The book addresses the current demand for a scientific approach to advanced wireless technology and its future developments, including the current move from 4G to 5G wireless systems (2020), and the future to 6G wireless systems (2030). It gives a clear and in-depth presentation of both antennas and the adaptive signal processing that makes antennas powerful, maneuverable, and necessary for advanced wireless technology. Moving towards the increasing demand for a scientific approach to smart antennas, the book presents electromagnetic signal processing techniques to both control the antenna beam and to track the moving station, which is required for effective, fast, dynamic beamforming. In addition to presenting new, memory efficient and fast algorithms for smart antennas, another helpful feature of the book is the inclusion of complete listings of MATLABTM codes for powerful techniques such as Artificial Intelligence (AI) beamforming, Analytical Phase Shift technique and the traditional Least Mean Square method, The student, researcher or engineer may readily use these codes to gain confidence in understanding, as well as to develop and deploy powerful, new smart antenna techniques. The first part of the book presents a comprehensive description and analysis of basic antenna theory, starting from short dipole antennas to array antennas. This section also includes important concepts related to antenna parameters, electromagnetic wave propagation, the Friis equation, the radar equation and wave reflection and transmission through media. The second part of the book focuses on smart antennas, commencing from a look at traditional approach to beam forming before getting into the details of smart antennas. Complete derivation and description of the techniques for electromagnetic field signal processing techniques for adaptive beam forming are presented. Many new and research ideas are included in this section. A novel method for fast, low memory and accurate, maneuverable single beam generation is presented, as well as other methods for beamforming with fewer elements with a simple method for tracking the mobile antenna and station. In this section, for completeness, the use of antenna signal processing for synthetic aperture techniques for imaging are also presented, specifically the Inverse Synthetic Aperture Imaging technique. Some computer codes are given for the student and researcher to get started with new areas to explore. The third part of the book presents technological aspects of advanced wireless technology, including Artificial Intelligence driven steerable single beams, the 5G wireless system and the various devices needed to construct the system. While the books' main emphasis is theoretical understanding and design with the basic tools needed to develop powerful computer code for the smart antennas, it also provides the algorithms or codes in a number of important cases to show how the smart antenna computer codes may be developed using electromagnetic signal processing. Artificial Intelligence (AI) driven beam forming is presented using computationally fast and low-memory demanding technique for AI beam forming is presented with the different excitation functions available. The final chapter outlines certain techniques to develop smart antenna algorithms and computer codes for beginners, researchers, and engineers, and furthermore, to implement a part of what was learnt, including AI techniques.

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.

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.

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.

New technologies in engineering, physics and biomedicine are demanding increasingly complex methods of digital signal processing. By presenting the latest research work the authors demonstrate how real-time recurrent neural networks (RNNs) can be implemented to expand the range of traditional signal processing techniques and to help combat the problem of prediction. Within this text neural networks are considered as massively interconnected nonlinear adaptive filters.

• Analyses the relationships between RNNs and various nonlinear models and filters, and introduces spatio-temporal architectures together with the concepts of modularity and nesting

• Examines stability and relaxation within RNNs

• Presents on-line learning algorithms for nonlinear adaptive filters and introduces new paradigms which exploit the concepts of a priori and a posteriori errors, data-reusing adaptation, and normalisation

• Studies convergence and stability of on-line learning algorithms based upon optimisation techniques such as contraction mapping and fixed point iteration

• Describes strategies for the exploitation of inherent relationships between parameters in RNNs

• Discusses practical issues such as predictability and nonlinearity detecting and includes several practical applications in areas such as air pollutant modelling and prediction, attractor discovery and chaos, ECG signal processing, and speech processing

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.

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

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.

Understand the theory and function of wireless antennas with this comprehensive guide As wireless technology continues to develop, understanding of antenna properties and performance will only become more critical. Since antennas can be understood as junctions of waveguides, eigenmode analysis--the foundation of waveguide theory, concerned with the unexcited states of systems and their natural resonant characteristics--promises to be a crucial frontier in the study of antenna theory. Foundations of Antenna Radiation Theory incorporates the modal analysis, generic antenna properties and design methods discovered or developed in the last few decades, not being reflected in most antenna books, into a comprehensive introduction to the theory of antennas. This book puts readers into conversation with the latest research and situates students and researchers at the cutting edge of an important field of wireless technology. The book also includes: Detailed discussions of the solution methods for Maxwell equations and wave equations to provide a theoretical foundation for electromagnetic analysis of antennas Recent developments for antenna radiation in closed and open space, modal analysis and field expansions, dyadic Green's functions, time-domain theory, state-of-the-art antenna array synthesis methods, wireless power transmission systems, and more Innovative material derived from the author's own research Foundations of Antenna Radiation Theory is ideal for graduate or advanced undergraduate students studying antenna theory, as well as for reference by researchers, engineers, and industry professionals in the areas of wireless technology.

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."

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.

The massive volume of data generated in modern applications can overwhelm our ability to conveniently transmit, store, and index it. For many scenarios, building a compact summary of a dataset that is vastly smaller enables flexibility and efficiency in a range of queries over the data, in exchange for some approximation. This comprehensive introduction to data summarization, aimed at practitioners and students, showcases the algorithms, their behavior, and the mathematical underpinnings of their operation. The coverage starts with simple sums and approximate counts, building to more advanced probabilistic structures such as the Bloom Filter, distinct value summaries, sketches, and quantile summaries. Summaries are described for specific types of data, such as geometric data, graphs, and vectors and matrices. The authors offer detailed descriptions of and pseudocode for key algorithms that have been incorporated in systems from companies such as Google, Apple, Microsoft, Netflix and Twitter.

An Introduction to Audio Content Analysis Enables readers to understand the algorithmic analysis of musical audio signals with AI-driven approaches An Introduction to Audio Content Analysis serves as a comprehensive guide on audio content analysis explaining how signal processing and machine learning approaches can be utilized for the extraction of musical content from audio. It gives readers the algorithmic understanding to teach a computer to interpret music signals and thus allows for the design of tools for interacting with music. The work ties together topics from audio signal processing and machine learning, showing how to use audio content analysis to pick up musical characteristics automatically. A multitude of audio content analysis tasks related to the extraction of tonal, temporal, timbral, and intensity-related characteristics of the music signal are presented. Each task is introduced from both a musical and a technical perspective, detailing the algorithmic approach as well as providing practical guidance on implementation details and evaluation. To aid in reader comprehension, each task description begins with a short introduction to the most important musical and perceptual characteristics of the covered topic, followed by a detailed algorithmic model and its evaluation, and concluded with questions and exercises. For the interested reader, updated supplemental materials are provided via an accompanying website. Written by a well-known expert in the music industry, sample topics covered in Introduction to Audio Content Analysis include: Digital audio signals and their representation, common time-frequency transforms, audio features Pitch and fundamental frequency detection, key and chord Representation of dynamics in music and intensity-related features Beat histograms, onset and tempo detection, beat histograms, and detection of structure in music, and sequence alignment Audio fingerprinting, musical genre, mood, and instrument classification An invaluable guide for newcomers to audio signal processing and industry experts alike, An Introduction to Audio Content Analysis covers a wide range of introductory topics pertaining to music information retrieval and machine listening, allowing students and researchers to quickly gain core holistic knowledge in audio analysis and dig deeper into specific aspects of the field with the help of a large amount of references.

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.

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This second volume, Inference, builds on the foundational topics established in volume I to introduce students to techniques for inferring unknown variables and quantities, including Bayesian inference, Monte Carlo Markov Chain methods, maximum-likelihood estimation, hidden Markov models, Bayesian networks, and reinforcement learning. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including solutions for instructors), 180 solved examples, almost 200 figures, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Learning, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.

This book is devoted to the analysis of measurement signals which requires specific mathematical operations like Convolution, Deconvolution, Laplace, Fourier, Hilbert, Wavelet or Z transform which are all presented in the present book. The different problems refer to the modulation of signals, filtration of disturbance as well as to the orthogonal signals and their use in digital form for the measurement of current, voltage, power and frequency are also widely discussed. All the topics covered in this book are presented in detail and illustrated by means of examples in MathCad and LabVIEW. This book provides a useful source for researchers, scientists and engineers who in their daily work are required to deal with problems of measurement and signal processing and can also be helpful to undergraduate students of electrical engineering.

Adaptive filtering is a classical branch of digital signal processing (DSP). Industrial interest in adaptive filtering grows continuously with the increase in computer performance that allows ever more conplex algorithms to be run in real-time. Change detection is a type of adaptive filtering for non-stationary signals and is also the basic tool in fault detection and diagnosis.

Often considered as separate subjects Adaptive Filtering and Change Detection bridges a gap in the literature with a unified treatment of these areas, emphasizing that change detection is a natural extension of adaptive filters, and adaptive filters are the basic building blocks in all change detectors.
By providing a timely and innovative resource, applied engineers, researchers and postgraduate students studying DSP will all find this an indispensable and enlightening read!

• Presents a comprehensive and complete treatment of adapative filtering

• Provides a unifying framework for the theory of change detection based on a filtering approach

• Combines mathematical tools (algebra, calculus, statistics) and applications areas (airborne, automotive, communication systems, standard signal processing and automatic control applications)

• Features extensive examples and case studies which can be reproduced and investigated in an accompanying Matlab demo toolbox.

This book provides an interdisciplinary look at emerging trends in signal processing and biomedicine found at the intersection of healthcare, engineering, and computer science. It examines the vital role signal processing plays in enabling a new generation of technology based on big data, and looks at applications ranging from medical electronics to data mining of electronic medical records. Topics covered include analysis of medical images, machine learning, biomedical nanosensors, wireless technologies, and instrumentation and electrical stimulation. Biomedical Signal Processing: Innovation and Applications presents tutorials and examples of successful applications, and will appeal to a wide range of professionals, researchers, and students interested in applications of signal processing, medicine, and biology.

Offering a practical alternative to the conventional methods used in signal processing applications, this book discloses numerical techniques and explains how to evaluate the frequency-domain attributes of a waveform without resorting to actual transformation through Fourier methods. This book should prove of interest to practitioners in any field who may require the analysis, association, recognition or processing of signals, and undergraduate students of signal processing.

This book describes the basic functions of the European Digital Radio DAB+ (Digital Audio Broadcasting plus) with its direct possible applications in a simple way. The book refers to fundamentals of DABs 80+ norms and specifications. Presented subjects are indicating problems of DAB signal propagation and possible multimedia applications. The book provides about 130 figures for explaining new concepts in an easy to approach manner. Applications include, but are not limited to audio compression MPEG, OFDM, SFN phasor representation, multiplexes, MOT, and conditional access. The book is intended for those interested in decisions regarding radio at various levels, owners of radio stations, and designers of various multimedia applications of digital radio in the field of security, students of wireless systems, etc. * Presents the fundamental functions of DAB / DAB+ (Digital Audio Broadcasting) along with its applications * Outlines the European Digital Radio system * Explains the functions of worldwide emerging digital radio subsystems

The proceedings of the 4th Stability and Control Processes Conference are focused on modern applied mathematics, stability theory, and control processes. The conference was held in recognition of the 90th birthday of Professor Vladimir Ivanovich Zubov (1930-2000). This selection of papers reflects the wide-ranging nature of V. I. Zubov's work, which included contributions to the development of the qualitative theory of differential equations, the theory of rigid body motion, optimal control theory, and the theory of electromagnetic fields. It helps to advance many aspects of the theory of control systems, including questions of motion stability, nonlinear oscillations in control systems, navigation and reliability of control devices, vibration theory, and quantization of orbits. The disparate applications covered by the book - in mechanical systems, game theory, solid-state physics, socio-economic systems and medical and biological systems, control automata and navigation - are developments from Professor Zubov's in-depth studies on the theory of stability of motion, the theory of automatic control and the theory of the motions of optimal processes. Stability and Control Processes presents research continuing the legacy of V. I. Zubov and updates it with sections focused on intelligence-based control. These proceedings will be of interest to academics, professionals working in industry and researchers alike.

This second book by the author on WSNs focuses on the concepts of energy, and energy harvesting and management techniques. Definitions and terminologies are made clear without leaning on the relaxing assumption that they are already known or easily reachable, the reader is not to be diverted from the main course. Neatly drawn figures assist in viewing and imagining the offered topics. To make energy related topics felt and seen, the adopted technologies as well as their manufacturers are presented in details. With such a depth, this book is intended for a wide audience, it is meant to be helper and motivator, for the senior undergraduates, postgraduates, researchers, and practitioners; concepts and energy related applications are laid out, research and practical issues are backed by appropriate literature, and new trends are put under focus. For senior undergraduate students, it familiarizes with conceptual foundations and practical projects implementations. Also, it is intended for graduate students working on their thesis and in need of specific knowledge on WSNs and the related energy harvesting and management techniques. Moreover, it is targeting researchers and practitioners interested in features and applications of WSNs, and on the available energy harvesting and management projects and testbeds. Exercises at the end of each chapter are not just questions and answers; they are not limited to recapitulate ideas. Their design objective is not bound to be a methodical review of the provided concepts, but rather as a motivator for lot more of searching, finding, and comparing beyond what has been presented in the book.

This book presents the selected peer-reviewed papers from the International Conference on Communication Systems and Networks (ComNet) 2019. Highlighting the latest findings, ideas, developments and applications in all areas of advanced communication systems and networking, it covers a variety of topics, including next-generation wireless technologies such as 5G, new hardware platforms, antenna design, applications of artificial intelligence (AI), signal processing and optimization techniques. Given its scope, this book can be useful for beginners, researchers and professionals working in wireless communication and networks, and other allied fields.