UID:
almafu_9959239217502883
Format:
1 online resource (425 p.)
Edition:
3rd ed.
ISBN:
9786610746873
,
9781280746871
,
1280746874
,
9780080467689
,
0080467687
Series Statement:
Biomedical Engineering
Content:
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
Note:
Previous ed.: 1999.
,
Front Cover; Title Page; Copyright Page; Table of Contents; Preface; Dedication; Acknowledgments; List of symbols; 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
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2.4 Generalities2.5 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
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3.5.1 Frequency Range and Scaling3.5.2 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
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4.2.3 Gaussian Random Variable4.3 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 ?2 Approach; 4.5.2 Detailed Procedure for ?2 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
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4.8 Random numbers and signal characteristics4.8.1 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
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5.5.3 Consistency and Ergodicity
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English
Additional Edition:
ISBN 9781483299990
Additional Edition:
ISBN 1483299996
Additional Edition:
ISBN 9780120885817
Additional Edition:
ISBN 0120885816
Language:
English
