In a field as rapidly expanding as digital signal processing, even the topics relevant to the basics change over time both in their nature and their relative importance. It is important, therefore, to have an up-to-date text that not only covers the fundamentals, but that also follows a logical development that leaves no gaps readers must somehow bridge by themselves.

Digital Signal Processing with Examples in MATLAB® is just such a text. The presentation does not focus on DSP in isolation, but relates it to continuous signal processing and treats digital signals as samples of physical phenomena. The author also takes care to introduce important topics not usually addressed in signal processing texts, including the discrete cosine and wavelet transforms, multirate signal processing, signal coding and compression, least squares systems design, and adaptive signal processing. He also uses the industry-standard software MATLAB to provide examples of signal processing, system design, spectral analysis, filtering, coding and compression, and exercise solutions. All of the examples and functions used in the text are available online at www.crcpress.com.

Designed for a one-semester upper-level course but also ideal for self-study and reference, Digital Signal Processing with Examples in MATLAB is complete, self-contained, and rigorous. For basic DSP, it is quite simply the only book you need.

PREFACE

INTRODUCTION

Digital Signal Processing

How to Read this Text

Introduction to MATLAB

Signals, Vectors, and Arrays

Review of Vector and Matrix Algebra Using Matlab Notation

Geometric Series and Other Formulas

Matlab Functions in DSP

The Chapters Ahead

References

LEAST SQUARES, ORTHOGONALITY, AND THE FOURIER SERIES

Introduction

Least Squares

Orthogonality

The Discrete Fourier Series

Exercises

References

CORRELATION, FOURIER SPECTRA, AND THE SAMPLING THEOREM

Introduction

Correlation

The Discrete Fourier Transform (DFT)

Redundancy in the DFT

The FFT algorithm

Amplitude and Phase Spectra

The Inverse DFT

Properties of the DFT

Continuous Transforms

The Sampling Theorem

Waveform Reconstruction and Aliasing

Exercises

References

LINEAR SYSTEMS AND TRANSFER FUNCTIONS

Continuous and Discrete Linear Systems

Properties of Discrete Linear Systems

Discrete Convolution

The z-Transform and Linear Transfer Functions

Poles and Zeros

Transient Response and Stability

System Response via the Inverse z-Transform

Cascade, Parallel, and Feedback Structures

Direct Algorithms

State-Space Algorithms

Lattice Algorithms and Structures

FFT Algorithms

Discrete Linear Systems and Digital Filters

Exercises

References

FIR FILTER DESIGN

Introduction

An Ideal Lowpass Filter

The Realizable Version

Improving an FIR Filter with Window Functions

Highpass, Bandpass, and Bandstop Filters

A Complete FIR Filtering Example

Other Types of FIR Filters

Exercises

References

IIR FILTER DESIGN

Introduction

Linear Phase

Butterworth Filters

Chebyshev Filters

Frequency Translations

The Bilinear Transformation

IIR Digital Filters

Other Types of IIR Filters

Exercises

References

RANDOM SIGNAL AND SPECTRAL ESTIMATION

Introduction

Amplitude Distributions

Uniform, Gaussian, and Other Distributions

Power and Power Density Spectra

Properties of the Power Spectrum

Power Spectral Estimation

Data Windows in Spectral Estimation

The Cross-Power Spectrum

Algorithms

Exercises

References

LEAST-SQUARES SYSTEM DESIGN

Introduction

Applications of Least-Squares Design

System Design via the Mean-Squared Error

A Design Example

Least-Squares Design with Finite Signal Vectors

Correlation and Covariance Computation

Channel Equalization

System Identification

Interference Canceling

Linear Prediction and Recovery

Effects of Independent Broadband Noise

Exercises

References

ADAPTIVE SIGNAL PROCESSING

Introduction

The Mean-Squared Error Performance Surface

Searching the Performance Surface

Steepest Descent and the LMS Algorithm

LMS Example

Direct Descent and the RLS Algorithm

Measures of Adaptive System Performance

Other Adaptive Structures and Algorithms

Exercises

References

SIGNAL INFORMATION, CODING AND COMPRESSION

Introduction

Measuring Information

Two Ways to Compress Signals

Entropy Coding

Transform Coding and the Discrete Cosine Transform

Multirate Signal Decomposition and Subband Coding

Time-Frequency Analysis and Wavelet Transforms

Exercises

References

INDEX

"In a field as rapidly expanding as digital signal processing (DSP), even the basic topics change over time, both in nature and relative importance. It is important, therefore, to have an up-to-date text that not only covers the fundamentals but also follows a logical development that leaves no gaps that readers must somehow bridge by themselves. Digital Signal Processing with Examples in MATLAB is such a text."

- IEEE Signal Processing Magazine, Vol. 22, No. 4, July 2005

Resource | OS Platform | Updated | Description | Instructions |
---|---|---|---|---|

Platform type | March 16, 2011 | Downloads for "Digital Signal Processing with Examples in Matlab" may be found on Prof. Stearns' web page at the University of New Mexico. Go to http://ece.unm.edu/faculty/stearns/ , click on "Downloads", then on "DSP Downloads". |