In a field as rapidly expanding as digital signal processing, even the topics relevant to the basics change over time both in their nature and their relative importance. It is important, therefore, to have an up-to-date text that not only covers the fundamentals, but that also follows a logical development that leaves no gaps readers must somehow bridge by themselves. Digital Signal Processing with Examples in MATLAB(R) is just such a text. The presentation does not focus on DSP in isolation, but relates it to continuous signal processing and treats digital signals as samples of physical phenomena. The author also takes care to introduce important topics not usually addressed in signal processing texts, including the discrete cosine and wavelet transforms, multirate signal processing, signal coding and compression, least squares systems design, and adaptive signal processing. He also uses the industry-standard software MATLAB to provide examples of signal processing, system design, spectral analysis, filtering, coding and compression, and exercise solutions. All of the examples and functions used in the text are available online at www.crcpress.com.
Designed for a one-semester upper-level course but also ideal for self-study and reference, Digital Signal Processing with Examples in MATLAB is complete, self-contained, and rigorous. For basic DSP, it is quite simply the only book you need.
Table of Contents
PREFACE INTRODUCTION Digital Signal Processing How to Read this Text Introduction to MATLAB Signals, Vectors, and Arrays Review of Vector and Matrix Algebra Using Matlab Notation Geometric Series and Other Formulas Matlab Functions in DSP The Chapters Ahead References LEAST SQUARES, ORTHOGONALITY, AND THE FOURIER SERIES Introduction Least Squares Orthogonality The Discrete Fourier Series Exercises References CORRELATION, FOURIER SPECTRA, AND THE SAMPLING THEOREM Introduction Correlation The Discrete Fourier Transform (DFT) Redundancy in the DFT The FFT algorithm Amplitude and Phase Spectra The Inverse DFT Properties of the DFT Continuous Transforms The Sampling Theorem Waveform Reconstruction and Aliasing Exercises References LINEAR SYSTEMS AND TRANSFER FUNCTIONS Continuous and Discrete Linear Systems Properties of Discrete Linear Systems Discrete Convolution The z-Transform and Linear Transfer Functions Poles and Zeros Transient Response and Stability System Response via the Inverse z-Transform Cascade, Parallel, and Feedback Structures Direct Algorithms State-Space Algorithms Lattice Algorithms and Structures FFT Algorithms Discrete Linear Systems and Digital Filters Exercises References FIR FILTER DESIGN Introduction An Ideal Lowpass Filter The Realizable Version Improving an FIR Filter with Window Functions Highpass, Bandpass, and Bandstop Filters A Complete FIR Filtering Example Other Types of FIR Filters Exercises References IIR FILTER DESIGN Introduction Linear Phase Butterworth Filters Chebyshev Filters Frequency Translations The Bilinear Transformation IIR Digital Filters Other Types of IIR Filters Exercises References RANDOM SIGNAL AND SPECTRAL ESTIMATION Introduction Amplitude Distributions Uniform, Gaussian, and Other Distributions Power and Power Density Spectra Properties of the Power Spectrum Power Spectral Estimation Data Windows in Spectral Estimation The Cross-Power Spectrum Algorithms Exercises References LEAST-SQUARES SYSTEM DESIGN Introduction Applications of Least-Squares Design System Design via the Mean-Squared Error A Design Example Least-Squares Design with Finite Signal Vectors Correlation and Covariance Computation Channel Equalization System Identification Interference Canceling Linear Prediction and Recovery Effects of Independent Broadband Noise Exercises References ADAPTIVE SIGNAL PROCESSING Introduction The Mean-Squared Error Performance Surface Searching the Performance Surface Steepest Descent and the LMS Algorithm LMS Example Direct Descent and the RLS Algorithm Measures of Adaptive System Performance Other Adaptive Structures and Algorithms Exercises References SIGNAL INFORMATION, CODING AND COMPRESSION Introduction Measuring Information Two Ways to Compress Signals Entropy Coding Transform Coding and the Discrete Cosine Transform Multirate Signal Decomposition and Subband Coding Time-Frequency Analysis and Wavelet Transforms Exercises References INDEX