Statistical Signal Processing Of Complex Valued Data PDF Download
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Author | : Peter J. Schreier |
Publisher | : |
Total Pages | : 331 |
Release | : 2010 |
Genre | : |
ISBN | : 9780511678974 |
Download Statistical Signal Processing of Complex-valued Data Book in PDF, ePub and Kindle
Author | : Peter J. Schreier |
Publisher | : Cambridge University Press |
Total Pages | : 331 |
Release | : 2010-02-04 |
Genre | : Technology & Engineering |
ISBN | : 1139487620 |
Download Statistical Signal Processing of Complex-Valued Data Book in PDF, ePub and Kindle
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.
Author | : Eric Moreau |
Publisher | : John Wiley & Sons |
Total Pages | : 112 |
Release | : 2013-10-07 |
Genre | : Technology & Engineering |
ISBN | : 1848214596 |
Download Blind Identification and Separation of Complex-valued Signals Book in PDF, ePub and Kindle
Blind identification consists of estimating a multi-dimensional system only through the use of its output, and source separation, the blind estimation of the inverse of the system. Estimation is generally carried out using different statistics of the output. The authors of this book consider the blind identification and source separation problem in the complex-domain, where the available statistical properties are richer and include non-circularity of the sources – underlying components. They define identifiability conditions and present state-of-the-art algorithms that are based on algebraic methods as well as iterative algorithms based on maximum likelihood theory. Contents 1. Mathematical Preliminaries. 2. Estimation by Joint Diagonalization. 3. Maximum Likelihood ICA. About the Authors Eric Moreau is Professor of Electrical Engineering at the University of Toulon, France. His research interests concern statistical signal processing, high order statistics and matrix/tensor decompositions with applications to data analysis, telecommunications and radar. Tülay Adali is Professor of Electrical Engineering and Director of the Machine Learning for Signal Processing Laboratory at the University of Maryland, Baltimore County, USA. Her research interests concern statistical and adaptive signal processing, with an emphasis on nonlinear and complex-valued signal processing, and applications in biomedical data analysis and communications. Blind identification consists of estimating a multidimensional system through the use of only its output. Source separation is concerned with the blind estimation of the inverse of the system. The estimation is generally performed by using different statistics of the outputs. The authors consider the blind estimation of a multiple input/multiple output (MIMO) system that mixes a number of underlying signals of interest called sources. They also consider the case of direct estimation of the inverse system for the purpose of source separation. They then describe the estimation theory associated with the identifiability conditions and dedicated algebraic algorithms. The algorithms depend critically on (statistical and/or time frequency) properties of complex sources that will be precisely described.
Author | : Umberto Spagnolini |
Publisher | : John Wiley & Sons |
Total Pages | : 604 |
Release | : 2018-02-05 |
Genre | : Technology & Engineering |
ISBN | : 1119293979 |
Download Statistical Signal Processing in Engineering Book in PDF, ePub and Kindle
A problem-solving approach to statistical signal processing for practicing engineers, technicians, and graduate students This book takes a pragmatic approach in solving a set of common problems engineers and technicians encounter when processing signals. In writing it, the author drew on his vast theoretical and practical experience in the field to provide a quick-solution manual for technicians and engineers, offering field-tested solutions to most problems engineers can encounter. At the same time, the book delineates the basic concepts and applied mathematics underlying each solution so that readers can go deeper into the theory to gain a better idea of the solution’s limitations and potential pitfalls, and thus tailor the best solution for the specific engineering application. Uniquely, Statistical Signal Processing in Engineering can also function as a textbook for engineering graduates and post-graduates. Dr. Spagnolini, who has had a quarter of a century of experience teaching graduate-level courses in digital and statistical signal processing methods, provides a detailed axiomatic presentation of the conceptual and mathematical foundations of statistical signal processing that will challenge students’ analytical skills and motivate them to develop new applications on their own, or better understand the motivation underlining the existing solutions. Throughout the book, some real-world examples demonstrate how powerful a tool statistical signal processing is in practice across a wide range of applications. Takes an interdisciplinary approach, integrating basic concepts and tools for statistical signal processing Informed by its author’s vast experience as both a practitioner and teacher Offers a hands-on approach to solving problems in statistical signal processing Covers a broad range of applications, including communication systems, machine learning, wavefield and array processing, remote sensing, image filtering and distributed computations Features numerous real-world examples from a wide range of applications showing the mathematical concepts involved in practice Includes MATLAB code of many of the experiments in the book Statistical Signal Processing in Engineering is an indispensable working resource for electrical engineers, especially those working in the information and communication technology (ICT) industry. It is also an ideal text for engineering students at large, applied mathematics post-graduates and advanced undergraduates in electrical engineering, applied statistics, and pure mathematics, studying statistical signal processing.
Author | : Robert M. Gray |
Publisher | : Cambridge University Press |
Total Pages | : 479 |
Release | : 2004-12-02 |
Genre | : Technology & Engineering |
ISBN | : 1139456288 |
Download An Introduction to Statistical Signal Processing Book in PDF, ePub and Kindle
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.
Author | : Karim G. Oweiss |
Publisher | : Academic Press |
Total Pages | : 441 |
Release | : 2010-09-22 |
Genre | : Technology & Engineering |
ISBN | : 0080962963 |
Download Statistical Signal Processing for Neuroscience and Neurotechnology Book in PDF, ePub and Kindle
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
Author | : Are Hjørungnes |
Publisher | : Cambridge University Press |
Total Pages | : 271 |
Release | : 2011-02-24 |
Genre | : Technology & Engineering |
ISBN | : 1139498045 |
Download Complex-Valued Matrix Derivatives Book in PDF, ePub and Kindle
In this complete introduction to the theory of finding derivatives of scalar-, vector- and matrix-valued functions with respect to complex matrix variables, Hjørungnes describes an essential set of mathematical tools for solving research problems where unknown parameters are contained in complex-valued matrices. The first book examining complex-valued matrix derivatives from an engineering perspective, it uses numerous practical examples from signal processing and communications to demonstrate how these tools can be used to analyze and optimize the performance of engineering systems. Covering un-patterned and certain patterned matrices, this self-contained and easy-to-follow reference deals with applications in a range of areas including wireless communications, control theory, adaptive filtering, resource management and digital signal processing. Over 80 end-of-chapter exercises are provided, with a complete solutions manual available online.
Author | : Richard Shiavi |
Publisher | : Elsevier |
Total Pages | : 424 |
Release | : 2010-07-19 |
Genre | : Technology & Engineering |
ISBN | : 0080467687 |
Download Introduction to Applied Statistical Signal Analysis Book in PDF, ePub and Kindle
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.
Author | : Swagata Nandi |
Publisher | : Springer Nature |
Total Pages | : 265 |
Release | : 2020-08-21 |
Genre | : Computers |
ISBN | : 9811562806 |
Download Statistical Signal Processing Book in PDF, ePub and Kindle
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.
Author | : John G. Proakis |
Publisher | : |
Total Pages | : 584 |
Release | : 2002 |
Genre | : Computers |
ISBN | : |
Download Algorithms for Statistical Signal Processing Book in PDF, ePub and Kindle
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.