Digital signal processing with examples in MATLAB

Content: Introduction Digital Signal Processing (DSP) 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        Least Squares, Orthogonality, and the Fourier Series Introduction Least Squares Orthogonality Discrete Fourier Series        Correlation, Fourier Spectra, and the Sampling Theorem Introduction Correlation The Discrete Fourier Transform (DFT) Redundancy in the DFT The Fast Fourier Transform (FFT) Algorithm Amplitude and Phase Spectra The Inverse DFT Properties of the DFT Continuous Transforms, Linear Systems, and Convolution The Sampling Theorem Waveform Reconstruction and Aliasing Resampling Nonuniform and Log-Spaced Sampling        Linear Systems and Transfer Functions Continuous and Discrete Linear Systems Properties of Discrete Linear Systems Discrete Convolution The z-Transform and Linear Transfer Functions The Complex Z-Plane and the Chirp z-Transform 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 Functions Used in This Chapter        Finite Impulse Response Filter Design Introduction An Ideal Lowpass Filter The Realizable Version Improving a Finite Impulse Response (FIR) Filter with Window Functions Highpass, Bandpass, and Bandstop Filters A Complete FIR Filtering Example Other Types of FIR Filters Digital Differentiation A Hilbert Transformer        Infinite Impulse Response Filter Design Introduction Linear Phase Butterworth Filters Chebyshev Filters Frequency Translations The Bilinear Transformation Infinite Impulse Response (IIR) Digital Filters Digital Resonators and the Spectrogram The All-Pass Filter Digital Integration and Averaging        Random Signals 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        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        Adaptive Signal Processing Introduction The Mean-Squared Error Performance Surface Searching the Performance Surface Steepest Descent and the Least-Mean-Square (LMS) Algorithm LMS Examples Direct Descent and the Recursive-Least-Squares (RLS) Algorithm Measures of Adaptive System Performance Other Adaptive Structures and Algorithms        Signal Information, Coding, and Compression Introduction Measuring Information Two Ways to Compress Signals Adaptive Predictive Coding Entropy Coding Transform Coding and the Discrete Cosine Transform The Discrete Sine Transform Multirate Signal Decomposition and Subband Coding Time-Frequency Analysis and Wavelet Transforms        Models of Analog Systems Introduction Impulse-Invariant Approximation Final Value Theorems Pole-Zero Comparisons Approaches to Modeling Input-Invariant Models Other Linear Models Comparison of Linear Models Models of Multiple and Nonlinear Systems Concluding Remarks        Pattern Recognition with Support Vector Machines Introduction Pattern Recognition Principles Learning Support Vector Machines Multiclass Classification MATLAB Examples    Appendix: Table of Laplace and Z-Transforms    Index    Exercises and References appear at the end of each chapter.