Digital Signal Processing: Fundamentals and Applications
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Table of contents
- Cover
- Contentsv
- Prefacexiii
- About the Authorxvii
- Chapter 1: Introduction to Digital Signal Processing1
- Objectives1
- 1.1 Basic Concepts of Digital Signal Processing1
- 1.2 Basic Digital Signal Processing Examples in Block Diagrams3
- 1.2.1 Digital Filtering3
- 1.2.2 Signal Frequency (Spectrum) Analysis4
- 1.3 Overview of Typical Digital Signal Processing in Real-World Applications6
- 1.3.1 Digital Crossover Audio System6
- 1.3.2 Interference Cancellation in Electrocardiography7
- 1.3.3 Speech Coding and Compression7
- 1.3.4 Compact-Disc Recording System9
- 1.3.5 Digital Photo Image Enhancement10
- 1.4 Digital Signal Processing Applications11
- 1.5 Summary12
- References12
- Chapter 2: Signal Sampling and Quantization13
- Objectives13
- 2.1 Sampling of Continuous Signal13
- 2.2 Signal Reconstruction20
- 2.2.1 Practical Considerations for Signal Sampling: Anti-Aliasing Filtering25
- 2.2.2 Practical Considerations for Signal Reconstruction: Anti-Image Filter and Equalizer29
- 2.3 Analog-to-Digital Conversion, Digital-to-Analog Conversion, and Quantization35
- 2.4 Summary49
- 2.5 MATLAB Programs50
- 2.6 Problems51
- References56
- Chapter 3: Digital Signals and Systems57
- Objectives57
- 3.1 Digital Signals57
- 3.1.1 Common Digital Sequences58
- 3.1.2 Generation of Digital Signals62
- 3.2 Linear Time-Invariant, Causal Systems64
- 3.2.1 Linearity64
- 3.2.2 Time Invariance65
- 3.2.3 Causality67
- 3.3 Difference Equations and Impulse Responses68
- 3.3.1 Format of Difference Equation68
- 3.3.2 System Representation Using Its Impulse Response69
- 3.4 Bounded-in-and-Bounded-out Stability72
- 3.5 Digital Convolution74
- 3.6 Summary82
- 3.7 Problems83
- Chapter 4: Discrete Fourier Transform and Signal Spectrum87
- Objectives87
- 4.1 Discrete Fourier Transform87
- 4.1.1 Fourier Series Coefficients of Periodic Digital Signals88
- 4.1.2 Discrete Fourier Transform Formulas92
- 4.2 Amplitude Spectrum and Power Spectrum98
- 4.3 Spectral Estimation Using Window Functions110
- 4.4 Application to Speech Spectral Estimation120
- 4.5 Fast Fourier Transform120
- 4.5.1 Method of Decimation-in-Frequency122
- 4.5.2 Method of Decimation-in-Time127
- 4.6 Summary131
- 4.7 Problems131
- References134
- Chapter 5: The z-Transform135
- Objectives135
- 5.1 Definition135
- 5.2 Properties of the z-Transform139
- 5.3 Inverse z-Transform142
- 5.3.1 Partial Fraction Expansion Using MATLAB148
- 5.4 Solution of Difference Equations Using the z-Transform151
- 5.5 Summary155
- 5.6 Problems156
- Reference158
- Chapter 6: Digital Signal Processing Systems, Basic Filtering Types, and Digital Filter Realizations159
- Objectives159
- 6.1 The Difference Equation and Digital Filtering159
- 6.2 Difference Equation and Transfer Function165
- 6.2.1 Impulse Response, Step Response, and System Response169
- 6.3 The z-Plane Pole-Zero Plot and Stability171
- 6.4 Digital Filter Frequency Response179
- 6.5 Basic Types of Filtering188
- 6.6 Realization of Digital Filters195
- 6.6.1 Direct-Form I Realization195
- 6.6.2 Direct-Form II Realization196
- 6.6.3 Cascade (Series) Realization197
- 6.6.4 Parallel Realization198
- 6.7 Application: Speech Enhancement and Filtering202
- 6.7.1 Pre-Emphasis of Speech202
- 6.7.2 Bandpass Filtering of Speech205
- 6.8 Summary208
- 6.9 Problems209
- Reference214
- Chapter 7: Finite Impulse Response Filter Design215
- Objectives215
- 7.1 Finite Impulse Response Filter Format215
- 7.2 Fourier Transform Design217
- 7.3 Window Method229
- 7.4 Applications: Noise Reduction and Two-Band Digital Crossover253
- 7.4.1 Noise Reduction253
- 7.4.2 Speech Noise Reduction255
- 7.4.3 Two-Band Digital Crossover256
- 7.5 Frequency Sampling Design Method260
- 7.6 Optimal Design Method268
- 7.7 Realization Structures of Finite Impulse Response Filters280
- 7.7.1 Transversal Form280
- 7.7.2 Linear Phase Form282
- 7.8 Coefficient Accuracy Effects on Finite Impulse Response Filters283
- 7.9 Summary of Finite Impulse Response (FIR) Design Procedures and Selection of FIR Filter Design Me287
- 7.10 Summary290
- 7.11 MATLAB Programs291
- 7.12 Problems294
- References301
- Chapter 8: Infinite Impulse Response Filter Design303
- Objectives303
- 8.1 Infinite Impulse Response Filter Format303
- 8.2 Bilinear Transformation Design Method305
- 8.2.1 Analog Filters Using Lowpass Prototype Transformation306
- 8.2.2 Bilinear Transformation and Frequency Warping310
- 8.2.3 Bilinear Transformation Design Procedure317
- 8.3 Digital Butterworth and Chebyshev Filter Designs322
- 8.3.1 Lowpass Prototype Function and Its Order322
- 8.3.2 Lowpass and Highpass Filter Design Examples326
- 8.3.3 Bandpass and Bandstop Filter Design Examples336
- 8.4 Higher-Order Infinite Impulse Response Filter Design Using the Cascade Method343
- 8.5 Application: Digital Audio Equalizer346
- 8.6 Impulse Invariant Design Method350
- 8.7 Polo-Zero Placement Method for Simple Infinite Impulse Response Filters358
- 8.7.1 Second-Order Bandpass Filter Design359
- 8.7.2 Second-Order Bandstop (Notch) Filter Design360
- 8.7.3 First-Order Lowpass Filter Design362
- 8.7.4 First-Order Highpass Filter Design364
- 8.8 Realization Structures of Infinite Impulse Response Filters365
- 8.8.1 Realization of Infinite Impulse Response Filters in Direct-Form I and Direct-Form II366
- 8.8.2 Realization of Higher-Order Infinite Impulse Response Filters via the Cascade Form368
- 8.9 Application: 60-Hz Hum Eliminator and Heart Rate Detection Using Electrocardiography370
- 8.10 Coefficient Accuracy Effects on Infinite Impulse Response Filters377
- 8.11 Application: Generation and Detection of Dual-Tone Multifrequency Tones Using Goertzel Algorith381
- 8.11.1 Single-Tone Generator382
- 8.11.2 Dual-Tone Multifrequency Tone Generator384
- 8.11.3 Goertzel Algorithm386
- 8.11.4 Dual-Tone Multifrequency Tone Detection Using the Modified Goertzel Algorithm391
- 8.12 Summary of Infinite Impulse Response (IIR) Design Procedures and Selection of the IIR Filter De396
- 8.13 Summary401
- 8.14 Problems402
- References412
- Chapter 9: Hardware and Software for Digital Signal Processors413
- Objectives413
- 9.1 Digital Signal Processor Architecture413
- 9.2 Digital Signal Processor Hardware Units416
- 9.2.1 Multiplier and Accumulator416
- 9.2.2 Shifters417
- 9.2.3 Address Generators418
- 9.3 Digital Signal Processors and Manufactures419
- 9.4 Fixed-Point and Floating-Point Formats420
- 9.4.1 Fixed-Point Format420
- 9.4.2 Floating-Point Format429
- 9.4.3 IEEE Floating-Point Formats434
- 9.4.5 Fixed-Point Digital Signal Processors437
- 9.4.6 Floating-Point Processors439
- 9.5 Finite Impulse Response and Infinite Impulse Response Filter Implementation in Fixed-Point Syste441
- 9.6 Digital Signal Processing Programming Examples447
- 9.6.1 Overview of TMS320C67x DSK447
- 9.6.2 Concept of Real-Time Processing451
- 9.6.3 Linear Buffering452
- 9.6.4 Sample C Programs455
- 9.7 Summary460
- 9.8 Problems461
- References462
- Chapter 10: Adaptive Filters and Applications463
- Objectives463
- 10.1 Introduction to Least Mean Square Adaptive Finite Impulse Response Filters463
- 10.2 Basic Wiener Filter Theory and Least Mean Square Algorithm467
- 10.3 Applications: Noise Cancellation, System Modeling, and Line Enhancement473
- 10.3.1 Noise Cancellation473
- 10.3.2 System Modeling479
- 10.3.3 Line Enhancement Using Linear Prediction484
- 10.4 Other Application Examples486
- 10.4.1 Canceling Periodic Interferences Using Linear Prediction487
- 10.4.2 Electrocardiography Interference Cancellation488
- 10.4.3 Echo Cancellation in Long-Distance Telephone Circuits489
- 10.5 Summary491
- 10.6 Problems491
- References496
- Chapter 11: Waveform Quantization and Compression497
- Objectives497
- 11.1 Linear Midtread Quantization497
- 11.2 mu-law Companding501
- 11.2.1 Analog mu-Law Companding501
- 11.2.2 Digital mu-Law Companding506
- 11.3 Examples of Differential Pulse Code Modulation (DPCM), Delta Modulation, and Adaptive DPCM G.72510
- 11.3.1 Examples of Differential Pulse Code Modulation and Delta Modulation510
- 11.3.2 Adaptive Differential Pulse Code Modulation G.721515
- 11.4 Discrete Cosine Transform, Modified Discrete Cosine Transform, and Transform Coding in MPEG Aud522
- 11.4.1 Discrete Cosine Transform522
- 11.4.2 Modified Discrete Cosine Transform525
- 11.4.3 Transform Coding in MPEG Audio530
- 11.5 Summary533
- 11.6 MATLAB Programs534
- 11.7 Problems550
- References555
- Chapter 12: Multirate Digital Signal Processing, Oversampling of Analog-to-Digital Conversion, and U557
- Objectives557
- 12.1 Multirate Digital Signal Processing Basics557
- 12.1.1 Sampling Rate Reduction by an Integer Factor558
- 12.1.2 Sampling Rate Increase by an Integer Factor564
- 12.1.3 Changing Sampling Rate by a Non-Integer Factor L/M570
- 12.1.4 Application: CD Audio Player575
- 12.1.5 Multistage Decimation578
- 12.2 Polyphase Filter Structure and Implementation583
- 12.3 Oversampling of Analog-to-Digital Conversion589
- 12.3.1 Oversampling and Analog-to-Digital Conversion Resolution590
- 12.3.2 Sigma-DeltaModulation Analog-to-Digital Conversion593
- 12.4 Application Example: CD Player599
- 12.5 Undersampling of Bandpass Signals601
- 12.6 Summary609
- 12.7 Problems610
- References614
- Chapter 13: Image Processing Basics617
- 13.1 Image Processing Notation and Data Formats617
- 13.1.1 8-Bit Gray Level Images618
- 13.1.2 24-Bit Color Images619
- 13.1.3 8-Bit Color Images620
- 13.1.4 Intensity Images621
- 13.1.5 Red, Green, Blue Components and Grayscale Conversion622
- 13.1.6 MATLAB Functions for Format Conversion624
- 13.2 Image Histogram and Equalization625
- 13.2.1 Grayscale Histogram and Equalization625
- 13.2.2 24-Bit Color Image Equalization632
- 13.2.3 8-Bit Indexed Color Image Equalization633
- 13.2.4 MATLAB Functions for Equalization636
- 13.3 Image Level Adjustment and Contrast637
- 13.3.1 Linear Level Adjustment638
- 13.3.2 Adjusting the Level for Display641
- 13.3.3 Matlab Functions for Image Level Adjustment642
- 13.4 Image Filtering Enhancement642
- 13.4.1 Lowpass Noise Filtering643
- 13.4.2 Median Filtering646
- 13.4.3 Edge Detection651
- 13.4.4 MATLAB Functions for Image Filtering655
- 13.5 Image Pseudo-Color Generation and Detection657
- 13.6 Image Spectra661
- 13.7 Image Compression by Discrete Cosine Transform664
- 13.7.1 Two-Dimensional Discrete Cosine Transform666
- 13.7.2 Two-Dimensional JPEG Grayscale Image Compression Example669
- 13.7.3 JPEG Color Image Compression671
- 13.8 Creating a Video Sequence by Mixing Two Images677
- 13.9 Video Signal Basics677
- 13.9.1 Analog Video678
- 13.9.2 Digital Video685
- 13.10 Motion Estimation in Video687
- 13.11 Summary690
- 13.12 Problems692
- References698
- Appendix A: Introduction to the MATLAB Environment699
- A.1 Basic Commands and Syntax699
- A.2 MATLAB Array and Indexing703
- A.3 Plot Utilities: Subplot, Plot, Stem, and Stair704
- A.4 MATLAB Script Files704
- A.5 MATLAB Functions705
- References707
- Appendix B: Review of Analog Signal Processing709
- B.1 Fourier Series and Fourier Transform709
- B.1.1 Sine-Cosine Form709
- B.1.2 Amplitude-Phase Form710
- B.1.3 Complex Exponential Form711
- B.1.4 Spectral Plots714
- B.1.5 Fourier Transform721
- B.2 Laplace Transform726
- B.2.1 Laplace Transform and Its Table726
- B.2.2 Solving Differential Equations Using Laplace Transform727
- B.2.3 Transfer Function730
- B.3 Poles, Zeros, Stability, Convolution, and Sinusoidal Steady-State Response731
- B.3.1 Poles, Zeros, and Stability731
- B.3.2 Convolution733
- B.3.3 Sinusoidal Steady-State Response735
- B.4 Problems736
- References740
- Appendix C: Normalized Butterworth and Chebyshev Fucntions741
- C.1 Normalized Butterworth Function741
- C.2 Normalized Chebyshev Function744
- Appendix D: Sinusoidal Steady-State Response of Digital Filters749
- D.1 Sinusoidal Steady-State Response749
- D.2 Properties of the Sinusoidal Steady-State Response751
- Appendix E: Finite Impulse Response Filter Design Equations by the Frequency Sampling Design Method753
- Appendix F: Some Useful Mathematical Formulas757
- Bibliography761
- Answers to Selected Problems765
- Index791
- Color Plates817
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
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