Synopses & Reviews
In this supplementary text, MATLAB is used as a computing tool to explore traditional DSP topics and solve problems to gain insight. This greatly expands the range and complexity of problems that students can effectively study in the course. Since DSP applications are primarily algorithms implemented on a DSP processor or software, a fair amount of programming is required. Using interactive software such as MATLAB makes it possible to place more emphasis on learning new and difficult concepts than on programming algorithms. Interesting practical examples are discussed and useful problems are explored.
About the Author
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. Professor Ingle is coauthor of the books: DSP Laboratory Using the ADSP-2181 Microprocessor (Prentice-Hall, 1991), Discrete Systems Laboratory (Brooks-Cole, 2000), and Statistical and Adaptive Signal proc-essing (Artech House, 2005). 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 the following academic positions: Associate Professor of Electrical Engineering, 1969-1976; Professor of Electrical Engineering, 1976-1998; Associate Dean of the College of Engineering and Director of the Graduate School of Engineering, 1982-1984; Chairman of the Department of Electrical and Computer Engineering, 1984-1997. His professional experience and interests are in the general areas of digital communications and digital signal processing. He is the co-author of several books including Digital Communications (2008, 5th ed.), Introduction to Digital Signal Processing (2007, 4th ed.); Digital Signal Processing Laboratory (1991); Advanced Digital Signal Processing (1992); Digital Processing of Speech Signals (2000); Communication Systems Engineering, (2002, 2nd ed.); Digital Signal Processing Using MATLAB V.4 (2010, 3rd ed.); Contemporary Communication Systems Using MATLAB (2004, 2nd ed.); 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. Lattice 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.