimodal and multiway adaptive processing of brain signals, the new direction of brain signal research