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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.

Features

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.
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.



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.