[ELSEVIER Academic Press LOGO]

Biomedical Engineering, Electrical Engineering, Biomathematics

Introduction to Applied Statistical Signal Analysis

Guide to Biomedical and Electrical Engineering Applications, 3rd Ed

Richard Shiavi

Vanderbilt University, Nashville, Tennessee, U.S.A.

KEY FEATURES

' Notebooks provide an interactive mode of learning that can be utilized by professors or independent learners

' Online materials include detailed quantitative examples presented with Mat Lab implementation

' Over one hundred worked problems, real world applications, graphs, and notebook applications

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.

Contents:

Introduction to Terminology; Empirical Modeling and Approximation; Fourier Analysis; Probability Concepts and Signal Characteristics; Random Processes and Signal Correlation; Random Signals, Linear Systems, and Power Spectra; Spectral Analysis for Random Signals; Random Signal Modeling and Modern Spectral Estimation; Theory and Application of Cross Correlation and Coherence

Related titles:

Enderle; Introduction to Biomedical Engineering, 2 ed.; 0122386620; April 2005

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.

The third edition of "Introduction to Applied Statistical Signal Analysis" begins with a discussion of techniques for modeling and represents discrete data measurements, interpolation, and a definition of time series. Building from basic principles to illustrate complex application, this book provides a practical introduction to signal analysis techniques that are commonly used in a broad range of engineering areas including speech, biomedical signals, communications, and pattern recognition. <BR>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. <BR> Balanced perspective of the concepts, mathematical bases, and requirements for estimation <BR> Online materials include detailed quantitative examples presented with Mat Lab implementation <BR> Over one hundred worked problems, real world applications, graphs, and notebook applications to further the understanding of concepts

Introduction to Terminology; Empirical Modeling and Approximation; Fourier Analysis; Probability Concepts and Signal Characteristics; Random Processes and Signal Correlation; Random Signals, Linear Systems, and Power Spectra; Spectral Analysis for Random Signals; Random Signal Modeling and Modern Spectral Estimation; Theory and Application of Cross Correlation and Coherence