## Book description

This title provides the most important theoretical aspects of Image and Signal Processing (ISP) for both deterministic and random signals. The theory is supported by exercises and computer simulations relating to real applications.

More than 200 programs and functions are provided in the MATLAB® language, with useful comments and guidance, to enable numerical experiments to be carried out, thus allowing readers to develop a deeper understanding of both the theoretical and practical aspects of this subject.

1. Coverpage
2. Titlepage
4. Dedication
5. Contents
6. Preface
7. Notations and Abbreviations
8. Introduction to MATLAB
1. 1 Variables
2. 2 Operations and functions
3. 3 Graphically displaying results
4. 4 Converting numbers to character strings
5. 5 Input/output
6. 6 Program writing
9. 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 The sampling theorem
2. 2.2 Plotting a signal as a function of time
3. 2.3 Spectral representation
4. 2.4 Fast Fourier transform
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 Filter Implementation
1. 5.1 Filter implementation
2. 5.2 Filter banks
6. Chapter 6 An Introduction to Image Processing
1. 6.1 Introduction
2. 6.2 Geometric transformations of an image
3. 6.3 Frequential content of an image
4. 6.4 Linear filtering
5. 6.5 Other operations on images
6. 6.6 JPEG lossy compression
7. 6.7 Watermarking
10. Part II Random Signals
1. Chapter 7 Random Variables
1. 7.1 Random phenomena in signal processing
2. 7.2 Basic concepts of random variables
3. 7.3 Common probability distributions
4. 7.4 Generating an r.v. with any type of p.d.
5. 7.5 Uniform quantization
2. Chapter 8 Random Processes
1. 8.1 Introduction
2. 8.2 Wide-sense stationary processes
3. 8.3 Estimating the covariance
4. 8.4 Filtering formulae for WSS random processes
5. 8.5 MA, AR and ARMA time series
3. Chapter 9 Continuous Spectra Estimation
1. 9.1 Non-parametric estimation of the PSD
2. 9.2 Parametric estimation
4. Chapter 10 Discrete Spectra Estimation
1. 10.1 Estimating the amplitudes and the frequencies
2. 10.2 Periodograms and the resolution limit
3. 10.3 High resolution methods
5. Chapter 11 The Least Squares Method
1. 11.1 The projection theorem
2. 11.2 The least squares method
3. 11.3 Linear predictions of the WSS processes
4. 11.4 Wiener filtering
5. 11.5 The LMS (least mean square) algorithm
6. 11.6 Application: the Kalman algorithm
6. Chapter 12 Selected Topics
1. 12.1 Simulation of continuous-time systems
2. 12.2 Dual Tone Multi-Frequency (DTMF)
3. 12.3 Speech processing
4. 12.4 DTW
5. 12.5 Modifying the duration of an audio signal
6. 12.6 Quantization noise shaping
7. 12.7 Elimination of the background noise in audio
8. 12.8 Eliminating the impulse noise
9. 12.9 Tracking the cardiac rhythm of the fetus
10. 12.10 Extracting the contour of a coin
11. 12.11 Principal component analysis (PCA)
12. 12.12 Separating an instantaneous mixture
13. 12.13 Matched filters in radar telemetry
14. 12.14 Kalman filtering
15. 12.15 Compression
16. 12.16 Digital communications
17. 12.17 Linear equalization and the Viterbi algorithm
11. Part III Hints and Solutions
1. Chapter 13 Hints and Solutions
2. Chapter 14 Appendix
12. Bibliography
13. Index

## Product information

• Title: Digital Signal and Image Processing Using MATLAB
• Author(s):
• Release date: May 2006
• Publisher(s): Wiley
• ISBN: 9781905209132