Digital Signal Processing fundamentals and application 1st Edition by Li Tan 9780123740908 0080550576

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ISBN 10: 0080550576
ISBN 13: 9780123740908 
Author: Li Tan

This book will enable electrical engineers and technicians in the fields of the biomedical, computer, and electronics engineering, to master the essential fundamentals of DSP principles and practice. Coverage includes DSP principles, applications, and hardware issues with an emphasis on applications. Many instructive worked examples are used to illustrate the material and the use of mathematics is minimized for easier grasp of concepts.
In addition to introducing commercial DSP hardware and software, and industry standards that apply to DSP concepts and algorithms, topics covered include adaptive filtering with noise reduction and echo cancellations; speech compression; signal sampling, digital filter realizations; filter design; multimedia applications; over-sampling, etc. More advanced topics are also covered, such as adaptive filters, speech compression such as PCM, u-law, ADPCM, and multi-rate DSP and over-sampling ADC.

*Covers DSP principles and hardware issues with emphasis on applications and many worked examples
*Website with MATLAB programs for simulation and C programs for real-time DSP
*End of chapter problems are helpful in ensuring retention and understanding of what was just read

Digital Signal Processing fundamentals and application 1st Table of contents:

Chapter 1: Introduction to Digital Signal Processing
Objectives
1.1 Basic Concepts of Digital Signal Processing
1.2 Basic Digital Signal Processing Examples in Block Diagrams
1.2.1 Digital Filtering
1.2.2 Signal Frequency (Spectrum) Analysis
1.3 Overview of Typical Digital Signal Processing in Real-World Applications
1.3.1 Digital Crossover Audio System
1.3.2 Interference Cancellation in Electrocardiography
1.3.3 Speech Coding and Compression
1.3.4 Compact-Disc Recording System
1.3.5 Digital Photo Image Enhancement
1.4 Digital Signal Processing Applications
1.5 Summary
References

Chapter 2: Signal Sampling and Quantization
Objectives
2.1 Sampling of Continuous Signal
2.2 Signal Reconstruction
2.2.1 Practical Considerations for Signal Sampling: Anti-Aliasing Filtering
2.2.2 Practical Considerations for Signal Reconstruction: Anti-Image Filter and Equalizer
2.3 Analog-to-Digital Conversion, Digital-to-Analog Conversion, and Quantization
2.4 Summary
2.5 MATLAB Programs
2.6 Problems
References

Chapter 3: Digital Signals and Systems
Objectives
3.1 Digital Signals
3.1.1 Common Digital Sequences
3.1.2 Generation of Digital Signals
3.2 Linear Time-Invariant, Causal Systems
3.2.1 Linearity
3.2.2 Time Invariance
3.2.3 Causality
3.3 Difference Equations and Impulse Responses
3.3.1 Format of Difference Equation
3.3.2 System Representation Using Its Impulse Response
3.4 Bounded-in-and-Bounded-out Stability
3.5 Digital Convolution
3.6 Summary
3.7 Problems

Chapter 4: Discrete Fourier Transform and Signal Spectrum
Objectives
4.1 Discrete Fourier Transform
4.1.1 Fourier Series Coefficients of Periodic Digital Signals
4.1.2 Discrete Fourier Transform Formulas
4.2 Amplitude Spectrum and Power Spectrum
4.3 Spectral Estimation Using Window Functions
4.4 Application to Speech Spectral Estimation
4.5 Fast Fourier Transform
4.5.1 Method of Decimation-in-Frequency
4.5.2 Method of Decimation-in-Time
4.6 Summary
4.7 Problems
References

Chapter 5: The z-Transform
Objectives
5.1 Definition
5.2 Properties of the z-Transform
5.3 Inverse z-Transform
5.3.1 Partial Fraction Expansion Using MATLAB
5.4 Solution of Difference Equations Using the z-Transform
5.5 Summary
5.6 Problems
Reference

Chapter 6: Digital Signal Processing Systems, Basic Filtering Types, and Digital Filter Realizations
Objectives
6.1 The Difference Equation and Digital Filtering
6.2 Difference Equation and Transfer Function
6.2.1 Impulse Response, Step Response, and System Response
6.3 The z-Plane Pole-Zero Plot and Stability
6.4 Digital Filter Frequency Response
6.5 Basic Types of Filtering
6.6 Realization of Digital Filters
6.6.1 Direct-Form I Realization
6.6.2 Direct-Form II Realization
6.6.3 Cascade (Series) Realization
6.6.4 Parallel Realization
6.7 Application: Speech Enhancement and Filtering
6.7.1 Pre-Emphasis of Speech
6.7.2 Bandpass Filtering of Speech
6.8 Summary
6.9 Problems
Reference

Chapter 7: Finite Impulse Response Filter Design
Objectives
7.1 Finite Impulse Response Filter Format
7.2 Fourier Transform Design
7.3 Window Method
7.4 Applications: Noise Reduction and Two-Band Digital Crossover
7.4.1 Noise Reduction
7.4.2 Speech Noise Reduction
7.4.3 Two-Band Digital Crossover
7.5 Frequency Sampling Design Method
7.6 Optimal Design Method
7.7 Realization Structures of Finite Impulse Response Filters
7.7.1 Transversal Form
7.7.2 Linear Phase Form
7.8 Coefficient Accuracy Effects on Finite Impulse Response Filters
7.9 Summary of Finite Impulse Response (FIR) Design Procedures and Selection of FIR Filter Design Methods
7.10 Summary
7.11 MATLAB Programs
7.12 Problems
References

Chapter 8: Infinite Impulse Response Filter Design
Objectives
8.1 Infinite Impulse Response Filter Format
8.2 Bilinear Transformation Design Method
8.2.1 Analog Filters Using Lowpass Prototype Transformation
8.2.2 Bilinear Transformation and Frequency Warping
8.2.3 Bilinear Transformation Design Procedure
8.3 Digital Butterworth and Chebyshev Filter Designs
8.3.1 Lowpass Prototype Function and Its Order
8.3.2 Lowpass and Highpass Filter Design Examples
8.3.3 Bandpass and Bandstop Filter Design Examples
8.4 Higher-Order Infinite Impulse Response Filter Design Using the Cascade Method
8.5 Application: Digital Audio Equalizer
8.6 Impulse Invariant Design Method
8.7 Pole-Zero Placement Method for Simple Infinite Impulse Response Filters
8.7.1 Second-Order Bandpass Filter Design
8.7.2 Second-Order Bandstop (Notch) Filter Design
8.7.3 First-Order Lowpass Filter Design
8.7.4 First-Order Highpass Filter Design
8.8 Realization Structures of Infinite Impulse Response Filters
8.8.1 Realization of Infinite Impulse Response Filters in Direct-Form I and Direct-Form II
8.8.2 Realization of Higher-Order Infinite Impulse Response Filters via the Cascade Form
8.9 Application: 60-Hz Hum Eliminator and Heart Rate Detection Using Electrocardiography
8.10 Coefficient Accuracy Effects on Infinite Impulse Response Filters
8.11 Application: Generation and Detection of Dual-Tone Multifrequency Tones Using Goertzel Algorithm
8.11.1 Single-Tone Generator
8.11.2 Dual-Tone Multifrequency Tone Generator
8.11.3 Goertzel Algorithm
8.11.4 Dual-Tone Multifrequency Tone Detection Using the Modified Goertzel Algorithm
8.12 Summary of Infinite Impulse Response (IIR) Design Procedures and Selection of the IIR Filter Design Methods
8.13 Summary
8.14 Problems
References

Chapter 9: Hardware and Software for Digital Signal Processors
Objectives
9.1 Digital Signal Processor Architecture
9.2 Digital Signal Processor Hardware Units
9.2.1 Multiplier and Accumulator
9.2.2 Shifters
9.2.3 Address Generators
9.3 Digital Signal Processors and Manufactures
9.4 Fixed-Point and Floating-Point Formats
9.4.1 Fixed-Point Format
9.4.2 Floating-Point Format
9.4.3 IEEE Floating-Point Formats
9.4.5 Fixed-Point Digital Signal Processors
9.4.6 Floating-Point Processors
9.5 Finite Impulse Response and Infinite Impulse Response Filter Implementation in Fixed-Point Systems
9.6 Digital Signal Processing Programming Examples
9.6.1 Overview of TMS320C67x DSK
9.6.2 Concept of Real-Time Processing
9.6.3 Linear Buffering
9.6.4 Sample C Programs
9.7 Summary
9.8 Problems
References

Chapter 10: Adaptive Filters and Applications
Objectives
10.1 Introduction to Least Mean Square Adaptive Finite Impulse Response Filters
10.2 Basic Wiener Filter Theory and Least Mean Square Algorithm
10.3 Applications: Noise Cancellation, System Modeling, and Line Enhancement
10.3.1 Noise Cancellation
10.3.2 System Modeling
10.3.3 Line Enhancement Using Linear Prediction
10.4 Other Application Examples
10.4.1 Canceling Periodic Interferences Using Linear Prediction
10.4.2 Electrocardiography Interference Cancellation
10.4.3 Echo Cancellation in Long-Distance Telephone Circuits
10.5 Summary
10.6 Problems
References

Chapter 11: Waveform Quantization and Compression
Objectives
11.1 Linear Midtread Quantization
11.2 mu-law Companding
11.2.1 Analog mu-Law Companding
11.2.2 Digital mu-Law Companding
11.3 Examples of Differential Pulse Code Modulation (DPCM), Delta Modulation, and Adaptive DPCM G.72
11.3.1 Examples of Differential Pulse Code Modulation and Delta Modulation
11.3.2 Adaptive Differential Pulse Code Modulation G.721
11.4 Discrete Cosine Transform, Modified Discrete Cosine Transform, and Transform Coding in MPEG Audio
11.4.1 Discrete Cosine Transform
11.4.2 Modified Discrete Cosine Transform
11.4.3 Transform Coding in MPEG Audio
11.5 Summary
11.6 MATLAB Programs
11.7 Problems
References

Chapter 12: Multirate Digital Signal Processing, Oversampling of Analog-to-Digital Conversion, and Upconversion
Objectives
12.1 Multirate Digital Signal Processing Basics
12.1.1 Sampling Rate Reduction by an Integer Factor
12.1.2 Sampling Rate Increase by an Integer Factor
12.1.3 Changing Sampling Rate by a Non-Integer Factor L/M
12.1.4 Application: CD Audio Player
12.1.5 Multistage Decimation
12.2 Polyphase Filter Structure and Implementation
12.3 Oversampling of Analog-to-Digital Conversion
12.3.1 Oversampling and Analog-to-Digital Conversion Resolution
12.3.2 Sigma-Delta Modulation Analog-to-Digital Conversion
12.4 Application Example: CD Player
12.5 Undersampling of Bandpass Signals
12.6 Summary
12.7 Problems
References

Chapter 13: Image Processing Basics
13.1 Image Processing Notation and Data Formats
13.1.1 8-Bit Gray Level Images
13.1.2 24-Bit Color Images
13.1.3 8-Bit Color Images
13.1.4 Intensity Images
13.1.5 Red, Green, Blue Components and Grayscale Conversion
13.1.6 MATLAB Functions for Format Conversion
13.2 Image Histogram and Equalization
13.2.1 Grayscale Histogram and Equalization
13.2.2 24-Bit Color Image Equalization
13.2.3 8-Bit Indexed Color Image Equalization
13.2.4 MATLAB Functions for Equalization
13.3 Image Level Adjustment and Contrast
13.3.1 Linear Level Adjustment
13.3.2 Adjusting the Level for Display
13.3.3 Matlab Functions for Image Level Adjustment
13.4 Image Filtering Enhancement
13.4.1 Lowpass Noise Filtering
13.4.2 Median Filtering
13.4.3 Edge Detection
13.4.4 MATLAB Functions for Image Filtering
13.5 Image Pseudo-Color Generation and Detection
13.6 Image Spectra
13.7 Image Compression by Discrete Cosine Transform
13.7.1 Two-Dimensional Discrete Cosine Transform
13.7.2 Two-Dimensional JPEG Grayscale Image Compression Example
13.7.3 JPEG Color Image Compression
13.8 Creating a Video Sequence by Mixing Two Images
13.9 Video Signal Basics
13.9.1 Analog Video
13.9.2 Digital Video
13.10 Motion Estimation in Video
13.11 Summary
13.12 Problems
References

Appendix A: Introduction to the MATLAB Environment
A.1 Basic Commands and Syntax
A.2 MATLAB Array and Indexing
A.3 Plot Utilities: Subplot, Plot, Stem, and Stair
A.4 MATLAB Script Files
A.5 MATLAB Functions
References

Appendix B: Review of Analog Signal Processing
B.1 Fourier Series and Fourier Transform
B.1.1 Sine-Cosine Form
B.1.2 Amplitude-Phase Form
B.1.3 Complex Exponential Form
B.1.4 Spectral Plots
B.1.5 Fourier Transform
B.2 Laplace Transform
B.2.1 Laplace Transform and Its Table
B.2.2 Solving Differential Equations Using Laplace Transform
B.2.3 Transfer Function
B.3 Poles, Zeros, Stability, Convolution, and Sinusoidal Steady-State Response
B.3.1 Poles, Zeros, and Stability
B.3.2 Convolution
B.3.3 Sinusoidal Steady-State Response
B.4 Problems
References

Appendix C: Normalized Butterworth and Chebyshev Functions
C.1 Normalized Butterworth Function
C.2 Normalized Chebyshev Function

Appendix D: Sinusoidal Steady-State Response of Digital Filters
D.1 Sinusoidal Steady-State Response
D.2 Properties of the Sinusoidal Steady-State Response

Appendix E: Finite Impulse Response Filter Design Equations by the Frequency Sampling Design Method

Appendix F: Some Useful Mathematical Formulas

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