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Digital Signal Processing Using Matlab

Book Description

This book uses MATLAB as a computing tool to explore traditional DSP topics and solve problems. This greatly expands the range and complexity of problems that students can effectively study in signal processing courses. A large number of worked examples, computer simulations and applications are provided, along with theoretical aspects that are essential in order to gain a good understanding of the main topics. Practicing engineers may also find it useful as an introductory text on the subject.

Table of Contents

  1. Cover Page
  2. Title Page
  3. Copyright
  4. Table of Contents
  5. Preface: Why and How this Book was Written
  6. Chapter 1: Introduction
    1. 1.1. Brief introduction to MATLAB
    2. 1.2. Solved exercises
  7. Chapter 2: Discrete-Time Signals
    1. 2.1. Theoretical background
    2. 2.2. Solved exercises
    3. 2.3. Exercises
  8. Chapter 3: Discrete-Time Random Signals
    1. 3.1. Theoretical background
    2. 3.2. Solved exercises
    3. 3.3. Exercises
  9. Chapter 4: Statistical Tests and High Order Moments
    1. 4.1. Theoretical background
    2. 4.2. Solved exercises
    3. 4.3. Exercises
  10. Chapter 5: Discrete Fourier Transform of Discrete-Time Signals
    1. 5.1. Theoretical background
    2. 5.2. Solved exercises
    3. 5.3. Exercises
  11. Chapter 6: Linear and Invariant Discrete-Time Systems
    1. 6.1. Theoretical background
    2. 6.2. Solved exercises
    3. 6.3. Exercises
  12. Chapter 7: Infinite Impulse Response Filters
    1. 7.1. Theoretical background
    2. 7.2. Solved exercises
    3. 7.3. Exercises
  13. Chapter 8: Finite Impulse Response Filters
    1. 8.1. Theoretical background
    2. 8.2. Solved exercises
    3. 8.3. Exercises
  14. Chapter 9: Detection and Estimation
    1. 9.1. Theoretical background
    2. 9.2. Solved exercises
    3. 9.3 Exercises
  15. Chapter 10: Power Spectral Density Estimation
    1. 10.1. Theoretical background
    2. 10.2. Solved exercises
    3. 10.3. Exercises
  16. Chapter 11: Time-Frequency Analysis
    1. 11.1. Theoretical background
    2. 11.2. Solved exercises
    3. 11.3. Exercises
  17. Chapter 12: Parametrical Time-Frequency Methods
    1. 12.1. Theoretical background
    2. 12.2. Solved exercises
    3. 12.3. Exercises
  18. Chapter 13: Supervised Statistical Classification
    1. 13.1. Theoretical background
    2. 13.2. Solved exercises
    3. 13.3. Exercises
  19. Chapter 14: Data Compression
    1. 14.1. Theoretical background
    2. 14.2. Solved exercises
    3. 14.3. Exercises
  20. References
  21. Index