This book provides a comprehensive coverage of the state-of-the-art in understanding media popularity and trends in online social networks through social multimedia signals. With insights from the study of popularity and sharing patterns of online media, trend spread in social media, social network analysis for multimedia and visualizing diffusion of media in online social networks. In particular, the book will address the following important issues: Understanding social network phenomena from a signal processing point of view; The existence and popularity of multimedia as shared and social media, how content or origin of sharing activity can affect its spread and popularity; The network-signal duality principle, i.e., how the signal tells us key properties of information diffusion in networks; The social signal penetration hypothesis, i.e., how the popularity of media in one domain can affect the popularity of media in another. The book will help researchers, developers and business (advertising/marketing) individuals to comprehend the potential in exploring social multimedia signals collected from social network data quantitatively from a signal processing perspective.

Diagnostic Biomedical Signal and Image Processing Applications: With Deep Learning Methods presents comprehensive research on both medical imaging and medical signals analysis. The book discusses classification, segmentation, detection, tracking and retrieval applications of non-invasive methods such as EEG, ECG, EMG, MRI, fMRI, CT and X-RAY, amongst others. These image and signal modalities include real challenges, which are the main themes that medical imaging and medical signal processing researchers focus on today. The book also emphasizes removing noise and specifying dataset key properties, with each chapter containing details of one of the medical imaging or medical signal modalities. Focusing on solving real medical problems using new deep learning and CNN approaches, this book will appeal to research scholars, graduate students, faculty members, R&D engineers, and biomedical engineers who want to learn how medical signals and images play an important role in the early diagnosis and treatment of diseases.

Traditional Wireless Sensor Networks (WSNs) have tremendous applications, but their performance can be limited due to the limited processing and communication power of wireless sensor nodes. Cognitive Radio Sensor Networks: Applications, Architectures, and Challenges examines how wireless sensor nodes with cognitive radio capabilities can address these challenges and improve the spectrum utilization. This premier reference work presents a broader picture on the applications, architecture, challenges, and open research directions in the area of WSN research. It serves as a reference book for graduate students in courses on topics such as wireless sensor networks, cognitive radio networks, and emerging wireless technologies.

This book gathers outstanding research papers presented at the International Joint Conference on Computational Intelligence (IJCCI 2019), held at the University of Liberal Arts Bangladesh (ULAB), Dhaka, on 25-26 October 2019 and jointly organized by the University of Liberal Arts Bangladesh (ULAB), Bangladesh; Jahangirnagar University (JU), Bangladesh; and South Asian University (SAU), India. These proceedings present novel contributions in the areas of computational intelligence, and offer valuable reference material for advanced research. The topics covered include collective intelligence, soft computing, optimization, cloud computing, machine learning, intelligent software, robotics, data science, data security, big data analytics, and signal and natural language processing.

The use of neural networks is permeating every area of signal processing. They can provide powerful means for solving many problems, especially in nonlinear, real-time, adaptive, and blind signal processing. The Handbook of Neural Network Signal Processing brings together applications that were previously scattered among various publications to provide an up-to-date, detailed treatment of the subject from an engineering point of view.

The authors cover basic principles, modeling, algorithms, architectures, implementation procedures, and well-designed simulation examples of audio, video, speech, communication, geophysical, sonar, radar, medical, and many other signals. The subject of neural networks and their application to signal processing is constantly improving. You need a handy reference that will inform you of current applications in this new area. The Handbook of Neural Network Signal Processing provides this much needed service for all engineers and scientists in the field.

For undergraduate courses on Signals and Linear Systems.

This book contains a comprehensive set of computer exercises of varying levels of difficulty covering the fundamentals of signals and systems. The exercises require the reader to compare answers they compute in MATLAB® with results and predictions made based on their understanding of the material. The book is compatible with any introductory course or text on signals and systems.

Thisbookpresentsmaterialwhichismorealgorithmicallyorientedthanmost alternatives.Italsodealswithtopicsthatareatorbeyondthestateoftheart. Examples include practical and applicable wavelet and other multiresolution transform analysis. New areas are broached like the ridgelet and curvelet transforms. The reader will ?nd in this book an engineering approach to the interpretation of scienti?c data. Compared to the 1st Edition, various additions have been made throu- out, and the topics covered have been updated. The background or en- ronment of this book's topics include continuing interest in e-science and the virtual observatory, which are based on web based and increasingly web service based science and engineering. Additional colleagues whom we would like to acknowledge in this 2nd edition include: Bedros Afeyan, Nabila Aghanim, Emmanuel Cand' es, David Donoho, Jalal Fadili, and Sandrine Pires, We would like to particularly - knowledge Olivier Forni who contributed to the discussion on compression of hyperspectral data, Yassir Moudden on multiwavelength data analysis and Vicent Mart' ?nez on the genus function. The cover image to this 2nd edition is from the Deep Impact project. It was taken approximately 8 minutes after impact on 4 July 2005 with the CLEAR6 ?lter and deconvolved using the Richardson-Lucy method. We thank Don Lindler, Ivo Busko, Mike A'Hearn and the Deep Impact team for the processing of this image and for providing it to us.

Advances in Imaging and Electron Physics, Volume 226 merges two long-running serials, Advances in Electronics and Electron Physics and Advances in Optical and Electron Microscopy. Chapters in this release cover Characterization of nanomaterials properties using FE-TEM, Cold field-emission electron sources: From higher brightness to ultrafast beams, Every electron counts: Towards the development of aberration optimized and aberration corrected electron sources, and more. The series features articles on the physics of electron devices (especially semiconductor devices), particle optics at high and low energies, microlithography, image science, digital image processing, electromagnetic wave propagation, electron microscopy and the computing methods used in all these domains.

The current popular and scientific interest in virtual environments has provided a new impetus for investigating binaural and spatial hearing. However, the many intriguing phenomena of spatial hearing have long made it an exciting area of scientific inquiry. Psychophysical and physiological investigations of spatial hearing seem to be converging on common explanations of underlying mechanisms. These understandings have in turn been incorporated into sophisticated yet mathematically tractable models of binaural interaction. Thus, binaural and spatial hearing is one of the few areas in which professionals are soon likely to find adequate physiological explanations of complex psychological phenomena that can be reasonably and usefully approximated by mathematical and physical models. This volume grew out of the Conference on Binaural and Spatial Hearing, a four-day event held at Wright-Patterson Air Force Base in response to rapid developments in binaural and spatial hearing research and technology. Meant to be more than just a proceedings, it presents chapters that are longer than typical proceedings papers and contain considerably more review material, including extensive bibliographies in many cases. Arranged into topical sections, the chapters represent major thrusts in the recent literature. The authors of the first chapter in each section have been encouraged to take a broad perspective and review the current state of literature. Subsequent chapters in each section tend to be somewhat more narrowly focused, and often emphasize the authors' own work. Thus, each section provides overview, background, and current research on a particular topic. This book is significant in that it reviews the important work during the past 10 to 15 years, and provides greater breadth and depth than most of the previous works.

Intelligent Edge Computing for Cyber Physical Applications introduces state-of-the-art research methodologies, tools and techniques, challenges, and solutions with further research opportunities in the area of edge-based cyber-physical systems. The book presents a comprehensive review of recent literature and analysis of different techniques for building edge-based CPS. In addition, it describes how edge-based CPS can be built to seamlessly interact with physical machines for optimal performance, covering various aspects of edge computing architectures for dynamic resource provisioning, mobile edge computing, energy saving scenarios, and different security issues. Sections feature practical use cases of edge-computing which will help readers understand the workings of edge-based systems in detail, taking into account the need to present intellectual challenges while appealing to a broad readership, including academic researchers, practicing engineers and managers, and graduate students.

Case-based reasoning in design is becoming an important approach to computer-support for design as well as an important component in understanding the design process. Design has become a major focus for problem solving paradigms due to its complexity and open-ended nature. This book presents a clear description of how case-based reasoning can be applied to design problems, including the representation of design cases, indexing and retrieving design cases, and the range of paradigms for adapting design cases. With a focus on design, this book differs from others that provide a generalist view of case-based reasoning.
This volume provides two important contributions to the area:
* a general description of the issues and alternatives in applying case-based reasoning to design, and
* a description of specific implementations of case-based design.
Through this combination, the reader will learn about both the general issues and the practical problems in supporting design through case-based reasoning. This book was prepared to fill a gap in the literature on the unique problems that design introduces to computational paradigms developed in computer science. It also addresses the needs of computational support for design problem solving from both theoretical and practical perspectives.

This book is concerned with the processing of signals that have been sampled and digitized. The authors present algorithms for the optimization, random simulation, and numerical integration of probability densities for applications of Bayesian inference to signal processing. In particular, methods are developed for the computation of marginal densities and evidence, and are applied to previously intractable problems either involving large numbers of parameters or where the signal model is of a complex form. The emphasis is on the applications of these methods notably to the restoration of digital audio recordings and biomedical data. After a chapter which sets out the main principles of Bayesian inference applied to signal processing, subsequent chapters cover numerical approaches to these techniques, the use of Markov chain Monte Carlo methods, the identification of abrupt changes in data using the Bayesian piecewise linear model, and identifying missing samples in digital audio signals.

Integrates rational approximation with adaptive filtering, providing viable, numerically reliable procedures for creating adaptive infinite impulse response (IIR) filters. The choice of filter structure to adapt, algorithm design and the approximation properties for each type of algorithm are also addressed. This work recasts the theory of adaptive IIR filters by concentrating on recursive lattice filters, freeing systems from the need for direct-form filters.;A solutions manual is available for instructors only. College or university bookstores may order five or more copies at a special student price which is available upon request.

This book focuses on artifi cial intelligence in the field of digital signal processing and wireless communication. The implementation of machine learning and deep learning in audio, image, and video processing is presented, while adaptive signal processing and biomedical signal processing are also explored through DL algorithms, as well as 5G and green communication. Finally, metaheuristic algorithms of related mathematical problems are explored.

Gaussian linear modelling cannot address current signal processing demands. In moderncontexts, suchasIndependentComponentAnalysis(ICA), progresshasbeen made speci?cally by imposing non-Gaussian and/or non-linear assumptions. Hence, standard Wiener and Kalman theories no longer enjoy their traditional hegemony in the ?eld, revealing the standard computational engines for these problems. In their place, diverse principles have been explored, leading to a consequent diversity in the implied computational algorithms. The traditional on-line and data-intensive pre- cupations of signal processing continue to demand that these algorithms be tractable. Increasingly, full probability modelling (the so-called Bayesian approach)-or partial probability modelling using the likelihood function-is the pathway for - sign of these algorithms. However, the results are often intractable, and so the area of distributional approximation is of increasing relevance in signal processing. The Expectation-Maximization (EM) algorithm and Laplace approximation, for ex- ple, are standard approaches to handling dif?cult models, but these approximations (certainty equivalence, and Gaussian, respectively) are often too drastic to handle the high-dimensional, multi-modal and/or strongly correlated problems that are - countered. Since the 1990s, stochastic simulation methods have come to dominate Bayesian signal processing. Markov Chain Monte Carlo (MCMC) sampling, and - lated methods, are appreciated for their ability to simulate possibly high-dimensional distributions to arbitrary levels of accuracy. More recently, the particle ?ltering - proach has addressed on-line stochastic simulation. Nevertheless, the wider acce- ability of these methods-and, to some extent, Bayesian signal processing itself- has been undermined by the large computational demands they typically mak

Motion Correction in MR: Correction of Position, Motion, and Dynamic Changes, Volume Eight provides a comprehensive survey of the state-of-the-art in motion detection and correction in magnetic resonance imaging and magnetic resonance spectroscopy. The book describes the problem of correctly and consistently identifying and positioning the organ of interest and tracking it throughout the scan. The basic principles of how image artefacts arise because of position changes during scanning are described, along with retrospective and prospective techniques for eliminating these artefacts, including classical approaches and methods using machine learning. Internal navigator-based approaches as well as external systems for estimating motion are also presented, along with practical applications in each organ system and each MR modality covered. This book provides a technical basis for physicists and engineers to develop motion correction methods, giving guidance to technologists and radiologists for incorporating these methods in patient examinations.

For a one-quarter or one-semster course on Signals and Systems. This new edition delivers an accessible yet comprehensive analytical introduction to continuous-time and discrete-time signals and systems. It also incorporates a strong emphasis on solving problems and exploring concepts, using demos, downloaded data, and MATLAB(r) to demonstrate solutions for a wide range of problems in engineering and other fields such as financial data analysis. Its flexible structure adapts easily for courses taught by semester or by quarter.

Time-frequency analysis is a modern branch of harmonic analysis. It com prises all those parts of mathematics and its applications that use the struc ture of translations and modulations (or time-frequency shifts) for the anal ysis of functions and operators. Time-frequency analysis is a form of local Fourier analysis that treats time and frequency simultaneously and sym metrically. My goal is a systematic exposition of the foundations of time-frequency analysis, whence the title of the book. The topics range from the elemen tary theory of the short-time Fourier transform and classical results about the Wigner distribution via the recent theory of Gabor frames to quantita tive methods in time-frequency analysis and the theory of pseudodifferential operators. This book is motivated by applications in signal analysis and quantum mechanics, but it is not about these applications. The main ori entation is toward the detailed mathematical investigation of the rich and elegant structures underlying time-frequency analysis. Time-frequency analysis originates in the early development of quantum mechanics by H. Weyl, E. Wigner, and J. von Neumann around 1930, and in the theoretical foundation of information theory and signal analysis by D."

This book brings together many advanced topics in network and acoustic echo cancellation which are aimed towards enhancing the echo cancellation performance of next-generation telecommunication systems. The general subject nature relates to algorithms with increased convergence speed, improved detection of double-talk from near-end speech, robust immunity to undetected double-talk, increased computational efficiency, and multi-channel capability. The resulting compendium provides a coherent treatment of such topics not found otherwise in journals or other books. The chapters are related with a common terminology, but still can be read independently.

The ability to process signals at multiple sampling rates can help reduce costs and improve performance in many DSP applications ranging from signal compression to wireless communications, consumer entertainment products and sensor networks. The theory of multirate signal processing has witnessed a great deal of progress since the publication of the first textbook by Ronald E. Crochiere and Lawrence R. Rabiner in 1983. However, this progress has been mainly in the area of deterministic systems with emphasis on perfect-reconstruction filter banks, orthogonal filter banks, systems with the same sampling rate across all channels and tree-structured systems.

Signal processing is an essential topic for all practicing and aspiring electrical engineers to understand no matter what specific area they are involved in. Originally published by McGraw-Hill* and now reissued by Artech House, this definitive volume offers a unified, comprehensive and practical treatment of statistical and adaptive signal processing. Written by leading experts in industry and academia, the book covers the most important aspects of the subject, such as spectral estimation, signal modeling, adaptive filtering, and array processing. This unique resource provides balanced coverage of implementation issues, applications, and theory, making it a smart choice for professional engineers and students alike. The book presents clear examples, problem sets, and computer experiments that help readers master the material and learn how to implement various methods presented in the chapters. This invaluable reference also includes a set of Matlab[registered] functions that engineers can use to solve real-world problems in the field. The book is packed with over 3,000 equations and more than 300 illustrations.

Plasmon Coupling Physics, Wave Effects and their Study by Electron Spectroscopies, Volume 222 in the Advances in Imaging and Electron Physics serial, merges two long-running serials, Advances in Electronics and Electron Physics and Advances in Optical and Electron Microscopy. The series features articles on the physics of electron devices (especially semiconductor devices), particle optics at high and low energies, microlithography, image science, digital image processing, electromagnetic wave propagation, electron microscopy and the computing methods used in all these domains. Specific chapters in this release cover Phase retrieval methods applied to coherent imaging, X-ray phase-contrast imaging: a broad overview of some fundamentals, Graphene and borophene as nanoscopic materials for electronics - with review of the physics, and more.

1.1 Digital Optics as a Subject Improvement of the quality of optical devices has always been the central task of experimental optics. In modern terms, improvements in sensitivity and resolution have equated higher quality with greater informational throughput. For most of today's applications, optics and electronics have, in essence, solved the problem of generating high quality pictures with great informational ca pacity. Effective use of the enormous amount of information contained in the images necessitates processing pictures, holograms, and interferograms. The manner in which information might be extracted from optical entities has be come a topic of current interest. The informational aspects of optical signals and systems might serve as a basis for attacking this question by making use of information theory and signal communication theory, and by enlisting modern tools and methods for data processing (the most important and powerful of which are those of digi tal computation). Exploiting modern advances in electronics has allowed new wavelength ranges and new kinds of radiation to be used in optics. Comput ers have extended our knowledge of the informational essence of radiation. Thus, computerized optical devices enhance not only the optical capabilities of sight, but also its analytical capabilities as well, thus opening qualitatively new horizons to all the areas in which optical devices have found application."