An Introduction to Statistical Signal Processing
An Introduction to Statistical Signal Processing

Author: Robert M. Gray

Publisher: Cambridge University Press

Published: 2004-12-02

Total Pages: 479

ISBN-13: 1139456288

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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 Statistical Signal Processing with Applications
Introduction to Statistical Signal Processing with Applications

Author: Mandyam Dhati Srinath

Publisher:

Published: 1996

Total Pages: 450

ISBN-13: 9780131252950

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An Introduction to Statistical Signal Processing with Applications covers basic techniques in the processing of stochastic signals and illustrate their use in a variety of specific applications. The book presents both detection and estimation in a clear, concise fashion and reflects recent developments and shifting emphases in the field.

An Introduction to Statistical Signal Processing with Applications
An Introduction to Statistical Signal Processing with Applications

Author: Mandyam Dhati Srinath

Publisher: John Wiley & Sons

Published: 1979

Total Pages: 522

ISBN-13:

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In An Introduction to Statistical Signal Processing with Applications, these three author/educators cover basic techniques in the processing of stochastic signals and illustrate their use in a variety of specific applications.

Introduction to Applied Statistical Signal Analysis
Introduction to Applied Statistical Signal Analysis

Author: Richard Shiavi

Publisher: Elsevier

Published: 2010-07-19

Total Pages: 424

ISBN-13: 0080467687

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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 Signal Processing
Statistical Signal Processing

Author: Debasis Kundu

Publisher: Springer Science & Business Media

Published: 2012-05-24

Total Pages: 142

ISBN-13: 8132206282

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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.

Statistical Signal Processing for Neuroscience and Neurotechnology
Statistical Signal Processing for Neuroscience and Neurotechnology

Author: Karim G. Oweiss

Publisher: Academic Press

Published: 2010-09-22

Total Pages: 441

ISBN-13: 0080962963

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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

Digital and Statistical Signal Processing
Digital and Statistical Signal Processing

Author: Anastasia Veloni

Publisher: CRC Press

Published: 2018-10-03

Total Pages: 377

ISBN-13: 042901757X

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Nowadays, many aspects of electrical and electronic engineering are essentially applications of DSP. This is due to the focus on processing information in the form of digital signals, using certain DSP hardware designed to execute software. Fundamental topics in digital signal processing are introduced with theory, analytical tables, and applications with simulation tools. The book provides a collection of solved problems on digital signal processing and statistical signal processing. The solutions are based directly on the math-formulas given in extensive tables throughout the book, so the reader can solve practical problems on signal processing quickly and efficiently. FEATURES Explains how applications of DSP can be implemented in certain programming environments designed for real time systems, ex. biomedical signal analysis and medical image processing. Pairs theory with basic concepts and supporting analytical tables. Includes an extensive collection of solved problems throughout the text. Fosters the ability to solve practical problems on signal processing without focusing on extended theory. Covers the modeling process and addresses broader fundamental issues.

Fundamentals of Statistical Signal Processing
Fundamentals of Statistical Signal Processing

Author: Steven M. Kay

Publisher: Pearson Education

Published: 2013

Total Pages: 496

ISBN-13: 013280803X

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"For those involved in the design and implementation of signal processing algorithms, this book strikes a balance between highly theoretical expositions and the more practical treatments, covering only those approaches necessary for obtaining an optimal estimator and analyzing its performance. Author Steven M. Kay discusses classical estimation followed by Bayesian estimation, and illustrates the theory with numerous pedagogical and real-world examples."--Cover, volume 1.