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.

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.

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. 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®. 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.