Digital signal processing (DSP) Books
Pearson Education Signals and Systems
Book SynopsisTable of Contents 1. Signals and Systems. 2. Linear Time-Invariant Systems. 3. Fourier Series Representation of Periodic Signals. 4. The Continuous-Time Fourier Transform. 5. The Discrete-Time Fourier Transform. 6. Time- and Frequency Characterization of Signals and Systems. 7. Sampling. 8. Communication Systems. 9. The Laplace Transform. 10. The Z-Transform. 11. Linear Feedback Systems. Bibliography. Answers. Index.
£77.99
Cambridge University Press Practical Smoothing
Book SynopsisThis is a practical guide to P-splines, a simple, flexible and powerful tool for smoothing. P-splines combine regression on B-splines with simple, discrete, roughness penalties. They were introduced by the authors in 1996 and have been used in many diverse applications. The regression basis makes it straightforward to handle non-normal data, like in generalized linear models. The authors demonstrate optimal smoothing, using mixed model technology and Bayesian estimation, in addition to classical tools like cross-validation and AIC, covering theory and applications with code in R. Going far beyond simple smoothing, they also show how to use P-splines for regression on signals, varying-coefficient models, quantile and expectile smoothing, and composite links for grouped data. Penalties are the crucial elements of P-splines; with proper modifications they can handle periodic and circular data as well as shape constraints. Combining penalties with tensor products of B-splines extends theseTrade Review'The title says it all. This is a practical book which shows how P-splines are used in an astonishingly wide range of settings. If you use P-splines already the book is indispensable; if you don't, then reading it will convince you it's time to start. Every example comes with an R-program available on the book's web-site, an important feature for the experienced user and novice alike.' Iain Currie, Heriot-Watt University'This book is an enlightening and at the same time extremely enjoyable read. It will serve the applied statistician who is looking for practical solutions but also the connoisseur in search of elegant concepts. The accompanying website offers reproducible code and invites to promptly enter the fascinating universe of P-splines.' Jutta Gampe, Max Planck Institute for Demographic Research'Everything you always wanted to know about P-splines, from the inventors themselves. Paul H.C. Eilers and Brian D. Marx make a compelling case for their claim that P-splines are the best practical smoother out there, providing intuition, methodology, applications, and R code that clearly demonstrate the power, flexibility, and wide applicability of this approach to smoothing.' Jeffrey Simonoff, New York University'This is the book that everyone working on smoothing models should keep handy. At last we have a manuscript that shows the real power of P-splines, their versatility, and the different perspectives you can take to use them. Chapters 1 to 3 will certainly appeal to those who want to start working in this field, and to researchers that need to deepen their knowledge of this technique. Scientists and practitioners from other areas will find chapters 4 to 8 very useful for the wide range of examples and applications. The companion package and the fact that all results (even figures) are reproducible is a real bonus. Thank you Paul and Brian for being truthful to your motto: 'show, don't tell'.' Maria Durbán, University Carlos III de MadridTable of Contents1. Introduction; 2. Bases, penalties, and likelihoods; 3. Optimal smoothing in action; 4. Multidimensional smoothing; 5. Smoothing of scale and shape; 6. Complex counts and composite links; 7. Signal regression; 8. Special subjects; A. P-splines for the impatient; B. P-splines and competitors; C. Computational details; D. Array algorithms; E. Mixed model equations; F. Standard errors in detail; G. The website.
£49.39
Pearson Education (US) Fundamentals of Statistical Signal Processing
Book SynopsisPLEASE PROVIDE ???Table of Contents(NOTE: Most chapters begin with an Introduction and Summary.) 1. Introduction. Detection Theory in Signal Processing. The Detection Problem. The Mathematical Detection Problem. Hierarchy of Detection Problems. Role of Asymptotics. Some Notes to the Reader. 2. Summary of Important PDFs. Fundamental Probability Density Functionshfil Penalty - M and Properties. Quadratic Forms of Gaussian Random Variables. Asymptotic Gaussian PDF. Monte Carlo Performance Evaluation. Number of Required Monte Carlo Trials. Normal Probability Paper. MATLAB Program to Compute Gaussian Right-Tail Probability and its Inverse. MATLAB Program to Compute Central and Noncentral c 2 Right-Tail Probability. MATLAB Program for Monte Carlo Computer Simulation. 3. Statistical Decision Theory I. Neyman-Pearson Theorem. Receiver Operating Characteristics. Irrelevant Data. Minimum Probability of Error. Bayes Risk. Multiple Hypothesis Testing. Neyman-Pearson Theorem. Minimum Bayes Risk Detector - Binary Hypothesis. Minimum Bayes Risk Detector - Multiple Hypotheses. 4. Deterministic Signals. Matched Filters. Generalized Matched Filters. Multiple Signals. Linear Model. Signal Processing Examples. Reduced Form of the Linear Model1. 5. Random Signals. Estimator-Correlator. Linear Model1. Estimator-Correlator for Large Data Records. General Gaussian Detection. Signal Processing Example. Detection Performance of the Estimator-Correlator. 6. Statistical Decision Theory II. Composite Hypothesis Testing. Composite Hypothesis Testing Approaches. Performance of GLRT for Large Data Records. Equivalent Large Data Records Tests. Locally Most Powerful Detectors. Multiple Hypothesis Testing. Asymptotically Equivalent Tests - No Nuisance Parameters. Asymptotically Equivalent Tests - Nuisance Parameters. Asymptotic PDF of GLRT. Asymptotic Detection Performance of LMP Test. Alternate Derivation of Locally Most Powerful Test. Derivation of Generalized ML Rule. 7. Deterministic Signals with Unknown Parameters. Signal Modeling and Detection Performance. Unknown Amplitude. Unknown Arrival Time. Sinusoidal Detection. Classical Linear Model. Signal Processing Examples. Asymptotic Performance of the Energy Detector. Derivation of GLRT for Classical Linear Model. 8. Random Signals with Unknown Parameters. Incompletely Known Signal Covariance. Large Data Record Approximations. Weak Signal Detection. Signal Processing Example. Derivation of PDF for Periodic Gaussian Random Process. 9. Unknown Noise Parameters. General Considerations. White Gaussian Noise. Colored WSS Gaussian Noise. Signal Processing Example. Derivation of GLRT for Classical Linear Model for s 2 Unknown. Rao Test for General Linear Model with Unknown Noise Parameters. Asymptotically Equivalent Rao Test for Signal Processing Example. 10. NonGaussian Noise. NonGaussian Noise Characteristics. Known Deterministic Signals. Deterministic Signals with Unknown Parameters. Signal Processing Example. Asymptotic Performance of NP Detector for Weak Signals. BRao Test for Linear Model Signal with IID NonGaussian Noise. 11. Summary of Detectors. Detection Approaches. Linear Model. Choosing a Detector. Other Approaches and Other Texts. 12. Model Change Detection. Description of Problem. Extensions to the Basic Problem. Multiple Change Times. Signal Processing Examples. General Dynamic Programming Approach to Segmentation. MATLAB Program for Dynamic Programming. 13. Complex/Vector Extensions, and Array Processing. Known PDFs. PDFs with Unknown Parameters. Detectors for Vector Observations. Estimator-Correlator for Large Data Records. Signal Processing Examples. PDF of GLRT for Complex Linear Model. Review of Important Concepts. Random Processes and Time Series Modeling.
£103.29
John Wiley & Sons Inc Detection Estimation and Modulation Theory Part I
Book SynopsisHarry Van Trees s Detection, Estimation, and Modulation Theory, Part I is one of the great time-tested classics in the field of signal processing. This new edition has been thoroughly revised and expanded, making it again the most comprehensive and up-to-date treatment of the subject.Table of ContentsPreface xv Preface to the First Edition xix 1 Introduction 1 1.1 Introduction 1 1.2 Topical Outline 1 1.3 Possible Approaches 11 1.4 Organization 14 2 Classical Detection Theory 17 2.1 Introduction 17 2.2 Simple Binary Hypothesis Tests 20 2.3 m Hypotheses 51 2.4 Performance Bounds and Approximations 63 2.5 Monte Carlo Simulation 80 2.6 Summary 109 2.7 Problems 110 3 General Gaussian Detection 125 3.1 Detection of Gaussian Random Vectors 126 3.2 Equal Covariance Matrices 138 3.3 Equal Mean Vectors 174 3.4 General Gaussian 197 3.5 m Hypotheses 209 3.6 Summary 213 3.7 Problems 215 4 Classical Parameter Estimation 230 4.1 Introduction 230 4.2 Scalar Parameter Estimation 232 4.3 Multiple Parameter Estimation 293 4.4 Global Bayesian Bounds 332 4.5 Composite Hypotheses 348 4.6 Summary 375 4.7 Problems 377 5 General Gaussian Estimation 400 5.1 Introduction 400 5.2 Nonrandom Parameters 401 5.3 Random Parameters 483 5.4 Sequential Estimation 495 5.5 Summary 507 5.6 Problems 510 6 Representation of Random Processes 519 6.1 Introduction 519 6.2 Orthonormal Expansions: Deterministic Signals 520 6.3 Random Process Characterization 528 6.4 Homogeous Integral Equations and Eigenfunctions 540 6.5 Vector Random Processes 564 6.6 Summary 568 6.7 Problems 569 7 Detection of Signals–Estimation of Signal Parameters 584 7.1 Introduction 584 7.2 Detection and Estimation in White Gaussian Noise 591 7.3 Detection and Estimation in Nonwhite Gaussian Noise 629 7.4 Signals with Unwanted Parameters: The Composite Hypothesis Problem 675 7.5 Multiple Channels 712 7.6 Multiple Parameter Estimation 716 7.7 Summary 721 7.8 Problems 722 8 Estimation of Continuous-Time Random Processes 771 8.1 Optimum Linear Processors 771 8.2 Realizable Linear Filters: Stationary Processes, Infinite Past: Wiener Filters 787 8.3 Gaussian–Markov Processes: Kalman Filter 807 8.4 Bayesian Estimation of Non-Gaussian Models 842 8.5 Summary 852 8.6 Problems 855 9 Estimation of Discrete–Time Random Processes 880 9.1 Introduction 880 9.2 Discrete-Time Wiener Filtering 882 9.3 Discrete-Time Kalman Filter 919 9.4 Summary 1016 9.5 Problems 1016 10 Detection of Gaussian Signals 1030 10.1 Introduction 1030 10.2 Detection of Continuous-Time Gaussian Processes 1030 10.3 Detection of Discrete-Time Gaussian Processes 1067 10.4 Summary 1076 10.5 Problems 1077 11 Epilogue 1084 11.1 Classical Detection and Estimation Theory 1084 11.2 Representation of Random Processes 1093 11.3 Detection of Signals and Estimation of Signal Parameters 1095 11.4 Linear Estimation of Random Processes 1098 11.5 Observations 1105 11.6 Conclusion 1106 Appendix A: Probability Distributions and Mathematical Functions 1107 Appendix B: Example Index 1119 References 1125 Index 1145
£87.26
McGraw-Hill Education Signals and Systems Analysis Using Transform
Book SynopsisSignals and Systems: Analysis Using Transform Methods and MATLAB has been extensively updated, while retaining the emphasis on fundamental applications and theory. The text includes a wealth of exercises, including drill exercises, and more challenging conceptual problems.McGraw-Hill''s Connect, is also available as an optional, add on item. Connect is the only integrated learning system that empowers students by continuously adapting to deliver precisely what they need, when they need it, how they need it, so that class time is more effective. Connect allows the professor to assign homework, quizzes, and tests easily and automatically grades and records the scores of the student''s work. Problems are randomized to prevent sharing of answers an may also have a multi-step solution which helps move the students'' learning along if they experience difficulty.Table of Contents1) Introduction2) Mathematical Description of Continuous-Time Signals3) Discrete-Time Signal Description4) Description of Systems5) Time-Domain System Analysis6) Continuous-Time Fourier Methods7) Discrete-Time Fourier Methods8) The Laplace Transform9) The z Transform10) Sampling and Signal Processing11) Frequency Response Analysis12) Laplace System Analysis13) z-Transform System Analysis14) Filter Analysis and DesignAppendix I – Useful Mathematical RelationsAppendix II – Continuous-Time Fourier Series PairsAppendix III – Discrete Fourier Transform PairsAppendix IV – Continuous-Time Fourier Transform PairsAppendix V – Discrete-Time Fourier Transform PairsAppendix VI – Tables of Laplace Transform PairsAppendix VII – z-Transform PairsBibliographyIndex
£53.09
McGraw-Hill Education - Europe Signals and Systems Analysis Using Transform
Book SynopsisSignals and Systems: Analysis Using Transform Methods and MATLAB has been extensively updated, while retaining the emphasis on fundamental applications and theory. The text includes a wealth of exercises, including drill exercises, and more challenging conceptual problems.McGraw-Hill's Connect, is also available as an optional, add on item. Connect is the only integrated learning system that empowers students by continuously adapting to deliver precisely what they need, when they need it, how they need it, so that class time is more effective. Connect allows the professor to assign homework, quizzes, and tests easily and automatically grades and records the scores of the student's work. Problems are randomized to prevent sharing of answers an may also have a "multi-step solution" which helps move the students' learning along if they experience difficulty.Table of Contents1) Introduction2) Mathematical Description of Continuous-Time Signals3) Discrete-Time Signal Description4) Description of Systems5) Time-Domain System Analysis6) Continuous-Time Fourier Methods7) Discrete-Time Fourier Methods8) The Laplace Transform9) The z Transform10) Sampling and Signal Processing11) Frequency Response Analysis12) Laplace System Analysis13) z-Transform System Analysis14) Filter Analysis and DesignAppendix I – Useful Mathematical RelationsAppendix II – Continuous-Time Fourier Series PairsAppendix III – Discrete Fourier Transform PairsAppendix IV – Continuous-Time Fourier Transform PairsAppendix V – Discrete-Time Fourier Transform PairsAppendix VI – Tables of Laplace Transform PairsAppendix VII – z-Transform PairsBibliographyIndex
£117.24
Elsevier Science Publishing Co Inc Handbook of Green Information and Communication
Book SynopsisA guide on the fundamental concepts, applications, algorithms, protocols, new trends and challenges, and research results. It offers information on the core and specialized issues in the field, making it suitable for both the new and experienced researcher.Table of ContentsCognitive Strategies for Green Two-Tier Cellular Networks: A Critical OverviewA Survey of Contemporary Technologies for Smart Home Energy ManagementEmbedded Computing in the Emerging Smart GridIEEE 802.15.4 Based Wireless Sensor Network Design for Smart Grid CommunicationsSmart Grid Communications NetworksWireless Technologies, Protocols, Issues and StandardsIntercell Interference Coordination: Towards A Greener Cellular Network; Energy-efficient Radio Resource Management for Green Radio SystemsGreen computing and Communication ArchitectureGreen Computing Platforms for Biomedical SystemsGreen Datacenter Infrastructures in the Cloud Computing EraEnergy Efficient Cloud Computing: A Green Migration of the Traditional ITGreen Data Centers; Energy-Efficient Sensor NetworksEnergy Efficient Next Generation Wireless CommunicationsEnergy Efficient MIMO-OFDM SystemsConstrained Green Base Station Deployment with Resource Allocation in Wireless NetworksGreen Broadband Access NetworksOverview of Energy Saving Techniques for Mobile and Wireless Access NetworksTowards Energy-Oriented Telecommunication NetworksEnergy-Efficient Peer-to-Peer Networking and OverlaysPower Management for 4G Broadband Wireless Access NetworksGreen Optical Core NetworksAnalysis and Development of Green-Aware Security Mechanisms for Modern Internet ApplicationsUsing Ant Colony Agents for Designing Energy-Efficient Routing Protocols for Wireless Ad Hoc and Sensor NetworksSmart Grid Communications: Opportunities and ChallengesA Survey on Smart Grid Communications: From an Architecture Overview to Standardization ActivitiesTowards Energy Efficiency in Next Generation Green Mobile Networks: a Queueing Theory Perspective
£88.50
Pearson Education (US) Understanding Digital Signal Processing
Book SynopsisRichard G. Lyons is a consulting Systems Engineer and lecturer with Besser Associates in Mountain View, California. He is author of the book Understanding Digital Signal Processing, editor and contributor to the book Streamlining Digital Signal Processing, and has authored numerous articles on DSP. Lyons has taught DSP at the University of California Santa Cruz Extension and recently received the IEEE Signal Processing Society's 2012 Educator of the Year award.Table of ContentsPreface xv About the Author xxiii Chapter 1: Discrete Sequences and Systems 1 1.1 Discrete Sequences and their Notation 2 1.2 Signal Amplitude, Magnitude, Power 8 1.3 Signal Processing Operational Symbols 10 1.4 Introduction to Discrete Linear Time-Invariant Systems 12 1.5 Discrete Linear Systems 12 1.6 Time-Invariant Systems 17 1.7 The Commutative Property of Linear Time-Invariant Systems 18 1.8 Analyzing Linear Time-Invariant Systems 19 References 21 Chapter 1 Problems 23 Chapter 2: Periodic Sampling 33 2.1 Aliasing: Signal Ambiguity in the Frequency Domain 33 2.2 Sampling Lowpass Signals 38 2.3 Sampling Bandpass Signals 42 2.4 Practical Aspects of Bandpass Sampling 45 References 49 Chapter 2 Problems 50 Chapter 3: The Discrete Fourier Transform 59 3.1 Understanding the DFT Equation 60 3.2 DFT Symmetry 73 3.3 DFT Linearity 75 3.4 DFT Magnitudes 75 3.5 DFT Frequency Axis 77 3.6 DFT Shifting Theorem 77 3.7 Inverse DFT 80 3.8 DFT Leakage 81 3.9 Windows 89 3.10 DFT Scalloping Loss 96 3.11 DFT Resolution, Zero Padding, and Frequency-Domain Sampling 98 3.12 DFT Processing Gain 102 3.13 The DFT of Rectangular Functions 105 3.14 Interpreting the DFT Using the Discrete-Time Fourier Transform 120 References 124 Chapter 3 Problems 125 Chapter 4: The Fast Fourier Transform 135 4.1 Relationship of the FFT to the DFT 136 4.2 Hints on Using FFTs in Practice 137 4.3 Derivation of the Radix-2 FFT Algorithm 141 4.4 FFT Input/Output Data Index Bit Reversal 149 4.5 Radix-2 FFT Butterfly Structures 151 4.6 Alternate Single-Butterfly Structures 154 References 158 Chapter 4 Problems 160 Chapter 5: Finite Impulse Response Filters 169 5.1 An Introduction to Finite Impulse Response (FIR) Filters 170 5.2 Convolution in FIR Filters 175 5.3 Lowpass FIR Filter Design 186 5.4 Bandpass FIR Filter Design 201 5.5 Highpass FIR Filter Design 203 5.6 Parks-McClellan Exchange FIR Filter Design Method 204 5.7 Half-band FIR Filters 207 5.8 Phase Response of FIR Filters 209 5.9 A Generic Description of Discrete Convolution 214 5.10 Analyzing FIR Filters 226 References 235 Chapter 5 Problems 238 Chapter 6: Infinite Impulse Response Filters 253 6.1 An Introduction to Infinite Impulse Response Filters 254 6.2 The Laplace Transform 257 6.3 The z-Transform 270 6.4 Using the z-Transform to Analyze IIR Filters 274 6.5 Using Poles and Zeros to Analyze IIR Filters 282 6.6 Alternate IIR Filter Structures 289 6.7 Pitfalls in Building IIR Filters 292 6.8 Improving IIR Filters with Cascaded Structures 295 6.9 Scaling the Gain of IIR Filters 300 6.10 Impulse Invariance IIR Filter Design Method 303 6.11 Bilinear Transform IIR Filter Design Method 319 6.12 Optimized IIR Filter Design Method 330 6.13 A Brief Comparison of IIR and FIR Filters 332 References 333 Chapter 6 Problems 336 Chapter 7: Specialized Digital Networks and Filters 361 7.1 Differentiators 361 7.2 Integrators 370 7.3 Matched Filters 376 7.4 Interpolated Lowpass FIR Filters 381 7.5 Frequency Sampling Filters: The Lost Art 392 References 426 Chapter 7 Problems 429 Chapter 8: Quadrature Signals 439 8.1 Why Care about Quadrature Signals? 440 8.2 The Notation of Complex Numbers 440 8.3 Representing Real Signals Using Complex Phasors 446 8.4 A Few Thoughts on Negative Frequency 450 8.5 Quadrature Signals in the Frequency Domain 451 8.6 Bandpass Quadrature Signals in the Frequency Domain 454 8.7 Complex Down-Conversion 456 8.8 A Complex Down-Conversion Example 458 8.9 An Alternate Down-Conversion Method 462 References 464 Chapter 8 Problems 465 Chapter 9: The Discrete Hilbert Transform 479 9.1 Hilbert Transform Definition 480 9.2 Why Care about the Hilbert Transform? 482 9.3 Impulse Response of a Hilbert Transformer 487 9.4 Designing a Discrete Hilbert Transformer 489 9.5 Time-Domain Analytic Signal Generation 495 9.6 Comparing Analytical Signal Generation Methods 497 References 498 Chapter 9 Problems 499 Chapter 10: Sample Rate Conversion 507 10.1 Decimation 508 10.2 Two-Stage Decimation 510 10.3 Properties of Downsampling 514 10.4 Interpolation 516 10.5 Properties of Interpolation 518 10.6 Combining Decimation and Interpolation 521 10.7 Polyphase Filters 522 10.8 Two-Stage Interpolation 528 10.9 z-Transform Analysis of Multirate Systems 533 10.10 Polyphase Filter Implementations 535 10.11 Sample Rate Conversion by Rational Factors 540 10.12 Sample Rate Conversion with Half-band Filters 543 10.13 Sample Rate Conversion with IFIR Filters 548 10.14 Cascaded Integrator-Comb Filters 550 References 566 Chapter 10 Problems 568 Chapter 11: Signal Averaging 589 11.1 Coherent Averaging 590 11.2 Incoherent Averaging 597 11.3 Averaging Multiple Fast Fourier Transforms 600 11.4 Averaging Phase Angles 603 11.5 Filtering Aspects of Time-Domain Averaging 604 11.6 Exponential Averaging 608 References 615 Chapter 11 Problems 617 Chapter 12: Digital Data Formats and their Effects 623 12.1 Fixed-Point Binary Formats 623 12.2 Binary Number Precision and Dynamic Range 632 12.3 Effects of Finite Fixed-Point Binary Word Length 634 12.4 Floating-Point Binary Formats 652 12.5 Block Floating-Point Binary Format 658 References 658 Chapter 12 Problems 661 Chapter 13: Digital Signal Processing Tricks 671 13.1 Frequency Translation without Multiplication 671 13.2 High-Speed Vector Magnitude Approximation 679 13.3 Frequency-Domain Windowing 683 13.4 Fast Multiplication of Complex Numbers 686 13.5 Efficiently Performing the FFT of Real Sequences 687 13.6 Computing the Inverse FFT Using the Forward FFT 699 13.7 Simplified FIR Filter Structure 702 13.8 Reducing A/D Converter Quantization Noise 704 13.9 A/D Converter Testing Techniques 709 13.10 Fast FIR Filtering Using the FFT 716 13.11 Generating Normally Distributed Random Data 722 13.12 Zero-Phase Filtering 725 13.13 Sharpened FIR Filters 726 13.14 Interpolating a Bandpass Signal 728 13.15 Spectral Peak Location Algorithm 730 13.16 Computing FFT Twiddle Factors 734 13.17 Single Tone Detection 737 13.18 The Sliding DFT 741 13.19 The Zoom FFT 749 13.20 A Practical Spectrum Analyzer 753 13.21 An Efficient Arctangent Approximation 756 13.22 Frequency Demodulation Algorithms 758 13.23 DC Removal 761 13.24 Improving Traditional CIC Filters 765 13.25 Smoothing Impulsive Noise 770 13.26 Efficient Polynomial Evaluation 772 13.27 Designing Very High-Order FIR Filters 775 13.28 Time-Domain Interpolation Using the FFT 778 13.29 Frequency Translation Using Decimation 781 13.30 Automatic Gain Control (AGC) 783 13.31 Approximate Envelope Detection 784 13.32 AQuadrature Oscillator 786 13.33 Specialized Exponential Averaging 789 13.34 Filtering Narrowband Noise Using Filter Nulls 792 13.35 Efficient Computation of Signal Variance 797 13.36 Real-time Computation of Signal Averages and Variances 799 13.37 Building Hilbert Transformers from Half-band Filters 802 13.38 Complex Vector Rotation with Arctangents 805 13.39 An Efficient Differentiating Network 810 13.40 Linear-Phase DC-Removal Filter 812 13.41 Avoiding Overflow in Magnitude Computations 815 13.42 Efficient Linear Interpolation 815 13.43 Alternate Complex Down-conversion Schemes 816 13.44 Signal Transition Detection 820 13.45 Spectral Flipping around Signal Center Frequency 821 13.46 Computing Missing Signal Samples 823 13.47 Computing Large DFTs Using Small FFTs 826 13.48 Computing Filter Group Delay without Arctangents 830 13.49 Computing a Forward and Inverse FFT Using a Single FFT 831 13.50 Improved Narrowband Lowpass IIR Filters 833 13.51 A Stable Goertzel Algorithm 838 References 840 Appendix A: The Arithmetic of Complex Numbers 847 A.1 Graphical Representation of Real and Complex Numbers 847 A.2 Arithmetic Representation of Complex Numbers 848 A.3 Arithmetic Operations of Complex Numbers 850 A.4 Some Practical Implications of Using Complex Numbers 856 Appendix B: Closed Form of a Geometric Series 859 Appendix C: Time Reversal and the DFT 863 Appendix D: Mean, Variance, and Standard Deviation 867 D.1 Statistical Measures 867 D.2 Statistics of Short Sequences 870 D.3 Statistics of Summed Sequences 872 D.4 Standard Deviation (RMS) of a Continuous Sinewave 874 D.5 Estimating Signal-to-Noise Ratios 875 D.6 The Mean and Variance of Random Functions 879 D.7 The Normal Probability Density Function 882 Appendix E: Decibels (DB and DBM) 885 E.1 Using Logarithms to Determine Relative Signal Power 885 E.2 Some Useful Decibel Numbers 889 E.3 Absolute Power Using Decibels 891 Appendix F: Digital Filter Terminology 893 Appendix G: Frequency Sampling Filter Derivations 903 G.1 Frequency Response of a Comb Filter 903 G.2 Single Complex FSF Frequency Response 904 G.3 Multisection Complex FSF Phase 905 G.4 Multisection Complex FSF Frequency Response 906 G.5 Real FSF Transfer Function 908 G.6 Type-IV FSF Frequency Response 910 Appendix H: Frequency Sampling Filter Design Tables 913 Appendix I: Computing Chebyshev Window Sequences 927 I.1 Chebyshev Windows for FIR Filter Design 927 I.2 Chebyshev Windows for Spectrum Analysis 929 Index 931
£80.09
Pearson Education Digital Signal Processing
Book Synopsis Emmanuel Ifeachor is Professor of Intelligent Electronic Systems and Director of the Centre for Communications, Networks and Information Systems at the University of Plymouth, UK. Barrie Jervis is Professor of Electronic Engineering at Sheffield Hallam University, UK. This book evolved from the authors' extensive experience in teaching practically oriented courses in DSP to both undergraduates and engineers in industry. Their own research in applied DSP has influenced the contents of the book and provided many of the examples and case studies. Table of Contents 1. Introduction. 2. Analog I/O interface for real-time DSP systems. 3. Discrete transforms. 4. The z-transform and its applications in signal processing. 5. Correlation and convolution. 6. A framework for digital filter design. 7. Finite impulse response (FIR) filter design. 8. Design of infinite impulse response (IIR) digital filters. 9. Multirate digital signal processing. 10. Adaptive digital filters. 11. Spectrum estimation and analysis. 12. General- and special-purpose digital signal processors. 13. Analysis of finite wordlength effects in fixed-point DSP systems. 14. Applications and design studies.
£64.99
Elsevier Science Advanced Methods in Biomedical Signal Processing
Book SynopsisTable of Contents1. Feature engineering 2. Heart rate variability 3. Understanding the suitabillity of parametric modeling techniques in detecting the changes in the HRV signals acquired from cannabis consuming and nonconsuming Indian paddy-field workers 4. Patient-specific ECG beat classification using EMD and deep learning-based technique 5. Empirical wavelet transform and deep learning-based technique for ECG beat classification 6. Development of an Internet-of-Things (IoT)-based pill monitoring device for geriatric patients 7. Biomedical robotics 8. Combating COVID-19 by implying machine learning predictions and projections 9. Deep learning methods for analysis of neural signals: From conventional neural network to graph neural network 10. Improved extraction of the extreme thermal regions of breast IR images 11. New metrics to asses the subtle changes of the heart's electromagnetic field 12. The role of optimal and modified lead systems in electrocardiogram 13. Adaptive rate EEG processing and machine learning-based efficient recognition of epilepsy 14. Multimodal microscopy: A novel low-cost microscope designed for food and biological applications
£103.50
Elsevier Science & Technology Signal Processing and Machine Learning Theory
Book SynopsisTable of Contents1. Introduction to Signal Processing and Machine Learning Theory 2. Continuous-Time Signals and Systems 3. Discrete-Time Signals and Systems 4. Random Signals and Stochastic Processes 5. Sampling and Quantization 6. Digital Filter Structures and Their Implementation 7. Multi-rate Signal Processing for Software Radio Architectures 8. Modern Transform Design for Practical Audio/Image/Video Coding Applications 9. Discrete Multi-Scale Transforms in Signal Processing 10. Frames in Signal Processing 11. Parametric Estimation 12. Adaptive Filters 13. Signal Processing over Graphs 14. Tensors for Signal Processing and Machine Learning 15. Non-convex Optimization for Machine Learning 16. Dictionary Learning and Sparse Representation
£114.30
Elsevier Science Dimensions of Uncertainty in Communication
Book SynopsisTable of Contents1. Model selection 2. Performance bounds from epistemic uncertainty 3. Moment bounds 4. Interval analysis 5. Probability boxes 6. Dependence bounds 7. Beyond probability
£86.36
Springer Signal Processing Methods for Music Transcription
Book SynopsisFoundations.- to Music Transcription.- An Introduction to Statistical Signal Processing and Spectrum Estimation.- Sparse Adaptive Representations for Musical Signals.- Rhythm and Timbre Analysis.- Beat Tracking and Musical Metre Analysis.- Unpitched Percussion Transcription.- Automatic Classification of Pitched Musical Instrument Sounds.- Multiple Fundamental Frequency Analysis.- Multiple Fundamental Frequency Estimation Based on Generative Models.- Auditory Model-Based Methods for Multiple Fundamental Frequency Estimation.- Unsupervised Learning Methods for Source Separation in Monaural Music Signals.- Entire Systems, Acoustic and Musicological Modelling.- Auditory Scene Analysis in Music Signals.- Music Scene Description.- Singing Transcription.Table of ContentsFoundations.- to Music Transcription.- An Introduction to Statistical Signal Processing and Spectrum Estimation.- Sparse Adaptive Representations for Musical Signals.- Rhythm and Timbre Analysis.- Beat Tracking and Musical Metre Analysis.- Unpitched Percussion Transcription.- Automatic Classification of Pitched Musical Instrument Sounds.- Multiple Fundamental Frequency Analysis.- Multiple Fundamental Frequency Estimation Based on Generative Models.- Auditory Model-Based Methods for Multiple Fundamental Frequency Estimation.- Unsupervised Learning Methods for Source Separation in Monaural Music Signals.- Entire Systems, Acoustic and Musicological Modelling.- Auditory Scene Analysis in Music Signals.- Music Scene Description.- Singing Transcription.
£123.49
Springer-Verlag New York Inc. Handbook of Signal Processing in Acoustics
Book SynopsisAcoustic Signals and Systems.- Signals and Systems.- Acoustic Data Acquisition.- Spectral Analysis and Correlation.- The FFT and Tone Identification.- Measuring Transfer-Functions and Impulse Responses.- Digital Sequences.- Filters.- Adaptive Processing.- Beamforming and Wavenumber Processing.- Auditory System and Hearing.- Anatomy, Physiology and Function of the Auditory System.- Physiological Measures of Auditory Function.- Auditory Processing Models.- Speech Intelligibility.- Signal Processing in Hearing Aids.- Psychoacoustics.- Methods for Psychoacoustics in Relation to Long-Term Sounds.- Masking and Critical Bands.- Aspects of Modeling Pitch Perception.- Calculation of Loudness for Normal and Hearing-Impaired Listeners.- Psychoacoustical Roughness.- Musical Acoustics.- Automatic Music Transcription.- Music Structure Analysis from Acoustic Signals.- Computer Music Synthesis and Composition.- Singing Voice Analysis, Synthesis, and Modeling.- Instrument Modeling and Synthesis.- DigitTrade ReviewFrom the reviews:“The ‘Handbook of Signal Processing in Acoustics’ provides an excellent reference for practicing acousticians and engineers. … encompasses essential background material, technical details, standards, and practical tips. It is aimed to a public with some knowledge of signal processing, and it is meant to be used as a reference. … Signal processing techniques which find major application in different areas of acoustics are well presented from different perspectives … . this compendium is an excellent reference for engineers and professionals working in acoustics.” (Joaquin E. Moran, Noise Control Engineering Journal, Vol. 58 (6), November-December, 2010)Table of Contents1. Acoustical oceanography Models for Propagation Codes Transducer Arrays: structure, data acquisition, signal generation, calibration Sonar MFP Tomography Other Inverse Techniques Signal and Noise Characteristics 2. Active Noise Control Principles of adaptive techniques Plant modeling Sound/vibration field sensing Actuator characteristics and requirements Performance limitations Multi-channel systems Performance and complexity 3. Animal bioacoustics Recording and monitoring systems Models of echolocation Hearing performance and modelling Characteristics of calls Stimuli generation Locating and tracking Archives and Databases of signals 4. Architectural acoustics Room models Measurement of transmissions, absorption, reverberation, etc. Sound fields (definitions, criteria, measurement, typical values) MLS and other coded signals Auralization: Modelling techniques, listening modes, processing requirements, existing systems, performace Artificial reverberation Sound reinforcement Acoustic privacy 5. Audio engineering Transducer modeling Loudspeaker performance characteristics Audio recording and playback formats Audio-visual interaction ADC, DAC, and Codec technologies Multi-channel sound and Virtual audio Restoration Digital audio editing Effects generation 6. Auditory System, Hearing Modeling of hearing Thresholds and Masking Frequency and level discrimination Binaural hearing and spatialization HRTF HATS and other physical models Hearing aids Auditory illusions 7. Education in acoustics 8. Electroacoustics Microphone types and their characteristics Vibration sensors and their characteristics Acoustic actuators and their characteristics Smart sensors and actuators 9. Engineering acoustics 10. Infrasonics Background noise and source signals Sensors and their characteristics Propagation models Event detection Data archiving Source identification 11. Musical Acoustics Computer music synthesis and composition Computer music recognition and analysis Singing voice analysis, synthesis, and processing Instrument measurement, modeling and synthesis Coding and compression of music 12. Noise Noise source modeling Acoustic holography Atmospheric sound propagation Source localization Noise evaluation and Annoyance thresholds 13. Non-linear acoustics Propagation equations and codes Example non-linear systems Parametric array Measurement methods Detection of non-linearities 14. Psychoacoustics Perceptual models Cochlear implants Auditory alarms 15. Seismology Seismic Coda Acoustic Profiling Propagation modes and properties for modeling Seismo-acoustic coupling 16. Speech Characteristics of speech as signals Synthesis Recognition Intelligibility and quality metrics Corpus for tests Coding and compression Display and analysis 17. Strutural acoustics and vibration BEM, FEM, EA, etc. Actuator design and deployment Propagation and radiation Machine diagnostics and prognosis Modeling, measuring and analyzing shock Materials testing 18. Telecomm POTS Wideband Echo supression Hearing aids Handset, Headset, and Wireless standards Systems for handicapped users 19. Ultrasonics
£539.99
Springer Spin Waves
Book Synopsisto Magnetism.- Quantum Theory of Spin Waves.- Magnetic Susceptibilities.- Electromagnetic Waves in Anisotropic-Dispersive Media.- Magnetostatic Modes.- Propagation Characteristics and Excitation of Dipolar Spin Waves.- Variational Formulation for Magnetostatic Modes.- Optical-Spin Wave Interactions.- Nonlinear Interactions.- Novel Applications.Trade Reviewws, Issue 2010 j)Table of Contentsto Magnetism.- Quantum Theory of Spin Waves.- Magnetic Susceptibilities.- Electromagnetic Waves in Anisotropic-Dispersive Media.- Magnetostatic Modes.- Propagation Characteristics and Excitation of Dipolar Spin Waves.- Variational Formulation for Magnetostatic Modes.- Optical-Spin Wave Interactions.- Nonlinear Interactions.- Novel Applications.
£142.49
John Wiley & Sons Inc Multimedia Signal Processing
Book SynopsisMultimedia Signal Processing is a comprehensive and accessible text to the theory and applications of digital signal processing (DSP). The applications of DSP are pervasive and include multimedia systems, cellular communication, adaptive network management, radar, pattern recognition, medical signal processing, financial data forecasting, artificial intelligence, decision making, control systems and search engines. This book is organised in to three major parts making it a coherent and structured presentation of the theory and applications of digital signal processing. A range of important topics are covered in basic signal processing, model-based statistical signal processing and their applications. Part 1: Basic Digital Signal Processing gives an introduction to the topic, discussing sampling and quantization, Fourier analysis and synthesis, Z-transform, and digital filters. Part 2: Model-based Signal Processing covers probability and infoTrade Review"A valuable and accessible text … .Suited not only for senior undergraduates and postgraduates but also for researchers and engineers." (Zentralblatt Math, 2008/17)Table of ContentsPreface. Acknowledgement. Symbols. Abbreviations. Part I Basic Digital Signal Processing. 1 Introduction. 1.1 Signals and Information. 1.2 Signal Processing Methods. 1.3 Applications of Digital Signal Processing. 1.4 Summary. 2 Fourier Analysis and Synthesis. 2.1 Introduction. 2.2 Fourier Series: Representation of Periodic Signals. 2.3 Fourier Transform: Representation of Nonperiodic Signals. 2.4 Discrete Fourier Transform. 2.5 Short-Time Fourier Transform. 2.6 Fast Fourier Transform (FFT). 2.7 2-D Discrete Fourier Transform (2-D DFT). 2.8 Discrete Cosine Transform (DCT). 2.9 Some Applications of the Fourier Transform. 2.10 Summary. 3 z-Transform. 3.1 Introduction. 3.2 Derivation of the z-Transform. 3.3 The z-Plane and the Unit Circle. 3.4 Properties of z-Transform. 3.5 z-Transfer Function, Poles (Resonance) and Zeros (Anti-resonance). 3.6 z-Transform of Analysis of Exponential Transient Signals. 3.7 Inverse z-Transform. 3.8 Summary. 4 Digital Filters. 4.1 Introduction. 4.2 Linear Time-Invariant Digital Filters. 4.3 Recursive and Non-Recursive Filters. 4.4 Filtering Operation: Sum of Vector Products, A Comparison of Convolution and Correlation. 4.5 Filter Structures: Direct, Cascade and Parallel Forms. 4.6 Linear Phase FIR Filters. 4.7 Design of Digital FIR Filter-banks. 4.8 Quadrature Mirror Sub-band Filters. 4.9 Design of Infinite Impulse Response (IIR) Filters by Pole–zero Placements. 4.10 Issues in the Design and Implementation of a Digital Filter. 4.11 Summary. 5 Sampling and Quantisation. 5.1 Introduction. 5.2 Sampling a Continuous-Time Signal. 5.3 Quantisation. 5.4 Sampling Rate Conversion: Interpolation and Decimation. 5.5 Summary. Part II Model-Based Signal Processing. 6 Information Theory and Probability Models. 6.1 Introduction: Probability and Information Models. 6.2 Random Processes. 6.3 Probability Models of Random Signals. 6.4 Information Models. 6.5 Stationary and Non-Stationary Random Processes. 6.6 Statistics (Expected Values) of a Random Process. 6.7 Some Useful Practical Classes of Random Processes. 6.8 Transformation of a Random Process. 6.9 Search Engines: Citation Ranking. 6.10 Summary. 7 Bayesian Inference. 7.1 Bayesian Estimation Theory: Basic Definitions. 7.2 Bayesian Estimation. 7.3 Expectation Maximisation Method. 7.4 Cramer–Rao Bound on the Minimum Estimator Variance. 7.5 Design of Gaussian Mixture Models (GMM). 7.6 Bayesian Classification. 7.7 Modelling the Space of a Random Process. 7.8 Summary. 8 Least Square Error, Wiener–Kolmogorov Filters. 8.1 Least Square Error Estimation: Wiener–Kolmogorov Filter. 8.2 Block-Data Formulation of the Wiener Filter. 8.3 Interpretation of Wiener Filter as Projection in Vector Space. 8.4 Analysis of the Least Mean Square Error Signal. 8.5 Formulation of Wiener Filters in the Frequency Domain. 8.6 Some Applications of Wiener Filters. 8.7 Implementation of Wiener Filters. 8.8 Summary. 9 Adaptive Filters: Kalman, RLS, LMS. 9.1 Introduction. 9.2 State-Space Kalman Filters. 9.3 Sample Adaptive Filters. 9.4 Recursive Least Square (RLS) Adaptive Filters. 9.5 The Steepest-Descent Method. 9.6 LMS Filter. 9.7 Summary. 10 Linear Prediction Models. 10.1 Linear Prediction Coding. 10.2 Forward, Backward and Lattice Predictors. 10.3 Short-Term and Long-Term Predictors. 10.4 MAP Estimation of Predictor Coefficients. 10.5 Formant-Tracking LP Models. 10.6 Sub-Band Linear Prediction Model. 10.7 Signal Restoration Using Linear Prediction Models. 10.8 Summary. 11 Hidden Markov Models. 11.1 Statistical Models for Non-Stationary Processes. 11.2 Hidden Markov Models. 11.3 Training Hidden Markov Models. 11.4 Decoding Signals Using Hidden Markov Models. 11.5 HMM in DNA and Protein Sequences. 11.6 HMMs for Modelling Speech and Noise. 11.7 Summary. 12 Eigenvector Analysis, Principal Component Analysis and Independent Component Analysis. 12.1 Introduction – Linear Systems and Eigenanalysis. 12.2 Eigenvectors and Eigenvalues. 12.3 Principal Component Analysis (PCA). 12.4 Independent Component Analysis. 12.5 Summary. Part III Applications of Digital Signal Processing to Speech, Music and Telecommunications. 13 Music Signal Processing and Auditory Perception. 13.1 Introduction. 13.2 Musical Notes, Intervals and Scales. 13.3 Musical Instruments. 13.4 Review of Basic Physics of Sounds. 13.5 Music Signal Features and Models. 13.6 Anatomy of the Ear and the Hearing Process. 13.7 Psychoacoustics of Hearing. 13.8 Music Coding (Compression). 13.9 High Quality Audio Coding: MPEG Audio Layer-3 (MP3). 13.10 Stereo Music Coding. 13.11 Summary. 14 Speech Processing. 14.1 Speech Communication. 14.2 Acoustic Theory of Speech: The Source–filter Model. 14.3 Speech Models and Features. 14.4 Linear Prediction Models of Speech. 14.5 Harmonic Plus Noise Model of Speech. 14.6 Fundamental Frequency (Pitch) Information. 14.7 Speech Coding. 14.8 Speech Recognition. 14.9 Summary. 15 Speech Enhancement. 15.1 Introduction. 15.2 Single-Input Speech Enhancement Methods. 15.3 Speech Bandwidth Extension – Spectral Extrapolation. 15.4 Interpolation of Lost Speech Segments – Packet Loss Concealment. 15.5 Multi-Input Speech Enhancement Methods. 15.6 Speech Distortion Measurements. 15.7 Summary. 16 Echo Cancellation. 16.1 Introduction: Acoustic and Hybrid Echo. 16.2 Telephone Line Hybrid Echo. 16.3 Hybrid (Telephone Line) Echo Suppression. 16.4 Adaptive Echo Cancellation. 16.5 Acoustic Echo. 16.6 Sub-Band Acoustic Echo Cancellation. 16.7 Echo Cancellation with Linear Prediction Pre-whitening. 16.8 Multi-Input Multi-Output Echo Cancellation. 16.9 Summary. 17 Channel Equalisation and Blind Deconvolution. 17.1 Introduction. 17.2 Blind Equalisation Using Channel Input Power Spectrum. 17.3 Equalisation Based on Linear Prediction Models. 17.4 Bayesian Blind Deconvolution and Equalisation. 17.5 Blind Equalisation for Digital Communication Channels. 17.6 Equalisation Based on Higher-Order Statistics. 17.7 Summary. 18 Signal Processing in Mobile Communication. 18.1 Introduction to Cellular Communication. 18.2 Communication Signal Processing in Mobile Systems. 18.3 Capacity, Noise, and Spectral Efficiency. 18.4 Multi-path and Fading in Mobile Communication. 18.5 Smart Antennas – Space–Time Signal Processing. 18.6 Summary. Index.
£97.16
John Wiley & Sons Inc Complex Valued Nonlinear Adaptive Filters
Book SynopsisThe filtering of real world signals requires an adaptive mode of operation to deal with the statistically nonstationary nature of the data. Feedback and nonlinearity within filtering architectures are needed to cater for long time dependencies and possibly nonlinear signal generating mechanisms.Table of ContentsPreface xiii Acknowledgements xvii 1 The Magic of Complex Numbers 1 1.1 History of Complex Numbers 2 1.2 History of Mathematical Notation 8 1.3 Development of Complex Valued Adaptive Signal Processing 9 2 Why Signal Processing in the Complex Domain? 13 2.1 Some Examples of Complex Valued Signal Processing 13 2.2 Modelling in C is Not Only Convenient But Also Natural 19 2.3 Why Complex Modelling of Real Valued Processes? 20 2.4 Exploiting the Phase Information 23 2.5 Other Applications of Complex Domain Processing of Real Valued Signals 26 2.6 Additional Benefits of Complex Domain Processing 29 3 Adaptive Filtering Architectures 33 3.1 Linear and Nonlinear Stochastic Models 34 3.2 Linear and Nonlinear Adaptive Filtering Architectures 35 3.3 State Space Representation and Canonical Forms 39 4 Complex Nonlinear Activation Functions 43 4.1 Properties of Complex Functions 43 4.2 Universal Function Approximation 46 4.3 Nonlinear Activation Functions for Complex Neural Networks 48 4.4 Generalised Splitting Activation Functions (GSAF) 53 4.5 Summary: Choice of the Complex Activation Function 54 5 Elements of CR Calculus 55 5.1 Continuous Complex Functions 56 5.2 The Cauchy–Riemann Equations 56 5.3 Generalised Derivatives of Functions of Complex Variable 57 5.4 CR-derivatives of Cost Functions 62 6 Complex Valued Adaptive Filters 69 6.1 Adaptive Filtering Configurations 70 6.2 The Complex Least Mean Square Algorithm 73 6.3 Nonlinear Feedforward Complex Adaptive Filters 80 6.4 Normalisation of Learning Algorithms 85 6.5 Performance of Feedforward Nonlinear Adaptive Filters 87 6.6 Summary: Choice of a Nonlinear Adaptive Filter 89 7 Adaptive Filters with Feedback 91 7.1 Training of IIR Adaptive Filters 92 7.2 Nonlinear Adaptive IIR Filters: Recurrent Perceptron 97 7.3 Training of Recurrent Neural Networks 99 7.4 Simulation Examples 102 8 Filters with an Adaptive Stepsize 107 8.1 Benveniste Type Variable Stepsize Algorithms 108 8.2 Complex Valued GNGD Algorithms 110 8.3 Simulation Examples 113 9 Filters with an Adaptive Amplitude of Nonlinearity 119 9.1 Dynamical Range Reduction 119 9.2 FIR Adaptive Filters with an Adaptive Nonlinearity 121 9.3 Recurrent Neural Networks with Trainable Amplitude of Activation Functions 122 9.4 Simulation Results 124 10 Data-reusing Algorithms for Complex Valued Adaptive Filters 129 10.1 The Data-reusing Complex Valued Least Mean Square (DRCLMS) Algorithm 129 10.2 Data-reusing Complex Nonlinear Adaptive Filters 131 10.3 Data-reusing Algorithms for Complex RNNs 134 11 Complex Mappings and M¨obius Transformations 137 11.1 Matrix Representation of a Complex Number 137 11.2 The M¨obius Transformation 140 11.3 Activation Functions and M¨obius Transformations 142 11.4 All-pass Systems as M¨obius Transformations 146 11.5 Fractional Delay Filters 147 12 Augmented Complex Statistics 151 12.1 Complex Random Variables (CRV) 152 12.2 Complex Circular Random Variables 158 12.3 Complex Signals 159 12.4 Second-order Characterisation of Complex Signals 161 13 Widely Linear Estimation and Augmented CLMS (ACLMS) 169 13.1 Minimum Mean Square Error (MMSE) Estimation in C 169 13.2 Complex White Noise 172 13.3 Autoregressive Modelling in C 173 13.4 The Augmented Complex LMS (ACLMS) Algorithm 175 13.5 Adaptive Prediction Based on ACLMS 178 14 Duality Between Complex Valued and Real Valued Filters 183 14.1 A Dual Channel Real Valued Adaptive Filter 184 14.2 Duality Between Real and Complex Valued Filters 186 14.3 Simulations 188 15 Widely Linear Filters with Feedback 191 15.1 The Widely Linear ARMA (WL-ARMA) Model 192 15.2 Widely Linear Adaptive Filters with Feedback 192 15.4 The Augmented Kalman Filter Algorithm for RNNs 198 15.5 Augmented Complex Unscented Kalman Filter (ACUKF) 200 15.6 Simulation Examples 203 16 Collaborative Adaptive Filtering 207 16.1 Parametric Signal Modality Characterisation 207 16.2 Standard Hybrid Filtering in R 209 16.3 Tracking the Linear/Nonlinear Nature of Complex Valued Signals 210 16.4 Split vs Fully Complex Signal Natures 214 16.5 Online Assessment of the Nature of Wind Signal 216 16.6 Collaborative Filters for General Complex Signals 217 17 Adaptive Filtering Based on EMD 221 17.1 The Empirical Mode Decomposition Algorithm 222 17.2 Complex Extensions of Empirical Mode Decomposition 226 17.3 Addressing the Problem of Uniqueness 230 17.4 Applications of Complex Extensions of EMD 230 18 Validation of Complex Representations – Is This Worthwhile? 233 18.1 Signal Modality Characterisation in R 234 18.2 Testing for the Validity of Complex Representation 239 18.3 Quantifying Benefits of Complex Valued Representation 243 Appendix A: Some Distinctive Properties of Calculus in C 245 Appendix B: Liouville's Theorem 251 Appendix C: Hypercomplex and Clifford Algebras 253 Appendix D: Real Valued Activation Functions 257 Appendix E: Elementary Transcendental Functions (ETF) 259 Appendix F: The O Notation and Standard Vector and Matrix Differentiation 263 Appendix G: Notions From Learning Theory 265 Appendix H: Notions from Approximation Theory 269 Appendix I: Terminology Used in the Field of Neural Networks 273 Appendix J: Complex Valued Pipelined Recurrent Neural Network (CPRNN) 275 Appendix K: Gradient Adaptive Step Size (GASS) Algorithms in R 279 Appendix L: Derivation of Partial Derivatives from Chapter 8 283 Appendix M: A Posteriori Learning 287 Appendix N: Notions from Stability Theory 291 Appendix O: Linear Relaxation 293 Appendix P: Contraction Mappings, Fixed Point Iteration and Fractals 299 References 309 Index 321
£106.35
John Wiley & Sons Inc Mimo Radar Signal Processing
Book SynopsisThe first book to present a systematic and coherent picture of MIMO radars Due to its potential to improve target detection and discrimination capability, Multiple-Input and Multiple-Output (MIMO) radar has generated significant attention and widespread interest in academia, industry, government labs, and funding agencies.Table of ContentsPREFACE. CONTRIBUTORS. 1 MIMO Radar — Diversity Means Superiority (Jian Li and Petre Stoica). 1.1 Introduction. 1.2 Problem Formulation. 1.3 Parameter Identifiability. 1.4 Nonparametric Adaptive Techniques for Parameter Estimation. 1.5 Parametric Techniques for Parameter Estimation. 1.6 Transmit Beampattern Designs. 1.7 Conclusions. Appendix IA Generalized Likelihood Ratio Test. Appendix 1B Lemma and Proof. Acknowledgments. References. 2 MIMO Radar: Concepts, Performance Enhancements, and Applications (Keith W. Forsythe and Daniel W. Bliss). 2.1 Introduction. 2.2 Notation. 2.3 MIMO Radar Virtual Aperture. 2.4 MIMO Radar in Clutter-Free Environments. 2.5 Optimality of MIMO Radar for Detection. 2.6 MIMO Radar with Moving Targets in Clutter: GMTI Radars. 2.7 Summary. Appendix 2A A Localization Principle. Appendix 2B Bounds on R(N). Appendix 2C An Operator Norm Inequality. Appendix 2D Negligible Terms. Appendix 2E Bound on Eigenvalues. Appendix 2F Some Inner Products. Appendix 2G An Invariant Inner Product. Appendix 2H Kro¨necker and Tensor Products. Acknowledgments. References. 3 Generalized MIMO Radar Ambiguity Functions (Geoffrey San Antonio, Daniel R. Fuhrmann, and Frank C. Robey). 3.1 Introduction. 3.2 Background. 3.3 MIMO Signal Model. 3.4 MIMO Parametric Channel Model. 3.5 MIMO Ambiguity Function. 3.6 Results and Examples. 3.7 Conclusion. References. 4 Performance Bounds and Techniques for Target Localization Using MIMO Radars (Joseph Tabrikian). 4.1 Introduction. 4.2 Problem Formulation. 4.3 Properties. 4.4 Target Localization. 4.5 Performance Lower Bound for Target Localization. 4.6 Simulation Results. 4.7 Discussion and Conclusions. Appendix 4A Log-Likelihood Derivation. Appendix 4B Transmit–Receive Pattern Derivation. Appendix 4C Fisher Information Matrix Derivation. References. 5 Adaptive Signal Design For MIMO Radars (Benjamin Friedlander). 5.1 Introduction. 5.2 Problem Formulation. 5.3 Estimation. 5.4 Detection. 5.5 MIMO Radar and Phased Arrays. Appendix 5A Theoretical SINR Calculation. References. 6 MIMO Radar Spacetime Adaptive Processing and Signal Design (Chun-Yang Chen and P. P. Vaidyanathan). 6.1 Introduction. 6.2 The Virtual Array Concept. 6.3 Spacetime Adaptive Processing in MIMO Radar. 6.4 Clutter Subspace in MIMO Radar. 6.5 New STAP Method for MIMO Radar. 6.6 Numerical Examples. 6.7 Signal Design of the STAP Radar System. 6.8 Conclusions. Acknowledgments. References. 7 Slow-Time MIMO SpaceTime Adaptive Processing (Vito F. Mecca, Dinesh Ramakrishnan, Frank C. Robey, and Jeffrey L. Krolik). 7.1 Introduction. 7.2 SIMO Radar Modeling and Processing. 7.3 Slow-Time MIMO Radar Modeling. 7.4 Slow-Time MIMO Radar Processing. 7.5 OTHr Propagation and Clutter Model. 7.6 Simulations Examples. 7.7 Conclusion. Acknowledgment. References. 8 MIMO as a Distributed Radar System (H. D. Griffiths, C. J. Baker, P. F. Sammartino, and M. Rangaswamy). 8.1 Introduction. 8.2 Systems. 8.3 Performance. 8.4 Conclusions. Acknowledgment. References. 9 Concepts and Applications of A MIMO Radar System with Widely Separated Antennas (Hana Godrich, Alexander M. Haimovich, and Rick S. Blum). 9.1 Background. 9.2 MIMO Radar Concept. 9.3 NonCoherent MIMO Radar Applications. 9.4 Coherent MIMO Radar Applications. 9.5 Chapter Summary. Appendix 9A Deriving the FIM. Appendix 9B Deriving the CRLB on the Location Estimate Error. Appendix 9C MLE of Time Delays — Error Statistics. Appendix 9D Deriving the Lowest GDOP for Special Cases. Acknowledgments. References. 10 SpaceTime Coding for MIMO Radar (Antonio De Maio and Marco Lops). 10.1 Introduction. 10.2 System Model. 10.3 Detection In MIMO Radars. 10.4 Spacetime Code Design. 10.5 The Interplay Between STC and Detection Performance. 10.6 Numerical Results. 10.7 Adaptive Implementation. 10.8 Conclusions. Acknowledgment. References. INDEX.
£126.85
John Wiley & Sons Inc Fundamentals Signal Processing
Book SynopsisFundamentals of Signal Processing for Sound and Vibration Engineers is based on Joe Hammond's many years of teaching experience at the Institute of Sound and Vibration Research, University of Southampton.Table of ContentsPreface. 1. Introduction to Signal Processing. 1.1 Descriptions of Physical Data (Signals). 1.2 Classification of Data. PART I: DETERMINISTIC SIGNALS. 2. Classification of Deterministic Data. 2.1 Periodic Signals. 2.2 Almost Periodic Signals. 2.3 Transient Signals. 2.4 Brief Summary and Concluding Remarks. 2.5 MATLAB Examples. 3. Fourier Series. 3.1 Periodic Signals and Fourier Series. 3.2 The Delta Function. 3.3 Fourier Series and the Delta Function. 3.4 The Complex Form of the Fourier Series. 3.5 Spectra. 3.6 Some Computational Considerations. 3.7 Brief Summary. 3.8 MATLAB Examples. 4. Fourier Integrals (Fourier Transform) and Continuous-Time Linear Systems. 4.1 The Fourier Integral. 4.2 Energy Spectra. 4.3 Some Examples of Fourier Transforms. 4.4 Properties of Fourier Transforms. 4.5 The Importance of Phase. 4.6 Echoes. 4.7 Continuous-Time Linear Time-Invariant Systems and Convolution. 4.8 Group Delay (Dispersion). 4.9 Minimum and Non-Minimum Phase Systems. 4.10 The Hilbert Transform. 4.11 The Effect of Data Truncation (Windowing). 4.12 Brief Summary. 4.13 MATLAB Examples. 5. Time Sampling and Aliasing. 5.1 The Fourier Transform of An Ideal Sampled Signal. 5.2 Aliasing and Anti-Aliasing Filters. 5.3 Analogue-to-Digital Conversion and Dynamic Range. 5.4 Some Other Considerations in Signal Acquisition. 5.5 Shannon’s Sampling Theorem (Signal Reconstruction). 5.6 Brief Summary. 5.7 MATLAB Examples. 6. The Discrete Fourier Transform. 6.1 Sequences and Linear Filters. 6.2 Frequency Domain Representation of Discrete Systems and Signals. 6.3 The Discrete Fourier Transform. 6.4 Properties of the DFT. 6.5 Convolution of Periodic Sequences. 6.6 The Fast Fourier Transform. 6.7 Brief Summary. 6.8 MATLAB Examples. PART II: INTRODUCTION TO RANDOM PROCESSES. 7. Random Processes. 7.1 Basic Probability Theory. 7.2 Random Variables and Probability Distributions. 7.3 Expectations of Functions of a Random Variable. 7.4 Brief Summary. 7.5 MATLAB Examples. 8. Stochastic Processes; Correlation Functions and Spectra. 8.1 Probability Distribution Associated with a Stochastic Process. 8.2 Moments of a Stochastic Process. 8.3 Stationarity. 8.4 The Second Moments of a Stochastic Process; Covariance. (Correlation) Functions. 8.5 Ergodicity and Time Averages. 8.6 Examples. 8.7 Spectra. 8.8 Brief Summary. 8.9 MATLAB Examples. 9. Linear System Response to Random Inputs: System Identification. 9.1 Single-Input, Single-Output Systems. 9.2 The Ordinary Coherence Function. 9.3 System Identification. 9.4 Brief Summary. 9.5 MATLAB Examples. 10. Estimation Methods and Statistical Considerations. 10.1 Estimator Errors and Accuracy. 10.2 Mean Value and Mean Square Value. 10.3 Correlation and Covariance Functions. 10.4 Power Spectral Density Function. 10.5 Cross-spectral Density Function. 10.6 Coherence Function. 10.7 Frequency Response Function. 10.8 Brief Summary. 10.9 MATLAB Examples. 11. Multiple-Input/Response Systems. 11.1 Description of Multiple-Input, Multiple-Output (MIMO) Systems. 11.2 Residual Random Variables, Partial and Multiple Coherence Functions. 11.3 Principal Component Analysis. Appendices. References. Index.
£79.16
John Wiley & Sons Inc Ultrafast AllOptical Signal Processing Devices
Book SynopsisSemiconductor-based Ultra-Fast All-Optical Signal Processing Devices a key technology for the next generation of ultrahigh bandwidth optical communication systems! The introduction of ultra-fast communication systems based on all-optical signal processing is considered to be one of the most promising ways to handle the rapidly increasing global communication traffic. Such systems will enable real time super-high definition moving pictures such as high reality TV-conference, remote diagnosis and surgery, cinema entertainment and many other applications with small power consumption. The key issue to realize such systems is to develop ultra-fast optical devices such as light sources, all-optical gates and wavelength converters. Ultra-Fast All-Optical Signal Processing Devices discusses the state of the art development of semiconductor-based ultrafast all-optical devices, and their various signal processing applications for bit-rates 100Gb/s to 1Tb/s. UltTable of ContentsContributors ix Preface xi 1 Introduction 1Hiroshi Ishikawa 1.1 Evolution of Optical Communication Systems and Device Technologies 1 1.2 Increasing Communication Traffic and Power Consumption 2 1.3 Future Networks and Technologies 4 1.3.1 Future Networks 4 1.3.2 Schemes for Huge Capacity Transmission 5 1.4 Ultrafast All-Optical Signal Processing Devices 6 1.4.1 Challenges 6 1.4.2 Basics of the Nonlinear Optical Process 7 1.5 Overview of the Devices and Their Concepts 11 1.6 Summary 13 References 13 2 Light Sources 15Yoh Ogawa and Hitoshi Murai 2.1 Requirement for Light Sources 15 2.1.1 Optical Short Pulse Source 16 2.1.2 Optical Time Division Multiplexer 19 2.2 Mode-locked Laser Diodes 20 2.2.1 Active Mode Locking 20 2.2.2 Passive Mode Locking 23 2.2.3 Hybrid Mode Locking 25 2.2.4 Optical Synchronous Mode Locking 27 2.2.5 Application for Clock Extraction 29 2.3 Electro-absorption Modulator Based Signal Source 30 2.3.1 Overview of Electro-absorption Modulator 30 2.3.2 Optical Short Pulse Generation Using EAM 33 2.3.3 Optical Time Division Multiplexer Based on EAMs 38 2.3.4 160-Gb/s Optical Signal Generation 41 2.3.5 Detection of a 160-Gb/s OTDM Signal 43 2.3.6 Transmission Issues 46 2.4 Summary 47 References 47 3 Semiconductor Optical Amplifier Based Ultrafast Signal Processing Devices 53Hidemi Tsuchida and Shigeru Nakamura 3.1 Introduction 53 3.2 Fundamentals of SOA 53 3.3 SOA as an Ultrafast Nonlinear Medium 56 3.4 Use of Ultrafast Response Component by Filtering 57 3.4.1 Theoretical Background 57 3.4.2 Signal Processing Using the Fast Response Component of SOA 60 3.5 Symmetric Mach–Zehnder (SMZ) All-Optical Gate 64 3.5.1 Fundamentals of the SMZ All-Optical Gate 64 3.5.2 Technology of Integrating Optical Circuits for an SMZ All-Optical Gate 67 3.5.3 Optical Demultiplexing 68 3.5.4 Wavelength Conversion and Signal Regeneration 73 3.6 Summary 83 References 83 4 Uni-traveling-carrier Photodiode (UTC-PD) and PD-EAM Optical Gate Integrating a UTC-PD and a TravelingWave Electro-absorption Modulator 89Hiroshi Ito and Satoshi Kodama 4.1 Introduction 89 4.2 Uni-traveling-carrier Photodiode (UTC-PD) 91 4.2.1 Operation 91 4.2.2 Fabrication and Characterization 96 4.2.3 Characteristics of the UTC-PD 98 4.2.4 Photo Receivers 114 4.3 Concept of a New Opto-electronic Integrated Device 117 4.3.1 Importance of High-output PDs 117 4.3.2 Monolithic Digital OEIC 118 4.3.3 Monolithic PD-EAM Optical Gate 118 4.4 PD-EAM Optical Gate Integrating UTC-PD and TW-EAM 119 4.4.1 Basic Structure 119 4.4.2 Design 120 4.4.3 Optical Gating Characteristics of PD-EAM 123 4.4.4 Fabrication 125 4.4.5 Gating Characteristics 127 4.4.6 Applications for Ultrafast All-Optical Signal Processing 131 4.4.7 Future Work 143 4.5 Summary and Prospects 147 References 148 5 Intersub-band Transition All-Optical Gate Switches 155Nobuo Suzuki, Ryoichi Akimoto, Hiroshi Ishikawa and Hidemi Tsuchida 5.1 Operation Principle 155 5.1.1 Transition Wavelength 156 5.1.2 Matrix Element 157 5.1.3 Saturable Absorption 157 5.1.4 Absorption Recovery Time 158 5.1.5 Dephasing Time and Spectral Linewidth 160 5.1.6 Gate Operation in Waveguide Structure 162 5.2 GaN/AlN ISBT Gate 164 5.2.1 Absorption Spectra 165 5.2.2 Saturation of Absorption in Waveguides 168 5.2.3 Ultrafast Optical Gate 170 5.3 (CdS/ZnSe)/BeTe ISBT Gate 172 5.3.1 Growth of CdS/ ZnSe/ BeTe QWs and ISBT Absorption Spectra 173 5.3.2 Waveguide Structure for a CdS/ ZnSe/ BeTe Gate 177 5.3.3 Characteristics of a CdS/ ZnSe/ BeTe Gate 181 5.4 InGaAs/AlAs/AlAsSb ISBT Gate 183 5.4.1 Device Structure and its Fabrication 183 5.4.2 Saturation Characteristics and Time Response 184 5.5 Cross-phase Modulation in an InGaAs/AlAs/AlAsSb-based ISBT Gate 186 5.5.1 Cross-phase Modulation Effect and its Mechanisms 187 5.5.2 Application to Wavelength Conversion 192 5.6 Summary 195 References 196 6 Wavelength Conversion Devices 201Haruhiko Kuwatsuka 6.1 Introduction 201 6.2 Wavelength Conversion Schemes 202 6.2.1 Optical Gate Switch Type 202 6.2.2 Coherent Type Conversion 204 6.3 Physics of Four-wave Mixing in LDs or SOAs 205 6.3.1 Model 205 6.3.2 Asymmetric χ(3) for Positive and Negative Detuning 210 6.3.3 Symmetric χ(3) in Quantum Dot SOAs 212 6.4 Wavelength Conversion of Short Pulses Using FWM in Semiconductor Devices 214 6.4.1 Model 214 6.4.2 The Effect of the Stop Band in DFB-LDs 217 6.4.3 The Effect of the Depletion of Gain 218 6.4.4 The Pulse Width Broadening in FWM Wavelength Conversion 219 6.5 Experimental Results ofWavelength Conversion Using FWM in SOAs or LDs 220 6.5.1 Wavelength Conversion of Short Pulses Using a DFB-LD 220 6.5.2 Wavelength Conversion of 160-Gb/s OTDM Signal Using a Quantum Dot SOAs 221 6.5.3 Format-free Wavelength Conversion 222 6.5.4 Chromatic Dispersion Compensation of Optical Fibers Using FWM in DFB-LDs 224 6.6 The Future View ofWavelength Conversion Using FWM 225 6.7 Summary 226 References 226 7 Summary and Future Prospects 231Hiroshi Ishikawa 7.1 Introduction 231 7.2 Transmission Experiments 231 7.2.1 FESTA Experiments 231 7.2.2 Test Bed Field Experiment 235 7.2.3 Recent Transmission Experiments above 160-Gb/s 236 7.3 Requirements on Devices and Prospects 238 7.3.1 Devices Described in this Book 238 7.3.2 Necessity for New Functionality Devices and Technology 240 7.4 Summary 241 References 242 Index 243
£115.16
John Wiley & Sons Inc Discover Signal Processing
Book SynopsisSignal processing is now a multidisciplinary topic, and one that has applications in many fields including, but not limited to, science, engineering, medicine, finance and the behavioural sciences. Modern software libraries that include dedicated languages and packages designed to simplify the development and application of signal processing techniques are now readily available; however this ease of application means that an understanding of the various techniques is imperative. It is critical that the student or practitioner is able to choose an appropriate processing technique, be aware of potential errors involved and understand how to control them. Discover Signal Processing exploits the rationale of learning by doing; actually attempting and performing a task is the most effective way to remember and understand. It presents the reader with a diverse range of exercises; some intended to recall or practice simple concepts, others more complex & aimed at developing a real Table of ContentsPreface. About the Author. Notation. Part A: The Exercises. 1 Introduction. Overview. The Exercises. Exercise 1.1. Exercise 1.2. Solutions and Summaries. 2 Signals. Overview. The Exercises. Exercise 2.1. Exercise 2.2. Exercise 2.3. Exercise 2.4. Solutions and Summaries. 3 Fourier Methods. Overview. The Exercises. Exercise 3.1. Exercise 3.2. Exercise 3.3. Exercise 3.4. Exercise 3.5. Exercise 3.6. Exercise 3.7. Exercise 3.8. Exercise 3.9. Exercise 3.10. Exercise 3.11. Exercise 3.12. Solutions and Summaries. 4 Linear Systems. Overview. The Exercises. Exercise 4.1. Exercise 4.2. Exercise 4.3. Solutions and Summaries. 5 Filters. Overview. The Exercises. Exercise 5.1. Exercise 5.2. Exercise 5.3. Exercise 5.4. Solutions and Summaries. 6 Time Domain Averaging (TDA). Overview. The Exercises. Exercise 6.1. Exercise 6.2. Exercise 6.3. Exercise 6.4. Solutions and Summaries. 7 Spectral Analysis. Overview. The Exercises. Exercise 7.1(a). Exercise 7.1(b). Exercise 7.2. Exercise 7.3. Exercise 7.4. Exercise 7.5. Exercise 7.6. Exercise 7.7. Exercise 7.8. Exercise 7.9. Solutions and Summaries. 8 Envelope Detection. Overview. The Exercises. Exercise 8.1. Solutions and Summaries. 9 The Spectrogram. Overview. The Exercises. Exercise 9.1. Exercise 9.2. Solutions and Summaries. 10 Sampling. Overview. The Exercises. Exercise 10.1. Exercise 10.2. Exercise 10.3. Exercise 10.4. Solutions and Summaries. 11 Identifi cation – Transfer Functions. Overview. The Exercises. Exercise 11.1. Exercise 11.2. Exercise 11.3. Exercise 11.4. Solutions and Summaries. 12 Model-based Signal Processing. Overview. The Exercises. Exercise 12.1. Exercise 12.2. Exercise 12.3. Solutions and Summaries. 13 Diagnostic Applications for Rotating Machines. Overview. The Exercises. Exercise 13.1. Exercise 13.2. Exercise 13.3. Exercise 13.4. Exercise 13.5. Solutions and Summaries. 14 Systems with Delays. Overview. The Exercises. Exercise 14.1. Exercise 14.2. Exercise 14.3. Solutions and Summaries. Part B. 1 Introduction. 1.1 General Objectives. 1.2 Basic Processing. 1.3 Why the Frequency Domain? 1.4 An Introductory Example. 2 Introduction to Signals. 2.1 Signal Classification. 2.2 Signal Descriptions. 2.3 Correlation Functions. 2.4 Estimation and Errors. 3 Fourier Methods. 3.1 Fourier Series. 3.2 Fourier (Integral) Transform. 3.3 The Uncertainty Principle. 3.4 The Discrete Fourier Transform (DFT). 3.5 The DFT and the Fast Fourier Transform (FFT). 3.6 Discontinuities and Windows. 4 Linear Systems. 4.1 Continuous Systems. 4.2 Discrete Systems. 4.3 A Specifi c Case of a Continuous Linear Systems – Accelerometers. Appendix 4.A The Lightly Damped SDOF System. 5 Filters. 5.1 Preliminaries. 5.2 Analog and Digital Filters. 5.3 Filter Classifi cation and Specifications. 5.4 IIR Filters. 5.5 FIR Filters. 5.6 The Importance of Linear Phase Filters. 5.7 Design Tools. 6 Time Domain Averaging (Synchronous Averaging). 6.1 Principle. 6.2 Rejection of Nonsynchronous Components. 6.3 TDA with Decaying Memory Process. 7 Spectral Analysis. 7.1 Introduction. 7.2 Representation of Signals in the Frequency Domain. 7.3 Errors and their Control. 7.4 Spectral Analysis: Practical Considerations. 8 Envelopes. 8.1 Introduction. 8.2 The Hilbert Transform (HT). 8.3 Analytic Signals. 8.4 Narrow Band (NB) Signals and their Envelope. 9 The Spectrogram. 9.1 Introduction. 9.2 Time Frequency Methods. 9.3 The Short Time Fourier Transform (STFT) and the Spectrogram. 10 Data Acquisition. 10.1 Data Acquisition and Signal Processing Systems. 10.2 Amplitude Quantization. 10.3 Quantization in Time: The Sampling Theorem. 10.4 Antialiasing Filters. 11 Input/Output Identifi cation. 11.1 Objectives and Overview. 11.2 Frequency Domain Identifi cation: The Noiseless Case. 11.3 Identifi cation with Noise Corrupted Signals. 11.4 Error Mechanisms and their Control in the Identifi cation Process. 11.5 Estimation Errors for the Coherence Function. 12 Model-based Signal Processing. 12.1 General. 12.2 Signal Models. 12.3 Modeling of Signals. 12.4 Model-based Spectral Analysis. 12.5 Model or Selection. 12.6 Model-based Diagnostics. Appendix 12.A The Correlation Matrix. 13 Machinery Diagnostics: Bearings and Gears. 13.1 Diagnostics and Rotating Machinery. 13.2 Structural Effects. 13.3 Rotating Imbalance. 13.4 Modeling of Roller Bearing Vibration Signals. 13.5 Bearing Vibrations: Structural Effects and Envelopes. 13.6 Modeling of Gear Vibration Signals. 14 Delays and Echoes. 14.1 System with Pure Delays. 14.2 Correlation Functions. 14.3 Cepstral Analysis. References. Index.
£81.65
John Wiley & Sons Inc DAFX
Book SynopsisRapid development in different fields of Digital Audio Effects (DAFX) has led to new algorithms. The Second Edition of DAFX - Digital Audio Effects investigates digital signal processing, its application to sound, and how its effects on sound can be used within music.Table of ContentsPreface. List of Contributors. 1 Introduction (V. Verfaille, M. Holters, U. Zölzer). 1.1 Digital Audio Effects DAFX with MATLAB. 1.2 Classifications of DAFX. 1.3 Fundamentals of Digital Signal Processing. 1.4 Conclusion. Bibliography. 2 Filters and Delays (P. Dutilleux, M. Holters, S. Disch, U. Zölzer). 2.1 Introduction. 2.2 Basic Filters. 2.3 Equalizers. 2.4 Time-varying Filters. 2.5 Basic Delay Structures. 2.6 Delay-based Audio Effects. 2.7 Conclusion. Sound and Music. Bibliography. 3 Modulators and Demodulators (P. Dutilleux, M. Holters, S. Disch, U. Zölzer). 3.1 Introduction. 3.2 Modulators. 3.3 Demodulators. 3.4 Applications. 3.5 Conclusion. Sound and Music. Bibliography. 4 Nonlinear Processing (P. Dutilleux, K. Dempwolf, M. Holters, U. Zölzer). 4.1 Introduction. 4.2 Dynamic Range Control. 4.3 Musical Distortion and Saturation Effects. 4.4 Exciters and Enhancers. 4.5 Conclusion. Sound and Music. Bibliography. 5 Spatial Effects (V. Pulkki, T. Lokki, D. Rocchesso). 5.1 Introduction. 5.2 Concepts of spatial hearing. 5.3 Basic spatial effects for stereophonic loudspeaker and headphone playback. 5.4 Binaural techniques in spatial audio. 5.5 Spatial audio effects for multichannel loudspeaker layouts. 5.6 Reverberation. 5.7 Modeling of room acoustics. 5.8 Other spatial effects. 5.9 Conclusion. 5.10 Acknowledgements. References. 6 Time-Segment Processing (P. Dutilleux, G. De Poli, A. von dem Knesebeck, U. Zölzer). 6.1 Introduction. 6.2 Variable Speed Replay. 6.3 Time Stretching. 6.4 Pitch Shifting. 6.5 Time Shuffling and Granulation. 6.6 Conclusion. Sound and Music. References. 7 Time-Frequency Processing (D. Arfib, F. Keiler, U. Zölzer, V. Verfaille, J. Bonada). 7.1 Introduction. 7.2 Phase Vocoder Basics. 7.3 Phase Vocoder Implementations. 7.4 Phase Vocoder Effects. 7.5 Conclusion. References. 8 Source-Filter Processing (D. Arfib, F. Keiler, U. Zölzer, V. Verfaille). 8.1 Introduction. 8.2 Source-Filter Separation. 8.3 Source-Filter Transformations. 8.4 Conclusion. References. 9 Adaptive Digital Audio Effects (V. Verfaille, D. Arfib, F. Keiler, A. von dem Knesebeck, U. Zölzer). 9.1 Introduction. 9.2 Sound-Feature Extraction. 9.3 Mapping Sound Features to Control Parameters. 9.4 Examples of Adaptive DAFX. 9.5 Conclusions. References. 10 Spectral Processing (J. Bonada, X. Serra, X. Amatriain, A. Loscos). 10.1 Introduction. 10.2 Spectral Models. 10.3 Techniques. 10.4 Effects. 10.5 Conclusions. References. 11 Time and Frequency Warping-Musical Signals (G. Evangelista). 11.1 Introduction. 11.2 Warping. 11.3 Musical Uses of Warping. 11.4 Conclusion. References. 12 Virtual Analog Effects (V. Välimäki, S. Bilbao, J. O. Smith, J. S. Abel, J. Pakarinen, D. Berners). 12.1 Introduction. 12.2 Virtual Analog Filters. 12.3 Circuit-Based Valve Emulation. 12.4 Electromechanical Effects. 12.5 Tape-Based Echo Simulation. 12.6 Antiquing of Audio Files. 12.7 Conclusion. References. 13 Automatic Mixing (E. Perez-Gonzalez, J. D. Reiss). 13.1 Introduction. 13.2 AM-DAFX. 13.3 Cross-adaptive AM-DAFX. 13.4 AM-DAFX Implementations. 13.5 Conclusion. References. 14 Sound Source Separation (G. Evangelista, S. Marchand, M. D. Plumbley, E. Vincent). 14.1 Introduction. 14.2 Binaural Source Separation. 14.3 Source Separation from Single-Channel Signals. 14.4 Applications. 14.5 Conclusions. Acknowledgments. References. Glossary. Index.
£79.16
John Wiley & Sons Inc Digital Design of Signal Processing Systems
Book SynopsisDigital Design of Signal Processing Systems discusses a spectrum of architectures and methods for effective implementation of algorithms in hardware (HW). Encompassing all facets of the subject this book includes conversion of algorithms from floating-point to fixed-point format, parallel architectures for basic computational blocks, Verilog Hardware Description Language (HDL), SystemVerilog and coding guidelines for synthesis. The book also covers system level design of Multi Processor System on Chip (MPSoC); a consideration of different design methodologies including Network on Chip (NoC) and Kahn Process Network (KPN) based connectivity among processing elements. A special emphasis is placed on implementing streaming applications like a digital communication system in HW. Several novel architectures for implementing commonly used algorithms in signal processing are also revealed. With a comprehensive coverage of topics the book provides an appropriate mix of examples to iTrade Review"It can be used in a course on advanced digital design and VLSI signal processing at the senior undergraduate or graduate level." (Booknews, 1 April 2011)Table of ContentsPreface. Acknowledgement. 1 Overview. 1.1 Introduction. 1.2 Fueling the Innovation: Moore’s Law. 1.3 Digital Systems. 1.4 Examples of Digital Systems. 1.5 Components of the Digital Design Process. 1.6 Competing Objectives in Digital Process. 1.7 Synchronous Digital Hardware Systems. 1.8 Design Strategies. References. 2. Using a Hardware Description Language. 2.1 Overview. 2.2 About Verilog. 2.3 System Design Flow. 2.4 Logic Synthesis. 2.5 Using the Verilog HDL. 2.6 Four Levels of Abstraction. 2.7 Verification in Hardware Design. 2.8 Example of a Verification Setup. 2.9 SystemVerilog. Exercises. References. 3. System Design Flow and Fixed-Point Arithmetic. 3.1 Overview. 3.2 System Design Flow. 3.3 Representations and Numbers. 3.4 Floating-point Format. 3.5 Qn.m Format for Fixed-point Arithmetic. 3.6 Floating-Point to Fixed-Point Conversion. 3.7 Block Floating-Point Format. 3.8 Forms of Digital Filter. Exercises. References. 4. Mapping on Fully Dedicated Architecture. 4.1 Introduction. 4.2 Discrete Real-Time Systems. 4.3 Synchronous Digital Hardware Systems. 4.4 Kahn Process Network. 4.5 Methods of Representing DSP Systems. 4.6 Performance Measures. 4.7 Fully Dedicated Architecture. 4.8 DFG to HW Synthesis. Exercises. References. 5. Design Options for Basic Building Blocks. 5.1 Introduction. 5.2 Embedded Processors and Arithmetic Units in FPGAs. 5.3 Instantiation of Embedded Blocks. 5.4 Basic Building Blocks: Introduction. 5.5 Adders. 5.6 Barrel Shifter. 5.7 Cary Save Adder and Compressors. 5.8 Parallel Multipliers. 5.9 Two’s Complement Signed Multiplier. 5.10 Compression Trees for Multi-operand Addition. 5.11 Algorithm Transformations for CSA. Exercises. References. 6. Multiplier-less Multiplication by Constants. 6.1 Introduction. 6.2 Canonic Sign Digit Representation. 6.3 Minimum Signed Digit Representation. 6.4 Multiplication by Constant in Signal Processing Algorithm. 6.5 Optimized DFG Transformation. 6.6 Fully Dedicated Architecture for Direct-form FIR Filter. 6.7 Complexity Reduction. 6.8 Distributed Arithmetic. 6.9 FFT Architecture using FIR Filter Structure. Exercises. References. 7. Pipelining, Retiming, Look-ahead Transformation and Polyphase Decomposition. 7.1 Introduction. 7.2 Pipelining and Retiming. 7.3 Digital Design of Feedback Systems. 7.4 C-slow Retiming. 7.5 Look-ahead Transformation for IIR filters. 7.6 Look-ahead Transformation for Generalized IIR Filters. 7.7 Polyphase Structure for Decimation and Interpolation Applications. 7.8 IIR Filter for Decimation and Interpolation. Exercises. References. 8. Unfolding and Folding Architectures. 8.1 Introduction. 8.2 Unfolding. 8.3 Sampling Rate Considerations. 8.4 Unfolding Techniques. 8.5 Folding Techniques. 8.6 Mathematical Transformation for Folding. 8.7 Algorithmic Transformation. Exercises. References. 9.Designs based on Finite State Machines. 9.1 Introduction. 9.2 Examples of Time-shared Architecture Design. 9.3 Sequencing and Control. 9.4 Algorithmic State Machine Representation. 9.5 FSM Optimization for Low Power and Area. 9.6 Designing for Testability. 9.7 Methods for Reducing Power Dissipation. Exercises. References. 10. Micro-programmed State Machines. 10.1 Introduction. 10.2 Micro-programmed Controller. 10.3 Counter-based State Machine. 10.4 Subroutine Support. 10.5 Nested Subroutine Support. 10.6 Nested Loop Support. 10.7 Examples. Exercises. References. 11. Micro-programmed Adaptive Filtering Applications. 11.1 Introduction. 11.2 Adaptive Filters Configurations. 11.3 Adaptive Algorithms. 11.4 Channel Equalizer using NLMS. 11.5 Echo Canceller. 11.6 Adaptive Algorithms with Micro-programmed State Machines. Exercises. References. 12 CORDIC-based DDFS Architectures. 12.1 Introduction. 12.2 Direct Digital Frequency Synthesizer. 12.3 Design of a Basic DDFS. 12.4 The CORDIC Algorithm. 12.5 Hardware Mapping of Modified CORDIC Algorithm. Exercises. References. 13. Digital Design of Communication Systems. 13.1 Introduction. 13.2 Top-level Design Options. 13.3 Typical Digital Communication System. Exercises. References. Index.
£89.06
John Wiley & Sons Inc healthmonitoringaerospacestructures
Book SynopsisMaintenance and continuous health monitoring of air, land and sea structures is one of the most important concerns in a wide range of industries including transportation and civil engineering. Effective maintenance minimises not only the cost of ownership of structures but also improves safety and the perception of safety.Trade Review"...very relevant and timely...strongly recommend this multidisciplinary book...an integrated volume of real value..." (Measurement and Control, Vol 37(5), June 2004)Table of ContentsList of Contributors. Preface. 1. Introduction (G. Bartelds, J.H. Heida, J. McFeat and C. Boller). 1.1 Health and Usage Monitoring in Aircraft Structures – Why and How? 1.2 Smart Solution in Aircraft Monitoring. 1.3 End-User Requirements. 1.3.1 Damage Detection. 1.3.2 Load History Monitoring. 1.4 Assessment of Monitoring Technologies. 1.5 Background of Technology Qualification Process. 1.6 Technology Qualification. 1.6.1 Philosophy. 1.6.2 Performance and Operating Requirements. 1.6.3 Qualification Evidence – Requirements and Provision. 1.6.4 Risks. 1.7 Flight Vehicle Certification. 1.8 Summary. References. 2. Aircraft Structural Health and Usage Monitoring (C. Boller and W.J. Staszewski). 2.1 Introduction. 2.2 Aircraft Structural Damage. 2.3 Ageing Aircraft Problem. 2.4 LifeCycle Cost of Aerospace Structures. 2.4.1 Background. 2.4.2 Example. 2.5 Aircraft Structural Design. 2.5.1 Background. 2.5.2 Aircraft Design Process. 2.6 Damage Monitoring Systems in Aircraft. 2.6.1 Loads Monitoring. 2.6.2 Fatigue Monitoring. 2.6.3 Load Models. 2.6.4 Disadvantages of Current Loads Monitoring Systems. 2.6.5 Damage Monitoring and Inspections. 2.7 Non-Destructive Testing. 2.7.1 Visual Inspection. 2.7.2 Ultrasonic Inspection. 2.7.3 Eddy Current. 2.7.4 Acoustic Emission. 2.7.5 Radiography, Thermography and Shearography. 2.7.6 Summary. 2.8 Structural Health Monitoring. 2.8.1 Vibration and Modal Analysis. 2.8.2 Impact Damage Detection. 2.9 Emerging Monitoring Techniques and Sensor Technologies. 2.9.1 Smart Structures and Materials. 2.9.2 Damage Detection Techniques. 2.9.3 Sensor Technologies. 2.9.4 Intelligent Signal Processing. 2.10 Conclusions. References. 3. Operational Load Monitoring Using Optical Fibre Sensors (P. Foote, M. Breidne, K. Levin, P. Papadopolous, I. Read, M. Signorazzi, L.K. Nilsson, R. Stubbe and A. Claesson). 3.1 Introduction. 3.2 Fibre Optics. 3.2.1 Optical Fibres. 3.2.2 Optical Fibre Sensors. 3.2.3 Fibre Bragg Grating Sensors. 3.3 Sensor Target Specifications. 3.4 Reliability of Fibre Bragg Grating Sensors. 3.4.1 Fibre Strength Degradation. 3.4.2 Grating Decay. 3.4.3 Summary. 3.5 Fibre Coating Technology. 3.5.1 Polyimide Chemistry and Processing. 3.5.2 Polyimide Adhesion to Silica. 3.5.3 Silane Adhesion Promoters. 3.5.4 Experimental Example. 3.5.5 Summary. 3.6 Example of Surface Mounted Operational Load Monitoring Sensor System. 3.6.1 Sensors. 3.6.2 Optical Signal Processor. 3.6.3 Optical Interconnections. 3.7 Optical Fibre Strain Rosette. 3.8 Example of Embedded Optical Impact Detection System. 3.9 Summary. References. 4. Damage Detection Using Stress and Ultrasonic Waves (W.J. Staszewski, C. Boller, S. Grondel, C. Biemans, E. O’Brien, C. Delebarre and G.R. Tomlinson). 4.1 Introduction. 4.2 Acoustic Emission. 4.2.1 Background. 4.2.2 Transducers. 4.2.3 Signal Processing. 4.2.4 Testing and Calibration. 4.3 Ultrasonics. 4.3.1 Background. 4.3.2 Inspection Modes. 4.3.3 Transducers. 4.3.4 Display Modes. 4.4 Acousto-Ultrasonics. 4.5 Guided Wave Ultrasonics. 4.5.1 Background. 4.5.2 Guided Waves. 4.5.3 Lamb Waves. 4.5.4 Monitoring Strategy. 4.6 Piezoelectric Transducers. 4.6.1 Piezoelectricity and Piezoelectric Materials. 4.6.2 Constitutive Equations. 4.6.3 Properties. 4.7 Passive Damage Detection Examples. 4.7.1 Crack Monitoring Using Acoustic Emission. 4.7.2 Impact Damage Detection in Composite Materials. 4.8 Active Damage Detection Examples. 4.8.1 Crack Monitoring in Metallic Structures Using Broadband Acousto-Ultrasonics. 4.8.2 Impact Damage Detection in Composite Structures Using Lamb Waves. 4.9 Summary. References. 5. Signal Processing for Damage Detection (W.J. Staszewski and K. Worden). 5.1 Introduction. 5.2 Data Pre-Processing. 5.2.1 Signal Smoothing. 5.2.2 Signal Smoothing Filters. 5.3 Signal Features for Damage Identification. 5.3.1 Feature Extraction. 5.3.2 Feature Selection. 5.4 Time–Domain Analysis. 5.5 Spectral Analysis. 5.6 Instantaneous Phase and Frequency. 5.7 Time–Frequency Analysis. 5.8 Wavelet Analysis. 5.8.1 Continuous Wavelet Transform. 5.8.2 Discrete Wavelet Transform. 5.9 Dimensionality Reduction Using Linear and Nonlinear Transformation. 5.9.1 Principal Component Analysis. 5.9.2 Sammon Mapping. 5.10 Data Compression Using Wavelets. 5.11 Wavelet-Based Denoising. 5.12 Pattern Recognition for Damage Identification. 5.13 Artificial Neural Networks. 5.13.1 Parallel Processing Paradigm. 5.13.2 The Artificial Neuron. 5.13.3 Multi-Layer Networks. 5.13.4 Multi-Layer Perceptron Neural Networks and Others. 5.13.5 Applications. 5.14 Impact Detection in Structures Using Pattern Recognition. 5.14.1 Detection of Impact Positions. 5.14.2 Detection of Impact Energy. 5.15 Data Fusion. 5.16 Optimised Sensor Distributions. 5.16.1 Informativeness of Sensors. 5.16.2 Optimal Sensor Location. 5.17 Sensor Validation. 5.18 Conclusions. References. 6. Structural Health Monitoring Evaluation Tests (P.A. Lloyd, R. Pressland, J. McFeat, I. Read, P. Foote, J.P. Dupuis, E. O’Brien, L. Reithler, S. Grondel, C. Delebarre, K. Levin, C. Boller, C. Biemans and W.J. Staszewski). 6.1 Introduction. 6.2 Large-Scale Metallic Evaluator. 6.2.1 Lamb Wave Results from Riveted Metallic Specimens. 6.2.2 Acoustic Emission Results from a Full-Scale Fatigue Test. 6.3 Large-Scale Composite Evaluator. 6.3.1 Test Article. 6.3.2 Sensor and Specimen Integration. 6.3.3 Impact Tests. 6.3.4 Damage Detection Results – Distributed Optical Fibre Sensors. 6.3.5 Damage Detection Results – Bragg Grating Sensors. 6.3.6 Lamb Wave Damage Detection System. 6.4 Flight Tests. 6.4.1 Flying Test-Bed. 6.4.2 Acoustic Emission Optical Damage Detection System. 6.4.3 Bragg Grating Optical Load Measurement System. 6.4.4 Fibre Optic Load Measurement Rosette System. 6.5 Summary. References. Index.
£100.76
John Wiley & Sons Inc Digital Signal Processing Using MATLAB for
Book SynopsisQuickly Engages in Applying Algorithmic Techniques to Solve Practical Signal Processing Problems With its active, hands-on learning approach, this text enables readers to master the underlying principles of digital signal processing and its many applications in industries such as digital television, mobile and broadband communications, and medical/scientific devices. Carefully developed MATLAB examples throughout the text illustrate the mathematical concepts and use of digital signal processing algorithms. Readers will develop a deeper understanding of how to apply the algorithms by manipulating the codes in the examples to see their effect. Moreover, plenty of exercises help to put knowledge into practice solving real-world signal processing challenges. Following an introductory chapter, the text explores: Sampled signals and digital processing Random signals Representing signals and systems TemporTrade Review"Intended for undergraduate or graduate students in engineering or related disciplines, this introductory volume examines key theories in signal processing and presents this information optimized for use with MATLAB technical computing software." (Book News, 1 October 2011) Table of ContentsPreface xi Chapter 1. What Is Signal Processing? 1 1.1 Chapter Objectives 1 1.2 Introduction 1 1.3 Book Objectives 2 1.4 DSP and ITS Applications 3 1.5 Application Case Studies Using DSP 4 1.6 Overview of Learning Objectives 12 1.7 Conventions Used in This Book 15 1.8 Chapter Summary 16 Chapter 2. Matlab for Signal Processing 19 2.1 Chapter Objectives 19 2.2 Introduction 19 2.3 What Is MATLAB? 19 2.4 Getting Started 20 2.5 Everything Is a Matrix 20 2.6 Interactive Use 21 2.7 Testing and Looping 23 2.8 Functions and Variables 25 2.9 Plotting and Graphing 30 2.10 Loading and Saving Data 31 2.11 Multidimensional Arrays 35 2.12 Bitwise Operators 37 2.13 Vectorizing Code 38 2.14 Using MATLAB for Processing Signals 40 2.15 Chapter Summary 43 Chapter 3. Sampled Signals and Digital Processing 45 3.1 Chapter Objectives 45 3.2 Introduction 45 3.3 Processing Signals Using Computer Algorithms 45 3.4 Digital Representation of Numbers 47 3.5 Sampling 61 3.6 Quantization 64 3.7 Image Display 74 3.8 Aliasing 81 3.9 Reconstruction 84 3.10 Block Diagrams and Difference Equations 88 3.11 Linearity, Superposition, and Time Invariance 92 3.12 Practical Issues and Computational Efficiency 95 3.13 Chapter Summary 98 Chapter 4. Random Signals 103 4.1 Chapter Objectives 103 4.2 Introduction 103 4.3 Random and Deterministic Signals 103 4.4 Random Number Generation 105 4.5 Statistical Parameters 106 4.6 Probability Functions 108 4.7 Common Distributions 112 4.8 Continuous and Discrete Variables 114 4.9 Signal Characterization 116 4.10 Histogram Operators 117 4.11 Median Filters 122 4.12 Chapter Summary 125 Chapter 5. Representing Signals and Systems 127 5.1 Chapter Objectives 127 5.2 Introduction 127 5.3 Discrete-Time Waveform Generation 127 5.4 The z Transform 137 5.5 Polynomial Approach 144 5.6 Poles, Zeros, and Stability 146 5.7 Transfer Functions and Frequency Response 152 5.8 Vector Interpretation of Frequency Response 153 5.9 Convolution 156 5.10 Chapter Summary 160 Chapter 6. Temporal and Spatial Signal Processing 165 6.1 Chapter Objectives 165 6.2 Introduction 165 6.3 Correlation 165 6.4 Linear Prediction 177 6.5 Noise Estimation and Optimal Filtering 183 6.6 Tomography 188 6.7 Chapter Summary 201 Chapter 7. Frequency Analysis of Signals 203 7.1 Chapter Objectives 203 7.2 Introduction 203 7.3 Fourier Series 203 7.4 How Do the Fourier Series Coefficient Equations Come About? 209 7.5 Phase-Shifted Waveforms 211 7.6 The Fourier Transform 212 7.7 Aliasing in Discrete-Time Sampling 231 7.8 The FFT as a Sample Interpolator 233 7.9 Sampling a Signal over a Finite Time Window 236 7.10 Time-Frequency Distributions 240 7.11 Buffering and Windowing 241 7.12 The FFT 243 7.13 The DCT 252 7.14 Chapter Summary 266 Chapter 8. Discrete-Time Filters 271 8.1 Chapter Objectives 271 8.2 Introduction 271 8.3 What Do We Mean by “Filtering”? 272 8.4 Filter Specification, Design, and Implementation 274 8.5 Filter Responses 282 8.6 Nonrecursive Filter Design 285 8.7 Ideal Reconstruction Filter 293 8.8 Filters with Linear Phase 294 8.9 Fast Algorithms for Filtering, Convolution, and Correlation 298 8.10 Chapter Summary 311 Chapter 9. Recursive Filters 315 9.1 Chapter Objectives 315 9.2 Introduction 315 9.3 Essential Analog System Theory 319 9.4 Continuous-Time Recursive Filters 326 9.5 Comparing Continuous-Time Filters 339 9.6 Converting Continuous-Time Filters to Discrete Filters 340 9.7 Scaling and Transformation of Continuous Filters 361 9.8 Summary of Digital Filter Design via Analog Approximation 371 9.9 Chapter Summary 372 Bibliography 375 Index 379
£82.76
John Wiley & Sons Inc Polynomial Signal Processing
Book SynopsisDespite our growing understanding of the properties and capabilities of nonlinear filters, there persists the belief among engineers that these filters are too complex to implement. This book debunks the myth that all nonlinear filters are complex with its coverage of the polynomial filter.Trade Review"A first-year graduate-level text that provides an overview of the state of the art in the area of nonlinear signal processing known as polynomial signal processing." (SciTech Book News Vol. 25, No. 2 June 2001) "The text is clear and easy to follow - an excellent way of getting started in this area." (Ultramicroscopy, Vol.87, 2001)Table of ContentsVolterra Series Expansions. Realization of Truncated Volterra Filters. Multidimensional Volterra Filters. Parameter Estimation. Frequency-Domain Methods for Volterra System Identification. Adaptive Truncated Volterra Filters. Recursive Polynomial Systems. Inversion and Time Series Analysis. Applications of Polynomial Filters. Some Related Topics and Recent Developments. Appendices. References. Index.
£167.36
John Wiley & Sons Inc Analog MOS Integrated Circuits for Signal
Book SynopsisDescribes the operating principles of analog MOS integrated circuits and how to design and use such circuits. The initial section explores general properties of analog MOS integrated circuits and the math and physics background required. The remainder of the book is devoted to the design of circuits.Table of ContentsTransformation Methods. MOS Devices as Circuit Elements. MOS Operational Amplifiers. Switched-Capacitor Filters. Nonfiltering Applications of Switched-Capacitor Circuits. Nonideal Effects in Switched-Capacitor Circuits. Systems Considerations and Applications. Index.
£226.76
John Wiley & Sons Inc Nonlinear and Adaptive Control Design
Book SynopsisUsing a pedagogical style along with detailed proofs and illustrative examples, this book opens a view to the largely unexplored area of nonlinear systems with uncertainties. The focus is on adaptive nonlinear control results introduced with the new recursive design methodology--adaptive backstepping.Table of ContentsSTATE FEEDBACK. Design Tools for Stabilization. Adaptive Backstepping Design. Tuning Functions Design. Modular Design with Passive Identifiers. Modular Design with Swapping Identifiers. OUTPUT FEEDBACK. Output-Feedback Design Tools. Tuning Functions Designs. Modular Designs. Linear Systems. Appendices. Bibliography. Index.
£168.26
John Wiley and Sons Ltd Signals and Systems
Book SynopsisThis title provides an integrated treatment of continuous-time and discrete-time forms of signals and systems intended to reflect their roles in engineering practice.Table of ContentsChapter 1. Introduction. Chapter 2. Time-Domain Representations of Linear Time-Invariant Systems. Chapter 3. Fourier Representations of Signals and Linear Time Invariant Systems. Chapter 4. Applications of Fourier Representations to Mixed Signal Classes. Chapter 5. Application to Communication Systems. Chapter 6. Representing Signals by Using Continuous-Time Complex Exponentials: The Laplace Transform. Chapter 7. Representing Signals by Using Discrete-Time Complex Exponentials: The z-Transform. Chapter 8. Application to Filters and Equalizers. Chapter 9. Application to Linear Feedback Systems. Chapter 10. Epilogue. Appendix A: Selected Mathematical Identities. Appendix B: Partial-Fraction Expansions. Appendix C: Tables of Fourier Representations and Properties. Appendix D: Tables of Laplace Transforms and Properties. Appendix E: Tables of z-Transforms and Properties.Appendix F: Introduction to MATLAB.Index.
£122.50
John Wiley & Sons Inc Optical Filter Design A Signal Processing
Book SynopsisWith more and more information being transmitted over fiber optic cables, optical filtering is becoming crucial to the smooth operation of optical communication networks. This book presents digital signal processing techniques for the design of optical filters, covering filters used in narrow band filtering and optical signal processing.Table of ContentsFundamentals of Electromagnetic Waves and Waveguides. Digital Filter Concepts for Optical Filters. Multi-Stage MA Architectures. Multi- Stage AR Architectures. Multi-Stage ARMA Filters. Optical Measurements and Filter Analysis. Future Directions. Index.
£151.16
John Wiley & Sons Inc ModelBased Signal Processing Adaptive and
Book SynopsisModel-Based Signal Processing develops the "model-based approach" to signal processing for a variety of useful model sets including the popularly termed "physics-based" models. It presents a unique viewpoint of signal processing from the model-based perspective.Trade Review"Given its extensive, but very cohesive and accessible coverage…this book could be very well appreciated by both students and specialists in the field." (Computing Reviews.com, August 1, 2006) "...belongs in the library of every practicing signal processor." (Journal of the Acoustical Society of America, May 2006)Table of ContentsPreface. Acknowledgments. 1. Introduction. 2. Discrete Random Signals ans Systems. 3. Estimation Theory. 4. AR, MA, ARMAX, Lattice, Exponential, Wave Model-Based Processors. 5. Linear State-Space Model-Based Processors. 6. Nonlinear State-Space Model-Based Processors. 7. Adaptive AR, MA, ARMAX, Exponential Model-Based Processors. 8. Adaptive State-Space Model-Based Processors. 9. Applied Physics-Based Processors. Appendix A: Probability and Statistics Overview. Appendix B: Sequential MBP and UD-Factorization. Appendix C: SSpack_PC: An Interactive Model-Based Processing Software Package. Index.
£153.85
John Wiley & Sons Inc VLSI Digital Signal Processing Systems Design and
Book SynopsisExpertly combining the fields of computer architecture theory and digital signal processing (DSP), this comprehensive, single-volume resource provides everything circuit designers and computer professionals need to stay on top of the rapid changes in VLSI (Very Large Scale Integration) design for DSP.Trade Review"Globally there hardly exist more than a dozen book references on the subject of DSP hardware design. Among them…[Parhi's book is one of the] incontestable leaders, in both depth and breadth." (Analog Dialogue)Table of ContentsIntroduction to Digital Signal Processing Systems. Iteration Bound. Pipelining and Parallel Processing. Retiming. Unfolding. Folding. Systolic Architecture Design. Fast Convolution. Algorithmic Strength Reduction in Filters and Transforms. Pipelined and Parallel Recursive and Adaptive Filters. Scaling and Roundoff Noise. Digital Lattice Filter Structures. Bit-Level Arithmetic Architectures. Redundant Arithmetic. Numerical Strength Reduction. Synchronous, Wave, and Asynchronous Pipelines. Low-Power Design. Programmable Digital Signal Processors. Appendices. Index.
£143.95
John Wiley & Sons Inc Random Processes
Book SynopsisAn understanding of random processes is crucial to many engineering fields-including communication theory, computer vision, and digital signal processing in electrical and computer engineering, and vibrational theory and stress analysis in mechanical engineering.Trade Review"The reader will find an excellent presentation ranging from the basic concepts of probability theory to the advanced topics of RP, filtering, estimation and detection." (IIE Transactions on Operations Engineering)Table of ContentsPreface xv 1 Experiments and Probability 1 2 Random Variables 37 3 Estimation of Random Variables 133 4 Random Processes 179 5 Linear Systems: Random Processes 247 6 Nonlinear Systems: Random Processes 295 7 Optimum Linear Filters: The Wiener Approach 335 8 Optimum Linear Systems: The Kalman Approach 383 9 Detection Theory: Discrete Observation 423 10 Detection Theory: Continuous Observation 511 Appendixes Index 599
£161.95
John Wiley & Sons Inc Synthetic Aperture Radar Signal Processing with
Book SynopsisAn up-to-date analysis of the SAR wavefront reconstruction signal theory and its digital implementation. With the advent of fast computing and digital information processing techniques, synthetic aperture radar (SAR) technology has become both more powerful and more accurate.Table of ContentsRange Imaging. Cross-Range Imaging. SAR Radiation Pattern. Generic Synthetic Aperture Radar. Spotlight Synthetic Aperture Radar. Stripmap Synthetic Aperture Radar. Circular Synthetic Aperture Radar. Monopulse Synthetic Aperture Radar. Bibliography. Index.
£161.06
John Wiley & Sons Inc Kalman Filtering and Neural Networks Adaptive and
Book SynopsisKalman filtering is a well-established topic in the field of control and signal processing and represents by far the most refined method for the design of neural networks. This book takes a nontraditional nonlinear approach and reflects the fact that most practical applications are nonlinear.Trade Review"Although the traditional approach to the subject is usually linear, this book recognizes and deals with the fact that real problems are most often nonlinear." (SciTech Book News, Vol. 25, No. 4, December 2001)Table of ContentsPreface. Contributors. Kalman Filters (S. Haykin). Parameter-Based Kalman Filter Training: Theory and Implementaion (G. Puskorius and L. Feldkamp). Learning Shape and Motion from Image Sequences (G. Patel, et al.). Chaotic Dynamics (G. Patel and S. Haykin). Dual Extended Kalman Filter Methods (E. Wan and A. Nelson). Learning Nonlinear Dynamical System Using the Expectation-Maximization Algorithm (S. Roweis and Z. Ghahramani). The Unscencted Kalman Filter (E. Wan and R. van der Merwe). Index.
£126.85
John Wiley & Sons Inc Scattering Theories Theories and Applications
Book SynopsisWave scattering by discrete scatterers is an interdisciplinary area of research with many applications in such areas as atomic physics, medical imaging, geoscience and remote sensing. This three-volume work is an expanded and updated version of the authors 1985 book, Theory of Microwave Remote Sensing.Table of ContentsPREFACE xi CHAPTER 1 INTRODUCTION TO ELECTROMAGNETIC SCATTERING BY A SINGLE PARTICLE 1 1 Basic Scattering Parameters 2 1.1 Scattering Amplitudes and Cross Sections 2 1.2 Scattering Amplitude Matrix 6 2 Rayleigh Scattering 9 2.1 Rayleigh Scattering by a Small Particle 9 2.2 Rayleigh Scattering by a Sphere 10 2.3 Rayleigh Scattering by an Ellipsoid 12 2.4 Scattering Dyads 14 3 Integral Representations of Scattering and Born Approximation 16 3.1 Integral Expression for Scattering Amplitude 16 3.2 Born Approximation 18 4 Plane Waves, Cylindrical Waves, and Spherical Waves 21 4.1 Cartesian Coordinates: Plane Waves 21 4.2 Cylindrical Waves 22 4.3 Spherical Waves 24 5 Acoustic Scattering 30 6 Scattering by Spheres, Cylinders, and Disks 32 6.1 Mie Scattering 32 6.2 Scattering by a Finite Length Cylinder Using the Infinite Cylinder Approximation 41 6.3 Scattering by a Disk Based on the Infinite Disk Approximation 46 References and Additional Readings 52CHAPTER 2 BASIC THEORY OF ELECTROMAGNETIC SCATTERING 53 1 Dyadic Green's Function 54 1.1 Green's Functions 54 1.2 Plane Wave Representation 55 1.3 Cylindrical Waves 57 1.4 Spherical Waves 59 2 Huygens' Principle and Extinction Theorem 60 3 Active Remote Sensing and Bistatic Scattering Coefficients 66 4 Optical Theorem 68 5 Reciprocity and Symmetry 73 5.1 Reciprocity 73 5.2 Reciprocal Relations for Bistatic Scattering Coefficients and Scattering Amplitudes 75 5.3 Symmetry Relations for Dyadic Green's Function 79 6 Eulerian Angles of Rotation 81 7 T-Matrix 83 7.1 T-Matrix and Relation to Scattering Amplitudes 83 7.2 Unitarity and Symmetry 88 8 Extended Boundary Condition 91 8.1 Extended Boundary Condition Technique 91 8.2 Spheres 97 8.2.1 Scattering and Absorption for Arbitrary Excitation 100 8.2.2 Mie Scattering of Coated Sphere 102 8.3 Spheroids 104 References and Additional Readings 106CHAPTER 3 FUNDAMENTALS OF RANDOM SCATTERING 107 1 Radar Equation for Conglomeration of Scatterers 108 2 Stokes Parameters and Phase Matrices 116 2.1 Elliptical Polarization, Stokes Parameters, Partial Polarization 116 2.2 Stokes Matrix 123 2.3 Scattering per Unit Volume and Phase Matrix 124 2.4 Rayleigh Phase Matrix 127 2.5 Phase Matrix of Random Media 129 3 Fluctuating Fields 131 3.1 Coherent and Incoherent Fields 131 3.2 Probability Distribution of Scattered Fields and Polarimetric Description 132 4 Specific Intensity 140 5 Passive Remote Sensing 145 5.1 Planck's Radiation Law and Brightness Temperature 145 5.2 KirchhofT's Law 149 5.3 Fluctuation Dissipation Theorem 152 5.4 Emissivity of Four Stokes Parameters 155 6 Correlation Function of Fields 161 References and Additional Readings 165 CHAPTER 4 CHARACTERISTICS OF DISCRETE SCATTERERS AND ROUGH SURFACES 167 1 Ice 168 2 Snow 170 3 Vegetation 171 4 Atmosphere 172 5 Correlation Function and Pair Distribution Function 173 5.1 Correlation Function 174 5.2 Pair Distribution Function 176 6 Gaussian Rough Surface and Spectral Density 179 7 Soil and Rocky Surfaces 184 8 Ocean Surface 185 References and Additional Readings 195 CHAPTER 5 SCATTERING AND EMISSION BY LAYERED MEDIA 199 1 Incoherent Approach of Radiative Transfer 200 2 Wave Approach 203 2.1 Reflection and Transmission 203 2.2 Dyadic Green's Function for Stratified Medium 207 2.3 Brightness Temperatures for a Stratified Medium with Temperature Distribution 212 3 Comparison Between Incoherent Approach and Coherent Approach 217 4 Applications to Passive Remote Sensing of Soil 220 References and Additional Readings 229 CHAPTER 6 SINGLE SCATTERING AND APPLICATIONS 231 1 Single Scattering and Particle Position Correlation 232 2 Applications of Single Scattering 237 2.1 Synthetic Aperture Radar 237 2.2 Interferometric SAR 248 2.3 Active Remote Sensing of Half-Space Random Media 252 References and Additional Readings 258 CHAPTER 7 RADIATIVE TRANSFER THEORY 259 1 Scalar Radiative Transfer Theory 260 2 Vector Radiative Transfer Theory 269 2.1 Phase Matrix of Independent Scattering 269 2.2 Extinction Matrix 272 2.3 Emission Vector 275 2.4 Boundary Conditions 283 References and Additional Readings 286 CHAPTER 8 SOLUTION TECHNIQUES OF RADIATIVE TRANSFER THEORY 287 1 Iterative Method 288 1.1 Iterative Procedure 288 1.2 Integral Equation for Scattering Problems 293 1.3 Active Remote Sensing of a Half-Space of Spherical Particles 298 1.4 Active Remote Sensing of a Layer of Nonspherical Particles 303 1.4.1 Numerical Illustrations with Finite Dielectric Cylinders 310 1.5 Second-Order Scattering from Isotropic Point Scatterers 322 2 Discrete Ordinate-Eigenanalysis Method 324 2.1 Radiative Transfer Solution for Laminar Structures 324 2.2 Numerical Procedure of Discrete Ordinate Method: Normal Incidence 328 2.3 Active Remote Sensing: Oblique Incidence 337 2.4 Discrete Ordinate Method for Passive Remote Sensing 343 2.5 Passive Remote Sensing of a Three-Dimensional Random Medium 349 2.6 Passive Remote Sensing of a Layer of Mie Scatterers Overlying a Dielectric Half-Space 352 3 Invariant Imbedding 362 3.1 One-Dimensional Problem 363 3.2 Passive Remote Sensing of a Three-Dimensional Scattering Medium with Inhomogeneous Profiles 370 3.3 Passive Remote Sensing of a Three-Dimensional Random Medium 373 3.4 Thermal Emission of Layers of Spherical Scatterers in the Presence of Inhomogeneous Absorption and Temperature Profiles 374 4 Diffusion Approximation 380 References and Additional Readings 386 CHAPTER 9 ONE-DIMENSIONAL RANDOM ROUGH SURFACE SCATTERING 389 1 Introduction 390 2 Statistics of Random Rough Surface 392 2.1 Statistics, Correlation Function and Spectral Density 392 2.2 Characteristic Functions 396 3 Small Perturbation Method 397 3.1 Dirichlet Problem for One-Dimensional Surface 397 3.2 Neumann Problem for One-Dimensional Surface 403 4 Kirchhoff Approach 407 4.1 Dirichlet Problem for One-Dimensional Surface 408 4.2 Neumann Problem for One-Dimensional Surface 415 References and Additional Readings 417 INDEX 419
£145.76
John Wiley & Sons Inc Scattering Numerical Numerical Simulations
Book SynopsisA timely and authoritative guide to the state of the art of wave scattering Scattering of Electromagnetic Waves offers in three volumes a complete and up-to-date treatment of wave scattering by random discrete scatterers and rough surfaces.Trade Review"this graduate textbook presents numerical simulation techniques and results for electromagnetic wave scattering in random media and rough surfaces..." (SciTech Book News, Vol. 25, No. 3, September 2001)Table of ContentsPREFACE xix CHAPTER 1 MONTE CARLO SIMULATIONS OF LAYERED MEDIA 1 1 One-Dimensional Layered Media with Permittivity Fluctuations 2 1.1 Continuous Random Medium 2 1.2 Generation of One-Dimensional Continuous Gaussian Random Medium 4 1.3 Numerical Results and Applications to Antarctica 5 2 Random Discrete Layering and Applications 8 References and Additional Readings 12 CHAPTER 2 INTEGRAL EQUATION FORMULATIONS AND BASIC NUMERICAL METHODS 13 1 Integral Equation Formulation for Scattering Problems 14 1.1 Surface Integral Equations 14 1.2 Volume Integral Equations 17 1.3 Dyadic Green's Function Singularity and Electrostatics 19 2 Method of Moments 23 3 Discrete Dipole Approximation (DDA) 27 3.1 Small Cubes 28 3.2 Radiative Corrections 29 3.3 Other Shapes 31 4 Product of Toeplitz Matrix and Column Vector 37 4.1 Discrete Fourier Transform and Convolutions 38 4.2 FFT for Product of Toeplitz Matrix and Column Vector 42 5 Conjugate Gradient Method 46 5.1 Steepest Descent Method 46 5.2 Real Symmetric Positive Definite Matrix 48 5.3 General Real Matrix and Complex Matrix 52 References and Additional Readings 57 CHAPTER 3 SCATTERING AND EMISSION BY A PERIODIC ROUGH SURFACE 61 1 Dirichlet Boundary Conditions 62 1.1 Surface Integral Equation 62 1.2 Floquet's Theorem and Bloch Condition 63 1.3 2-D Green's Function in 1-D Lattice 64 1.4 Bistatic Scattering Coefficients 67 2 Dielectric Periodic Surface: T-Matrix Method 68 2.1 Formulation in Longitudinal Field Components 69 2.2 Surface Field Integral Equations and Coupled Matrix Equations 74 2.3 Emissivity and Comparison with Experiments 81 3 Scattering of Waves Obliquely Incident on Periodic Rough Surfaces: Integral Equation Approach 85 3.1 Formulation 85 3.2 Polarimetric Brightness Temperatures 89 4 Ewald's Method 93 4.1 Preliminaries 93 4.2 3-D Green's Function in 3-D Lattices 98 4.3 3-D Green's Function in 2-D Lattices 102 4.4 Numerical Results 105 References and Additional Readings 110 CHAPTER 4 RANDOM ROUGH SURFACE SIMULATIONS 111 1 Perfect Electric Conductor (Non-Penetrable Surface) 114 1.1 Integral Equation 114 1.2 Matrix Equation: Dirichlet Boundary Condition (EFIE for TE Case) 1161.3 Tapering of Incident Waves and Calculation of Scattered Waves 118 1.4 Random Rough Surface Generation 124 1.4.1 Gaussian Rough Surface 124 1.4.2 Fractal Rough Surface 132 1.5 Neumann Boundary Condition (MFIE for TM Case) 134 2 Two-Media Problem 137 2.1 TE and TM Waves 139 2.2 Absorptivity, Emissivity and Reflectivity 141 2.3 Impedance Matrix Elements: Numerical Integrations 143 2.4 Simulation Results 145 2.4.1 Gaussian Surface and Comparisons with Analytical Methods 145 2.4.2 Dirichlet Case of Gaussian Surface with Ocean Spectrum and Fractal Surface 150 2.4.3 Bistatic Scattering for Two Media Problem with Ocean Spectrum 151 3 Topics of Numerical Simulations 154 3.1 Periodic Boundary Condition 154 3.2 MFIE for TE Case of PEC 158 3.3 Impedance Boundary Condition 161 4 Microwave Emission of Rough Ocean Surfaces 163 5 Waves Scattering from Real-Life Rough Surface Profiles 166 5.1 Introduction 166 5.2 Rough Surface Generated by Three Methods 167 5.3 Numerical Results of the Three Methods 169 References and Additional Readings 175 CHAPTER 5 FAST COMPUTATIONAL METHODS FOR SOLVING ROUGH SURFACE SCATTERING PROBLEMS 177 1 Banded Matrix Canonical Grid Method for Two-Dimensional Scattering for PEC Case 1791.1 Introduction 179 1.2 Formulation and Computational Procedure 180 1.3 Product of a Weak Matrix and a Surface Unknown Column Vector 187 1.4 Convergence and Neighborhood Distance 188 1.5 Results of Composite Surfaces and Grazing Angle Problems 189 2 Physics-Based Two-Grid Method for Lossy Dielectric Surfaces 196 2.1 Introduction 196 2.2 Formulation and Single-Grid Implementation 198 2.3 Physics-Based Two-Grid Method Combined with Banded Matrix Iterative Approach/Canonical Grid Method 200 2.4 Bistatic Scattering Coefficient and Emissivity 203 3 Steepest Descent Fast Multipole Method 212 3.1 Steepest Descent Path for Green's Function 213 3.2 Multi-Level Impedance Matrix Decomposition and Grouping 216 3.3 Multi-Level Discretization of Angles and Interpolation 222 3.4 Steepest Descent Expression of Multi-Level Impedance Matrix Elements 226 3.5 SDFMM Algorithm 235 3.6 Numerical Results 242 4 Method of Ordered Multiple Interactions (MOMI) 242 4.1 Matrix Equations Based on MFIE for TE and TM Waves for PEC 242 4.2 Iterative Approach 245 4.3 Numerical Results 247 5 Physics-Based Two-Grid Method Combined with the Multilevel Fast Multipole Method 249 5.1 Single Grid and PBTG 249 5.2 Computational Complexity of the Combined Algorithm of the PBTG with the MLFMM 252 5.3 Gaussian Rough Surfaces and CPU Comparison 254 5.4 Non-Gaussian Surfaces 257 References and Additional Readings 263 CHAPTER 6 THREE-DIMENSIONAL WAVE SCATTERING FROM TWO-DIMENSIONAL ROUGH SURFACES 267 1 Scattering by Non-Penetrable Media 270 1.1 Scalar Wave Scattering 270 1.1.1 Formulation and Numerical Method 270 1.1.2 Results and Discussion 273 1.1.3 Convergence of SMFSIA 277 1.2 Electromagnetic Wave Scattering by Perfectly Conducting Surfaces 278 1.2.1 Surface Integral Equation 278 1.2.2 Surface Integral Equation for Rough Surface Scattering 280 1.2.3 Computation Methods 281 1.2.4 Numerical Simulation Results 286 2 Integral Equations for Dielectric Surfaces 293 2.1 Electromagnetic Fields with Electric and Magnetic Sources 293 2.2 Physical Problem and Equivalent Exterior and Interior Problems 296 2.2.1 Equivalent Exterior Problem, Equivalent Currents and Integral Equations 296 2.2.2 Equivalent Interior Problem, Equivalent Currents and Integral Equations 298 2.3 Surface Integral Equations for Equivalent Surface Currents, Tangential and Normal Components of Fields 300 3 Two-Dimensional Rough Dielectric Surfaces with Sparse Matrix Canonical Grid Method 304 3.1 Integral Equation and SMCG Method 304 3.2 Numerical Results of Bistatic Scattering Coefficient 318 4 Scattering by Lossy Dielectric Surfaces with PBTG Method 326 4.1 Introduction 326 4.2 Formulation and Single Grid Implementation 328 4.3 Physics-Based Two-Grid Method 329 4.4 Numerical Results and Comparison with Second Order Perturbation Method 334 4.5 Numerical Simulations of Emissivity of Soils with Rough Surfaces at Microwave Frequencies 343 5 Four Stokes Parameters Based on Tangential Surface Fields 350 6 Parallel Implementation of SMCG on Low Cost Beowulf System 354 6.1 Introduction 354 6.2 Low-Cost Beowulf Cluster 355 6.3 Parallel Implementation of the SMCG Method and the PBTG Method 356 6.4 Numerical Results 360 References and Additional Readings 366 CHAPTER 7 VOLUME SCATTERING SIMULATIONS 371 1 Combining Simulations of Collective Volume Scattering Effects with Radiative Transfer Theory 373 2 Foldy-Lax Self-Consistent Multiple Scattering Equations 376 2.1 Final Exciting Field and Multiple Scattering Equation 376 2.2 Foldy-Lax Equations for Point Scatterers 379 2.3 The JV-Particle Scattering Amplitude 382 3 Analytical Solutions of Point Scatterers 382 3.1 Phase Function and Extinction Coefficient for Uniformly Distributed Point Scatterers 382 3.2 Scattering by Collection of Clusters 389 4 Monte Carlo Simulation Results of Point Scatterers 392 References and Additional Readings 401 CHAPTER 8 PARTICLE POSITIONS FOR DENSE MEDIA CHARACTERIZATIONS AND SIMULATIONS 403 1 Pair Distribution Functions and Structure Factors 404 1.1 Introduction 404 1.2 Percus Yevick Equation and Pair Distribution Function for Hard Spheres 406 1.3 Calculation of Structure Factor and Pair Distribution Function 409 2 Percus—Yevick Pair Distribution Functions for Multiple Sizes 411 3 Monte Carlo Simulations of Particle Positions 414 3.1 Metropolis Monte Carlo Technique 415 3.2 Sequential Addition Method 418 3.3 Numerical Results 418 4 Sticky Particles 424 4.1 Percus-Yevick Pair Distribution Function for Sticky Spheres 424 4.2 Pair Distribution Function of Adhesive Sphere Mixture 429 4.3 Monte Carlo Simulation of Adhesive Spheres 434 5 Particle Placement Algorithm for Spheroids 444 5.1 Contact Functions of Two Ellipsoids 445 5.2 Illustrations of Contact Functions 446 References and Additional Readings 450 CHAPTER 9 SIMULATIONS OF TWO-DIMENSIONAL DENSE MEDIA 453 1 Introduction 454 1.1 Extinction as a Function of Concentration 454 1.2 Extinction as a Function of Frequency 456 2 Random Positions of Cylinders 458 2.1 Monte Carlo Simulations of Positions of Hard Cylinders 458 2.2 Simulations of Pair Distribution Functions 460 2.3 Percus-Yevick Approximation of Pair Distribution Functions 461 2.4 Results of Simulations 463 2.5 Monte Carlo Simulations of Sticky Disks 463 3 Monte Carlo Simulations of Scattering by Cylinders 469 3.1 Scattering by a Single Cylinder 469 3.2 Foldy-Lax Multiple Scattering Equations for Cylinders 476 3.3 Coherent Field, Incoherent Field, and Scattering Coefficient 480 3.4 Scattered Field and Internal Field Formulations 481 3.5 Low Frequency Formulas 482 3.6 Independent Scattering 484 3.7 Simulation Results for Sticky and Non-Sticky Cylinders 485 4 Sparse-Matrix Canonical-Grid Method for Scattering by Many Cylinders 486 4.1 Introduction 486 4.2 The Two-Dimensional Scattering Problem of Many Dielectric Cylinders 489 4.3 Numerical Results of Scattering and CPU Comparisons 490 References and Additional Readings 493 CHAPTER 10 DENSE MEDIA MODELS AND THREE-DIMENSIONAL SIMULATIONS 495 1 Introduction 496 2 Simple Analytical Models For Scattering From a Dense Medium 496 2.1 Effective Permittivity 496 2.2 Scattering Attenuation and Coherent Propagation Constant 500 2.3 Coherent Reflection and Incoherent Scattering From a Half-Space of Scatterers 505 2.4 A Simple Dense Media Radiative Transfer Theory 510 3 Simulations Using Volume Integral Equations 512 3.1 Volume Integral Equation 512 3.2 Simulation of Densely Packed Dielectric Spheres 514 3.3 Densely Packed Spheroids 518 4 Numerical Simulations Using T-Matrix Formalism 533 4.1 Multiple Scattering Equations 533 4.2 Computational Considerations 541 4.3 Results and Comparisons with Analytic Theory 545 4.4 Simulation of Absorption Coefficient 547 References and Additional Readings 548 CHAPTER 11 ANGULAR CORRELATION FUNCTION AND DETECTION OF BURIED OBJECT 551 1 Introduction 552 2 Two-Dimensional Simulations of Angular Memory Effect and Detection of Buried Object 553 2.1 Introduction 553 2.2 Simple and General Derivation of Memory Effect 553 2.3 ACF of Random Rough Surfaces with Different Averaging Methods 555 2.4 Scattering by a Buried Object Under a Rough Surface 557 3 Angular Correlation Function of Scattering by a Buried Object Under a 2-D Random Rough Surface (3-D Scattering) 564 3.1 Introduction 564 3.2 Formulation of Integral Equations 565 3.3 Statistics of Scattered Fields 570 3.4 Numerical Illustrations of ACF and PACF 571 4 Angular Correlation Function Applied to Correlation Imaging in Target Detection 575 4.1 Introduction 575 4.2 Formulation of Imaging 578 4.3 Simulations of SAR Data and ACF Processing 580 References and Additional Readings 591 CHAPTER 12 MULTIPLE SCATTERING BY CYLINDERS IN THE PRESENCE OF BOUNDARIES 593 1 Introduction 594 2 Scattering by Dielectric Cylinders Above a Dielectric Half-Space 594 2.1 Scattering from a Layer of Vertical Cylinders: First-Order Solution 594 2.2 First- and Second-Order Solutions 603 2.3 Results of Monte Carlo Simulations 613 3 Scattering by Cylinders in the Presence of Two Reflective Boundaries 622 3.1 Vector Cylindrical Wave Expansion of Dyadic Green's Function Between Two Perfect Conductors 622 3.2 Dyadic Green's Function of a Cylindrical Scatterer Between Two PEC 629 3.3 Dyadic Green's Function with Multiple Cylinders 631 3.4 Excitation of Magnetic Ring Currents 635 3.4.1 First Order Solution 637 3.4.2 Numerical Results 638 References and Additional Readings 640 CHAPTER 13 ELECTROMAGNETIC WAVES SCATTERING BY VEGETATION 641 1 Introduction 642 2 Plant Modeling by Using L-Systems 644 2.1 Lindenmayer Systems 644 2.2 Turtle Interpretation of L-Systems 646 2.3 Computer Simulations of Stochastic L-Systems and Input Files 649 3 Scattering from Trees Generated by L-Systems Based on Coherent Addition Approximation 654 3.1 Single Scattering by a Particle in the Presence of Reflective Boundary 655 3.1.1 Electric Field and Dyadic Green's Function 655 3.1.2 Scattering by a Single Particle 656 3.2 Scattering by Trees 659 4 Coherent Addition Approximation with Attenuation 667 5 Scattering from Plants Generated by L-Systems Based on Discrete Dipole Approximation 669 5.1 Formulation of Discrete Dipole Approximation (DDA) Method 670 5.2 Scattering by Simple Trees 672 5.3 Scattering by Honda Trees 677 6 Rice Canopy Scattering Model 685 6.1 Model Description 685 6.2 Model Simulation 689 References and Additional Readings 691 INDEX 693
£151.16
John Wiley & Sons Inc Scattering of Electromagnetic Waves
Book SynopsisA timely and authoritative guide to the state of the art of wave scattering Scattering of Electromagnetic Waves offers in three volumes a complete and up-to-date treatment of wave scattering by random discrete scatterers and rough surfaces. Written by leading scientists who have made important contributions to wave scattering over three decades, this new work explains the principles, methods, and applications of this rapidly expanding, interdisciplinary field. It covers both introductory and advanced material and provides students and researchers in remote sensing as well as imaging, optics, and electromagnetic theory with a one-stop reference to a wealth of current research results. Plus, Scattering of Electromagnetic Waves contains detailed discussions of both analytical and numerical methods, including cutting-edge techniques for the recovery of earth/land parametric information. The three volumes are entitled respectively Theories and Applications, Numerical Simulation, andTrade Review"Here they [the authors] delve deeper into the topics raised in the first two volumes..." (SciTech Book News, Vol. 25, No. 3, September 2001)Table of ContentsPREFACE xiii CHAPTER 1 TWO-DIMENSIONAL RANDOM ROUGH SURFACE SCATTERING BASED ON SMALL PERTURBATION METHOD 1 1 Electromagnetic Wave Scattering by a Perfect Electric Conductor 2 1.1 Zeroth- and First-Order Solutions 7 1.2 Second-Order Solutions 11 2 Electromagnetic Wave Scattering by a Dielectric Rough Surface 18 2.1 Zeroth- and First-Order Solutions 27 2.2 Second-Order Solutions 36 3 Coherent Reflection, Emissivities, and Bistatic Scattering Coefficients of Random Dielectric Surfaces 47 3.1 Coherent Reflection 48 3.2 Emissivities of Four Stokes Parameters 51 3.3 Bistatic Scattering Coefficients 58 References and Additional Readings 61 CHAPTER 2 KIRCHHOFF APPROACH AND RELATED METHODS FOR ROUGH SURFACE SCATTERING 65 1 Kirchhoff Approach 66 1.1 Perfectly Conducting Rough Surface 66 1.2 Dielectric Rough Surfaces 72 1.3 Second-Order Slope Corrections 94 2 Phase Perturbation Method 101 3 Emissivity Based on Composite Surface Model 108 References and Additional Readings 118 CHAPTER 3 VOLUME SCATTERING: CASCADE OF LAYERS 121 1 Single Scattering Solution of a Thin Layer, Coherent Wave, and Effective Propagation Constant 122 2 Transition Operator 128 3 Electromagnetic Wave Case of a Thin Layer and Extinction Matrix 130 4 First- and Second-Order Solutions: Incoherent Waves 135 5 Cascading of Layers: From First- and Second-Order Wave Solutions to Radiative Transfer Equation 143 6 Effects of Clustering 150 References and Additional Readings 160 CHAPTER 4 ANALYTIC WAVE THEORY FOR A MEDIUM WITH PERMITTIVITY FLUCTUATIONS 161 1 Dyson's Equation for the Mean Field 162 1.1 Bilocal Approximation 167 1.2 Nonlinear Approximation 170 2 Second Moment of the Field 171 2.1 Bethe-Salpeter Equation 171 2.2 Energy Conservation 175 3 Strong Permittivity Fluctuations 178 3.1 Random Medium with Spherically Symmetric Correlation Function 179 3.2 Very Low Frequency Effective Permittivity 181 3.3 Effective Permittivity Under the Bilocal Approximation 182 3.4 Backscattering Coefficients 185 3.5 Results of Effective Permittivity and Bistatic Coefficients 187 References and Additional Readings 194 CHAPTER 5 MULTIPLE SCATTERING THEORY FOR DISCRETE SCATTERERS 197 1 Transition Operator 198 2 Multiple Scattering Equations 203 3 Approximations of Multiple Scattering Equations 204 3.1 Configurational Average of Multiple Scattering Equations 205 3.2 Effective Field Approximation (EFA, Foldy's Approximation) 207 3.3 Quasi-crystalline Approximation (QCA) 210 3.4 Coherent Potential (CP) 213 3.5 Quasi-crystalline Approximation with Coherent Potential (QCA-CP) 216 3.6 Low-Frequency Solutions 219 3.7 QCA-CP for Multiple Species of Particles 224 4 Ward's Identity and Energy Conservation 226 5 Derivation of Radiative Transfer Equation from Ladder Approximation 232 References and Additional Readings 241 CHAPTER 6 QUASI-CRYSTALLINE APPROXIMATION IN DENSE MEDIA SCATTERING 245 1 Scattering of Electromagnetic Waves from a Half-Space of Dielectric Scatterers— Normal Incidence 246 1.1 Coherent Wave Propagation 247 1.2 Effective Phase Velocity and Attenuation Rate in the Low-Frequency Limit 257 1.3 Dispersion Relations at Higher Frequencies 259 2 Scattering of Electromagnetic Waves from a Half-Space of Dielectric Scatterers—Oblique Incidence 266 2.1 Dispersion Relation and Coherent Reflected Wave 266 2.2 Vertically and Horizontally Polarized Incidence 275 3 Cases with Size Distributions 280 3.1 Coherent Field 281 3.2 Incoherent Field Using Distorted Born Approximation 287 4 Dense Media Radiative Transfer Theory Based on Quasi-crystalline Approximation 300 4.1 Phase Matrix, Extinction, Scattering, and Absorption Coefficients 301 4.2 Brightness Temperature Computed with QCA-based DMRT 307 4.3 Numerical Results for Sticky and Non-Sticky Particles 309 References and Additional Readings 319 CHAPTER 7 DENSE MEDIA SCATTERING 323 1 Introduction 324 2 Effective Propagation Constants, Mean Green's Function, and Mean Field for Half-Space DiscreteRandom Medium of Multiple Species 325 3 Derivation of Dense Media Radiative Transfer Equation (DMRT) 329 4 Dense Media Radiative Transfer Equations for Active Remote Sensing 340 5 General Relation between Active and Passive Remote Sensing with Temperature Distribution 344 6 Dense Media Radiative Transfer Equations for Passive Remote Sensing 349 7 Numerical Illustrations of Active and Passive Remote Sensing 351 References and Additional Readings 357 CHAPTER 8 BACKSCATTERING ENHANCEMENT 359 1 Introduction 360 1.1 Volume Scattering 361 1.2 Volume Scattering in the Presence of Reflective Boundary 362 2 Second-Order Volume Scattering Theory of Isotropic Point Scatterers 366 3 Summation of Ladder Terms and Cyclical Terms for Isotropic Point Scatterers 374 3.1 Formulation 375 3.2 Numerical Illustrations 380 4 Anisotropic Scatterers and Diffusion Approximation 385 4.1 Summation of Ladder Terms and Cyclical Terms 386 4.2 Unidirectional Point Source Green's Function 391 4.3 Second-Order Multiple-Scattering Theory 393 4.4 Diffusion Approximation 395 4.5 Numerical Results 399 References and Additional Readings 403 INDEX 407
£151.16
John Wiley & Sons Inc Signal Theory Methods in Multispectral Remote
Book SynopsisAn outgrowth of the author''s extensive experience teaching senior and graduate level students, this is both a thorough introduction and a solid professional reference. * Material covered has been developed based on a 35-year research program associated with such systems as the Landsat satellite program and later satellite and aircraft programs. * Covers existing aircraft and satellite programs and several future programs *An Instructor''s Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.Table of ContentsPreface. PART I: INTRODUCTION. Chapter 1. Introduction and Background. PART II: THE BASICS FOR CONVENTIONAL MULTISPECTRAL DATA. Chapter 2. Radiation and Sensor Systems in Remote Sensing. Chapter 3. Pattern Recognition in Remote Sensing. PART III: ADDITIONAL DETAILS. Chapter 4. Training a Classifier. Chapter 5. Hyperspectral Data Characteristics. Chapter 6. Feature Definition. Chapter 7. A Data Analysis Paradigm and Examples. Chapter 8. Use of Spatial Variations. Chapter 9. Noise in Remote Sensing Systems. Chapter 10. Multispectral Image Data Preprocessing. Appendix. An Outline of Probability Theory. Exercises. Index.
£184.46
John Wiley & Sons Inc Recurrent Neural Networks for Prediction Learning
Book SynopsisNeural networks consist of interconnected groups of neurons which function as processing units and aim to reconstruct the operation of the human brain.Table of ContentsPreface. Introduction. Fundamentals. Network Architectures for Prediction. Activation Functions Used in Neural Networks. Recurrent Neural Networks Architectures. Neural Networks as Nonlinear Adaptive Filters. Stability Issues in RNN Architectures. Data-Reusing Adaptive Learning Algorithms. A Class of Normalised Algorithms for Online Training of Recurrent Neural Networks. Convergence of Online Learning Algorithms in Neural Networks. Some Practical Considerations of Predictability and Learning Algorithms for Various Signals. Exploiting Inherent Relationships Between Parameters in Recurrent Neural Networks. Appendix A: The O Notation and Vector and Matrix Differentiation. Appendix B: Concepts from the Approximation Theory. Appendix C: Complex Sigmoid Activation Functions, Holomorphic Mappings and Modular Groups. Appendix D: Learning Algorithms for RNNs. Appendix E: Terminology Used in the Field of Neural Networks. Appendix F: On the A Posteriori Approach in Science and Engineering. Appendix G: Contraction Mapping Theorems. Appendix H: Linear GAS Relaxation. Appendix I: The Main Notions in Stability Theory. Appendix J: Deasonsonalising Time Series. References. Index.
£157.45
John Wiley & Sons Inc Synthetic Aperture Radar
Book SynopsisThe use of synthetic aperture radar (SAR) represents a new era in remote sensing technology. A complete handbook for anyone who must design an SAR system capable of reliably producing high quality image data products, free from image artifacts and calibrated in terms of the target backscatter coefficient. Combines fundamentals underlying the SAR imaging process and the practical system engineering required to produce quality images from a real SAR system. Beginning with a broad overview of SAR technology, it goes on to examine SAR system capabilities and components and detail the techniques required for design and development of the SAR ground data system with emphasis on the correlation processing. Intended for SAR system engineers and researchers, it is generously illustrated for maximum clarity.Table of ContentsThe Radar Equation. The Matched Filter and Pulse Compression. Imaging and the Rectangular Algorithm. Ancillary Processes in Image Formation. SAR Flight System. Radiometric Calibration of SAR Data. Geometric Calibration of SAR Data. The SAR Ground System. Other Imaging Algorithms. Appendices. List of Acronyms. Index.
£211.46
Wiley Neural Networks for Optimization and Signal
Book SynopsisA topical introduction on the ability of artificial neural networks to not only solve on-line a wide range of optimization problems but also to create new techniques and architectures. Provides in-depth coverage of mathematical modeling along with illustrative computer simulation results.Table of ContentsMathematical Preliminaries of Neurocomputing. Architectures and Electronic Implementation of Neural Network Models. Unconstrained Optimization and Learning Algorithms. Neural Networks for Linear, Quadratic Programming and Linear Complementarity Problems. A Neural Network Approach to the On-Line Solution of a System of Linear Algebraic Equations and Related Problems. Neural Networks for Matrix Algebra Problems. Neural Networks for Continuous, Nonlinear, Constrained Optimization Problems. Neural Networks for Estimation, Identification and Prediction. Neural Networks for Discrete and Combinatorial Optimization Problems. Appendices. Subject Index.
£218.66
John Wiley & Sons Inc Signal Analysis
Book SynopsisSignal analysis gives an insight into the properties of signals and stochastic processes by methodology. Linear transforms are integral to the continuing growth of signal processes as they characterize and classify signals. In particular, those transforms that provide time-frequency signal analysis are attracting greater numbers of researchers and are becoming an area of considerable importance. The key characteristic of these transforms, along with a certain time-frequency localization called the wavelet transform and various types of multirate filter banks, is their high computational efficiency. It is this computational efficiently which accounts for their increased application. This book provides a complete overview and introduction to signal analysis. It presents classical and modern signal analysis methods in a sequential structure starting with the background to signal theory. Progressing through the book the author introduces more advanced topics in an easy to understand style.Trade Review"...excellent and interesting reading for digital signal processing engineers and designers and for postgraduate students in electrical and computer faculties." (Mathematical Reviews, 2002d)Table of ContentsSignals and Signal Spaces. Integral Signal Representations. Discrete Signal Representations. Examples of Discrete Transforms. Transforms and Filters for Stochastic Processes. Filter Banks. Short-Time Fourier Analysis. Wavelet Transform. Non-Linear Time-Frequency Distributions. Bibliography. Index.
£181.76
Wiley Identification of TimeVarying Processes
Book SynopsisTime varying process identification (TVPI) techniques facilitate adaptive noise reduction, echo cancellation and predictive coding of signals. This treatment addresses the identification of time-varying characteristics of dynamic processes.Trade Review"...a comprehensive treatment...well-written and successful in combining mathematics and practical understanding of real world applications..." (Automatica, No.38, 2002)Table of ContentsModeling Essentials. Models of Nonstationary Processes. Process Segmentation. Weighted Least Squares. Least Mean Squares. Basis Functions. Kalman Filtering. Practical Issues. Epilogue. References. Index.
£181.76
Cambridge University Press Introduction to Coding Theory
Book SynopsisThis 2006 book introduces the reader to the theoretical foundations of error-correcting codes, with an emphasis on Reed-Solomon codes and their derivative codes. It is designed to be accessible to a broad readership, including students of computer science, electrical engineering, and mathematics, from senior-undergraduate to graduate level.Trade Review'… a most welcome addition. … well tested as a course text. Features include, the extensive collections of interesting and nontrivial problems at the end of chapters, the clear and insightful explanations of some of the deeper aspects of the subject and the extensive, interesting and useful historical notes on the development of the subject. This is an excellent volume that will reward the participants in any course that uses it with a deep understanding and appreciation for the subject.' Ian F, Blake, University of Toronto'This book introduces the reader to the theoretical foundations of error-correctiong codes ... While mathematical rigor is maintained, the text is designed to be accessible to a broad readership, including students of computer science, electrical engineering, and mathematics, from senior undergraduate to graduate level.' L'enseignement mathematique'The mathematical style of this book is clear, concise and scholarly with a pleasing layout. There are numerous exercises, many with hints and many introducing further new concepts. Altogether this is an excellent book covering a wide range of topics in this area, and including an extensive bibliography.' Publication of the International Statistical Institute'The reader will find many well-chosen examples throughout the book and will be challenged by over 300 exercises, many of which have hints. Some of the exercises develop concepts that are not contained within the main body of the text. For example, the very first problem of the book, filling up more than an entire page of the text, introduces the AWGN channel and requires the reader to check the crossover probability of a memoryless binary symmetric channel. Zentralblatt MATHTable of ContentsPreface; 1. Introduction; 2. Linear codes; 3. Introduction to finite fields; 4. Bounds on the parameters of codes; 5. Reed-Solomon codes and related codes; 6. Decoding of Reed-Solomon codes; 7. Structure of finite fields; 8. Cyclic codes; 9. List decoding of Reed-Solomon codes; 10. Codes in the Lee metric; 11. MDS codes; 12. Concatenated codes; 13. Graph codes; 14. Trellis codes and convolutional codes; Appendix A. Basics in modern algebra; Bibliography; List of symbols; Index.
£75.99
Cambridge University Press Quantum Processes Systems and Information
Book SynopsisA new and exciting approach to the basics of quantum theory, this undergraduate textbook contains extensive discussions of conceptual puzzles and over 800 exercises and problems. In addition to the standard topics covered in other textbooks, it covers communication and measurement, quantum entanglement, entropy and thermodynamics, and quantum information processing.Trade Review'This is a fantastic book, with one of the authors no less than the very inventor of the word and idea of a qubit. When I opened the book for the first time, I found I couldn't stop reading through it and working out some of the problems. … There's no book out there I would recommend more for learning the mechanics of this quantum world.' Chris Fuchs, Perimeter Institute for Theoretical Physics'One of the most original and insightful introductions to quantum mechanics ever written, this book is also an excellent introduction to the emerging field of quantum information science.' Michael Nielsen, co-author of Quantum Computation and Quantum Information'This superb new book by Ben Schumacher and Mike Westmoreland is perfectly suited for a modern undergraduate course on quantum mechanics that emphasizes fundamental notions from quantum information science, such as entanglement, Bell's theorem, quantum teleportation, quantum cryptography, and quantum error correction. The authors, who are themselves important contributors to the subject, have complete mastery of the material, and they write clearly and engagingly.' John Preskill, California Institute of Technology'This is a wonderful book! It covers the usual topics of a first course in quantum mechanics and much more, and it does so with an unusual conceptual depth. The inclusion of information theoretic ideas not only enriches the presentation of the basic theory - for example in helping to articulate the conditions under which quantum coherence is lost - it also opens up the large area of physics in which both quantum mechanical and information theoretic concepts play central roles.' William K. Wootters, Williams College'With its comprehensive presentation of both quantum mechanics and QIC, this book, written by two pioneers of this emerging new approach to computing, is really one of a kind … Most concepts that one would find in traditional nonrelativistic quantum mechanics physics books are presented in a clear and well thought-out manner (but be prepared for a bit more work when dealing with subtle notions such as quantum relative entropy or mutual information) … The brilliant pedagogical approach taken by the authors, who are able to present quite abstract notions using a clear and sprightly style, together with the quality of the editing … will provide both students and researchers interested in the growing field of QIC with a pleasant and informative read.' Computing Reviews'… a very impressive piece of work. It has clearly been refined over some time; the explanations and proofs that are scattered throughout the text are clearly written and elegant, and common themes are picked up repeatedly with increasing sophistication as the book goes along …' Mathematical Reviews'A bright undergraduate would get a tremendous grounding in modern quantum theory from reading this book, and solving the problems therein. So would many postgraduate students and academics wanting to get into the heart of quantum information research.' Howard M. Wiseman, Quantum Information ProcessingTable of Contents1. Bits and quanta; 2. Qubits; 3. States and observables; 4. Distinguishability and information; 5. Quantum dynamics; 6. Entanglement; 7. Information and ebits; 8. Density operators; 9. Open systems; 10. A particle in space; 11. Dynamics of a free particle; 12. Spin and rotation; 13. Ladder systems; 14. Many particles; 15. Stationary states in 1-D; 16. Bound states in 3-D; 17. Perturbation theory; 18. Quantum information processing; 19. Classical and quantum entropy; 20. Error correction; Appendixes; Index.
£62.99
Cambridge University Press Hilbert Transforms Volume 1 124 Encyclopedia of Mathematics and its Applications Series Number 124
Book SynopsisWritten to suit a wide audience (including physical sciences), these two volumes will become the reference of choice on the Hilbert transform, whatever the subject background of the reader. The author explains all the common Hilbert transforms, mathematical techniques for evaluating them, and has detailed discussions of their application.Trade Review"The author gives detailed and exhaustive information on almost all properties of the Hilbert transform... the selected topics are presented in an easy-to-use style." Lasha Ephremidze, Mathematical ReviewsTable of ContentsPreface; List of symbols; List of abbreviations; Volume I: 1. Introduction; 2. Review of some background mathematics; 3. Derivation of the Hilbert transform relations; 4. Some basic properties of the Hilbert transform; 5. Relationship between the Hilbert transform and some common transforms; 6. The Hilbert transform of periodic functions; 7. Inequalities for the Hilbert transform; 8. Asymptotic behavior of the Hilbert transform; 9. Hilbert transforms of some special functions; 10. Hilbert transforms involving distributions; 11. The finite Hilbert transform; 12. Some singular integral equations; 13. Discrete Hilbert transforms; 14. Numerical evaluation of Hilbert transforms; References; Subject index; Author index.
£127.30
Cambridge University Press Probability Random Processes and Statistical Analysis
Book SynopsisTogether with the fundamental topics, this book covers advanced theories and engineering applications, including the EM algorithm, hidden Markov models, and queueing and loss systems. A solutions manual, lecture slides and MATLAB programs all available online make this ideal for classroom teaching as well as a valuable reference for professionals.Trade Review'This book provides a very comprehensive, well-written and modern approach to the fundamentals of probability and random processes, together with their applications in the statistical analysis of data and signals. … It provides a one-stop, unified treatment that gives the reader an understanding of the models, methodologies and underlying principles behind many of the most important statistical problems arising in engineering and the sciences today.' Dean H. Vincent Poor, Princeton University'This is a well-written up-to-date graduate text on probabilty and random processes. It is unique in combining statistical analysis with the probabilistic material. As noted by the authors, the material, as presented, can be used in a variety of current application areas, ranging from communications to bioinformatics. I particularly liked the historical introduction, which should make the field exciting to the student, as well as the introductory chapter on probability, which clearly describes for the student the distinction between the relative frequency and axiomatic approaches to probability. I recommend it unhesitatingly. It deserves to become a leading text in the field.' Professor Emeritus Mischa Schwartz, Columbia University'Hisashi Kobayashi, Brian L. Mark, and William Turin are highly experienced university teachers and scientists. Based on this background their book covers not only fundamentals but also a large range of applications. Some of them are treated in a textbook for the first time. … Without any doubt the book will be extremely valuable to graduate students and to scientists in universities and industry as well. Congratulations to the authors!' Prof. Dr.-Ing. Eberhard Hänsler, Technische Universität Darmstadt'An up-to-date and comprehensive book with all the fundamentals in Probability, Random Processes, Stochastic Analysis, and their interplays and applications, which lays a solid foundation for the students in related areas. It is also an ideal textbook with five relatively independent but logically interconnected parts and the corresponding solution manuals and lecture slides. Furthermore, to my best knowledge, the similar editing in Part IV and Part V can't be found elsewhere.' Zhisheng Niu, Tsinghua UniversityTable of Contents1. Introduction; Part I. Probability, Random Variables and Statistics: 2. Probability; 3. Discrete random variables; 4. Continuous random variables; 5. Functions of random variables and their distributions; 6. Fundamentals of statistical analysis; 7. Distributions derived from the normal distribution; Part II. Transform Methods, Bounds and Limits: 8. Moment generating function and characteristic function; 9. Generating function and Laplace transform; 10. Inequalities, bounds and large deviation approximation; 11. Convergence of a sequence of random variables, and the limit theorems; Part III. Random Processes: 12. Random process; 13. Spectral representation of random processes and time series; 14. Poisson process, birth-death process, and renewal process; 15. Discrete-time Markov chains; 16. Semi-Markov processes and continuous-time Markov chains; 17. Random walk, Brownian motion, diffusion and itô processes; Part IV. Statistical Inference: 18. Estimation and decision theory; 19. Estimation algorithms; Part V. Applications and Advanced Topics: 20. Hidden Markov models and applications; 21. Probabilistic models in machine learning; 22. Filtering and prediction of random processes; 23. Queuing and loss models.
£70.99
