Introduction to Applied Statistical Signal Analysis
eBook - PDF

Introduction to Applied Statistical Signal Analysis

Guide to Biomedical and Electrical Engineering Applications

  1. 424 pages
  2. English
  3. PDF
  4. Available on iOS & Android
eBook - PDF

Introduction to Applied Statistical Signal Analysis

Guide to Biomedical and Electrical Engineering Applications

About this book

Introduction to Applied Statistical Signal Analysis, Third Edition, 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.Topics presented include mathematical bases, requirements for estimation, and detailed quantitative examples for implementing techniques for classical signal analysis. This book includes over one hundred worked problems and real world applications. Many of the examples and exercises use measured signals, most of which are from the biomedical domain.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.- 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.

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Yes, you can access Introduction to Applied Statistical Signal Analysis by Richard Shiavi in PDF and/or ePUB format, as well as other popular books in Computer Science & Computer Engineering. We have over one million books available in our catalogue for you to explore.

Information

Table of contents

  1. Front Cover
  2. Title Page
  3. Copyright Page
  4. Table of Contents
  5. Preface
  6. Dedication
  7. Acknowledgments
  8. List of symbols
  9. Chapter 1 Introduction and terminology
  10. Chapter 2 Empirical modeling and approximation
  11. Chapter 3 Fourier analysis
  12. Chapter 4 Probability concepts and signal characteristics
  13. Chapter 5 Introduction to random processes and signal properties
  14. Chapter 6 Random signals, linear systems, and power spectra
  15. Chapter 7 Spectral analysis for random signals: Nonparametric methods
  16. Chapter 8 Random signal modeling and parametric spectral estimation
  17. Chapter 9 Theory and application of cross correlation and coherence
  18. Chapter 10 Envelopes and kernel functions
  19. Appendices
  20. Index