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Table of contents:
For senior/graduate-level courses in Discrete-Time Signal Processing. THE definitive, authoritative text on DSP -- ideal for those with an introductory-level knowledge of signals and systems. Written by prominent, DSP pioneers, it provides thorough treatment of the fundamental theorems and properties of discrete-time linear systems, filtering, sampling, and discrete-time Fourier Analysis. By focusing on the general and universal concepts in discrete-time signal processing, it remains vital and relevant to the new challenges arising in the field --without limiting itself to specific technologies with relatively short life spans.
Contents:
1
Introduction
2
Discrete-Time Signals and Systems
Introduction
Discrete-time Signals: Sequences
Discrete-time Systems
Linear Time-Invariant Systems
Properties of Linear Time-Invariant Systems
Linear Constant-Coefficient Difference Equations
Frequency-Domain Representation of Discrete-Time Signals and Systems
Representation of Sequence by Fourier Transforms
Symmetry Properties of the Fourier Transform
Fourier Transform Theorems
Discrete-Time Random Signals
Summary
3
The z-Transform
Introduction
The z-Transform
Properties of the Region of Convergence for the z-Transform
The Inverse z-Transform
z-Transform Properties
Summary
4
Sampling of Continuous-Time Signals
Introduction
Periodic Sampling
Frequency-Domain Representation of Sampling
Reconstruction of a Bandlimited Signal from its Samples
Discrete-Time Processing of Continuous-Time Signals
Continuous-Time Processing of Discrete-Time Signals
Changing the Sampling Rate Using Discrete-Time Processing
Practical Considerations
Oversampling and Noise Shaping
Summary
5
Transform Analysis of Linear Time-Invariant Systems
Introduction
The Frequency Response of LTI Systems
System Functions for Systems Characterized by Linea
Frequency Response for Rational System Functions
Relationship Between Magnitude and Phase
All-Pass Systems
Minimum-Phase Systems
Linear Systems with Generalized Linear Phase
Summary
6
Structures for Discrete-Time Systems
Introduction
Block Diagram Representation of Linear Constant-Coefficient Difference Equations
Signal Flow Graph Representation of Linear Constant-Coefficient Difference Equations
Basic Structures for IIR Systems
Transposed Forms
Basic Network Structures for FIR Systems
Overview of Finite-Precision Numerical Effects
The Effects of Coefficient Quantization
Effects of Roundoff Noise in Digital Filters
Zero-Input Limit Cycles in Fixed-Point Realizations of IIR Digital Filters
Summary
7
Filter Design Techniques
Introduction
Design of Discrete-Time IIR Filters from Continuous-Time Filters
Design of FIR Filters by Windowing
Examples of FIR Filter Design by the Kaiser Window Method
Optimum Approximations of FIR Filters
Examples of FIR Equiripple Approximation
Comments on IIR and FIR Digital Filters
Summary
8
The Discrete Fourier Transform
Introduction
Representation of Periodic Sequences: the Discrete Fourier Series
Summary of Properties of the DFS Representation of Periodic Sequences
The Fourier Transform of Periodic Signals
Sampling the Fourier Transform
Fourier Representation of Finite-Duration Sequences: The Discrete-Fourier Transform
Properties of the Discrete Fourier Transform
Summary of Properties of the Discrete Fourier Transform
Linear Convolution Using the Discrete Fourier Transform
The Discrete Cosine Transform (DCT)
Summary
9
Computation of the Discrete Fourier Transform
Introduction
Efficient Computation of the Discrete Fourier Transform
The Goertzel Algorithm Decimation-in-Time FFT Algorithms
Decimation-in-Frequency FFT Algorithms
Practical Considerations Implementation of the DFT Using Convolution
Summary
10
Fourier Analysis of Signals Using the Discrete Fourier Transform
Introduction
Fourier Analysis of Signals Using the DFT
DFT Analysis of Sinusoidal Signals
The Time-Dependent Fourier Transform
Block Convolution Using the Time-Dependent Fourier Transform
Fourier Analysis of Nonstationary Signals
Fourier Analysis of Stationary Random Signals: the Periodogram
Spectrum Analysis of Random Signals Using Estimates of the Autocorrelation Sequence
Summary
11
Discrete Hilbert Transforms
Introduction
Real and Imaginary Part Sufficiency of the Fourier Transform for Causal Sequences
Sufficiency Theorems for Finite-Length Sequences
Relationships Between Magnitude and Phase
Hilbert Transform Relations for Complex Sequences
Summary
Appendix A: Random Signals
Discrete-Time Random Process
Averages
Properties of Correlation and Covariance Sequences
Transform Representation of Random Signals
Appendix B: Continuous-Time Filters
Butterworth Lowpass Filters
Chebyshev Filters
Elliptic Filters.
Brief Description:
Intended for senior/graduate-level courses in Discrete-Time Signal Processing, this book is suitable for those with an introductory-level knowledge of signals and systems. It provides a treatment of the fundamental theorems and properties of discrete-time linear systems, filtering, sampling, and discrete-time Fourier Analysis.
For Pricing and Availability Click Here
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