Digital Signal Processing Fundamentals and Applications 3rd Edition by Lizhe Tan, Jean Jiang 0128150719 9780128150719

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ISBN 10: 0128150719 

ISBN 13: 9780128150719

Author: Lizhe Tan, Jean Jiang 

Digital Signal Processing: Fundamentals and Applications, Third Edition, not only introduces students to the fundamental principles of DSP, it also provides a working knowledge that they take with them into their engineering careers. Many instructive, worked examples are used to illustrate the material, and the use of mathematics is minimized for an easier grasp of concepts. As such, this title is also useful as a reference for non-engineering students and practicing engineers. The book goes beyond DSP theory, showing the implementation of algorithms in hardware and software.

Digital Signal Processing Fundamentals and Applications 3rd table of contents: 

Chapter 1: Introduction to Digital Signal Processing
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. Vibration Signature Analysis for Defected Gear Tooth
1.3.6. Digital Image Enhancement
1.4. Digital Signal Processing Applications
1.5. Summary
Chapter 2: Signal Sampling and Quantization
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
Chapter 3: Digital Signals and Systems
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. Digital Convolution
3.5. Bounded-Input and Bounded-Output Stability
3.6. Summary
3.7. Problems
Chapter 4: Discrete Fourier Transform and Signal Spectrum
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 Signal 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
Chapter 5: The z-Transform
5.1. Definition
5.2. Properties of the z-Transform
5.3. Inverse z-Transform
5.3.1. Partial Fraction Expansion and Look-Up Table
5.3.2. Partial Fraction Expansion Using MATLAB
5.3.3. Power Series Method
5.3.4. Inversion Formula Method
5.4. Solution of Difference Equations Using the z-Transform
5.5. Two-Sided z-Transform
5.6. Summary
5.7. Problems
Chapter 6: Digital Signal Processing Systems, Basic Filtering Types, and Digital Filter Realizations
6.1. 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: Signal Enhancement and Filtering
6.7.1. Preemphasis of Speech
6.7.2. Bandpass Filtering of Speech
6.7.3. Enhancement of ECG Signal Using Notch Filtering
6.8. Summary
6.9. Problems
Chapter 7: Finite Impulse Response Filter Design
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. Noise Reduction in Vibration Signal
7.4.4. Two-Band Digital Crossover
7.5. Frequency Sampling Design Method
7.6. Optimal Design Method
7.7. Design of FIR Differentiator and Hilbert Transformer
7.8. Realization Structures of Finite Impulse Response Filters
7.8.1. Transversal Form
7.8.2. Linear Phase Form
7.9. Coefficient Accuracy Effects on Finite Impulse Response Filters
7.10. Summary of FIR Design Procedures and Selection of the FIR Filter Design Methods in Practice
7.11. Summary
7.12. MATLAB Programs
7.13. Problems
Chapter 8: Infinite Impulse Response Filter Design
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 DTMF Tones Using the 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 D
8.13. Summary
8.14. Problems
Chapter 9: Adaptive Filters and Applications
9.1. Introduction to Least Mean Square Adaptive Finite Impulse Response Filters
9.2. Basic Wiener Filter Theory and Adaptive Algorithms
9.2.1. Wiener Filter Theory and Linear Prediction
9.2.1.1. Basic Wiener Filter Theory
9.2.1.2. Forward Linear Prediction
9.2.2. Steepest Descent Algorithm
9.2.3. Least Mean Square Algorithm
9.2.4. Recursive Least Squares Algorithm
9.3. Applications: Noise Cancellation, System Modeling, and Line Enhancement
9.3.1. Noise Cancellation
9.3.2. System Modeling
9.3.3. Line Enhancement Using Linear Prediction
9.4. Other Application Examples
9.4.1. Canceling Periodic Interferences Using Linear Prediction
9.4.2. Electrocardiography Interference Cancellation
9.4.3. Echo Cancellation in Long-Distance Telephone Circuits
9.5. Summary
9.6. Problems
Chapter 10: Waveform Quantization and Compression
10.1. Linear Midtread Quantization
10.2. μ-Law Companding
10.2.1. Analog μ-Law Companding
10.2.2. Digital μ-Law Companding
10.3. Examples of Differential Pulse Code Modulation (DPCM), Delta Modulation, and Adaptive DPCM G.7
10.3.1. Examples of DPCM and Delta Modulation
10.3.2. Adaptive Differential Pulse Code Modulation G.721
10.4. Discrete Cosine Transform, Modified Discrete Cosine Transform, and Transform Coding in MPEG Au
10.4.1. Discrete Cosine Transform
10.4.2. Modified Discrete Cosine Transform
10.4.3. Transform Coding in MPEG Audio
10.5. Summary
10.6. MATLAB Programs
10.7. Problems
Chapter 11: Multirate Digital Signal Processing, Oversampling of Analog-to-Digital Conversion, and U
11.1. Multirate Digital Signal Processing Basics
11.1.1. Sampling Rate Reduction by an Integer Factor
11.1.2. Sampling Rate Increase by an Integer Factor
11.1.3. Changing Sampling Rate by a Non-Integer Factor L/M
11.1.4. Application: CD Audio Player
11.1.5. Multistage Decimation
11.2. Polyphase Filter Structure and Implementation
11.3. Oversampling of Analog-To-Digital Conversion
11.3.1. Oversampling and ADC Resolution
11.3.2. Sigma-Delta Modulation ADC
11.4. Application Example: CD Player
11.5. Undersampling of Bandpass Signals
11.6. Summary
11.7. Problems
11.8. MATLAB Problems
MATLAB Project
Chapter 12: Subband and Wavelet-Based Coding
12.1. Subband Coding Basics
12.2. Subband Decomposition and Two-Channel Perfect Reconstruction-Quadrature Mirror Filter Bank
12.3. Subband Coding of Signals
12.4. Wavelet Basics and Families of Wavelets
12.5. Multiresolution Equations
12.6. Discrete Wavelet Transform
12.7. Wavelet Transform Coding of Signals
12.8. MATLAB Programs
12.9. Summary
12.10. Problems
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. RGB 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
13.7.4. Image Compression Using Wavelet Transform Coding
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
Chapter 14: Hardware and Software for Digital Signal Processors
14.1. Digital Signal Processor Architecture
14.2. DSP Hardware Units
14.2.1. Multiplier and Accumulator
14.2.2. Shifters
14.2.3. Address Generators
14.3. DSPs and Manufactures
14.4. Fixed-Point and Floating-Point Formats
14.4.1. Fixed-Point Format
14.4.2. Floating-Point Format
14.4.3. IEEE Floating-Point Formats
14.4.4. Fixed-Point DSPs
14.4.5. Floating-Point DSPs
14.5. Finite Impulse Response and Infinite Impulse Response Filter Implementations in Fixed-Point Sy
14.6. Digital Signal Processing Programming Examples
14.6.1. Overview of TMS320C67x DSK
14.6.2. Concept of Real-Time Processing
14.6.3. Linear Buffering
14.6.4. Sample C Programs
14.7. Additional Real-Time DSP Examples
14.7.1. Adaptive Filtering Using the TMS320C6713 DSK
14.7.2. Signal Quantization Using the TMS320C6713 DSK
14.7.3. Sampling Rate Conversion Using the TMS320C6713 DSK
14.8. Summary
14.9. Problems

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