# Introduction to Applied Statistical Signal Analysis Books

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## Introduction to Applied Statistical Signal Analysis

- Author : Richard Shiavi
- Publisher :
- Release : 1999
- ISBN : UOM:39015047480713
- Language : En, Es, Fr & De

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This book provides a balanced perspective of the concept, mathematical bases, requirements for estimation, and detailed quantitative examples of the implementation of the techniques for classical signal analysis. The presentation integrates theory and implementation, practical examples, homework exercises which range from pencil and paper format to computer-based format problems, to instructional notebooks. The notebooks provide a mode of learning that is interactive and suited for self-pacing and independent learning. * "real-world" applications * real data available for exercises and projects * notebooks for interactive learning * graphical explanation of concepts * exercises emphasizing concepts * CD-ROM with MATLAB implementation

## Introduction to Applied Statistical Signal Analysis

- Author : Richard Shiavi
- Publisher : Academic Press
- Release : 2006-12-27
- ISBN : 1483299996
- Language : En, Es, Fr & De

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Introduction to Applied Statistical Signal Analysis 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. Introduction to Applied Statistical Signal Analysis intertwines theory and implementation with practical examples and exercises. Topics presented in detail include: mathematical bases, requirements for estimation and detailed quantitative examples for implementing techniques for classical signal analysis. This book will help readers understand real-world applications of signal analysis as they relate to biomedical engineering. 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. . Accompanied by MATLAB notebooks that provide an interactive mode of learning which can be utilized by professors or independent learners, available from the Companion website. . 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. Copies of these are available for download from the Companion website. . Please visit http: //books.elsevier.com/companions/9780120885817 to access accompanying material."

## Introduction to Applied Statistical Signal Analysis

- Author : Richard Shiavi
- Publisher : Elsevier
- Release : 2010-07-19
- ISBN : 9780080467689
- Language : En, Es, Fr & De

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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.

## An Introduction to Statistical Signal Processing

- Author : Robert M. Gray
- Publisher : Cambridge University Press
- Release : 2004-12-02
- ISBN : 1139456288
- Language : En, Es, Fr & De

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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.

## Statistical Signal Processing for Neuroscience and Neurotechnology

- Author : Karim G. Oweiss
- Publisher : Academic Press
- Release : 2010-09-22
- ISBN : 0080962963
- Language : En, Es, Fr & De

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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