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Digital Signal Processing with Examples in MATLAB®, Second Edition

By Samuel D. Stearns, Donald R. Hush

CRC Press – 2011 – 516 pages

Series: Electrical Engineering & Applied Signal Processing Series

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    978-1-43-983782-5
    April 4th 2011

Description

Based on fundamental principles from mathematics, linear systems, and signal analysis, digital signal processing (DSP) algorithms are useful for extracting information from signals collected all around us. Combined with today’s powerful computing capabilities, they can be used in a wide range of application areas, including engineering, communications, geophysics, computer science, information technology, medicine, and biometrics.

Updated and expanded, Digital Signal Processing with Examples in MATLAB®, Second Edition introduces the basic aspects of signal processing and presents the fundamentals of DSP. It also relates DSP to continuous signal processing, rather than treating it as an isolated operation.

New to the Second Edition

  • Discussion of current DSP applications
  • New chapters on analog systems models and pattern recognition using support vector machines
  • New sections on the chirp z-transform, resampling, waveform reconstruction, discrete sine transform, and logarithmic and nonuniform sampling
  • A more comprehensive table of transforms

Developing the fundamentals of DSP from the ground up, this bestselling text continues to provide readers with a solid foundation for further work in most areas of signal processing. For novices, the authors review the basic mathematics required to understand DSP systems and offer a brief introduction to MATLAB. They also include end-of-chapter exercises that not only provide examples of the topics discussed, but also introduce topics and applications not covered in the chapters.

Reviews

"This book will guide you through the mathematics and electrical engineering theory using real-world applications. It will also use MATLAB®, a software tool that allows you to easily implement signal-processing techniques using the computer and to view the signals graphically. … The reader of this text is fortunate to be guided by two wonderful teachers who translate the issues and understanding of using signal processing in the real world to examples and applications that open the door to this fascinating subject."

—From the Foreword by Dr. Delores M. Etter, Texas Instruments Distinguished Chair in Engineering Education and director of the Caruth Institute for Engineering Education, Southern Methodist University, Dallas, Texas, USA

Praise for the First Edition

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

It is a pleasure to recommend this book to the serious student of digital signal processing. It is carefully written and illustrated by many useful examples and exercises, and the material is selected to cover the relevant topics in this rapidly developing field of knowledge.

—the late Professor Richard W. Hamming, Bell Laboratories

Contents

Introduction

Digital Signal Processing (DSP)

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

Least Squares, Orthogonality, and the Fourier Series

Introduction

Least Squares

Orthogonality

Discrete Fourier Series

Correlation, Fourier Spectra, and the Sampling Theorem

Introduction

Correlation

The Discrete Fourier Transform (DFT)

Redundancy in the DFT

The Fast Fourier Transform (FFT) Algorithm

Amplitude and Phase Spectra

The Inverse DFT

Properties of the DFT

Continuous Transforms, Linear Systems, and Convolution

The Sampling Theorem

Waveform Reconstruction and Aliasing

Resampling

Nonuniform and Log-Spaced Sampling

Linear Systems and Transfer Functions

Continuous and Discrete Linear Systems

Properties of Discrete Linear Systems

Discrete Convolution

The z-Transform and Linear Transfer Functions

The Complex Z-Plane and the Chirp z-Transform

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

Functions Used in This Chapter

Finite Impulse Response Filter Design

Introduction

An Ideal Lowpass Filter

The Realizable Version

Improving a Finite Impulse Response (FIR) Filter with Window Functions

Highpass, Bandpass, and Bandstop Filters

A Complete FIR Filtering Example

Other Types of FIR Filters

Digital Differentiation

A Hilbert Transformer

Infinite Impulse Response Filter Design

Introduction

Linear Phase

Butterworth Filters

Chebyshev Filters

Frequency Translations

The Bilinear Transformation

Infinite Impulse Response (IIR) Digital Filters

Digital Resonators and the Spectrogram

The All-Pass Filter

Digital Integration and Averaging

Random Signals 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

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

Adaptive Signal Processing

Introduction

The Mean-Squared Error Performance Surface

Searching the Performance Surface

Steepest Descent and the Least-Mean-Square (LMS) Algorithm

LMS Examples

Direct Descent and the Recursive-Least-Squares (RLS) Algorithm

Measures of Adaptive System Performance

Other Adaptive Structures and Algorithms

Signal Information, Coding, and Compression

Introduction

Measuring Information

Two Ways to Compress Signals

Adaptive Predictive Coding

Entropy Coding

Transform Coding and the Discrete Cosine Transform

The Discrete Sine Transform

Multirate Signal Decomposition and Subband Coding

Time–Frequency Analysis and Wavelet Transforms

Models of Analog Systems

Introduction

Impulse-Invariant Approximation

Final Value Theorems

Pole–Zero Comparisons

Approaches to Modeling

Input-Invariant Models

Other Linear Models

Comparison of Linear Models

Models of Multiple and Nonlinear Systems

Concluding Remarks

Pattern Recognition with Support Vector Machines

Introduction

Pattern Recognition Principles

Learning

Support Vector Machines

Multiclass Classification

MATLAB Examples

Appendix: Table of Laplace and Z-Transforms

Index

Exercises and References appear at the end of each chapter.

Author Bio

Samuel D. Stearns is a professor emeritus at the University of New Mexico, where has been involved in adjunct teaching and research since 1960. An IEEE fellow, Dr. Stearns was also a distinguished member of the technical staff at Sandia National Laboratories for 27 years. His principal technical areas are DSP and adaptive signal processing.

Don R. Hush is a technical staff member at the Los Alamos National Laboratory. An IEEE senior member, Dr. Hush was previously a technical staff member at Sandia National Laboratories and a professor at the University of New Mexico. He was also an associate editor for IEEE Transactions on Neural Networks and IEEE Signal Processing Magazine.

Name: Digital Signal Processing with Examples in MATLAB®, Second Edition (Hardback)CRC Press 
Description: By Samuel D. Stearns, Donald R. Hush. Based on fundamental principles from mathematics, linear systems, and signal analysis, digital signal processing (DSP) algorithms are useful for extracting information from signals collected all around us. Combined with today’s powerful...
Categories: Circuits & Devices, Digital Signal Processing, Electrical Engineering Communications, Electrical & Electronic Engineering