Statistical Signal Processing
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An Introduction To Statistical Signal Processing
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Author : Robert M. Gray
language : en
Publisher: Cambridge University Press
Release Date : 2004-12-02
An Introduction To Statistical Signal Processing written by Robert M. Gray and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-12-02 with Technology & Engineering categories.
This book describes the essential tools and techniques of statistical signal processing. At every stage theoretical ideas are linked to specific applications in communications and signal processing using a range of carefully chosen examples. The book begins with a development of basic probability, random objects, expectation, and second order moment theory followed by a wide variety of examples of the most popular random process models and their basic uses and properties. Specific applications to the analysis of random signals and systems for communicating, estimating, detecting, modulating, and other processing of signals are interspersed throughout the book. Hundreds of homework problems are included and the book is ideal for graduate students of electrical engineering and applied mathematics. It is also a useful reference for researchers in signal processing and communications.
Introduction To Applied Statistical Signal Analysis
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Author : Richard Shiavi
language : en
Publisher: Elsevier
Release Date : 2010-07-19
Introduction To Applied Statistical Signal Analysis written by Richard Shiavi and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-07-19 with Technology & Engineering categories.
Introduction to Applied Statistical Signal Analysis, Third Edition, is designed for the experienced individual with a basic background in mathematics, science, and computer. With this predisposed knowledge, the reader will coast through the practical introduction and move on to signal analysis techniques, commonly used in a broad range of engineering areas such as biomedical engineering, communications, geophysics, and speech. Topics presented include mathematical bases, requirements for estimation, and detailed quantitative examples for implementing techniques for classical signal analysis. This book includes over one hundred worked problems and real world applications. Many of the examples and exercises use measured signals, most of which are from the biomedical domain. The presentation style is designed for the upper level undergraduate or graduate student who needs a theoretical introduction to the basic principles of statistical modeling and the knowledge to implement them practically. Includes over one hundred worked problems and real world applications. Many of the examples and exercises in the book use measured signals, many from the biomedical domain.
Statistical Digital Signal Processing And Modeling
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Author : Monson H. Hayes
language : en
Publisher: John Wiley & Sons
Release Date : 1996-04-19
Statistical Digital Signal Processing And Modeling written by Monson H. Hayes 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 1996-04-19 with Technology & Engineering categories.
This new text responds to the dramatic growth in digital signal processing (DSP) over the past decade, and is the product of many years of teaching an advanced DSP course at Georgia Tech. While the focal point of the text is signal modeling, it integrates and explores the relationships of signal modeling to the important problems of optimal filtering, spectrum estimation, and adaptive filtering. Coverage is equally divided between the theory and philosophy of statistical signal processing, and the algorithms that are used to solve related problems. The text reflects the author's philosophy that a deep understanding of signal processing is accomplished best through working problems. For this reason, the book is loaded with worked examples, homework problems, and MATLAB computer exercises. While the examples serve to illustrate the ideas developed in the book, the problems seek to motivate and challenge the student and the computer exercises allow the student to experiment with signal processing algorithms on complex signals. Professor Hayes is recognized as a leader in the signal processing community, particularly for his work in signal reconstruction and image processing. This text is suitable for senior/graduate level courses in advanced DSP or digital filtering found in Electrical Engineering Departments. Prerequisites include basic courses in DSP and probability theory.
Algorithms For Statistical Signal Processing
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Author : John G. Proakis
language : en
Publisher:
Release Date : 2002
Algorithms For Statistical Signal Processing written by John G. Proakis and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Computers categories.
Keeping pace with the expanding, ever more complex applications of DSP, this authoritative presentation of computational algorithms for statistical signal processing focuses on "advanced topics" ignored by other books on the subject. Algorithms for Convolution and DFT. Linear Prediction and Optimum Linear Filters. Least-Squares Methods for System Modeling and Filter Design. Adaptive Filters. Recursive Least-Squares Algorithms for Array Signal Processing. QRD-Based Fast Adaptive Filter Algorithms. Power Spectrum Estimation. Signal Analysis with Higher-Order Spectra. For Electrical Engineers, Computer Engineers, Computer Scientists, and Applied Mathematicians.
Statistical Signal Processing Of Complex Valued Data
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Author : Peter J. Schreier
language : en
Publisher: Cambridge University Press
Release Date : 2010-02-04
Statistical Signal Processing Of Complex Valued Data written by Peter J. Schreier and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-02-04 with Technology & Engineering categories.
Complex-valued random signals are embedded in the very fabric of science and engineering, yet the usual assumptions made about their statistical behavior are often a poor representation of the underlying physics. This book deals with improper and noncircular complex signals, which do not conform to classical assumptions, and it demonstrates how correct treatment of these signals can have significant payoffs. The book begins with detailed coverage of the fundamental theory and presents a variety of tools and algorithms for dealing with improper and noncircular signals. It provides a comprehensive account of the main applications, covering detection, estimation, and signal analysis of stationary, nonstationary, and cyclostationary processes. Providing a systematic development from the origin of complex signals to their probabilistic description makes the theory accessible to newcomers. This book is ideal for graduate students and researchers working with complex data in a range of research areas from communications to oceanography.
Statistical Signal Processing
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Author : Debasis Kundu
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-05-24
Statistical Signal Processing written by Debasis Kundu 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 2012-05-24 with Computers categories.
Signal processing may broadly be considered to involve the recovery of information from physical observations. The received signal is usually disturbed by thermal, electrical, atmospheric or intentional interferences. Due to the random nature of the signal, statistical techniques play an important role in analyzing the signal. Statistics is also used in the formulation of the appropriate models to describe the behavior of the system, the development of appropriate techniques for estimation of model parameters and the assessment of the model performances. Statistical signal processing basically refers to the analysis of random signals using appropriate statistical techniques. The main aim of this book is to introduce different signal processing models which have been used in analyzing periodic data, and different statistical and computational issues involved in solving them. We discuss in detail the sinusoidal frequency model which has been used extensively in analyzing periodic data occuring in various fields. We have tried to introduce different associated models and higher dimensional statistical signal processing models which have been further discussed in the literature. Different real data sets have been analyzed to illustrate how different models can be used in practice. Several open problems have been indicated for future research.
Introduction To Statistical Signal Processing With Applications
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Author : Mandyam D. Srinath
language : en
Publisher:
Release Date : 1999
Introduction To Statistical Signal Processing With Applications written by Mandyam D. Srinath and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with categories.
Statistical Signal Processing
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Author : Swagata Nandi
language : en
Publisher: Springer
Release Date : 2020-10-16
Statistical Signal Processing written by Swagata Nandi and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-16 with Computers categories.
This book introduces readers to various signal processing models that have been used in analyzing periodic data, and discusses the statistical and computational methods involved. Signal processing can broadly be considered to be the recovery of information from physical observations. The received signals are usually disturbed by thermal, electrical, atmospheric or intentional interferences, and due to their random nature, statistical techniques play an important role in their analysis. Statistics is also used in the formulation of appropriate models to describe the behavior of systems, the development of appropriate techniques for estimation of model parameters and the assessment of the model performances. Analyzing different real-world data sets to illustrate how different models can be used in practice, and highlighting open problems for future research, the book is a valuable resource for senior undergraduate and graduate students specializing in mathematics or statistics.
Statistical Signal Processing For Neuroscience And Neurotechnology
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Author : Karim G. Oweiss
language : en
Publisher: Academic Press
Release Date : 2010-09-22
Statistical Signal Processing For Neuroscience And Neurotechnology written by Karim G. Oweiss and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-09-22 with Technology & Engineering categories.
This is a uniquely comprehensive reference that summarizes the state of the art of signal processing theory and techniques for solving emerging problems in neuroscience, and which clearly presents new theory, algorithms, software and hardware tools that are specifically tailored to the nature of the neurobiological environment. It gives a broad overview of the basic principles, theories and methods in statistical signal processing for basic and applied neuroscience problems.Written by experts in the field, the book is an ideal reference for researchers working in the field of neural engineering, neural interface, computational neuroscience, neuroinformatics, neuropsychology and neural physiology. By giving a broad overview of the basic principles, theories and methods, it is also an ideal introduction to statistical signal processing in neuroscience. - A comprehensive overview of the specific problems in neuroscience that require application of existing and development of new theory, techniques, and technology by the signal processing community - Contains state-of-the-art signal processing, information theory, and machine learning algorithms and techniques for neuroscience research - Presents quantitative and information-driven science that has been, or can be, applied to basic and translational neuroscience problems
Nonlinear Signal Processing
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Author : Gonzalo R. Arce
language : en
Publisher: John Wiley & Sons
Release Date : 2005-01-03
Nonlinear Signal Processing written by Gonzalo R. Arce 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 2005-01-03 with Science categories.
Nonlinear Signal Processing: A Statistical Approach focuses on unifying the study of a broad and important class of nonlinear signal processing algorithms which emerge from statistical estimation principles, and where the underlying signals are non-Gaussian, rather than Gaussian, processes. Notably, by concentrating on just two non-Gaussian models, a large set of tools is developed that encompass a large portion of the nonlinear signal processing tools proposed in the literature over the past several decades. Key features include: * Numerous problems at the end of each chapter to aid development and understanding * Examples and case studies provided throughout the book in a wide range of applications bring the text to life and place the theory into context * A set of 60+ MATLAB software m-files allowing the reader to quickly design and apply any of the nonlinear signal processing algorithms described in the book to an application of interest is available on the accompanying FTP site.