Digital Signal Processing Using MATLAB: A Problem Solving Companion / Edition 4

by Vinay K. Ingle, John G. Proakis

Overview

Learn to use MATLAB as a useful computing tool for exploring traditional Digital Signal Processing (DSP) topics and solving problems to gain insight with this supplementary text. DIGITAL SIGNAL PROCESSING USING MATLAB: A PROBLEM SOLVING COMPANION, 4E greatly expands the range and complexity of problems that you can effectively study. Since DSP applications are primarily algorithms implemented on a DSP processor or software, they require a significant amount of programming. Using interactive software, such as MATLAB, enables you to focus on mastering new and challenging concepts rather than concentrating on programming algorithms. This edition discusses interesting, practical examples and explores useful problems. New online chapters introduce advanced topics, such as optimal filters, linear prediction, and adaptive filters, which are essential in furthering your academic studies at the graduate level.

Product Details

ISBN-13:	9781305635128
Publisher:	CL Engineering
Publication date:	01/01/2016
Series:	Activate Learning with these NEW titles from Engineering!
Edition description:	New Edition
Pages:	672
Product dimensions:	7.30(w) x 9.10(h) x 1.00(d)

About the Author

Dr. Vinay K. Ingle is an Associate Professor of Electrical and Computer Engineering at Northeastern University. He received his Ph.D. in electrical and computer engineering from Rensselaer Polytechnic Institute in 1981. He has broad research experience and has taught courses on topics including signal and image processing, stochastic processes, and estimation theory. Dr. Ingle has co-authored numerous higher level books including DSP LABORATORY USING THE ADSP-2181 MICROPROCESSOR (Prentice Hall, 1991), DISCRETE SYSTEMS LABORATORY (Brooks-Cole, 2000), STATISTICAL AND ADAPTIVE SIGNAL PROCESSING (Artech House, 2005), and APPLIED DIGITAL SIGNAL PROCESSING (Cambridge University Press, 2011).


Affiliation: University of California, San Diego and Northeastern University Bio: Dr. John Proakis is an Adjunct Professor at the University of California at San Diego and a Professor Emeritus at Northeastern University. He was a faculty member at Northeastern University from 1969 through 1998 and held several academic positions including Professor of Electrical Engineering, Associate Dean of the College of Engineering and Director of the Graduate School of Engineering, and Chairman of the Department of Electrical and Computer Engineering. His professional experience and interests focus in areas of digital communications and digital signal processing. He is co-author of several successful books, including DIGITAL COMMUNICATIONS, 5E (2008), INTRODUCTION TO DIGITAL SIGNAL PROCESSING, 4E (2007); DIGITAL SIGNAL PROCESSING LABORATORY (1991); ADVANCED DIGITAL SIGNAL PROCESSING (1992); DIGITAL PROCESSING OF SPEECH SIGNALS (2000); COMMUNICATION SYSTEMS ENGINEERING, 2E (2002); DIGITAL SIGNAL PROCESSING USING MATLAB V.4, 3E (2010); CONTEMPORARY COMMUNICATION SYSTEMS USING MATLAB, 2E (2004); ALGORITHMS FOR STATISTICAL SIGNAL PROCESSING (2002); FUNDAMENTALS OF COMMUNICATION SYSTEMS (2005).

Table of Contents

1. INTRODUCTION. Overview of Digital Signal Processing. A Brief Introduction to MATLAB. Applications of Digital Signal Processing. Brief Overview of the Book. 2. DISCRETE-TIME SIGNALS AND SYSTEMS. Discrete-time Signals. Discrete Systems. Convolution. Difference Equations. 3. THE DISCRETE-TIME FOURIER ANALYSIS. The Discrete-time Fourier Transform (DTFT). The Properties of the DTFT. The Frequency Domain Representation of LTI Systems. Sampling and Reconstruction of Analog Signals. 4. THE z-TRANSFORM. The Bilateral z-Transform. Important Properties of the z-Transform. Inversion of the z-Transform. System Representation in the z-Domain. Solutions of the Difference Equations. 5. THE DISCRETE FOURIER TRANSFORM. The Discrete Fourier Series. Sampling and Reconstruction in the z-Domain. The Discrete Fourier Transform. Properties of the Discrete Fourier Transform. Linear Convolution Using the DFT. The Fast Fourier Transform. 6. IMPLEMENTATION OF DISCRETE-TIME FILTERS. Basic Elements. IIR Filter Structures. FIR Filter Structures. Overview of Finite-Precision Numerical Effects. Representation of Numbers. The Process of Quantization and Error Characterizations. Quantization of Filter Coefficients. 7. FIR FILTER DESIGN. Preliminaries. Properties of Linear-phase FIR Filters. Window Design Techniques. Optimal Equiripple Design Technique. 8. IIR FILTER DESIGN. Some Preliminaries. Some Special Filter Types. Characteristics of Prototype Analog Filters. Analog-to-Digital Filter Transformations. Lowpass Filter Design Using MATLAB. Frequency-band Transformations. 9. SAMPLING RATE CONVERSION. Introduction. Decimation by a Factor D. Interpolation by a Factor I. Sampling Rate Conversion by a Rational Factor I/D. FIR Filter Designs for Sampling Rate Conversion. FIR Filter Structures for Sampling Rate Conversion. 10. ROUND-OFF EFFECTS IN DIGITAL FILTERS. Analysis of A/D Quantization Noise. Round-off Effects in IIR Digital Filters. Round-off Effects in FIR Digital Filters. 11. APPLICATIONS IN ADAPTIVE FILTERING. LMS Algorithm for Coefficient Adjustment. System Identification of System Modeling. Suppression of Narrowband Interference in a Wideband Signal. Adaptive Line Enhancement. Adaptive Channel Equalization. 12. APPLICATIONS IN COMMUNICATIONS Pulse-Code Modulation. Differential PCM (DPCM). Adaptive PCM and DPCM (ADPCM). Delta Modulation (DM). Linear Predictive Coding (LPC) of Speech. Dual-tone Multifrequency (DTMF) Signals. Binary Digital Communications. Spread-Spectrum Communications. 13. RANDOM PROCESSES Random Variable, A Pair of Random Variables, Random Signals, Power Spectral Density, Stationary Random processes through LTI Systems, Useful Random Processes. 14. LINEAR PREDICTION AND OPTIMUM LINEAR FILTERS Innovation Representation of a Stationary Random Processes, Forward and Backward Linear Prediction, Solutions of Normal equations, Properties of Linear Prediction-Error Filters, AR Lattice and ARMA Lattice Filters, Wiener Filters for Filtering and Prediction. 15. ADAPTIVE FILTERS Applications of Adaptive Filters: System Identification and modeling, Adaptive Channel equalization, Echo cancellation, Suppression of Narrowband Interference in Wideband Signal, Adaptive Line Enhancer, Adaptive Noise Cancelling, Linear Predictive Coding of Speech Signals, Adaptive Arrays, Adaptive Direct Form FIR Filters: The LMS Algorithm, The RLS Algorithm for the Direct Form FIR Filters.

Editorial Reviews

"I like the way the authors discuss the solutions to the problems. They provide enough steps in their solution to help the student keep up, but not so many steps that the student doesn't have to think This book is very accessible compared to many texts. It is written clearly...It has excellent integration with MATLAB. I like the fact that the authors have no trouble introducing functions to complement the MATLAB functions."

"This book has developed a suite of MATLAB functions that can be used to determine standard digital filter structures including the direct form, cascade form, parallel form, and lattice form. The MATLAB scripts are provided with detailed explanations. Numerous examples along with block diagrams and discussions are included to show how these functions are used. Overall, I find the authors' treatment of the subject, including the MATLAB functions and examples, is among the best compared with other textbooks I know of In terms of the treatment of using MATLAB for DSP teaching at the undergraduate level, counting the number and variety of MATLAB examples and problems, this is the most competitive book on the market in my opinion."

From the Publisher