Digital Signal and Image Processing using MATLAB, Volume 1: Fundamentals, 2nd Edition

Table of contents

1. Cover
2. Contents
3. Title Page
4. Copyright Page
5. Foreword
6. Notations and Abbreviations
7. Introduction to Matlab
   1. 1 Variables and Constants
   2. 2 Operations and Functions
   3. 3 Programming Structures
   4. 4 Graphically Displaying Results
   5. 5 Converting Numbers to Character Strings
   6. 6 Input/output
   7. 7 Program Writing
8. Part I: Deterministic Signals
   1. Chapter 1: Signal Fundamentals
      1. 1.1 The Concept of Signal
      2. 1.2 The Concept of System
      3. 1.3 Summary
   2. Chapter 2: Discrete Time Signals and Sampling
      1. 2.1 Fundamentals of Sampling
      2. 2.2 Plotting a Signal as a Function of Time
      3. 2.3 Spectral Representation
   3. Chapter 3: Spectral Observation
      1. 3.1 Spectral Accuracy and Resolution
      2. 3.2 Short Term Fourier Transform
      3. 3.3 Summing Up
      4. 3.4 Application Examples and Exercises
   4. Chapter 4: Linear Filters
      1. 4.1 Definitions and Properties
      2. 4.2 The Z-transform
      3. 4.3 Transforms and Linear Filtering
      4. 4.4 Difference Equations and Rational Tf Filters
      5. 4.5 Connection Between Gain and Poles/zeros
      6. 4.6 Minimum Phase Filters
      7. 4.7 Filter Design Methods
      8. 4.8 Oversampling and Undersampling
   5. Chapter 5: An Introduction to Image Processing
      1. 5.1 Introduction
      2. 5.2 Color Spaces
      3. 5.3 Geometric Transformations of an Image
      4. 5.4 Frequential Content of an Image
      5. 5.5 Linear Filtering
      6. 5.6 Other Operations on Images
      7. 5.7 Jpeg Lossy Compression
9. Part II: Random Signals
   1. Chapter 6: Random Variables
      1. 6.1 Random Phenomena in Signal Processing
      2. 6.2 Basic Concepts of Random Variables
      3. 6.3 Common Probability Distributions
      4. 6.4 Generating an R.v. with any Type of P.d
      5. 6.5 Uniform Quantization
   2. Chapter 7: Random Processes
      1. 7.1 Introduction
      2. 7.2 Wide-sense Stationary Processes
      3. 7.3 Estimating the Covariance
      4. 7.4 Filtering Formulae for Wss Random Processes
      5. 7.5 Ma, Ar and Arma Time Series
   3. Chapter 8: Spectra Estimation
      1. 8.1 Non-parametric Estimation of the Psd
      2. 8.2 Ar Estimation
      3. 8.3 Estimating the Amplitudes and the Frequencies
      4. 8.4 Periodograms and the Resolution Limit
   4. Chapter 9: The Least Squares Method
      1. 9.1 The Projection Theorem
      2. 9.2 The Least Squares Method
      3. 9.3 Linear Predictions of the Wss Processes
      4. 9.4 Wiener Filtering
      5. 9.5 The Lms (Least Mean Square) Algorithm
10. Part III: Appendices
    1. Chapter 10: Hints and Solutions
       1. H1 Signal Fundamentals
       2. H2 Discrete Time Signals and Sampling
       3. H3 Spectral Observation
       4. H4 Linear Filters
       5. H5 An Introduction to Image Processing
       6. H6 Random Variables
       7. H7 Random Processes
       8. H8 Spectra Estimation
       9. H9 The Least Squares Method
    2. Chapter 11: Appendix
       1. A1 Fourier Transform
       2. A2 Discrete Time Fourier Transform
       3. A3 Discrete Fourier Transform
       4. A4 Z-transform
11. Bibliography
12. Index
13. Other titles