Higher Education


Digital Signal Processing Using MATLAB® : A Problem Solving Companion

Author(s): Vinay K. Ingle | John G. Proakis

ISBN: 9789386668110

4th Edition

Copyright: 2017

India Release: 2018


Binding: Paperback

Pages: 672

Trim Size: 241 x 181 mm

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Help your student learn to maximize MATLAB® as a computing tool to explore traditional Digital Signal Processing (DSP) topics, solve problems and gain insights. An extremely valuable supplementary text, DIGITAL SIGNAL PROCESSING USING MATLAB®: A PROBLEM SOLVING COMPANION, 4E greatly expands the range and complexity of problems that students can effectively study in your course. 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®, makes it possible to place more emphasis on learning new and difficult concepts than on programming algorithms. This engaging supplemental text introduces interesting practical examples and shows students how to explore useful problems. New, optional online chapters introduce advanced topics, such as optimal filters, linear prediction, and adaptive filters, to further prepare your students for graduate-level success.

  • BOOK SERVES AS IDEAL SUPPLMENT TO YOUR DSP TEXT. This book is an excellent MATLAB® supplement with flexible coverage that works well to support virtually any traditional DSP text you prefer for your course.
  • AUTHORS INTRODUCE KEY TOPICS EARLIER FOR INITIAL MASTERY. Your students are introduced to fundamental functions, such as Number Representation, Process of Quantization, and Error Characterization, early in the book for initial success.
  • BOOK SIMPLIFIES DISCUSSION ON THE PARKS?MCCLELLAN ALGORITHM. The authors use their experience to clearly present the Parks-McClellan algorithm to enable easier understanding of this complex topic.
  • COVERAGE PROVIDES SIMPLIFIED, CONCISE DISCUSSION OF RANDOM VARIABLES AND RANDOM PROCESSES. Students study random variables and random processes, including bandpass processes, in a clear presentation that is suitable for undergraduate as well as graduate students.
  • NEW GRADUATE-LEVEL MATERIAL PREPARES STUDENTS FOR SUCCESS IN ADVANCED STUDIES. The authors provide clear introductions to more complex topics, such as linear prediction, optimal filters and adaptive filters with applications to communications systems, system identification, LPC coding of speech, and adaptive arrays.
  • MASTERY OF MATLAB® ENABLES STUDENTS TO EXPLORE MORE COMPLEX DSP PROBLEMS. This book clearly presents the concepts and emphasizes the applications of MATLAB® to make it possible for your students to study more complex DSP problems than are normally taught in undergraduate-level courses.
  • COVERAGE HIGHLIGHTS MATLAB® FUNCTIONS AND SCRIPTS WITH INSIGHTS INTO KEY PROCEDURES. Thorough presentation enables your students to modify problem values and parameters and study scripts that offer meaningful insights into MATLAB® procedures.
  • BOOK ADDRESSES BOTH BASIC AND ADVANCED TOPICS. Extensive integration of MATLAB® features and capabilities introduces basic and advanced topics, making this a valuable resource for new students or those already familiar with MATLAB® functions.
  • DETAILED COVERAGE EXPLORES ANALYSIS AND DESIGN OF FILTERS. The authors address important topics in great detail, including the analysis and design of filters and spectrum analyzers.
  • THIS EDITION INTEGRATES THE MOST CURRENT VERSION OF MATLAB®. Your students learn to master this useful program and maximize the latest features and capabilities of MATLAB®, including enhancements for handling large data, new hardware support and integrated documentation
  • DISCUSSION OF LATTICE/LADDER FILTER IS NOW PRESENTED LATER IN THE BOOK. This edition's coverage of lattice/ladder filters has moved from Chapter 6 to Chapter 14 -- the online chapter on Linear Prediction and Optimum Filters -- for a more logical presentation of information.
  • NEW CONCISE ON-LINE CHAPTER DISCUSSES RANDOM VARIABLES AND RANDOM PROCESSES. You now have the flexibility to introduce random variable and random processes, including bandpass processes, in this timely online chapter. The authors make these topics easier to understand with extensive MATLAB® examples.
  • NEW ON-LINE CHAPTER EXAMINES LINEAR PREDICTION AND OPTIMAL FILTERS. You can further prepare students for graduate studies with close examination of linear prediction and optimal filters, as well as coverage of lattice filters.
  • NEW ON-LINE CHAPTER STUDIES ADAPTIVE FILTERS. This flexible, online chapter contains easy-to-understand LMS and RLS algorithms with an extensive set of practical applications, including system identification, echo and noise cancellation, and adaptive arrays. All algorithms and applications are explained and analyzed using MATLAB®.


Overview of Digital Signal Processing. A Brief Introduction to MATLAB®. Applications of Digital Signal Processing. Brief Overview of the Book.


Discrete-time Signals. Discrete Systems. Convolution. Difference Equations.


The Discrete-time Fourier Transform (DTFT). The Properties of the DTFT. The Frequency Domain Representation of LTI Systems. Sampling and Reconstruction of Analog Signals.


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.


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.


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.


Preliminaries. Properties of Linear-phase FIR Filters. Window Design Techniques. Optimal Equiripple Design Technique.


Some Preliminaries. Some Special Filter Types. Characteristics of Prototype Analog Filters. Analog-to-Digital Filter Transformations. Lowpass Filter Design Using MATLAB®. Frequency-band Transformations.


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.


Analysis of A/D Quantization Noise. Round-off Effects in IIR Digital Filters. Round-off Effects in FIR Digital Filters.


LMS Algorithm for Coefficient Adjustment. System Identification of System Modeling. Suppression of Narrowband Interference in a Wideband Signal. Adaptive Line Enhancement. Adaptive Channel Equalization.


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.


Random Variable, A Pair of Random Variables, Random Signals, Power Spectral Density, Stationary Random processes through LTI Systems, Useful Random Processes.


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.


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.



Vinay K. Ingle, Northeastern University

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).


John G. Proakis, Northeastern University

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).