Digital signal processing (DSP) Books

Book SynopsisThis book offers a comprehensive introduction to advanced methods for image and video analysis and processing. It covers deraining, dehazing, inpainting, fusion, watermarking and stitching. It describes techniques for face and lip recognition, facial expression recognition, lip reading in videos, moving object tracking, dynamic scene classification, among others. The book combines the latest machine learning methods with computer vision applications, covering topics such as event recognition based on deep learning，dynamic scene classification based on topic model, person re-identification based on metric learning and behavior analysis. It also offers a systematic introduction to image evaluation criteria showing how to use them in different experimental contexts. The book offers an example-based practical guide to researchers, professionals and graduate students dealing with advanced problems in image analysis and computer vision.Table of ContentsIntroduction and Overview.- Matlab Functions of Image and Video.- Image and Video Segmentation.- Feature Extraction and Representation.- Common Evaluation Criterion.- Image Correction.- Image Inpainting.- Fusions.- Image Stitching.- Image Watermarking.

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Book SynopsisThis Springer brief provides the necessary foundations to understand differential privacy and describes practical algorithms enforcing this concept for the publication of real-time statistics based on sensitive data. Several scenarios of interest are considered, depending on the kind of estimator to be implemented and the potential availability of prior public information about the data, which can be used greatly to improve the estimators' performance. The brief encourages the proper use of large datasets based on private data obtained from individuals in the world of the Internet of Things and participatory sensing. For the benefit of the reader, several examples are discussed to illustrate the concepts and evaluate the performance of the algorithms described. These examples relate to traffic estimation, sensing in smart buildings, and syndromic surveillance to detect epidemic outbreaks.Table of ContentsChapter 1. Defining Privacy Preserving Data Analysis.- Chapter 2. Basic Differentially Private Mechanism.- Chapter 3. A Two-Stage Architecture for Differentially Private Filtering.- Chapter 4. Differentially Private Filtering for Stationary Stochastic Collective Signals.- Chapter 5. Differentially Private Kalman Filtering.- Chapter 6. Differentially Private Nonlinear Observers.- Chapter 7. Conclusion.

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Book SynopsisThis textbook introduces basic and advanced embedded system topics through Arm Cortex M microcontrollers, covering programmable microcontroller usage starting from basic to advanced concepts using the STMicroelectronics Discovery development board. Designed for use in upper-level undergraduate and graduate courses on microcontrollers, microprocessor systems, and embedded systems, the book explores fundamental and advanced topics, real-time operating systems via FreeRTOS and Mbed OS, and then offers a solid grounding in digital signal processing, digital control, and digital image processing concepts — with emphasis placed on the usage of a microcontroller for these advanced topics. The book uses C language, “the” programming language for microcontrollers, C++ language, and MicroPython, which allows Python language usage on a microcontroller. Sample codes and course slides are available for readers and instructors, and a solutions manual is available to instructors. The book will also be an ideal reference for practicing engineers and electronics hobbyists who wish to become familiar with basic and advanced microcontroller concepts.Table of ContentsChapter 1. Introduction.- Chapter 2. Microcontroller Architecture.- Chapter 3. Software Development Platforms.- Chapter 4. Digital Input and Output.- Chapter 5. Interrupts and Power Management.- Chapter 6. Timing Operations.- Chapter 7. Conversion Between Analog and Digital Values.- Chapter 8. Digital Communication.- Chapter 9. Memory Operations.- Chapter 10. Real-Time Operating Systems.- Chapter 11. LCD, Touch Screen and Graphical User Interface Formation.- Chapter 12. Introduction to Digital Signal Processing.- Chapter 13. Introduction to Digital Control.- Chapter 14. Introduction to Digital Image Processing.- Chapter 15. Advanced Topics.

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Book SynopsisThis book evaluates the impact of relevant factors affecting the results of speech quality assessment studies carried out in crowdsourcing. The author describes how these factors relate to the test structure, the effect of environmental background noise, and the influence of language differences. He details multiple user-centered studies that have been conducted to derive guidelines for reliable collection of speech quality scores in crowdsourcing. Specifically, different questions are addressed such as the optimal number of speech samples to include in a listening task, the influence of the environmental background noise in the speech quality ratings, as well as methods for classifying background noise from web audio recordings, or the impact of language proficiency in the user perception of speech quality. Ultimately, the results of these studies contributed to the definition of the ITU-T Recommendation P.808 that defines the guidelines to conduct speech quality studies in crowdsourcing.Table of Contents1. Introduction.2. Related Work.3. Method.4. Test Structure.5. Impact of Background Noise.6. Influence of Language.7. Conclusion.

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Book SynopsisThe book systematically introduces theories of frequently-used modern signal processing methods and technologies, and focuses discussions on stochastic signal, parameter estimation, modern spectral estimation, adaptive filter, high-order signal analysis and non-linear transformation in time-domain signal analysis. With abundant exercises, the book is an essential reference for graduate students in electrical engineering and information science. Table of ContentsTable of content:Chapter 1: Stochastic signal - correlation function, covariance function, power spectral density, signal identification, signal transformation, linear system with random input signalChapter 2: Parameter estimation - estimators, Fisher information and Cramer-Rao inequality, Bayes estimation, maximum likelihood estimation, least-square estimationChapter 3: Modern spectral estimation - discrete stochastic process, non-parametric spectral analysis, stationary ARMA process and spectral density, ARMA spectral estimation, ARMA identification, maximum entropy spectrum estimation, Pisarenko harmonic decomposition, extended Prony method, MUSIC, ESPRITChapter 4: Adaptive filter - Wiener filter for continuous time, Optimization, Kalman filter, LMS adaptive algorithm and filter, RLS adaptive algorithm, operator theory for adaptive filter, adaptive line enhancer, trap filter, generalized sidelobe canceller, blind adaptive multi-user detectionChapter 5: High-order statistical analysis - matrix and cumulative domain, high-order spectral, non-Gussian signal and linear system, FIR system identification, ARMA model identification, harmonic retrieval in color noise, time delay estimation, double spectral and application in signal classificationChapter 6: Linear transformation in time-domain signal analysis - local transformation, analytic signal, Fourier transformation, Gabor transformation, wavelet transformation and framework theory, multi-resolution analysis, quadrature filter, bi-quadrature filter, Gabor atoms and applications in radar signal detectionChapter 7: Nonlinear transformation in time-domain signal analysis - time domain distribution, Wigner-Ville distribution, fuzzy function, Cohen qusi-time domain distribution, evaluation and optimization of time domain distribution, time domain distribution for FM signal

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Book SynopsisThis book presents a novel approach to the analysis and design of all-digital phase-locked loops (ADPLLs), technology widely used in wireless communication devices. The authors provide an overview of ADPLL architectures, time-to-digital converters (TDCs) and noise shaping. Realistic examples illustrate how to analyze and simulate phase noise in the presence of sigma-delta modulation and time-to-digital conversion. Readers will gain a deep understanding of ADPLLs and the central role played by noise-shaping. A range of ADPLL and TDC architectures are presented in unified manner. Analytical and simulation tools are discussed in detail. Matlab code is included that can be reused to design, simulate and analyze the ADPLL architectures that are presented in the book.Table of ContentsIntroduction.- Phase Digitization in All-Digital PLLs.- A Unifying Framework for TDC Architectures.- Analytical Predictions of Phase Noise in ADPLLs.- Advantages of Noise Shaping and Dither.- Efficient Modeling and Simulation of Accumulator-Based ADPLLs.- Modelling and Estimating Phase Noise with Matlab.

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Book SynopsisThis improved and updated second edition covers the theory, development, and design of electro-acoustic transducers for underwater applications. This highly regarded text discusses the basics of piezoelectric and magnetostrictive transducers that are currently being used as well as promising new designs. It presents the basic acoustics as well as the specific acoustics data needed in transducer design and evaluation. A broad range of designs of projectors and hydrophones are described in detail along with methods of modeling, evaluation, and measurement. Analysis of projector and hydrophone transducer arrays, including the effects of mutual radiation impedance and numerical models for elements and arrays, are also covered. The book includes new advances in transducer design and transducer materials and has been completely reorganized to be suitable for use as a textbook, as well as a reference or handbook. The new edition contains corrections to the first edition, end-of-chapter exercises, and solutions to selected exercises. Each chapter includes a short introduction, end-of-chapter summary, and an extensive reference list offering the reader more detailed information and historical context. A glossary of key terms is also included at the end.Table of ContentsChapter 1: Introduction. 1.1 Brief History of Underwater Sound Transducers. 1.2 Underwater Transducer Applications. 1.3 General Description of Linear Electroacoustic Transduction. 1.4 Transducer Characteristics. 1.5 Transducer Arrays.- Chapter 2: Electroacoustic Transduction. 2.1 Piezoelectric Transducers. 2.2 Electroconstrictive Transducers. 2.3 Magnetostrictive Transducers. 2.4 Electrostatic Transducers. 2.5 Variable Reluctance Transducers. 2.6 Moving Coil Transducers. 2.7 Comparison of Transducer Mechanisms. 2.8 Equivalent Circuits. 2.9 Thermal Considerations. 2.10 Extended Equivalent Circuits.- Chapter 3: Transducer Models. 3.1 Lumped Parameter Models and Equivalent Circuits. 3.2 Distributed Models. 3.3 Matrix Models. 3.4 Finite Element Models.- Chapter 4: Transducer Characteristics. 4.1 Resonance Frequency. 4.2 The Mechanical Quality Factor. 4.3 Characteristic Mechanical Impedance. 4.4 Electromechanical Coupling Coefficient. 4.5 Parameter Based Figure of Merit (FOM).- Chapter 5: Transducers as Projectors. 5.1 Principles of Operation. 5.2 Ring and Spherical Transducers. 5.3 Piston Transducers. 5.4 Transmission Line Transducers. 5.5 Flextensional Transducers. 5.6 Flexural Transducers. 5.7 Modal Transducers. 5.8 Low Profile Transducers.- Chapter 6: Transducers as Hydrophones. 6.1 Principles of Operation. 6.2 Cylindrical and Spherical Hydrophones. 6.3 Planar Hydrophones. 6.4 Bender Hydrophones. 6.5 Vector Hydrophones. 6.6 Plane Wave Diffraction Constant. 6.7 Hydrophone Thermal Noise.- Chapter 7: Projector Arrays. 7.1 Array Directivity Functions. 7.2 Mutual Radiation Impedance and the Array Equations. 7.3 Calculation of Mutual Radiation Impedance. 7.4 Arrays of Non-FVD Transducers. 7.5 Volume Arrays. 7.6 Near Field of Projector Array. 7.7 The Nonlinear Parametric Array.Doubly Steered Arrays.- Chapter 8: Hydrophone Arrays. 8.1 Hydrophone Array Directional Response. 8.2 Array Gain. 8.3 Sources and Properties of Noise in Arrays. 8.4 Reduction of Array Noise. 8.5 Arrays of Vector Sensors. 8.6 Steered Planar Circular Arrays. 8.7 Array Absorption and Transparency.- Chapter 9: Transducer Evaluation and Measurement. 9.1 Electrical Measurement of Transducers in Air. 9.2 Measurement of Transducers in Water. 9.3 Measurement of Transducer Efficiency. 9.4 Acoustic Responses of Transducers. 9.5 Reciprocity Calibration. 9.6 Tuned Responses. 9.7 Near-Field Measurements. 9.8 Calibrated reference Transducers.- Chapter 10: Acoustic Radiation from Transducers. 10.1 The Acoustic Radiation Problem. 10.2 Far Field Acoustic Radiation 10.3 Near-Field Acoustic Radiation. 10.4 Radiation Impedance. 10.5 Dipole Coupling to a Parasitic Monopole.- Chapter 11: Mathematical Models for Acoustic Radiation. 11.1 Mutual Radiation Impedance. 11.2 Green's Theorem and Acoustic Reciprocity. 11.3 Scattering and the Diffraction Constant. 11.4 Numerical Models for Acoustic Calculations.- Chapter 12: Nonlinear Mechanisms and Their Effects. 12.1 Nonlinear Mechanisms in Lumped Parameter Transducers. 12.2 Analysis of Nonlinear Effects. 12.3 Nonlinear Analysis of Distributed-Parameter Transducers. 12.4 Nonlinear Effects on the Electromechanical Coupling Coefficient.- Appendix.- Glossary of Terms.- Solutions for Odd-Numbered Exercises.-Index.

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Book SynopsisThis improved and updated second edition covers the theory, development, and design of electro-acoustic transducers for underwater applications. This highly regarded text discusses the basics of piezoelectric and magnetostrictive transducers that are currently being used as well as promising new designs. It presents the basic acoustics as well as the specific acoustics data needed in transducer design and evaluation. A broad range of designs of projectors and hydrophones are described in detail along with methods of modeling, evaluation, and measurement. Analysis of projector and hydrophone transducer arrays, including the effects of mutual radiation impedance and numerical models for elements and arrays, are also covered. The book includes new advances in transducer design and transducer materials and has been completely reorganized to be suitable for use as a textbook, as well as a reference or handbook. The new edition contains corrections to the first edition, end-of-chapter exercises, and solutions to selected exercises. Each chapter includes a short introduction, end-of-chapter summary, and an extensive reference list offering the reader more detailed information and historical context. A glossary of key terms is also included at the end.Table of ContentsChapter 1: Introduction. 1.1 Brief History of Underwater Sound Transducers. 1.2 Underwater Transducer Applications. 1.3 General Description of Linear Electroacoustic Transduction. 1.4 Transducer Characteristics. 1.5 Transducer Arrays.- Chapter 2: Electroacoustic Transduction. 2.1 Piezoelectric Transducers. 2.2 Electroconstrictive Transducers. 2.3 Magnetostrictive Transducers. 2.4 Electrostatic Transducers. 2.5 Variable Reluctance Transducers. 2.6 Moving Coil Transducers. 2.7 Comparison of Transducer Mechanisms. 2.8 Equivalent Circuits. 2.9 Thermal Considerations. 2.10 Extended Equivalent Circuits.- Chapter 3: Transducer Models. 3.1 Lumped Parameter Models and Equivalent Circuits. 3.2 Distributed Models. 3.3 Matrix Models. 3.4 Finite Element Models.- Chapter 4: Transducer Characteristics. 4.1 Resonance Frequency. 4.2 The Mechanical Quality Factor. 4.3 Characteristic Mechanical Impedance. 4.4 Electromechanical Coupling Coefficient. 4.5 Parameter Based Figure of Merit (FOM).- Chapter 5: Transducers as Projectors. 5.1 Principles of Operation. 5.2 Ring and Spherical Transducers. 5.3 Piston Transducers. 5.4 Transmission Line Transducers. 5.5 Flextensional Transducers. 5.6 Flexural Transducers. 5.7 Modal Transducers. 5.8 Low Profile Transducers.- Chapter 6: Transducers as Hydrophones. 6.1 Principles of Operation. 6.2 Cylindrical and Spherical Hydrophones. 6.3 Planar Hydrophones. 6.4 Bender Hydrophones. 6.5 Vector Hydrophones. 6.6 Plane Wave Diffraction Constant. 6.7 Hydrophone Thermal Noise.- Chapter 7: Projector Arrays. 7.1 Array Directivity Functions. 7.2 Mutual Radiation Impedance and the Array Equations. 7.3 Calculation of Mutual Radiation Impedance. 7.4 Arrays of Non-FVD Transducers. 7.5 Volume Arrays. 7.6 Near Field of Projector Array. 7.7 The Nonlinear Parametric Array.Doubly Steered Arrays.- Chapter 8: Hydrophone Arrays. 8.1 Hydrophone Array Directional Response. 8.2 Array Gain. 8.3 Sources and Properties of Noise in Arrays. 8.4 Reduction of Array Noise. 8.5 Arrays of Vector Sensors. 8.6 Steered Planar Circular Arrays. 8.7 Array Absorption and Transparency.- Chapter 9: Transducer Evaluation and Measurement. 9.1 Electrical Measurement of Transducers in Air. 9.2 Measurement of Transducers in Water. 9.3 Measurement of Transducer Efficiency. 9.4 Acoustic Responses of Transducers. 9.5 Reciprocity Calibration. 9.6 Tuned Responses. 9.7 Near-Field Measurements. 9.8 Calibrated reference Transducers.- Chapter 10: Acoustic Radiation from Transducers. 10.1 The Acoustic Radiation Problem. 10.2 Far Field Acoustic Radiation 10.3 Near-Field Acoustic Radiation. 10.4 Radiation Impedance. 10.5 Dipole Coupling to a Parasitic Monopole.- Chapter 11: Mathematical Models for Acoustic Radiation. 11.1 Mutual Radiation Impedance. 11.2 Green's Theorem and Acoustic Reciprocity. 11.3 Scattering and the Diffraction Constant. 11.4 Numerical Models for Acoustic Calculations.- Chapter 12: Nonlinear Mechanisms and Their Effects. 12.1 Nonlinear Mechanisms in Lumped Parameter Transducers. 12.2 Analysis of Nonlinear Effects. 12.3 Nonlinear Analysis of Distributed-Parameter Transducers. 12.4 Nonlinear Effects on the Electromechanical Coupling Coefficient.- Appendix.- Glossary of Terms.- Solutions for Odd-Numbered Exercises.-Index.

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Book SynopsisSignal processing is the discipline of extracting information from collections of measurements. To be effective, the measurements must be organized and then filtered, detected, or transformed to expose the desired information. Distortions caused by uncertainty, noise, and clutter degrade the performance of practical signal processing systems.In aggressively uncertain situations, the full truth about an underlying signal cannot be known. This book develops the theory and practice of signal processing systems for these situations that extract useful, qualitative information using the mathematics of topology -- the study of spaces under continuous transformations. Since the collection of continuous transformations is large and varied, tools which are topologically-motivated are automatically insensitive to substantial distortion. The target audience comprises practitioners as well as researchers, but the book may also be beneficial for graduate students.Trade ReviewFrom the book reviews:“This text provides a nice exposition of the topological ideas used to extract information from signals and the practical details of signal processing. … Robinson’s intended audience is first year graduate students in both engineering and mathematics, and advanced undergraduates. … Throughout the text there are numerous examples and diagrams. Each chapter also ends with some open questions. These features make the book quite readable.” (Michele Intermont, MAA Reviews, February, 2015)“Three major goals for this book: firstly to show that topological invariants provide qualitative information about signals that is both relevant and practical, second to show that the signal processing concepts of filtering, detection, and noise correspond respectively to the concepts of sheaves, functoriality and sequences, and third to advocate for the use of sheaf theory in signal processing. … The target audience is practitioners so that the theoretical notions are covered with the practitioner in mind with motivations emphasized.” (Jonathan Hodgson, zbMATH, Vol. 1294, 2014)Table of ContentsIntroduction and informal discussion.- Parametrization.- Signals.- Detection.- Transforms.- Noise.

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Book SynopsisThe focus of this book is on providing students with insights into geometry that can help them understand deep learning from a unified perspective. Rather than describing deep learning as an implementation technique, as is usually the case in many existing deep learning books, here, deep learning is explained as an ultimate form of signal processing techniques that can be imagined. To support this claim, an overview of classical kernel machine learning approaches is presented, and their advantages and limitations are explained. Following a detailed explanation of the basic building blocks of deep neural networks from a biological and algorithmic point of view, the latest tools such as attention, normalization, Transformer, BERT, GPT-3, and others are described. Here, too, the focus is on the fact that in these heuristic approaches, there is an important, beautiful geometric structure behind the intuition that enables a systematic understanding. A unified geometric analysis to understand the working mechanism of deep learning from high-dimensional geometry is offered. Then, different forms of generative models like GAN, VAE, normalizing flows, optimal transport, and so on are described from a unified geometric perspective, showing that they actually come from statistical distance-minimization problems.Because this book contains up-to-date information from both a practical and theoretical point of view, it can be used as an advanced deep learning textbook in universities or as a reference source for researchers interested in acquiring the latest deep learning algorithms and their underlying principles. In addition, the book has been prepared for a codeshare course for both engineering and mathematics students, thus much of the content is interdisciplinary and will appeal to students from both disciplines.Trade Review“This book is based on material that has been prepared for senior-level undergraduate classes, this book can be used for one-semester senior-level undergraduate and graduate-level classes.” (Arzu Ahmadova, zbMATH 1493.68003, 2022)Table of ContentsPart I Basic Tools for Machine Learning: 1. Mathematical Preliminaries.- 2. Linear and Kernel Classifiers.- 3. Linear, Logistic, and Kernel Regression.- 4. Reproducing Kernel Hilbert Space, Representer Theorem.- Part II Building Blocks of Deep Learning: 5. Biological Neural Networks.- 6. Artificial Neural Networks and Backpropagation.- 7. Convolutional Neural Networks.- 8. Graph Neural Networks.- 9. Normalization and Attention.- Part III Advanced Topics in Deep Learning.- 10. Geometry of Deep Neural Networks.- 11. Deep Learning Optimization.- 12. Generalization Capability of Deep Learning.- 13. Generative Models and Unsupervised Learning.- Summary and Outlook.- Bibliography.- Index.

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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

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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.

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Book SynopsisThis book discusses non-conventional digital signal processing based on direct processing of delta-sigma modulated bit-stream. The main attributes of low-pass delta-sigma analog-to-digital converters are: simple and inexpensive design, robustness of design to component tolerances, low-power consumption, high input impedance, high resolution (more than 20 bits) and possibility of direct arithmetic operation on its bit-stream. The author presents a number of theoretical and simulation results related to newly proposed linear and non-linear circuits such as delta-sigma adders, delta-sigma rectifiers, delta-sigma RMS and AGC circuits, delta-sigma frequency deviation meters, etc. The proposed circuits are not application limited and can be used in instrumentation, sensor application, bio-medical application, communications, etc. Presents novel linear and nonlinear circuits for direct processing of delta-sigma modulated bit-stream; The proposed circuits are supported by theoretical and simulation results; Recommends potential applications of the proposed circuits, and proposes ideas for further investigation. Table of ContentsChapter 1. Basics of Low-Pass Modulation.- Chapter 2. Linear Processing of Delta-Modulated Bit-Stream.- Chapter 3. Rectification of a Delta-Sigma Modulated Signal.- Chapter 4. Multiplication of Two Δ-Σ Bit-Streams.- Chapter 5. Digital Architecture for Δ-Σ RMS-to-DC Converter.- Chapter 6. Companding Circuits and Systems Based on Δ-Σ Modulation.- Chapter 7. A Δ-Σ Digital Stereo Multiplexing-Demultiplexing System.- Chapter 8. Δ-Σ Digital Amplitude Modulation System.- Chapter 9. Δ-Σ Methods for Frequency Deviation Measurement of a Known Nominal Frequency.- Chapter 10. Δ-Σ Automatic Gain Controller.- Chapter 11. Δ-Σ Integrator and Differentiator Circuits.

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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

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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.

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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

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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.

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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.

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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

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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.

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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.

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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

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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.

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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.

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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.

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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.

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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.

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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.

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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

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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.

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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

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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

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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

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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.

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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.

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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.

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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.

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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.

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Book SynopsisBrimming with top articles from experts in signal processing and biomedical engineering, Time Frequency and Wavelets in Biomedical Signal Processing introduces time--frequency, time--scale, wavelet transform methods, and their applications in biomedical signal processing.Table of ContentsList of Contributors. Preface. TIME-FREQUENCY ANALYSIS METHODS WITH BIOMEDICAL APPLICATIONS. Recent Advances in Time-Frequency Representations: SomeTheoretical Foundation (W. Williams). Biological Applications and Interpretations of Time-Frequency Signal Analysis (W. Williams). The Application of Advanced Time-Frequency Analysis Techniques to Doppler Ultrasound (S. Marple, et al.). Analysis of ECG Late Potentials Using Time-Frequency Methods (H. Dickhaus & H. Heinrich). Time-Frequency Distributions Applied to Uterine EMG: Characterization and Assessment (J. Duchene & D. Devedeux). Time-Frequency Analyses of the Electrogastrogram (Z. Lin and J. Chen). Recent Advances in Time-Frequency and Time-Scale Methods (C. Mello & M. Akay). WAVELETS, WAVELET PACKETS, AND MATCHING PURSUITS WITH BIOMEDICAL APPLICATIONS. Fast Algorithms for Wavelet Transform Computation (O. Rioul & P. Duhamel). Analysis of Cellular Vibrations in the Living Cochlea Using the Continuous Wavelet Transform and the Short-Time Fourier Transform (M. Teich, et al.). Alterative Processing Method Using Gabor Wavelets and the Wavelet Transform for the Analysis of Phonocardiogram Signals (M. Matalgah, et al.). Wavelet Feature Extraction from Neurophysiological Signals (M. Sun & R. Sclabassi). Experiments with Adapted Wavelet De-Noising for Medical Signals and Images (R. Coifman & M. Wickerhauser). Speech Enhancement for Hearing Aids (J. Rutledge). From Continuous Wavelet Transform to Wavelet Packets: Application to the Estimation of Pulmonary Microvascular Pressure (M. Karrakchou & M. Kunt). In Pursuit of Time-Frequency Representation of Brain Signals (P. Durka & K. Blinowska). EEG Spike Directors Based on Different Decompositions: A Comparative Study (L. Senhadji, et al.). WAVELETS AND MEDICAL IMAGING. A Discrete Dyadic Wavelet Transform for Multidimensional Feature Analysis (I. Koren & A. Laine). Hexagonal QMF Banks and Wavelets (S. Schuler & A. Laine). Inversion of the Radon Transform under Wavelet Constraints (B. Sahiner & A. Yagle). Wavelets Applied to Mammograms (W. Richardson). Hybrid Wavelet Transform for Image Enhancement forComputer-Assisted Diagnosis and Telemedicine Applications (L. Clarke, et al.). Medical Image Enhancement Using Wavelet Transform and Arithmetic Coding (P. Saipetch, et al.). Adapted Wavelet Encoding in Functional Magnetic Resonance Imaging (D. Healy, et al.). A Tutorial Overview of a Stabilization Algorithm for Limited-Angle Tomography (T. Olson). Wavelet Compression of Medical Images (A. Manduca). WAVELETS, NEURAL NETWORKS, AND FRACTALS. Single Side Scaling Wavelet Frame and Neural Network (Q. Zhang). Analysis of Evoked Potentials Using Wavelet Networks (H. Heinrich & H. Dickhaus). Self-Organizing Wavelet-Based Neural Networks (K. Kobayashi). On Wavelets and Fractal Processes (P. Flandrin). Fractal Analysis of Heart Rate Variability (R. Fischer & M. Akay). Index. Editor's Biography.

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