Help your students focus on the development, implementation, and application of modern DSP techniques with DIGITAL SIGNAL PROCESSING USING MATLAB®, 3E. Written using an engaging, informal style, this book inspires students to become actively involved in each topic discussed. Each chapter starts with a motivational section that includes practical examples students can solve using the techniques covered in the chapter. Every chapter concludes with a detailed case study example, a chapter summary with student learning outcomes, and a generous selection of homework problems cross-referenced to sections within the chapter. A comprehensive DSP Companion software accompanies this edition for you to use inside the classroom and your students to use outside of the classroom. The DSP Companion software operates with MATLAB® and provides a plethora of options for class demonstrations and interactive student explorations of analysis and design concepts.

PART I: SIGNAL AND SYSTEM ANALYSIS.
1. Signal Processing.
Motivation. Digital and Analog Processing. Total Harmonic Distortion (THD). A Notch Filter. Active Noise Control. Video Aliasing. Signals and Systems. Signal Classification. System Classification. Sampling of Continuous-time Signals. Sampling as Modulation. Aliasing. Reconstruction of Continuous-time Signals. Reconstruction Formula. Zero-order Hold. Delayed First-order Hold. Prefilters and Postfilters. Anti-aliasing Filter. Anti-imaging Filter. DAC and ADC Circuits. Digital-to-analog Conversion (DAC). Analog-to-digital Conversion (ADC). DSP Companion. Installation. Menu Options. GUI Modules. Functions. GUI Modules and Case Studies. Chapter Summary. Problems. Analysis. GUI Simulation. MATLAB® Computation.
2. Discrete-Time Systems in the Time Domain.
Motivation. Home Mortgage. Range Measurement with Radar. Discrete-time Signals. Signal Classification. Common Signals. Discrete-time Systems. Difference Equations. Zero-input response. Zero-state response. Block Diagrams. The Impulse Response. FIR Systems. IIR Systems. Convolution. Linear Convolution. Circular Convolution. Zero Padding. Deconvolution. Polynomial Arithmetic. Correlation. Linear Cross-correlation. Circular Cross-correlation. Stability in the Time Domain. GUI Modules and Case Studies. Chapter Summary. Problems. Analysis. GUI Simulation. MATLAB® Computation.
3. Discrete-time Systems in the Frequency Domain.
Motivation. Satellite Attitude Control. Modeling the Vocal Tract. Z-transform Pairs. Region of Convergence. Common Z-transform Pairs. Z-transform Properties. General Properties. Causal Properties. Inverse Z-transform. Noncausal Signals. Synthetic Division. Partial Fractions. Residue Method. Transfer Functions. The Transfer Function. Zero-state Response. Poles, Zeros, and Modes.
DC Gain. Signal Flow Graphs. Stability in the Frequency Domain. Input-output Representation. BIBO Stability. The Jury Test. Frequency Response. Frequency Response. Sinusoidal Inputs. Periodic Inputs. System Identification. Least-squares Fit. Persistently Exciting Inputs. GUI Modules and Case Studies. Chapter Summary. Problems. Analysis. GUI Simulation. MATLAB® Computation.
4. Fourier Transforms and Spectral Analysis.
Motivation. Fourier Series. DC Wall Transformer. Frequency Response. Discrete-time Fourier Transform (DTFT). DTFT. Properties of the DTFT. The Discrete Fourier Transform (DFT). DFT. Matrix Formulation. Fourier Series and Discrete Spectra. DFT Properties. Fast Fourier Transform (FFT). Decimation in Time FFT. FFT Computational Effort. Alternative FFT Implementations. Fast Convolution and Correlation. Fast Convolution. Fast Block Convolution. Fast Correlation. White Noise. Uniform White Noise. Gaussian White Noise. Auto-correlation. Auto-correlation of White Noise. Power Density Spectrum. Extracting Periodic Signals from Noise. Zero Padding and Spectral Resolution. Frequency Response using the DFT. Zero Padding. Spectral Resolution. The Spectrogram. Data Windows. Spectrogram. Power Density Spectrum Estimation. Bartlett''s Method. Welch''s Method. GUI Modules and Case Studies. Chapter Summary. Problems. Analysis. GUI Simulation. MATLAB® Computation.
Part II: Filter Design.
5. Filter Types and Characteristics.
Motivation. Filter Design Specifications. Filter Realization Structures. Frequency-selective Filters. Linear Design Specifications. Logarithmic Design Specifications (dB). Linear-phase Filters. Group Delay. Amplitude Response. Linear-phase Zeros. Zero-phase Filters. Minimum-phase and Allpass Filters. Minimum-phase Filters. Allpass Filters. Inverse Systems and Equalization. Quadrature Filters. Differentiator. Hilbert Transformer. Digital Oscillator. Notch Filters and Resonators. Notch Filters. Resonators. Narrowband Filters and Filter Banks. Narrowband Filters. Filter Banks. Adaptive Filters. Transversal Filters. Pseudo-filters. GUI Modules and Case Studies. Chapter Summary. Problems. Analysis. GUI Simulation. MATLAB® Computation.
6. FIR Filter Design.
Motivation. Numerical Differentiation. Signal-to-noise Ratio. Windowing Method. Truncated Impulse Response. Windowing. Frequency-sampling Method. Frequency Sampling. Transition-band Optimization. Least-squares Method. Equiripple Filter Design. Minimax Error Criterion. Parks-McClellan Algorithm. Differentiators and Hilbert Transformers. Differentiator Design. Hilbert Transformer Design. Quadrature Filter Design. Generation of a Quadrature Pair. Quadrature Filter Design. Equalizer Design. Filter Realization Structures. Direct Forms. Cascade Form. Lattice Form. Finite Word Length Effects. Binary Number Representation. Input Quantization Error. Coefficient Quantization Error. Roundoff Error, Overflow, and Scaling. GUI Modules and Cases Studies. Chapter Summary. Problems. Analysis. GUI Simulation. MATLAB® Computation.
7. IIR Filter Design.
Motivation. Tunable Plucked-string Filter. Colored Noise. Filter Design by Pole-zero Placement. Resonator. Notch Filter. Comb Filters. Filter Design Parameters. Classical Analog Filters. Butterworth Filters. Chebyshev-I Filters. Chebyshev-II Filters. Elliptic Filters. Bilinear Transformation Method. Frequency Transformations. Analog Frequency Transformations. Digital Frequency Transformations. Filter Realization Structures. Direct Forms. Parallel Form. Cascade Form. Finite Word Length Effects. Coefficient Quantization Error. Roundoff Error, Overflow, and Scaling. Limit Cycles. GUI Modules and Case Studies. Chapter Summary. Problems. Analysis. GUI Simulation. MATLAB® Computation.
Part III: Advanced Signal Processing.
8. Multirate Signal Processing.
Motivation. Narrowband. Intersample Delay Systems. Integer Sampling Rate Converters. Sampling Rate Decimator. Sampling Rate Interpolator. Rational Sampling Rate Converters. Single-stage Converters. Multistage Converters. Polyphase Filters. Polyphase Decimator. Polyphase Interpolator. Narrowband Filters. Filter Banks. Analysis and Synthesis Banks. Subfilter Design. Polyphase Representation. Perfect Reconstruction Filter Banks. Time-division multiplexing. Perfect Reconstruction. Transmultiplexors. Oversampled A-to-D Converters. Anti-aliasing Filters. Sigma-Delta ADCs. Oversampled DACs. Anti-imaging Filters. Passband Equalization. GUI Modules and Case Studies. Chapter Summary. Problems. Analysis. GUI Simulation. MATLAB® Computation.
9. Adaptive Signal Processing.
Motivation. System Identification. Channel Equalization. Signal Prediction. Noise Cancellation. Mean Square Error. Adaptive Transversal Filters. Cross-correlation Revisited. Mean Square Error. Least Mean Square (LMS) Method. Performance Analysis of the LMS Method. Step Size. Convergence Rate. Excess Mean Square Error. Modified LMS Methods. Normalized LMS Method. Correlation LMS Method. Leaky LMS Method. Adaptive Filter Design with Pseudo-filters. Pseudo-filters. Adaptive Filter Design. Linear-phase Adaptive Filters. Recursive Least Squares (RLS) Method. Performance Criterion. Recursive Formulation. Active Noise Control. The Filtered-x LMS Method. Secondary Path Identification. Signal-synthesis Method. Adaptive Function Approximation. Nonlinear Functions. Radial basis Functions (RBF). Raised-cosine RBF Networks. Nonlinear System Identification (NLMS). GUI Modules and Case Studies. Chapter Summary. Problems. Analysis. GUI Simulation. MATLAB® Computation. References and Further Reading.
Appendices.
1. Transform Tables.
Fourier Series. Fourier Transform. Laplace Transform. Z-transform. Discrete-time Fourier Transform (DTFT). Discrete Fourier Transform.
2. Mathematical Identities.
Complex Numbers. Euler''s Identity. Trigonometric Identities. Inequalities. Uniform White Noise.
Index.

Help your students focus on the development, implementation, and application of modern DSP techniques with DIGITAL SIGNAL PROCESSING USING MATLAB®, 3E. Written using an engaging, informal style, this book inspires students to become actively involved in each topic discussed. Each chapter starts with a motivational section that includes practical examples students can solve using the techniques covered in the chapter. Every chapter concludes with a detailed case study example, a chapter summary with student learning outcomes, and a generous selection of homework problems cross-referenced to sections within the chapter. A comprehensive DSP Companion software accompanies this edition for you to use inside the classroom and your students to use outside of the classroom. The DSP Companion software operates with MATLAB® and provides a plethora of options for class demonstrations and interactive student explorations of analysis and design concepts. EXPANDED TREATMENT OF THE LATEST TOPICS PREPARES STUDENTS FOR SUCCESS. Explicit treatment of the Noble identities simplifies the discussion of polyphase filter design. This edition offers an improved discussion of adaptive FIR filter design using pseudo-filters and a streamlined explanation of nonlinear discrete-time system identification. The authors have also expanded this edition's presentation of least-squares system identification of ARMA, AR, and MA models. NEW EXAMPLES, FIGURES, AND TABLES THROUGHOUT FURTHER CLARIFY KEY CONCEPTS. New tables show the relationships between precision and the oversampling rate in oversampled ADCs and DACs. New examples, such as one that details processing MP3 music files with encoders and decoders, engage students and make content memorable. UPDATED DSP COMPANION SOFTWARE OFFERS NEW FUNCTIONALITY FOR STUDENTS AND TIME-SAVING RESOURCES FOR INSTRUCTORS. DSP Companion Software provides separate Student and Faculty versions compatible with the latest versions of Windows® and MATLAB®. A new Definitions menu option lets students view all definitions, propositions, and algorithms that appear in this edition. The Instructor version now includes a new Presentations menu option that displays more than 90 PowerPoint® lectures -- one for each section of every chapter. BOOK'S READER-FRIENDLY, CONVERSATIONAL WRITING STYLE INVITES ENGAGEMENT FROM STUDENTS. The authors have purposely written this text using an engaging, informal style that motivates students to delve into each new topic with interest. BROAD RANGE OF TOPICS PREPARES STUDENTS FOR VARIETY OF ON-THE-JOB CHALLENGES. This edition is specifically designed to provide a breadth of meaningful material and flexibility of content to accommodate courses of different lengths. For your convenience, the book is divided into three major parts: Signal and System Analysis, Digital Filter Design, and Advanced Signal Processing. STRUCTURED CHAPTERS HIGHLIGHT KEY POINTS AND EMPHASIZE REAL WORLD APPLICATIONS. Each chapter starts with a Motivation section that highlights practical examples and challenges that professionals can solve using techniques covered in the chapter. Specially marked sections denote more advanced or specialized material that you have the flexibility to omit without loss of continuity. Each chapter concludes with a detailed case study example that uses the techniques introduced in the chapter. A generous selection of homework problems provide numerous options for practice. DSP SOFTWARE CHAPTER GUI MODULES ALLOW STUDENTS TO EXPLORE AND MASTER DESIGN TOPICS. The chapter GUI modules feature a common user interface that is simple to use and easy to learn. These modules allow students to analyze design topics without requiring additional programming experience.