Introduction to applied statistical signal analysis :: guide to biomedical and electrical engineering 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...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Amsterdam ; Boston :
Elsevier Academic Press,
©2007.
|
| Ausgabe: | 3rd ed. |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | 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. |
| Beschreibung: | 1 online resource (xxii, 402 pages) : illustrations |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9780080467689 0080467687 |
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| 245 | 1 | 0 | |a Introduction to applied statistical signal analysis : |b guide to biomedical and electrical engineering applications / |c Richard Shiavi. |
| 250 | |a 3rd ed. | ||
| 260 | |a Amsterdam ; |a Boston : |b Elsevier Academic Press, |c ©2007. | ||
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| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a 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. | |
| 588 | 0 | |a Print version record. | |
| 520 | |a 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. | ||
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| contents | 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. |
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| discipline | Elektrotechnik / Elektronik / Nachrichtentechnik |
| edition | 3rd ed. |
| format | Electronic eBook |
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| spelling | Shiavi, Richard. Introduction to applied statistical signal analysis : guide to biomedical and electrical engineering applications / Richard Shiavi. 3rd ed. Amsterdam ; Boston : Elsevier Academic Press, ©2007. 1 online resource (xxii, 402 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Includes bibliographical references and index. 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. Print version record. 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. Signal processing Statistical methods. Traitement du signal Méthodes statistiques. COMPUTERS Information Theory. bisacsh TECHNOLOGY & ENGINEERING Signals & Signal Processing. bisacsh Signal processing Statistical methods fast has work: Introduction to applied statistical signal analysis (Text) https://id.oclc.org/worldcat/entity/E39PCFRFTKCWx98cWQmQKd3mMK https://id.oclc.org/worldcat/ontology/hasWork Print version: Shiavi, Richard. Introduction to applied statistical signal analysis. 3rd ed. Amsterdam ; Boston : Elsevier Academic Press, ©2007 0120885816 9780120885817 (OCoLC)70059536 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=185744 Volltext |
| spellingShingle | Shiavi, Richard Introduction to applied statistical signal analysis : guide to biomedical and electrical engineering applications / 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. Signal processing Statistical methods. Traitement du signal Méthodes statistiques. COMPUTERS Information Theory. bisacsh TECHNOLOGY & ENGINEERING Signals & Signal Processing. bisacsh Signal processing Statistical methods fast |
| title | Introduction to applied statistical signal analysis : guide to biomedical and electrical engineering applications / |
| title_auth | Introduction to applied statistical signal analysis : guide to biomedical and electrical engineering applications / |
| title_exact_search | Introduction to applied statistical signal analysis : guide to biomedical and electrical engineering applications / |
| title_full | Introduction to applied statistical signal analysis : guide to biomedical and electrical engineering applications / Richard Shiavi. |
| title_fullStr | Introduction to applied statistical signal analysis : guide to biomedical and electrical engineering applications / Richard Shiavi. |
| title_full_unstemmed | Introduction to applied statistical signal analysis : guide to biomedical and electrical engineering applications / Richard Shiavi. |
| title_short | Introduction to applied statistical signal analysis : |
| title_sort | introduction to applied statistical signal analysis guide to biomedical and electrical engineering applications |
| title_sub | guide to biomedical and electrical engineering applications / |
| topic | Signal processing Statistical methods. Traitement du signal Méthodes statistiques. COMPUTERS Information Theory. bisacsh TECHNOLOGY & ENGINEERING Signals & Signal Processing. bisacsh Signal processing Statistical methods fast |
| topic_facet | Signal processing Statistical methods. Traitement du signal Méthodes statistiques. COMPUTERS Information Theory. TECHNOLOGY & ENGINEERING Signals & Signal Processing. Signal processing Statistical methods |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=185744 |
| work_keys_str_mv | AT shiavirichard introductiontoappliedstatisticalsignalanalysisguidetobiomedicalandelectricalengineeringapplications |