Digital signal processing : principles and applications / Thomas Holton, San Francisco State University.

Format:

Book

Author/Creator:

Holton, Thomas, author.

Contributor:

Rosengarten Family Fund.

Alumni and Friends Memorial Book Fund.

Language:

English

Subjects (All):

Signal processing--Digital techniques.

Signal processing.

Physical Description:

xxvii, 1032 pages : illustrations ; 26 cm

Place of Publication:

Cambridge, United Kingdom ; New York, NY : Cambridge University Press, 2021.

Summary:

"Combining clear explanations of elementary principles, advanced topics, and applications, with step-by-step mathematical derivations, this textbook provides a comprehensive yet accessible introduction to digital signal processing. All the key topics are covered, including discrete-time Fourier transform, z-transform, discrete Fourier transform, and A/D conversion, as well as more advanced topics such as FIR and IIR filtering algorithms, multi-rate systems, the discrete cosine transform, and spectral signal processing. Over 600 full-color illustrations, 200 fully worked examples, hundreds of end-of-chapter homework problems, and detailed computational examples of DSP algorithms implemented in Matlab and C aid understanding and help put knowledge into practice. A wealth of supplementary material accompanies the book online, including interactive programs for instructors, a full set of solutions, and Matlab laboratory exercises, making this the ideal text for senior undergraduate and graduate courses on digital signal processing"-- Provided by publisher.

Contents:

Machine generated contents note: 1. Discrete-time signals and systems

Introduction

1.1. Two signal processing paradigms

1.2. Advantages of digital signal processing

1.3. Applications of DSP

1.4. Signals

1.4.1. Signal classification

1.4.2. Discrete-time signals

1.5. Basic operations on signals

1.5.1. Shift

1.5.2. Flip

1.5.3. Flip and shift

1.5.4. Time decimation

1.5.5. Time expansion

1.5.6. Operation on multiple sequences

1.6. Basic sequences

1.6.1. Impulse

1.6.2. Unit step

1.6.3. Pulse

1.6.4. Power-law sequences

1.6.5. Sinusoidal sequences

1.6.6. Complex exponential sequences

1.6.7. Sequence classification

1.7. Systems

1.7.1. Discrete-time scalar multiplier

1.7.2. Offset

1.7.3. Squarer

1.7.4. Shift

1.7.5. Moving-window average

1.7.6. Summer

1.7.7. Switch

1.7.8. Linear constant-coefficient difference equation (LCCDE)

1.8. Linearity

1.8.1. The additivity property

1.8.2. The scaling property

1.8.3. Discrete-time scalar multiplier

1.8.4. Offset

1.8.5. Squarer

1.8.6. Shift

1.8.7. Moving-window average

1.8.8. Summer

1.8.9. Switch

1.8.10. Linear constant-coefficient difference equation

1.8.11. The "zero-in, zero-out" property of linear systems

1.9. Time in variance

1.9.1. Discrete-time scalar multiplier

1.9.2. Offset

1.9.3. Squarer

1.9.4. Shift

1.9.5. Moving-window average

1.9.6. Summer

1.9.7. Switch

1.9.8. Linear constant-coefficient difference equation (LCCDE)

1.10. Causality

1.10.1. Discrete-time scalar multiplier

1.10.2. Offset

1.10.3. Squarer

1.10.4. Shift

1.10.5. Moving-window average

1.10.6. Summer

1.10.7. Switch

1.10.8. Linear constant-coefficient difference equation (LCCDE)

1.11. Stability

1.11.1. Discrete-time scalar multiplier

1.11.2. Offset

1.11.3. Squarer

1.11.4. Shift

1.11.5. Moving-window average

1.11.6. Summer

1.11.7. Switch

1.11.8. Linear constant-coefficient difference equation (LCCDE)

Summary

Problems

2. Impulse response

2.1. FIR and IIR systems

2.1.1. Finite impulse response (FIR) systems

2.1.2. Infinite impulse response (IIR) systems

2.1.3. Response of a system to a flipped and shifted impulse

2.2. Convolution

2.2.1. Direct-summation method

2.2.2. Flip-and-shift method

2.2.3. Convolution examples

2.3. Properties of convolution

2.3.1. The commutative property

2.3.2. The associative property

2.3.3. The distributive property

2.4. Stability and causality

2.4.1. Stability

2.4.2. Causality

2.5. *Convolution reinterpreted

2.5.1. Convolution as polynomial multiplication

2.5.2. Convolution using Matlab

2.5.3. Convolution as matrix multiplication

2.6. *Deconvolution

2.7. * Convolution oflong sequences

2.7.1. Overlap-add method

2.7.2. Overlap-save method

2.8. Implementation issues

3. Discrete-time Fourier transform

3.1. Complex exponentials and sinusoids

3.1.1. Response of LTI systems to complex exponentials

3.1.2. Response of linear time-invariant systems to sinusoids

3.2. Discrete-time Fourier transform (DTFT)

3.2.1. Orthogonality of complex exponential sequences

3.2.2. Definition and derivation

3.2.3. Notation of the DTFT

3.2.4. Existence of the DTFT

3.2.5. The system function (again)

3.2.6. Periodicity of the DTFT

3.2.7. DTFT of finite-length sequences

3.2.8. DTFT of infinite-length sequences

3.3. Magnitude and phase description of the DTFT

3.3.1. Magnitude and phase of the DTFT of an impulse

3.3.2. Essential phase discontinuities

3.4. Important sequences and their transforms

3.5. Symmetry properties of the DTFT

3.5.1. Time reversal

3.5.2. Conjugate symmetry and antisymmetry

3.5.3. Even and odd symmetry

3.5.4. Consequences of symmetry

3.5.5. * Complex sequences

3.5.6. Symmetry summary

3.6. Response of a system to sinusoidal input

3.7. Linear-phase systems

3.7.1. Causal symmetric sequences

3.7.2. Causal antisymmetric sequences

3.7.3. Time delay and group delay

3.8. The inverse discrete-time Fourier transform

3.9. Using Matlab to compute and plot the DTFT

3.10. DTFT properties

3.10.1. Linearity

3.10.2. Delay (shifting) property

3.10.3. Complex modulation (frequency shift) property

3.10.4. Convolution

3.10.5. Using the convolution property of the DTFT to do filtering

3.10.6. Deconvolution and system identification using the convolution property

3.10.7. Convolution properties

3.10.8. Understanding filtering in the frequency domain

3.10.9. Multiplication (windowing) property

3.10.10. * Time- and band-limited systems

3.10.11. * Spectral and temporal ambiguity

3.10.12. Time-reversal property

3.10.13. Differentiation property

3.10.14. Parseval's theorem

3.10.15. DC-and 7i-value properties

3.10.16. * Using the DTFT to solve linear constant-coefficient difference equations

3.10.17. Summary of DTFT properties

3.11. * The relation between the DTFT and the Fourier series

4. z-transform

4.1. The z-transform

4.2. The singularities of H(z)

4.2.1. Pole-zero plots

4.2.2. Left-sided sequences

4.2.3. Relation between the z-transform and DTFT

4.2.4. Multiple poles and zeros

4.2.5. Finding the z-transform from the pole-zero plot

4.2.6. Complex poles and zeros

4.2.7. Some important transforms

4.2.8. Finite-length sequences

4.2.9. Plotting pole-zero plots with Matlab

4.3. * Linear-phase FIR systems

4.3.1. Complex zeros

4.3.2. Real zeros

4.4. The inverse z-transform

4.4.1. All-zero systems

4.4.2. Distinct real poles

4.4.3. Complex poles

4.4.4. Multiple (repeated) poles

4.4.5. Improper rational functions

4.4.6. Using Matlab to compute the inverse z-transform

4.5. Properties of the z-transform

4.5.1. Linearity

4.5.2. Shifting property

4.5.3. Differentiation property

4.5.4. Time reversal property

4.5.5. Convolution property

4.5.6. Applications of convolution

4.5.7. Initial-value theorem

4.5.8. Final-value theorem

4.6. Linear constant-coefficient difference equations (LCCDE)

4.6.1. LCCDE of FIR systems

4.6.2. LCCDE of IIR systems

4.6.3. Relation between LCCDE and H(z)

4.6.4. Using Matlab to solve LCCDEs

4.6.5. Inverse filter

4.7. * The unilateral r-transform

5. Frequency response

5.1. The computation of H(co) from H(z)

5.2. Systems with a single real zero

5.2.1. Direct computation

5.2.2. Graphical method

5.3. Systems with a single real pole

5.4. Multiple real poles and zeros

5.5. Complex poles and zeros

5.6. *3-D visualization of H(co) from H(z)

5.7. Allpass filter

5.7.1. Real allpass filter

5.7.2. Multiple poles and zeros

5.7.3. Allpass filters with complex poles and zeros

5.7.4. General allpass filter

5.7.5. Systems with the same magnitude

5.7.6. Practical applications of allpass filters

5.8. Minimum-phase-lag systems

6. A/D and D/A conversion

6.1. Overview of A/D and D/A conversion

6.2. Analog sampling and reconstruction

6.2.1. Analog sampling

6.2.2. The sampling theorem

6.2.3. The Nyquist sampling criterion

6.2.4. Oversampling, undersampling and critical sampling

6.2.5. * Sampling a cosine

6.3. Conversion from continuous time to discrete time and back

6.3.1. The continuous-to-discrete (C/D) converter

6.3.2. Spectrum of the discrete-time sequence

6.3.3. The discrete-to-continuous (D/C) converter

6.3.4. Summary

6.4. Anti-aliasing and reconstruction filters

6.4.1. The

anti-aliasing filter

6.4.2. A digital recording application

6.4.3. Reconstruction filter

6.4.4. Revised model of D/A conversion

6.5. Downsampling and upsampling

6.5.1. Downsampling

6.5.2. Decimation and aliasing

6.5.3. Oversampling A/D converter in a digital recording application

6.5.4. Upsampling

6.5.5. * Upsampling a cosine

6.5.6. Upsampling D/A converter in a digital recording application

6.5.7. Resampling

6.6. Matlab functions for sample-rate conversion

6.7. * Quantization

6.7.1. Model of quantization

6.7.2. Quantization error

6.7.3. Noise reduction by oversampling

6.7.4. * Noise-shaping A/D converters

6.7.5. * Sigma-delta A/D converters

6.8. * A/D converter architecture

6.9. * D/A converter architecture

7. Finite impulse response filters

7.1. Linear-phase FIR filters

7.1.1. Types of linear-phase filters

7.1.2. Basic properties of linear-phase filters

7.1.3. * Time-aligned and zero-phase FIR filters

7.2. Preliminaries of filter design

7.2.1. Specification of filter characteristics

7.2.2. The ideal lowpass filter

7.2.3. The optimum least-square-error FIR filter

7.2.4. Even- and odd-length causal filters

7.3. Window-based FIR filter design

7.3.1. Rectangular window filter

7.3.2. Raised cosine window niters

7.3.3. Kaiser window

Contents note continued: 7.4. Highpass, bandpass and bandstop FIR niters

7.5. Matlab implementation of window-based FIR niters

7.5.1. Matlab functions that implement FIR filtering

7.6. * Spline and raised-cosine FIR filters

7.6.1. FIR filters designed using splines

7.6.2. FIR filters designed using raised cosines

7.7. * Frequency-sampled FIR filter design

7.7.1. Inverse DFT

7.7.2. Frequency sampling as interpolation

7.7.3. Design formulas

7.7.4. Simultaneous equations

7.7.5. Matlab implementation of frequency-sampled filters

7.8. * Least-square-error FIR filter design

7.8.1. Discrete least-square-error FIR filters

7.8.2. * Integral least-square-error FIR filter design

7.9. * Optimal lowpass filter design

7.10. Multiband filters

7.11. * Differentiator

7.12. * Hilbert transformer

7.12.1. Derivation of the Hilbert transformer

7.12.2. FIR implementation of a Hilbert transformer

7.12.3. FFT implementation of a Hilbert transformer

7.12.4. Applications of the Hilbert transformer

8. Infinite impulse response filters

8.1. Definition of the IIR filter

8.2. Overview of analog filter design

8.2.1. Parameter definitions

8.2.2. Butterworth filter

8.2.3. * Chebyshev filter

8.2.4. * Inverse Chebyshev filter

8.2.5. * Elliptic filter

8.2.6. Summary

8.3. * Impulse invariance

8.3.1. Impulse-invariance approach

8.3.2. Impulse-invariance design procedure

8.3.3. Mapping of s-plane to z-plane

8.4. Bilinear transformation

8.4.1. Forward-difference approximation

8.4.2. Backward-difference approximation

8.4.3. Bilinear transformation

8.4.4. Bilinear-transformation procedure

8.4.5. Cascade of second-order sections

8.5. * Spectral transformations of IIR niters

8.5.1. Lowpass-to-lowpass transformation

8.5.2. Lowpass-to-highpass transformation

8.5.3. Lowpass-to-bandpass transformation

8.5.4. Lowpass-to-bandstop transformation

8.6. * Zero-phase IIR filtering

9. Filter architecture

9.1. Signal-flow graphs

9.2. Canonical filter architecture

9.2.1. First-order filters

9.2.2. Canonical filter architecture

9.3. Transposed filters

9.4. Cascade architecture

9.4.1. Allpass filters

9.4.2. Using Matlab to design cascade filters

9.5. Parallel architecture

9.5.1. Using Matlab to design parallel filters

9.6. FIR filters

9.7. * Lattice and lattice-ladder filters

9.7.1. FIR lattice filters

9.7.2. Specialized FIR lattice filters

9.7.3. IIR lattice filters

9.7.4. Allpass lattice filters

9.7.5. Lattice-ladder IIR filters

9.7.6. Stability of IIR filters revisited

9.8. * Coefficient quantization

9.8.1. Systems with poles

9.8.2. Systems with zeros

9.8.3. Systems with poles and zeros

9.8.4. Pairing poles and zeros

9.8.5. Coefficient quantization of lattice filters

9.9. * Implementation issues

9.9.1. Software implementation

9.9.2. Hardware implementation

10. Discrete Fourier transform (DFT)

10.1. Derivation of the DFT

10.1.1. The inverse discrete Fourier transform (IDFT)

10.1.2. Orthogonality of complex exponential sequences

10.1.3. Periodicity of the DFT

10.1.4. * Conditions for the reconstruction of a sequence from the DFT

10.2. DFT of basic signals

10.2.1. DFT of an impulse

10.2.2. DFT of a pulse

10.2.3. DFT of a constant

10.2.4. DFT of a complex exponential sequence

10.2.5. DFT of a sinusoid

10.2.6. Resolution and frequency mapping of the DFT

10.2.7. Summary (so far)

10.3. Properties of the DFT

10.3.1. Linearity

10.3.2. Complex conjugation

10.3.3. Symmetry properties of the DFT

10.3.4. Circular time shifting

10.3.5. Circular time reversal

10.3.6. Circular frequency shift

10.3.7. Circular convolution

10.3.8. Multiplication

10.3.9. Parseval's theorem

10.3.10. Summary of DFT properties

10.4. * Matrix representation of the DFT

10.5. * Using the DFT to increase resolution in the time and frequency domains

10.5.1. Increasing frequency resolution by zero-padding in the time domain

10.5.2. Upsampling in the time domain by zero-padding in the frequency domain

10.5.3. * Recovery of the DTFT from the DFT

11. Fast Fourier transform (FFT)

11.1. Radix-2 FFT transforms

11.1.1. Decimation-in-time FFT

11.1.2. Computational gain

11.1.3. Bit reversal

11.1.4. * Decimation-in-frequency FFT

11.2. ? Radix-4 FFT

11.2.1. The radix-4 decomposition

11.2.2. The radix-4 transform as a combination of radix-2 transforms

11.3. * Composite (mixed-radix) FFT

11.3.1. FFTs of composite size

11.3.2. Mixed radix-2 and radix-4 transform

11.3.3. Transposed and split-radix transforms

11.4. Inverse FFT

11.5. Matlab implementation

11.6. FFT of real sequences

11.6.1. Properties of DFTs (revisited)

11.6.2. TV-point FFT of two real A-point sequences

11.6.3. Appoint IFFT of two Appoint transforms

11.6.4. * A/2-point FFT of a real A-point sequence

11.6.5. * A/2-point IFFT of an A-point transform

11.7. FFT resolution

11.7.1. Increasing the resolution of the FFT

11.7.2. Decreasing the resolution of the FFT

11.8. Fast convolution using the FFT

11.8.1. Convolution of fixed-length input sequences

11.8.2. Block convolution using the FFT

11.8.3. ? Using both DIT and DIF transforms

11.8.4. Matlab support for convolution using the FFT

11.9. ? The Goertzel algorithm

11.10. Iterative and recursive implementations

11.10.1. Iterative implementation

11.10.2. Recursive implementation

11.11. Implementation issues

12. Discrete cosine transform (DCT)

12.1. The DCT

12.1.1. * Periodically extended sequences

12.1.2. "The" discrete cosine transform (DCT-II)

12.1.3. The inverse discrete cosine transform (IDCT)

12.1.4. The four principal DCT variants

12.1.5. Properties of the DCT

12.1.6. * Matrix form of the DCT and IDCT

12.1.7. * Energy compaction of the DCT

12.1.8. * Implementation of the DCT-II and IDCT-II

12.1.9. * The modified discrete cosine transform (MDCT)

12.2. MPEG audio compression

12.2.1. The MP3 encoder

12.2.2. Hybrid filter bank and MDCT

12.2.3. The psychoacoustic model

12.2.4. Bit allocation and quantization

12.2.5. Minimum entropy coding

12.3. JPEG image compression

12.3.1. Color processing

12.3.2. DCT transformation and quantization

12.3.3. Coefficient encoding

12.3.4. Implementation of 2D-DCT

13. Multirate systems

13.1. Polyphase downsampling

13.1.1. Review of downsampling

13.1.2. Polyphase implementation of downsampling

13.1.3. Downsampling summary

13.2. Polyphase upsampling

13.2.1. Review of upsampling

13.2.2. Polyphase implementation of upsampling

13.2.3. Upsampling summary

13.3. * Polyphase resampling

13.4. Transform analysis of polyphase systems

13.4.1. Basic decimation and expansion identities

13.4.2. Multirate identities of downsampling and upsampling

13.4.3. Transform analysis of polyphase downsampling

13.4.4. Transform analysis of polyphase upsampling

13.4.5. Transform analysis of polyphase resampling

13.4.6. * Matlab implementation of polyphase sample-rate conversion algorithms

13.5. Multistage systems for downsampling and upsampling

13.5.1. Multistage downsampling

13.5.2. Multistage upsampling

13.6. Multistage and multirate filtering

13.6.1. Multistage interpolated FIR (IFIR) filters

13.6.2. Multirate lowpass filtering

13.7. Special filters for multirate applications

13.7.1. Half-band filters

13.7.2. Polyphase downsampling and upsampling using half-band

filters

13.7.3. L-band (Nyquist) filters

13.8. ? Multirate filter banks

14. Spectral analysis

14.1. Basics of spectral analysis

14.1.1. Spectral effects of windowing

14.1.2. Effect of window choice

14.1.3. Spectral spread and leakage

14.1.4. Spectral effect of sampling

14.2. The short-time Fourier transform (STFT)

14.2.1. Constant overlap-add criterion

14.2.2. The spectrogram

14.2.3. * Implementation of the discrete STFT in Matlab

14.3. * Nonparametric methods of spectral estimation

14.3.1. The periodogram

14.3.2. Bartlett's method

14.3.3. The modified periodogram

14.3.4. Averaged modified periodogram

14.3.5. Welch's method

14.3.6. Discrete-time periodograms

14.3.7. Matlab implementation of the periodogram functions

14.4. * Parametric methods of spectral estimation

14.4.1. The ARMA model

14.4.2. The Levinson-Durbin algorithm

14.4.3. Matlab implementation of the Levinson-Durbin algorithm

14.5. Linear prediction

14.5.1. Predictor error and the estimation of model order

14.5.2. Linear predictive coding (LPC)

14.5.3. The source-filter model

14.5.4. Linear predictive coding architecture

14.5.5. * Alternate formulations of linear prediction equations

Appendix A Linear algebra

A.1. Systems of linear equations

Contents note continued: A.2. Solution of an inhomogeneous system of equations

A.2.1. Unique solution

A.2.2. Infinite number of solutions

A.2.3. No solution

A.3. Solution of a homogeneous system of equations

A.3.1. Trivial solution

A.3.2. Infinite number of solutions

A.4. Least-square-error optimization

Appendix B Numeric representations

B.1. Integer representation

B.1.1. Unsigned binary

B.1.2. Signed-magnitude binary

B.1.3. Two's-complement binary

B.1.4. Offset binary

B.1.5. Converting between binary formats

B.2. Fixed-point (fractional) representation

B.2.1. Rounding and truncation

B.2.2. Arithmetic of fractional numbers

B.3. Floating-point representation

B.4. Computer representation of numbers

Appendix C Matlab tutorial

C.1. Introduction to Matlab

C.1.1. What is Matlab?

C.1.2. What is Matlab not?

C.2. The elements of Matlab

C.2.1. Calculator functions

C.2.2. Variables

C.2.3. Matlab functions

C.3. Programming in Matlab

C.3.1. Scripts

C.3.2. Functions

C.3.3. Conditionals and loops

C.3.4. Classes

C.4. Matlab help

C.5. Plotting

C.6. The Matlab environment

C.6.1. Command window and editor

C.6.2. Debugging and writing "clean" code

Appendix D Probability and random processes

D.1. Probability distribution and density functions

D.1.1. Discrete probability density function

D.1.2. Continuous probability density function

D.1.3. Joint, marginal and conditional probability distributions

D.1.4. Expected value and moments

D.1.5. Covariance and correlation

D.2. Random processes

D.2.1. Statistics of a random process

D.2.2. Stationary random processes

D.2.3. White noise

D.2.4. Filtered random processes

D.2.5. The Wold decomposition

D.2.6. Estimators

D.2.7. Ergodic processes

D.3. Power spectral density

D.3.1. Definition

D.3.2. Power spectral density of a filtered random process

D.3.3. Power spectral density of noise

D.4. Matlab functions

D.4.1. Random number generators

D.4.2. Autocorrelation and crosscorrelation.

Notes:

Includes bibliographical references.

Local Notes:

Acquired for the Penn Libraries with assistance from the Rosengarten Family Fund.

Acquired for the Penn Libraries with assistance from the Alumni and Friends Memorial Book Fund.

Other Format:

Online version: Holton, Thomas, 1952- Digital signal processing

ISBN:

9781108418447

1108418449

OCLC:

1111772221

Publisher Number:

99990663552