Digital Signal Processing - INAOE - P
Tan: Digital Signaling Processing Perlims Final Proof page 1 23.6.2007 3:30am Compositor Name: MRajaDigital SignalProcessing
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Tan: Digital Signaling Processing Perlims Final Proof page 3 23.6.2007 3:30am Compositor Name: MRajaDigital SignalProcessingFundamentals andApplicationsLi TanDeVry UniversityDecatur, GeorgiaAMSTERDAM BOSTON HEIDELBERG LONDONNEW YORK OXFORD PARIS SAN DIEGOSAN FRANCISCO SINGAPORE SYDNEY TOKYOAcademic Press is an imprint of Elsevier
Tan: Digital Signaling Processing Perlims Final Proof page 4 23.6.2007 3:30am Compositor Name: MRajaAcademic Press is an imprint of Elsevier30 Corporate Drive, Suite 400, Burlington, MA 01803, USA525 B Street, Suite 1900, San Diego, California 92101-4495, USA84 Theobald’s Road, London WC1X 8RR, UKThis book is printed on acid-free paper.1Copyright ß 2008, Elsevier Inc. All rights reserved.No part of this publication may be reproduced or transmitted in any form or by any means,electronic or mechanical, including photocopy, recording, or any information storage andretrieval system, without permission in writing from the publisher.Permissions may be sought directly from Elsevier’s Science & Technology RightsDepartment in Oxford, UK: phone: (þ44) 1865 843830, fax: (þ44) 1865 853333,E-mail: permissions@elsevier.com. You may also complete your request on-line viathe Elsevier homepage (http://elsevier.com), by selecting ‘‘Support & Contact’’ then‘‘Copyright and Permission’’ and then ‘‘Obtaining Permissions.’’Library of Congress Cataloging-in-Publication DataApplication submitted.British Library Cataloguing-in-Publication DataA catalogue record for this book is available from the British Library.ISBN: 978-0-12-374090-8For information on all Academic Press publicationsvisit our Web site at www.books.elsevier.comPrinted in the United States of America07 08 09 10 11 9 8 7 6 5 4 32 1
Tan: Digital Signaling Processing Perlims Final Proof page 5 23.6.2007 3:30am Compositor Name: MRajaContentsPrefaceAbout the Author123Introduction to Digital Signal Processing1.1 Basic Concepts of Digital Signal Processing1.2 Basic Digital Signal Processing Examples in Block Diagrams1.2.1 Digital Filtering1.2.2 Signal Frequency (Spectrum) Analysis1.3 Overview of Typical Digital Signal Processing in Real-WorldApplications1.3.1 Digital Crossover Audio System1.3.2 Interference Cancellation in Electrocardiography1.3.3 Speech Coding and Compression1.3.4 Compact-Disc Recording System1.3.5 Digital Photo Image Enhancement1.4 Digital Signal Processing Applications1.5 Summaryxiiixvii1133466779101112Signal Sampling and Quantization2.1 Sampling of Continuous Signal2.2 Signal Reconstruction2.2.1 Practical Considerations for Signal Sampling:Anti-Aliasing Filtering2.2.2 Practical Considerations for Signal Reconstruction:Anti-Image Filter and Equalizer2.3 Analog-to-Digital Conversion, Digital-to-AnalogConversion, and Quantization2.4 Summary2.5 MATLAB Programs2.6 Problems13132035495051Digital Signals and Systems3.1 Digital Signals3.1.1 Common Digital Sequences3.1.2 Generation of Digital Signals575758622529
Tan: Digital Signaling Processing Perlims Final Proof page 6 23.6.2007 3:30am Compositor Name: MRajaviC O N T E N T S3.23.33.43.53.63.7Linear Time-Invariant, Causal Systems3.2.1 Linearity3.2.2 Time Invariance3.2.3 CausalityDifference Equations and Impulse Responses3.3.1 Format of Difference Equation3.3.2 System Representation Using Its Impulse ResponseBounded-in-and-Bounded-out StabilityDigital iscrete Fourier Transform and Signal Spectrum4.1 Discrete Fourier Transform4.1.1 Fourier Series Coefficients of Periodic Digital Signals4.1.2 Discrete Fourier Transform Formulas4.2 Amplitude Spectrum and Power Spectrum4.3 Spectral Estimation Using Window Functions4.4 Application to Speech Spectral Estimation4.5 Fast Fourier Transform4.5.1 Method of Decimation-in-Frequency4.5.2 Method of Decimation-in-Time4.6 Summary4.7 .31351351391421481511551566Digital Signal Processing Systems, Basic Filtering Types,and Digital Filter Realizations1596.1 The Difference Equation and Digital Filtering1596.2 Difference Equation and Transfer Function1656.2.1 Impulse Response, Step Response, and System Response 1696.3 The z-Plane Pole-Zero Plot and Stability1716.4 Digital Filter Frequency Response1796.5 Basic Types of Filtering188z-TransformDefinitionProperties of the z-TransformInverse z-Transform5.3.1 Partial Fraction Expansion Using MATLAB5.4 Solution of Difference Equations Using the z-Transform5.5 Summary5.6 Problems
Tan: Digital Signaling Processing Perlims Final Proof page 7 23.6.2007 3:30am Compositor Name: MRajaC O N T E N T S6.66.76.86.978Realization of Digital Filters6.6.1 Direct-Form I Realization6.6.2 Direct-Form II Realization6.6.3 Cascade (Series) Realization6.6.4 Parallel RealizationApplication: Speech Enhancement and Filtering6.7.1 Pre-Emphasis of Speech6.7.2 Bandpass Filtering of 8209Finite Impulse Response Filter Design7.1 Finite Impulse Response Filter Format7.2 Fourier Transform Design7.3 Window Method7.4 Applications: Noise Reduction andTwo-Band Digital Crossover7.4.1 Noise Reduction7.4.2 Speech Noise Reduction7.4.3 Two-Band Digital Crossover7.5 Frequency Sampling Design Method7.6 Optimal Design Method7.7 Realization Structures of Finite Impulse Response Filters7.7.1 Transversal Form7.7.2 Linear Phase Form7.8 Coefficient Accuracy Effects on Finite Impulse Response Filters7.9 Summary of Finite Impulse Response (FIR) Design Proceduresand Selection of FIR Filter Design Methods in Practice7.10 Summary7.11 MATLAB Programs7.12 Infinite Impulse Response Filter Design8.1 Infinite Impulse Response Filter Format8.2 Bilinear Transformation Design Method8.2.1 Analog Filters Using Lowpass Prototype Transformation8.2.2 Bilinear Transformation and Frequency Warping8.2.3 Bilinear Transformation Design Procedure8.3 Digital Butterworth and Chebyshev Filter Designs8.3.1 Lowpass Prototype Function and Its Order8.3.2 Lowpass and Highpass Filter Design Examples8.3.3 Bandpass and Bandstop Filter Design Examples303303305306310317322322326336287290291294
Tan: Digital Signaling Processing Perlims Final Proof page 8 23.6.2007 3:30am Compositor Name: MRajaviiiC O N T E N T er Infinite Impulse Response Filter DesignUsing the Cascade MethodApplication: Digital Audio EqualizerImpulse Invariant Design MethodPolo-Zero Placement Method for Simple Infinite ImpulseResponse Filters8.7.1 Second-Order Bandpass Filter Design8.7.2 Second-Order Bandstop (Notch) Filter Design8.7.3 First-Order Lowpass Filter Design8.7.4 First-Order Highpass Filter DesignRealization Structures of Infinite Impulse Response Filters8.8.1 Realization of Infinite Impulse Response Filters inDirect-Form I and Direct-Form II8.8.2 Realization of Higher-Order Infinite ImpulseResponse Filters via the Cascade FormApplication: 60-Hz Hum Eliminator and Heart RateDetection Using ElectrocardiographyCoefficient Accuracy Effects on Infinite ImpulseResponse FiltersApplication: Generation and Detection of Dual-ToneMultifrequency Tones Using Goertzel Algorithm8.11.1 Single-Tone Generator8.11.2 Dual-Tone Multifrequency Tone Generator8.11.3 Goertzel Algorithm8.11.4 Dual-Tone Multifrequency Tone DetectionUsing the Modified Goertzel AlgorithmSummary of Infinite Impulse Response (IIR) DesignProcedures and Selection of the IIR Filter Design Methodsin PracticeSummaryProblemsHardware and Software for Digital Signal Processors9.1 Digital Signal Processor Architecture9.2 Digital Signal Processor Hardware Units9.2.1 Multiplier and Accumulator9.2.2 Shifters9.2.3 Address Generators9.3 Digital Signal Processors and Manufactures9.4 Fixed-Point and Floating-Point Formats9.4.1 Fixed-Point Format9.4.2 Floating-Point Format9.4.3 IEEE Floating-Point 434
Tan: Digital Signaling Processing Perlims Final Proof page 9 23.6.2007 3:30am Compositor Name: MRajaC O N T E N T S9.59.69.79.89.4.5 Fixed-Point Digital Signal Processors9.4.6 Floating-Point ProcessorsFinite Impulse Response and Infinite Impulse ResponseFilter Implementation in Fixed-Point SystemsDigital Signal Processing Programming Examples9.6.1 Overview of TMS320C67x DSK9.6.2 Concept of Real-Time Processing9.6.3 Linear Buffering9.6.4 Sample C ProgramsSummaryProblems10 Adaptive Filters and Applications10.1 Introduction to Least Mean Square Adaptive FiniteImpulse Response Filters10.2 Basic Wiener Filter Theory and Least Mean Square Algorithm10.3 Applications: Noise Cancellation, System Modeling,and Line Enhancement10.3.1 Noise Cancellation10.3.2 System Modeling10.3.3 Line Enhancement Using Linear Prediction10.4 Other Application Examples10.4.1 Canceling Periodic Interferences UsingLinear Prediction10.4.2 Electrocardiography Interference Cancellation10.4.3 Echo Cancellation in Long-DistanceTelephone Circuits10.5 Summary10.6 Problems11 Waveform Quantization and Compression11.1 Linear Midtread Quantization11.2 m-law Companding11.2.1 Analog m-Law Companding11.2.2 Digital m-Law Companding11.3 Examples of Differential Pulse Code Modulation (DPCM),Delta Modulation, and Adaptive DPCM G.72111.3.1 Examples of Differential Pulse Code Modulationand Delta Modulation11.3.2 Adaptive Differential Pulse Code Modulation G.72111.4 Discrete Cosine Transform, Modified Discrete CosineTransform, and Transform Coding in MPEG Audio11.4.1 Discrete Cosine 0515522522
Tan: Digital Signaling Processing Perlims Final Proof page 10x23.6.2007 3:30am Compositor Name: MRajaC O N T E N T S11.4.2 Modified Discrete Cosine Transform11.4.3 Transform Coding in MPEG Audio11.5 Summary11.6 MATLAB Programs11.7 Problems12 Multirate Digital Signal Processing, Oversampling ofAnalog-to-Digital Conversion, and Undersampling ofBandpass Signals12.1 Multirate Digital Signal Processing Basics12.1.1 Sampling Rate Reduction by an Integer Factor12.1.2 Sampling Rate Increase by an Integer Factor12.1.3 Changing Sampling Rate by a Non-Integer Factor L/M12.1.4 Application: CD Audio Player12.1.5 Multistage Decimation12.2 Polyphase Filter Structure and Implementation12.3 Oversampling of Analog-to-Digital Conversion12.3.1 Oversampling and Analog-to-Digital ConversionResolution12.3.2 Sigma-Delta Modulation Analog-to-Digital Conversion12.4 Application Example: CD Player12.5 Undersampling of Bandpass Signals12.6 Summary12.7 Problems13 Image Processing Basics13.1 Image Processing Notation and Data Formats13.1.1 8-Bit Gray Level Images13.1.2 24-Bit Color Images13.1.3 8-Bit Color Images13.1.4 Intensity Images13.1.5 Red, Green, Blue Components and GrayscaleConversion13.1.6 MATLAB Functions for Format Conversion13.2 Image Histogram and Equalization13.2.1 Grayscale Histogram and Equalization13.2.2 24-Bit Color Image Equalization13.2.3 8-Bit Indexed Color Image Equalization13.2.4 MATLAB Functions for Equalization13.3 Image Level Adjustment and Contrast13.3.1 Linear Level Adjustment13.3.2 Adjusting the Level for 633636637638641
Tan: Digital Signaling Processing Perlims Final Proof page 1123.6.2007 3:30am Compositor Name: MRajaC O N T E N T S13.3.3 Matlab Functions for Image Level AdjustmentImage Filtering Enhancement13.4.1 Lowpass Noise Filtering13.4.2 Median Filtering13.4.3 Edge Detection13.4.4 MATLAB Functions for Image Filtering13.5Image Pseudo-Color Generation and Detection13.6Image Spectra13.7Image Compression by Discrete Cosine Transform13.7.1 Two-Dimensional Discrete Cosine Transform13.7.2 Two-Dimensional JPEG Grayscale ImageCompression Example13.7.3 JPEG Color Image Compression13.8Creating a Video Sequence by Mixing Two Images13.9Video Signal Basics13.9.1 Analog Video13.9.2 Digital Video13.10 Motion Estimation in Video13.11 Summary13.12 Problems13.4AppendixA.1A.2A.3A.4A.5A Introduction to the MATLAB EnvironmentBasic Commands and SyntaxMATLAB Array and IndexingPlot Utilities: Subplot, Plot, Stem, and StairMATLAB Script FilesMATLAB FunctionsAppendix B Review of Analog Signal Processing BasicsB.1Fourier Series and Fourier TransformB.1.1 Sine-Cosine FormB.1.2 Amplitude-Phase FormB.1.3 Complex Exponential FormB.1.4 Spectral PlotsB.1.5 Fourier TransformB.2Laplace TransformB.2.1 Laplace Transform and Its TableB.2.2 Solving Differential Equations UsingLaplace TransformB.2.3 Transfer FunctionB.3Poles, Zeros, Stability, Convolution, and SinusoidalSteady-State 714721726726727730731
Tan: Digital Signaling Processing Perlims Final Proof page 12xii23.6.2007 3:30am Compositor Name: MRajaC O N T E N T SB.4B.3.1 Poles, Zeros, and StabilityB.3.2 ConvolutionB.3.3 Sinusoidal Steady-State ResponseProblems731733735736Appendix C Normalized Butterworth and Chebyshev FucntionsC.1Normalized Butterworth FunctionC.2Normalized Chebyshev Function741741744Appendix D Sinusoidal Steady-State Response of Digital FiltersD.1 Sinusoidal Steady-State ResponseD.2 Properties of the Sinusoidal Steady-State Response749749751Appendix E Finite Impulse Response Filter Design Equations by theFrequency Sampling Design Method753Appendix F Some Useful Mathematical Formulas757Bibliography761Answers to Selected Problems765Index791
Tan: Digital Signaling Processing Perlims Final Proof page 1323.6.2007 3:30am Compositor Name: MRajaPrefaceTechnologies such as microprocessors, microcontrollers, and digital signal processors have become so advanced that they have had a dramatic impact on thedisciplines of electronics engineering, computer engineering, and biomedicalengineering. Technologists need to become familiar with digital signals andsystems and basic digital signal processing (DSP) techniques. The objective ofthis book is to introduce students to the fundamental principles of these subjectsand to provide a working knowledge such that they can apply DSP in theirengineering careers.The book is suitable for a sequence of two-semester courses at the senior levelin undergraduate electronics, computer, and biomedical engineering technologyprograms. Chapters 1 to 8 provide the topics for a one semester course, and asecond course can complete the rest of the chapters. This textbook can also beused in an introductory DSP course at the junior level in undergraduate electrical engineering programs at traditional colleges. Additionally, the bookshould be useful as a reference for undergraduate engineering students, sciencestudents, and practicing engineers.The material has been tested in two consecutive courses in signal processingsequence at DeVry University on the Decatur campus in Georgia. With thebackground established from this book, students can be well prepared to moveforward to take other senior-level courses that deal with digital signals andsystems for communications and controls.The textbook consists of 13 chapters, organized as follows:&Chapter 1 introduces concepts of DSP and presents a general DSP blockdiagram. Application examples are included.&Chapter 2 covers the sampling theorem described in time domain andfrequency domain and also covers signal reconstruction. Some practicalconsiderations for designing analog anti-aliasing lowpass filters and antiimage lowpass filters are included. The chapter ends with a section dealingwith analog-to-digital conversion (ADC) and digital-to-analog conversion(DAC), as well as signal quantization and encoding.&Chapter 3 introduces digital signals, linear time-invariant system concepts,difference equations, and digital convolutions.
Tan: Digital Signaling Processing Perlims Final Proof page 14xiv23.6.2007 3:30am Compositor Name: MRajaP R E F A C E&Chapter 4 introduces the discrete Fourier transform (DFT) and digitalsignal spectral calculations using the DFT. Applying the DFT to estimatethe speech spectrum is demonstrated. The chapter ends with a sectiondedicated to illustrating fast Fourier transform (FFT) algorithms.&Chapter 5 is devoted to the z-transform and difference equations.&Chapter 6 covers digital filtering using difference equations, transfer functions, system stability, digital filter frequency responses, and implementation methods such as the direct form I and direct form II.&Chapter 7 deals with various methods of finite impulse response (FIR)filter design, including the Fourier transform method for calculating FIRfilter coefficients, window method, frequency sampling design, and optimaldesign. Chapter 7 also includes applications using FIR filters for noisereduction and digital crossover system design.&Chapter 8 covers various methods of infinite impulse response (IIR) filterdesign, including the bilinear transformation (BLT) design, impulse invariant design, and pole-zero placement design. Applications using IIR filtersinclude audio equalizer design, biomedical signal enhancement, dual-tonemultifrequency (DTMF) tone generation and detection with the Goertzelalgorithm.&Chapter 9 introduces DSP architectures, software and hardware, andfixed-point and floating-point implementations of digital filters.&Chapter 10 covers adaptive filters with applications such as noise cancellation, system modeling, line enhancement, cancellation of periodic interferences, echo cancellation, and 60-Hz interference cancellation inbiomedical signals.&Chapter 11 is devoted to speech quantization and compression, includingpulse code modulation (PCM) coding, mu-law compression, adaptive differential pulse code modulation (ADPCM) coding, windowed modifieddiscrete cosine transform (W-MDCT) coding, and MPEG audio format,specifically MP3 (MPEG-1, layer 3).&Chapter 12 covers topics pertaining to multirate DSP and applications, aswell as principles of oversampling ADC, such as sigma-delta modulation.Undersampling for bandpass signals is also examined.&Finally, Chapter 13 covers image enhancement using histogram equalization and filtering methods, including edge detection. The chapter alsoexplores pseudo-color image generation and detection, two-dimensionalspectra, JPEG compression using DCT, and the mixing of two images to
Tan: Digital Signaling Processing Perlims Final Proof page 1523.6.2007 3:30am Compositor Name: MRajaP R E F A C Exvcreate a video sequence. Finally, motion compensation of the video sequenceis explored, which is a key element of video compression used in MPEG.MATLAB programs are listed wherever they are possible. Therefore, aMATLAB tutorial should be given to students who are new to the MATLABenvironment.&Appendix A serves as a MATLAB tutorial.&Appendix B reviews key fundamentals of analog signal processing. Topicsinclude Fourier series, Fourier transform, Laplace transform, and analogsystem basics.&Appendixes C, D, and E overview Butterworth and Chebyshev filters,sinusoidal steady-state responses in digital filters, and derivation of theFIR filter design equation via the frequency sampling method, respectively.&Appendix F offers general useful mathematical formulas.Instructor support, including solutions, can be found at http://textbooks.elsevier.com. MATLAB programs and exercises for students, plus Realtime C programs, can be found at .The author wishes to thank Dr. Samuel D. Stearns (professor at the University of New Mexico; Sandia National Laboratories, Albuquerque, NM),Dr. Delores M. Etler (professor at the United States Naval Academy atAnnapolis, MD) and Dr. Neeraj Magotra (Texas Instruments, former professorat the University of N
1.2.1 Digital Filtering 3 1.2.2 Signal Frequency (Spectrum) Analysis 4 1.3 Overview of Typical Digital Signal Processing in Real-World Applications 6 1.3.1 Digital Crossover Audio System 6 1.3.2 Interference Cancellation in Electrocardiography 7 1.3.3 Speech Coding and Compression 7 1.3.4 Compact-Disc Recording System 9 1.3.5 Digital Photo .
most of the digital signal processing concepts have benn well developed for a long time, digital signal processing is still a relatively new methodology. Many digital signal processing concepts were derived from the analog signal processing field, so you will find a lot o f similarities between the digital and analog signal processing.
A DSP System A/D DSP D/A Analog signal Analog signal Sampled data signal Analog signal Cts-time dst-amp staricase signal Digital signal Digital signal DSP System Antialiasing Filter Sample and Hold Reconstruction Filter A/D: Iconverts a sampled data signal value into a digital number, in part, through quantization of the amplitude
Modulation onto an analog signal m(t) baseband signal or modulating signal fc carrier signal s(t) modulated signal. Chap. 4 Data Encoding 2 1. Digital Data Digital Signals A digital signal is a sequence of discrete, dis
The 1980s also saw the introduction of the first Digital Signal Processors. Introduced in 1983 by Texas Instruments, the TMS320C10 was a microprocessor specifically designed to solve digital signal processing problems. Prior to its release, signal processing was mostly the domain of analog electronics. Digital signal processing
3. Digital Signal Processing – Monson H.Hayes – Schaum’s Outlines, McGraw-Hill,1999 4. Fundamentals of Digital Signal Processing using Matlab – Robert J Schilling, Sandra L Harris, Thomson 2007. 5. Digital Signal processing – A Practical Approach, Emmanue
DSP systems for real time ECG signal processing. In this design, high-speed floating point digital signal processor TMS320C6711 and TLC320AD535 dualchannel voice/data codec based DSP starter kit (DSK) was employed for processing the ECG. Electrocardiogram (ECG) signal frequency range varies between 0 Hz300 Hz and most -
That leaves signal 5 and DFT 8. Signal 5 can be written as a cosine times a rectangular pulse, so the DFT of signal 5 will be the convolution of a DFT of a cosine with the DFT of rectangular pulse — that is a sum of two shifted digital sinc functions. Signal DFT 1 4 2 6 3 1 4 2 5 8 6 7 7 3 8 5 18 EL 713: Digital Signal Processing .
small-group learning that incorporates a wide range of formal and informal instructional methods in which students interactively work together in small groups toward a common goal (Roseth, Garfield, and Ben-Zvi 2008; Springer, et al. 1999).
