The Theory Of Linear Prediction

Author: P. P. Vaidyanathan
Editor: Morgan & Claypool Publishers
ISBN: 1598295756
File Size: 42,30 MB
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Linear prediction theory has had a profound impact in the field of digital signal processing. Although the theory dates back to the early 1940s, its influence can still be seen in applications today. The theory is based on very elegant mathematics and leads to many beautiful insights into statistical signal processing. Although prediction is only a part of the more general topics of linear estimation, filtering, and smoothing, this book focuses on linear prediction. This has enabled detailed discussion of a number of issues that are normally not found in texts. For example, the theory of vector linear prediction is explained in considerable detail and so is the theory of line spectral processes. This focus and its small size make the book different from many excellent texts which cover the topic, including a few that are actually dedicated to linear prediction. There are several examples and computer-based demonstrations of the theory. Applications are mentioned wherever appropriate, but the focus is not on the detailed development of these applications. The writing style is meant to be suitable for self-study as well as for classroom use at the senior and first-year graduate levels. The text is self-contained for readers with introductory exposure to signal processing, random processes, and the theory of matrices, and a historical perspective and detailed outline are given in the first chapter.Table of Contents: Introduction / The Optimal Linear Prediction Problem / Levinson's Recursion / Lattice Structures for Linear Prediction / Autoregressive Modeling / Prediction Error Bound and Spectral Flatness / Line Spectral Processes / Linear Prediction Theory for Vector Processes / Appendix A: Linear Estimation of Random Variables / B: Proof of a Property of Autocorrelations / C: Stability of the Inverse Filter / Recursion Satisfied by AR Autocorrelations

Linear Prediction Theory

Author: Peter Strobach
Editor: Springer Science & Business Media
ISBN: 3642752063
File Size: 56,57 MB
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Lnear prediction theory and the related algorithms have matured to the point where they now form an integral part of many real-world adaptive systems. When it is necessary to extract information from a random process, we are frequently faced with the problem of analyzing and solving special systems of linear equations. In the general case these systems are overdetermined and may be characterized by additional properties, such as update and shift-invariance properties. Usually, one employs exact or approximate least-squares methods to solve the resulting class of linear equations. Mainly during the last decade, researchers in various fields have contributed techniques and nomenclature for this type of least-squares problem. This body of methods now constitutes what we call the theory of linear prediction. The immense interest that it has aroused clearly emerges from recent advances in processor technology, which provide the means to implement linear prediction algorithms, and to operate them in real time. The practical effect is the occurrence of a new class of high-performance adaptive systems for control, communications and system identification applications. This monograph presumes a background in discrete-time digital signal processing, including Z-transforms, and a basic knowledge of discrete-time random processes. One of the difficulties I have en countered while writing this book is that many engineers and computer scientists lack knowledge of fundamental mathematics and geometry.

Non Linear Predictive Control

Author: Basil Kouvaritakis
Editor: IET
ISBN: 9780852969847
File Size: 76,68 MB
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This book collates the important results which have emerged in the field of non-linear model based predictive control. Feedback linearisation, differential flatness, control Lyapunov functions, output feedback and neural networks are all discussed.

The Statistical Theory Of Linear Systems

Author: E. J. Hannanm
Editor: John Wiley & Sons Incorporated
ISBN:
File Size: 62,14 MB
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Focusing on linear dynamic systems evolving in discrete time, this study examines their importance in the development of new applications in various fields, pointing out their interconnections and potential use for workers in several disciplines.

Compression Of Ecg Using A Code Excited Linear Prediction Celp

Author: Lo Wang
Editor:
ISBN:
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Estimation Theory

Author: Source: Wikipedia
Editor: Books LLC, Wiki Series
ISBN: 9781156833926
File Size: 16,77 MB
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Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Pages: 118. Chapters: Likelihood function, Linear regression, Linear prediction, Likelihood principle, Maximum likelihood, Estimator, Point estimation, Interval estimation, Ordinary least squares, Expectation-maximization algorithm, Maximum spacing estimation, Fisher information, Ensemble Kalman filter, Delphi method, Bayesian spam filtering, Generalized method of moments, Bayes estimator, Cram r-Rao bound, Particle filter, M-estimator, Wiener filter, Matched filter, Mean squared error, Invariant estimator, Simple linear regression, Data assimilation, Tikhonov regularization, Trend estimation, James-Stein estimator, Fixed effects model, Extended Kalman filter, Location estimation in sensor networks, Minimax estimator, V-statistic, Consistent estimator, Rao-Blackwell theorem, Orthogonality principle, Stochastic optimization, Filtering problem, Maximum a posteriori estimation, Identifiability, U-statistic, Confidence region, Wiener deconvolution, Efficient estimator, Minimum mean square error, Kullback's inequality, Chebyshev center, Minimum-variance unbiased estimator, Kaplan-Meier estimator, Score, Recursive Bayesian estimation, Invariant extended Kalman filter, Hodges' estimator, Minimum distance estimation, Blind deconvolution, Extremum estimator, Best linear unbiased prediction, Mean and predicted response, Empirical probability, Backcasting, Stein's unbiased risk estimate, Restricted maximum likelihood, Nuisance parameter, Shrinkage estimator, Chapman-Robbins bound, Richardson-Lucy deconvolution, Motion estimation, Observed information, Zakai equation, Fraction of variance unexplained, Estimating equations, Adaptive estimator, Quasi-maximum likelihood, Risk function, Helmert-Wolf blocking, Forecast error, Auxiliary particle filter, Small area estimation, Spectral density estimation, Scoring algorithm, Testimator, L-estimator, Lehman...

The Use Of The Linear Prediction Of Speech In Computer Music Application

Author: James Anderson Moorer
Editor:
ISBN:
File Size: 37,63 MB
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Transactions Of The American Mathematical Society

Author: American Mathematical Society
Editor:
ISBN:
File Size: 64,32 MB
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Ire Transactions On Automatic Control

Author: Institute of Radio Engineers. Professional Group on Automatic Control
Editor:
ISBN:
File Size: 15,95 MB
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Statistical Theory And Method Abstracts

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ISBN:
File Size: 31,72 MB
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Statistical Decision Theory In Adaptive Control Systems

Author: Yoshifumi Sunahara
Editor:
ISBN:
File Size: 29,49 MB
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Plane Answers To Complex Questions

Author: Ronald Christensen
Editor: Springer Science & Business Media
ISBN: 1441998160
File Size: 39,30 MB
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This textbook provides a wide-ranging introduction to the use and theory of linear models for analyzing data. The author's emphasis is on providing a unified treatment of linear models, including analysis of variance models and regression models, based on projections, orthogonality, and other vector space ideas. Every chapter comes with numerous exercises and examples that make it ideal for a graduate-level course. All of the standard topics are covered in depth: ANOVA, estimation including Bayesian estimation, hypothesis testing, multiple comparisons, regression analysis, and experimental design models. In addition, the book covers topics that are not usually treated at this level, but which are important in their own right: balanced incomplete block designs, testing for lack of fit, testing for independence, models with singular covariance matrices, variance component estimation, best linear and best linear unbiased prediction, collinearity, and variable selection. This new edition includes a more extensive discussion of best prediction and associated ideas of R2, as well as new sections on inner products and perpendicular projections for more general spaces and Milliken and Graybill’s generalization of Tukey’s one degree of freedom for nonadditivity test.

Waveform Quantization And Coding

Author: Nuggehally S. Jayant
Editor:
ISBN:
File Size: 38,26 MB
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Multimedia Signal Processing

Author: Saeed V. Vaseghi
Editor: John Wiley & Sons
ISBN: 9780470066492
File Size: 18,11 MB
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Multimedia Signal Processing is a comprehensive and accessible text to the theory and applications of digital signal processing (DSP). The applications of DSP are pervasive and include multimedia systems, cellular communication, adaptive network management, radar, pattern recognition, medical signal processing, financial data forecasting, artificial intelligence, decision making, control systems and search engines. This book is organised in to three major parts making it a coherent and structured presentation of the theory and applications of digital signal processing. A range of important topics are covered in basic signal processing, model-based statistical signal processing and their applications. Part 1: Basic Digital Signal Processing gives an introduction to the topic, discussing sampling and quantization, Fourier analysis and synthesis, Z-transform, and digital filters. Part 2: Model-based Signal Processing covers probability and information models, Bayesian inference, Wiener filter, adaptive filters, linear prediction hidden Markov models and independent component analysis. Part 3: Applications of Signal Processing in Speech, Music and Telecommunications explains the topics of speech and music processing, echo cancellation, deconvolution and channel equalization, and mobile communication signal processing. Covers music signal processing, explains the anatomy and psychoacoustics of hearing and the design of MP3 music coder Examines speech processing technology including speech models, speech coding for mobile phones and speech recognition Covers single-input and multiple-inputs denoising methods, bandwidth extension and the recovery of lost speech packets in applications such as voice over IP (VoIP) Illustrated throughout, including numerous solved problems, Matlab experiments and demonstrations Companion website features Matlab and C++ programs with electronic copies of all figures. This book is ideal for researchers, postgraduates and senior undergraduates in the fields of digital signal processing, telecommunications and statistical data analysis. It will also be a valuable text to professional engineers in telecommunications and audio and signal processing industries.

Transactions Of The I R E Professional Group On Information Theory

Author: I.R.E. Professional Group on Information Theory
Editor:
ISBN:
File Size: 29,69 MB
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Theory And Application Of The Linear Model

Author: Franklin A. Graybill
Editor: Prindle Weber & Schmidt
ISBN:
File Size: 36,20 MB
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Mathematical concepts. Statistical concepts. The multidimensional normal distribution. Distributions of quadratic forms. Models. General linear model. Computing techniques. Applications of the general linear model. Sampling from the multivariate normal distribution. Multiple regression. Correlation. Some applications of the regression model. Desing models. Two-factor desing model. Components-of-variance models.

First Assp Workshop On Spectral Estimation August 17 18 1981

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ISBN:
File Size: 50,13 MB
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Linear Processes In Function Spaces

Author: Denis Bosq
Editor: Springer Science & Business Media
ISBN: 1461211549
File Size: 41,84 MB
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The main subject of this book is the estimation and forecasting of continuous time processes. It leads to a development of the theory of linear processes in function spaces. Mathematical tools are presented, as well as autoregressive processes in Hilbert and Banach spaces and general linear processes and statistical prediction. Implementation and numerical applications are also covered. The book assumes knowledge of classical probability theory and statistics.

1998 Ieee International Symposium On Information Theory

Author: IEEE Information Theory Society
Editor: Institute of Electrical & Electronics Engineers(IEEE)
ISBN:
File Size: 21,28 MB
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Adaptive Signal Processing

Author: L.D. Davisson
Editor: Springer
ISBN: 3709128404
File Size: 28,92 MB
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The four chapters of this volume, written by prominent workers in the field of adaptive processing and linear prediction, address a variety of problems, ranging from adaptive source coding to autoregressive spectral estimation. The first chapter, by T.C. Butash and L.D. Davisson, formulates the performance of an adaptive linear predictor in a series of theorems, with and without the Gaussian assumption, under the hypothesis that its coefficients are derived from either the (single) observation sequence to be predicted (dependent case) or a second, statistically independent realisation (independent case). The contribution by H.V. Poor reviews three recently developed general methodologies for designing signal predictors under nonclassical operating conditions, namely the robust predictor, the high-speed Levinson modeling, and the approximate conditional mean nonlinear predictor. W. Wax presents the key concepts and techniques for detecting, localizing and beamforming multiple narrowband sources by passive sensor arrays. Special coding algorithms and techniques based on the use of linear prediction now permit high-quality voice reproduction at remorably low bit rates. The paper by A. Gersho reviews some of the main ideas underlying the algorithms of major interest today.