Introduction to applied statistical signal analysis : guide to biomedical and electrical engineering applications /

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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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Shiavi, Richard
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Amsterdam ; Boston : Elsevier Academic Press, ©2007.
Edición:3rd ed.
Temas:
Signal processing > Statistical methods.
Traitement du signal > Méthodes statistiques.
COMPUTERS > Information Theory.
TECHNOLOGY & ENGINEERING > Signals & Signal Processing.
Signal processing > Statistical methods
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Cover
  • 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
  • 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&#4.

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