Introduction to Applied Statistical Signal Analysis Guide to Biomedical and Electrical Engineering Applications 3rd Edition by Shiavi Richard ISBN 0120885816 9781865843834

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ISBN 10: 0120885816 
ISBN 13: 9781865843834
Author: Shiavi Richard

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.

 Introduction to Applied Statistical Signal Analysis Guide to Biomedical and Electrical Engineering Applications 3rd Table of contents:

Chapter 1 Introduction and terminology
1.1 Introduction
1.2 Signal terminology
1.2.1 Domain Types
1.2.2 Amplitude Types
1.2.3 Basic Signal Forms
1.2.4 The Transformed Domain—The Frequency Domain
1.2.5 General Amplitude Properties
1.3 Analog to digital conversion
1.4 Measures of signal properties
1.4.1 Time Domain
1.4.2 Frequency Domain
References
Chapter 2 Empirical modeling and approximation
2.1 Introduction
2.2 Model development
2.3 Generalized least squares
2.4 Generalities
2.5 Models from linearization
2.6 Orthogonal polynomials
2.7 Interpolation and extrapolation
2.7.1 Lagrange Polynomials
2.7.2 Spline Interpolation
2.8 Overview
References
Exercises
Chapter 3 Fourier analysis
3.1 Introduction
3.2 Review of fourier series
3.2.1 Definition
3.2.2 Convergence
3.3 Overview of fourier transform relationships
3.3.1 Continuous versus Discrete Time
3.3.2 Discrete Time and Frequency
3.4 Discrete fourier transform
3.4.1 Definition Continued
3.4.2 Partial Summary of DFT Properties and Theorems
3.5 Fourier analysis
3.5.1 Frequency Range and Scaling
3.5.2 The Effect of Discretizing Frequency
3.5.3 The Effect of Truncation
3.5.4 Windowing
3.5.5 Resolution
3.5.6 Detrending
3.6 Procedural summary
3.7 Selected applications
References
Exercises
Appendices
Appendix 3.1 DFT of ionosphere data
Appendix 3.2 Review of properties of orthogonal functions
Appendix 3.3 The fourier transform
Appendix 3.4 Data and spectral windows
Chapter 4 Probability concepts and signal characteristics
4.1 Introduction
4.2 Introduction to random variables
4.2.1 Probability Descriptors
4.2.2 Moments of Random Variables
4.2.3 Gaussian Random Variable
4.3 Joint probability
4.3.1 Bivariate Distributions
4.3.2 Moments of Bivariate Distributions
4.4 Concept of sampling and estimation
4.4.1 Sample Moments
4.4.2 Significance of the Estimate
4.5 Density function estimation
4.5.1 General Principle for χ2 Approach
4.5.2 Detailed Procedure for χ2 Approach
4.5.3 Quantile-Quantile Approach
4.6 Correlation and regression
4.6.1 Estimate of Correlation
4.6.2 Simple Regression Model
4.7 General properties of estimators
4.7.1 Convergence
4.7.2 Recursion
4.7.3 Maximum Likelihood Estimation
4.8 Random numbers and signal characteristics
4.8.1 Random Number Generation
4.8.2 Change of Mean and Variance
4.8.3 Density Shaping
References
Exercises
Appendices
Appendix 4.1 Plots and formulas for five probability density functions
Chapter 5 Introduction to random processes and signal properties
5.1 Introduction
5.2 Definition of stationarity
5.3 Definition of moment functions
5.3.1 General Definitions
5.3.2 Moments of Stationary Processes
5.4 Time averages and ergodicity
5.5 Estimating correlation functions
5.5.1 Estimator Definition
5.5.2 Estimator Bias
5.5.3 Consistency and Ergodicity
5.5.4 Sampling Properties
5.5.5 Asymptotic Distributions
5.6 Correlation and signal structure
5.6.1 General Moving Average
5.6.2 First-Order MA
5.6.3 Second-Order MA
5.6.4 Overview
5.7 Assessing stationarity of signals
5.7.1 Multiple Segments—Parametric
5.7.2 Multiple Segments—Nonparametric
References
Exercises
Appendices
Appendix 5.1 Variance of autocovariance estimate
Appendix 5.2 Stationarity tests
Chapter 6 Random signals, linear systems, and power spectra
6.1 Introduction
6.2 Power spectra
6.2.1 Empirical Approach
6.2.2 Theoretical Approach
6.3 System definition review
6.3.1 Basic Definitions
6.3.2 Relationships between Input and Output
6.4 Systems and signal structure
6.4.1 Moving Average Process
6.4.2 Structure with Autoregressive Systems
6.4.3 Higher-Order AR Systems
6.5 Time series models for spectral density
References
Exercises
Chapter 7 Spectral analysis for random signals: Nonparametric methods
7.1 Spectral estimation concepts
7.1.1 Developing Procedures
7.1.2 Sampling Moments of Estimators
7.2 Sampling distribution for spectral estimators
7.2.1 Spectral Estimate for White Noise
7.2.2 Sampling Properties for General Random Processes
7.3 Consistent estimators—Direct methods
7.3.1 Periodogram Averaging
7.3.2 Confidence Limits
7.3.3 Summary of Procedure for Spectral Averaging
7.3.4 Welch Method
7.3.5 Spectral Smoothing
7.3.6 Additional Applications
7.4 Consistent estimators—Indirect methods
7.4.1 Spectral and Lag Windows
7.4.2 Important Details for Using FFT Algorithms
7.4.3 Statistical Characteristics of BT Approach
7.5 Autocorrelation estimation
References
Exercises
Appendices
Appendix 7.1 Variance of periodogram
Appendix 7.2 Proof of variance of BT spectral smoothing
Appendix 7.3 Window characteristics
Appendix 7.4 Lag window functions
Appendix 7.5 Spectral estimates from smoothing
Chapter 8 Random signal modeling and parametric spectral estimation
8.1 Introduction
8.2 Model development
8.3 Random data modeling approach
8.3.1 Basic Concepts
8.3.2 Solution of General Model
8.3.3 Model Order
8.3.4 Levinson-Durbin Algorithm
8.3.5 Burg Method
8.3.6 Summary of Signal Modeling
8.4 Power spectral density estimation
8.4.1 Definition and Properties
8.4.2 Statistical Properties
8.4.3 Other Spectral Estimation Methods
8.4.4 Comparison of Nonparametric and Parametric Methods
References
Exercises
Appendices
Appendix 8.1 Matrix form of Levinson-Durbin recursion
Chapter 9 Theory and application of cross correlation and coherence
9.1 Introduction
9.2 Properties of cross correlation functions
9.2.1 Theoretical Function
9.2.2 Estimators
9.3 Detection of time-limited signals
9.3.1 Basic Concepts
9.3.2 Application of Pulse Detection
9.3.3 Random Signals
9.3.4 Time Difference of Arrival
9.3.5 Marine Seismic Signal Analysis
9.3.6 Procedure for Estimation
9.4 Cross spectral density functions
9.4.1 Definition and Properties
9.4.2 Properties of Cross Spectral Estimators
9.5 Applications
9.6 Tests for correlation between time series
9.6.1 Coherence Estimators
9.6.2 Statistical Properties of Estimators
9.6.3 Confidence Limits
9.6.4 Procedure for Estimation
9.6.5 Application
References
Exercises
Chapter 10 Envelopes and kernel functions
10.1 The Hilbert transform and analytic functions
10.1.1 Introduction
10.1.2 Hilbert Transform
10.1.3 Analytic Signal
10.1.4 Discrete Hilbert Transform
10.2 Point processes and continuous signals via kernel functions
10.2.1 Concept
10.2.2 Nerve Activity and the Spike Density Function

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Shiavi Richard,Statistical Signal,Analysis,Biomedical,Electrical Engineering