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Author : Alexander Bronstein
Publisher :
ISBN 13 :
Total Pages : 22 pages
Book Rating : 4.:/5 (255 download)
Download or read book Asymptotic Performance Analysis of QML Multichannel Blind Deconvolution written by Alexander Bronstein and published by . This book was released on 2004 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author :
Publisher :
ISBN 13 :
Total Pages : 1308 pages
Book Rating : 4.3/5 (91 download)
Download or read book Independent Component Analysis and Blind Signal Separation written by and published by . This book was released on 2004 with total page 1308 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Xiaoan Sun
Publisher :
ISBN 13 :
Total Pages : 354 pages
Book Rating : 4.:/5 (516 download)
Download or read book Blind Source Separation and Multichannel Blind Deconvolution written by Xiaoan Sun and published by . This book was released on 2002 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Jerry M. Mendel
Publisher : Springer Science & Business Media
ISBN 13 : 1461233704
Total Pages : 233 pages
Book Rating : 4.4/5 (612 download)
Download or read book Maximum-Likelihood Deconvolution written by Jerry M. Mendel and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt: Convolution is the most important operation that describes the behavior of a linear time-invariant dynamical system. Deconvolution is the unraveling of convolution. It is the inverse problem of generating the system's input from knowledge about the system's output and dynamics. Deconvolution requires a careful balancing of bandwidth and signal-to-noise ratio effects. Maximum-likelihood deconvolution (MLD) is a design procedure that handles both effects. It draws upon ideas from Maximum Likelihood, when unknown parameters are random. It leads to linear and nonlinear signal processors that provide high-resolution estimates of a system's input. All aspects of MLD are described, from first principles in this book. The purpose of this volume is to explain MLD as simply as possible. To do this, the entire theory of MLD is presented in terms of a convolutional signal generating model and some relatively simple ideas from optimization theory. Earlier approaches to MLD, which are couched in the language of state-variable models and estimation theory, are unnecessary to understand the essence of MLD. MLD is a model-based signal processing procedure, because it is based on a signal model, namely the convolutional model. The book focuses on three aspects of MLD: (1) specification of a probability model for the system's measured output; (2) determination of an appropriate likelihood function; and (3) maximization of that likelihood function. Many practical algorithms are obtained. Computational aspects of MLD are described in great detail. Extensive simulations are provided, including real data applications.
Author : Ganesh R. Naik
Publisher : Springer
ISBN 13 : 3642550169
Total Pages : 549 pages
Book Rating : 4.6/5 (425 download)
Download or read book Blind Source Separation written by Ganesh R. Naik and published by Springer. This book was released on 2014-05-21 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: Blind Source Separation intends to report the new results of the efforts on the study of Blind Source Separation (BSS). The book collects novel research ideas and some training in BSS, independent component analysis (ICA), artificial intelligence and signal processing applications. Furthermore, the research results previously scattered in many journals and conferences worldwide are methodically edited and presented in a unified form. The book is likely to be of interest to university researchers, R&D engineers and graduate students in computer science and electronics who wish to learn the core principles, methods, algorithms and applications of BSS. Dr. Ganesh R. Naik works at University of Technology, Sydney, Australia; Dr. Wenwu Wang works at University of Surrey, UK.
Author : Pierre Comon
Publisher : Academic Press
ISBN 13 : 0080884946
Total Pages : 856 pages
Book Rating : 4.0/5 (88 download)
Download or read book Handbook of Blind Source Separation written by Pierre Comon and published by Academic Press. This book was released on 2010-02-17 with total page 856 pages. Available in PDF, EPUB and Kindle. Book excerpt: Edited by the people who were forerunners in creating the field, together with contributions from 34 leading international experts, this handbook provides the definitive reference on Blind Source Separation, giving a broad and comprehensive description of all the core principles and methods, numerical algorithms and major applications in the fields of telecommunications, biomedical engineering and audio, acoustic and speech processing. Going beyond a machine learning perspective, the book reflects recent results in signal processing and numerical analysis, and includes topics such as optimization criteria, mathematical tools, the design of numerical algorithms, convolutive mixtures, and time frequency approaches. This Handbook is an ideal reference for university researchers, R&D engineers and graduates wishing to learn the core principles, methods, algorithms, and applications of Blind Source Separation. Covers the principles and major techniques and methods in one book Edited by the pioneers in the field with contributions from 34 of the world’s experts Describes the main existing numerical algorithms and gives practical advice on their design Covers the latest cutting edge topics: second order methods; algebraic identification of under-determined mixtures, time-frequency methods, Bayesian approaches, blind identification under non negativity approaches, semi-blind methods for communications Shows the applications of the methods to key application areas such as telecommunications, biomedical engineering, speech, acoustic, audio and music processing, while also giving a general method for developing applications
Author : Boaz Porat
Publisher : Courier Dover Publications
ISBN 13 : 0486462986
Total Pages : 468 pages
Book Rating : 4.4/5 (864 download)
Download or read book Digital Processing of Random Signals written by Boaz Porat and published by Courier Dover Publications. This book was released on 2008-02-29 with total page 468 pages. Available in PDF, EPUB and Kindle. Book excerpt: This excellent advanced text rigorously covers several topics. Geared toward students of electrical engineering, its material is sufficiently general to be applicable to other engineering fields. 1994 edition.
Author : Noé Jiménez
Publisher :
ISBN 13 : 9783030843014
Total Pages : 0 pages
Book Rating : 4.8/5 (43 download)
Download or read book Acoustic Waves in Periodic Structures, Metamaterials, and Porous Media written by Noé Jiménez and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book delivers a comprehensive and up-to-date treatment of practical applications of metamaterials, structured media, and conventional porous materials. With increasing levels of urbanization, a growing demand for motorized transport, and inefficient urban planning, environmental noise exposure is rapidly becoming a pressing societal and health concern. Phononic and sonic crystals, acoustic metamaterials, and metasurfaces can revolutionize noise and vibration control and, in many cases, replace traditional porous materials for these applications. In this collection of contributed chapters, a group of international researchers reviews the essentials of acoustic wave propagation in metamaterials and porous absorbers with viscothermal losses, as well as the most recent advances in the design of acoustic metamaterial absorbers. The book features a detailed theoretical introduction describing commonly used modelling techniques such as plane wave expansion, multiple scattering theory, and the transfer matrix method. The following chapters give a detailed consideration of acoustic wave propagation in viscothermal fluids and porous media, and the extension of this theory to non-local models for fluid saturated metamaterials, along with a description of the relevant numerical methods. Finally, the book reviews a range of practical industrial applications, making it especially attractive as a white book targeted at the building, automotive, and aeronautic industries.
Author : Anastasios A. Tsiatis
Publisher : CRC Press
ISBN 13 : 1498769780
Total Pages : 602 pages
Book Rating : 4.4/5 (987 download)
Download or read book Dynamic Treatment Regimes written by Anastasios A. Tsiatis and published by CRC Press. This book was released on 2019-12-19 with total page 602 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dynamic Treatment Regimes: Statistical Methods for Precision Medicine provides a comprehensive introduction to statistical methodology for the evaluation and discovery of dynamic treatment regimes from data. Researchers and graduate students in statistics, data science, and related quantitative disciplines with a background in probability and statistical inference and popular statistical modeling techniques will be prepared for further study of this rapidly evolving field. A dynamic treatment regime is a set of sequential decision rules, each corresponding to a key decision point in a disease or disorder process, where each rule takes as input patient information and returns the treatment option he or she should receive. Thus, a treatment regime formalizes how a clinician synthesizes patient information and selects treatments in practice. Treatment regimes are of obvious relevance to precision medicine, which involves tailoring treatment selection to patient characteristics in an evidence-based way. Of critical importance to precision medicine is estimation of an optimal treatment regime, one that, if used to select treatments for the patient population, would lead to the most beneficial outcome on average. Key methods for estimation of an optimal treatment regime from data are motivated and described in detail. A dedicated companion website presents full accounts of application of the methods using a comprehensive R package developed by the authors. The authors’ website www.dtr-book.com includes updates, corrections, new papers, and links to useful websites.
Author : Erkki Oja
Publisher : John Wiley & Sons
ISBN 13 :
Total Pages : 208 pages
Book Rating : 4.3/5 (91 download)
Download or read book Subspace Methods of Pattern Recognition written by Erkki Oja and published by John Wiley & Sons. This book was released on 1983 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discusses the fundamentals of subspace methods & the different approaches taken; concentrates on the learning subspace method used for automatic speech recognition & more generally for the classification of spectra.
Author : Gang Li
Publisher : SciTech Publishing
ISBN 13 : 1839530758
Total Pages : 271 pages
Book Rating : 4.8/5 (395 download)
Download or read book Advanced Sparsity-Driven Models and Methods for Radar Applications written by Gang Li and published by SciTech Publishing. This book was released on 2020-12-03 with total page 271 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces advanced sparsity-driven models and methods and their applications in radar tasks such as detection, imaging and classification. Compressed sensing (CS) is one of the most active topics in the signal processing area. By exploiting and promoting the sparsity of the signals of interest, CS offers a new framework for reducing data without compromising the performance of signal recovery, or for enhancing resolution without increasing measurements.
Author : David L. DeMets
Publisher : Springer Science & Business Media
ISBN 13 : 0387301070
Total Pages : 389 pages
Book Rating : 4.3/5 (873 download)
Download or read book Data Monitoring in Clinical Trials written by David L. DeMets and published by Springer Science & Business Media. This book was released on 2006-06-22 with total page 389 pages. Available in PDF, EPUB and Kindle. Book excerpt: From the authors of "Fundamentals of Clinical Trials" which has sold over 15,000 copies world wide since its publication in 1998. No competition yet as the text does not focus on how to do clinical trials but on very specific situations that can be encountered during the process.
Author : Robert Shumway
Publisher : CRC Press
ISBN 13 : 1000008398
Total Pages : 200 pages
Book Rating : 4.0/5 ( download)
Download or read book Time Series written by Robert Shumway and published by CRC Press. This book was released on 2019-05-17 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: The goals of this text are to develop the skills and an appreciation for the richness and versatility of modern time series analysis as a tool for analyzing dependent data. A useful feature of the presentation is the inclusion of nontrivial data sets illustrating the richness of potential applications to problems in the biological, physical, and social sciences as well as medicine. The text presents a balanced and comprehensive treatment of both time and frequency domain methods with an emphasis on data analysis. Numerous examples using data illustrate solutions to problems such as discovering natural and anthropogenic climate change, evaluating pain perception experiments using functional magnetic resonance imaging, and the analysis of economic and financial problems. The text can be used for a one semester/quarter introductory time series course where the prerequisites are an understanding of linear regression, basic calculus-based probability skills, and math skills at the high school level. All of the numerical examples use the R statistical package without assuming that the reader has previously used the software. Robert H. Shumway is Professor Emeritus of Statistics, University of California, Davis. He is a Fellow of the American Statistical Association and has won the American Statistical Association Award for Outstanding Statistical Application. He is the author of numerous texts and served on editorial boards such as the Journal of Forecasting and the Journal of the American Statistical Association. David S. Stoffer is Professor of Statistics, University of Pittsburgh. He is a Fellow of the American Statistical Association and has won the American Statistical Association Award for Outstanding Statistical Application. He is currently on the editorial boards of the Journal of Forecasting, the Annals of Statistical Mathematics, and the Journal of Time Series Analysis. He served as a Program Director in the Division of Mathematical Sciences at the National Science Foundation and as an Associate Editor for the Journal of the American Statistical Association and the Journal of Business & Economic Statistics.
Author : Richard A. Davis
Publisher : CRC Press
ISBN 13 : 1466577746
Total Pages : 484 pages
Book Rating : 4.4/5 (665 download)
Download or read book Handbook of Discrete-Valued Time Series written by Richard A. Davis and published by CRC Press. This book was released on 2016-01-06 with total page 484 pages. Available in PDF, EPUB and Kindle. Book excerpt: Model a Wide Range of Count Time Series Handbook of Discrete-Valued Time Series presents state-of-the-art methods for modeling time series of counts and incorporates frequentist and Bayesian approaches for discrete-valued spatio-temporal data and multivariate data. While the book focuses on time series of counts, some of the techniques discussed ca
Author : Jose Casals
Publisher : CRC Press
ISBN 13 : 131536025X
Total Pages : 286 pages
Book Rating : 4.3/5 (153 download)
Download or read book State-Space Methods for Time Series Analysis written by Jose Casals and published by CRC Press. This book was released on 2018-09-03 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: The state-space approach provides a formal framework where any result or procedure developed for a basic model can be seamlessly applied to a standard formulation written in state-space form. Moreover, it can accommodate with a reasonable effort nonstandard situations, such as observation errors, aggregation constraints, or missing in-sample values. Exploring the advantages of this approach, State-Space Methods for Time Series Analysis: Theory, Applications and Software presents many computational procedures that can be applied to a previously specified linear model in state-space form. After discussing the formulation of the state-space model, the book illustrates the flexibility of the state-space representation and covers the main state estimation algorithms: filtering and smoothing. It then shows how to compute the Gaussian likelihood for unknown coefficients in the state-space matrices of a given model before introducing subspace methods and their application. It also discusses signal extraction, describes two algorithms to obtain the VARMAX matrices corresponding to any linear state-space model, and addresses several issues relating to the aggregation and disaggregation of time series. The book concludes with a cross-sectional extension to the classical state-space formulation in order to accommodate longitudinal or panel data. Missing data is a common occurrence here, and the book explains imputation procedures necessary to treat missingness in both exogenous and endogenous variables. Web Resource The authors’ E4 MATLAB® toolbox offers all the computational procedures, administrative and analytical functions, and related materials for time series analysis. This flexible, powerful, and free software tool enables readers to replicate the practical examples in the text and apply the procedures to their own work.
Author : Randal Douc
Publisher : CRC Press
ISBN 13 : 1466502347
Total Pages : 548 pages
Book Rating : 4.4/5 (665 download)
Download or read book Nonlinear Time Series written by Randal Douc and published by CRC Press. This book was released on 2014-01-06 with total page 548 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text emphasizes nonlinear models for a course in time series analysis. After introducing stochastic processes, Markov chains, Poisson processes, and ARMA models, the authors cover functional autoregressive, ARCH, threshold AR, and discrete time series models as well as several complementary approaches. They discuss the main limit theorems for Markov chains, useful inequalities, statistical techniques to infer model parameters, and GLMs. Moving on to HMM models, the book examines filtering and smoothing, parametric and nonparametric inference, advanced particle filtering, and numerical methods for inference.
Author : Jianqing Fan
Publisher : CRC Press
ISBN 13 : 0429527616
Total Pages : 942 pages
Book Rating : 4.4/5 (295 download)
Download or read book Statistical Foundations of Data Science written by Jianqing Fan and published by CRC Press. This book was released on 2020-09-21 with total page 942 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Foundations of Data Science gives a thorough introduction to commonly used statistical models, contemporary statistical machine learning techniques and algorithms, along with their mathematical insights and statistical theories. It aims to serve as a graduate-level textbook and a research monograph on high-dimensional statistics, sparsity and covariance learning, machine learning, and statistical inference. It includes ample exercises that involve both theoretical studies as well as empirical applications. The book begins with an introduction to the stylized features of big data and their impacts on statistical analysis. It then introduces multiple linear regression and expands the techniques of model building via nonparametric regression and kernel tricks. It provides a comprehensive account on sparsity explorations and model selections for multiple regression, generalized linear models, quantile regression, robust regression, hazards regression, among others. High-dimensional inference is also thoroughly addressed and so is feature screening. The book also provides a comprehensive account on high-dimensional covariance estimation, learning latent factors and hidden structures, as well as their applications to statistical estimation, inference, prediction and machine learning problems. It also introduces thoroughly statistical machine learning theory and methods for classification, clustering, and prediction. These include CART, random forests, boosting, support vector machines, clustering algorithms, sparse PCA, and deep learning.
