Industrial processes such as long-wall coal cutting and me- tal rolling, together with certain areas of 2D signal and image processing, exhibit a repetitive, or multipass struc- ture characterized by a series of sweeps of passes through a known set of dynamics. The output, or pass profile, produced on each pass explicitly contributes to that produced on the text. This interpass interaction can lead to the growth of oscillations, and hence a form of instability, in the se- quence of pass profiles which require control strategies that explicitly incorporate the essential repetitive struc- ture of the process in their decision making. This monograph is unique in developing the new techniques necessary for sy- stematic control systems design in the form of a stability theory and computationally feasible stability tests based on finite simulations and polynomial analysis. Its development requires a basic knowledge of linear frequency domain and state-space theory and a knowledge of basic functional ana- lysis would be beneficial. The text is aimed at researchers in the area of control and systems theory and should also be of interest to those working in the related area of signal and image processing.

Image processing is a fascinating applications area, not a fundamental science of sufficient generality to warrant studying it for its own sake. In this area, there are many opportunities to apply art and experience, as well as knowledge from a number of sciences and engineering disciplines, to the creation of products and processes for which society has an expressed need. Without this need, work in the field would be sterile, but with it, image processing can readily provide the interested scientist or engineer with a professioilal lifetime of challenging problems and corresponding rewards. This point of view motivates this book and has influenced the selection and treatment of topics. I have not attempted to 1 be encyclopedic; this service has already been performed by others. It will be noted that the word "digital" is not in the title of this book. While much of present-day image processing is implemented digitally, this work is not intended for those who think of image processing as a branch of digital signal processing, except, perhaps, to try to change their minds. Image gathering and image display, vital parts of the field with strong effects on image quality, are inherently analog, as are all of the channels and media now used, or likely to be used in the future, to record TV signals and to transmit them to the home.

Block pulse functions have been studied and applied extensively in the past fifteen years as a basic set of functions for signal characterizations in systems science and control. Written for staff, students and engineers in industry interested in digital signal processing for system analysis and design, this book presents the principles and techniques ofblock pulse functions in a systematic and uniform manner. Readers who are interested in using these functions in their own problems can obtain an overview of the existing methods and the developing tendency of this area. In comparison with other basic functions or polynomials, block pulse functions can lead more easily to recursive computations to solve concrete problems.

The four chapters of this volume, written by prominent workers in the field of adaptive processing and linear prediction, address a variety of problems, ranging from adaptive source coding to autoregressive spectral estimation. The first chapter, by T.C. Butash and L.D. Davisson, formulates the performance of an adaptive linear predictor in a series of theorems, with and without the Gaussian assumption, under the hypothesis that its coefficients are derived from either the (single) observation sequence to be predicted (dependent case) or a second, statistically independent realisation (independent case). The contribution by H.V. Poor reviews three recently developed general methodologies for designing signal predictors under nonclassical operating conditions, namely the robust predictor, the high-speed Levinson modeling, and the approximate conditional mean nonlinear predictor. W. Wax presents the key concepts and techniques for detecting, localizing and beamforming multiple narrowband sources by passive sensor arrays. Special coding algorithms and techniques based on the use of linear prediction now permit high-quality voice reproduction at remorably low bit rates. The paper by A. Gersho reviews some of the main ideas underlying the algorithms of major interest today.

Lewis Carroll once wrote a story about a king who wanted a very accurate map of his kingdom. The king had a pathologically fastidious eye for detail and consequently decided that the map was to be produced at a scale of 1:1. The scribes dutifully set to and, in time, the map was made. The map carried details of every tree, every rock and every blade of grass throughout the entire land. The problem occurred when they tried to use -it. First of all, the map was extraordinarily difficult to open out and line up with the countryside. Its sheer bulk meant that it took whole armies to carry it and a great host of bureaucrats and technicians to maintain the information. Such was the detail of the map that as soon as the wind blew strongly, whole sections needed to be redrawn. What was worse was that all the farmers protested because the map completely cut out the light from the sun and all the crops died. Eventually the howls of protest became so strong that the king was forced to take action. He did away with the old paper copy and decided to use the kingdom itself as the map. All lived happily ever after. There are, at least, two morals to this tale. First, you are almost certainly doomed to failure if you do not get the representation of the problem right.

This textbook gives details of recent developments in the field of image processing, machine vision and analysis. Based on the original book published in Czech, this English edition has been expanded to include 3D vision, neural networks and invariants.

The 1990 Grainger Lectures delivered at the University of Illinois, Urbana-Champaign, September 28 - October 1, 1990 were devoted to a critical reexamination of the foundations of adaptive control. In this volume the lectures are expanded by most recent developments and solutions for some long-standing open problems. Concepts and approaches presented are both novel and of fundamental importance for adaptive control research in the 1990s. The papers in Part I present unifications, reappraisals and new results on tunability, convergence and robustness of adaptive linear control, whereas the papers in Part II formulate new problems in adaptive control of nonlinear systems and solve them without any linear constraints imposed on the nonlinearities.

One of the most challenging problems in electrical engineering is the detection and localization of target in underwater acoustics. In particular, bearing estimation is usually obtained by the classical array processing method, beamforming. High-resolution methods have been introduced to overcome the main limitation of beamforming, poor resolution. This volume is composed of tutorials and state-of-the-art research papers on high-resolution methods applied to underwater acoustics. Most of these papers are related to the bearing estimation problem (detailed presentation of methods of their performances, both theoretical and at-sea, their use with an array of unknown geometry, etc) but extensions to the temporal and spectral domains are also included. This monograph on signal processing, underwater acoustics is intended for the use of electrical engineers, advanced students, researchers, industry engineers and scientists, technical navy people.

This volume contains papers presented during a four-day Workshop that took place at Rutgers University from 29 April to 2 May, 1991. The purpose of this workshop was to promote interaction among specialists in these areas byproviding for all an up-to-date picture of current issues and outstanding problems. The topics covered include singular stochasticcontrol, queuing networks, the mathematical theory of stochastic optimization and filtering, adaptive control and the estimation for random fields and its connections with simulated annealing, statistical mechanics, and combinatorial optimization.

The 1991 International Conference on Information Processing in Medical Imaging (IPMI '91) is the twelfth in the series and was held in Wye College, part of the University of London. The purpose of IPMI is to provide a forum for the detailed examination of methodological issues in computing which are at the heart of advances in medical image formation, manipulation and interpretation. This volume presents the proceedings of IPMI '91. Full-length scientific papers describing the latest techniques and results are organized into the following nine sections: - Image formation and reconstruction - Incorporation of priors in tomographic reconstruction - Multi-modal registration - Segmentation: specific applications - Segmentation: multi-scale, surfaces and topology - Anatomical models and variability - Factor analysis - Rule based systems and learning - Image quality, display and interaction. The volume also includes a set of color plates and a subject index. The book provides an up-to-date account of current work in the expanding and fast-moving area of image processing and medical imaging, and gives an overview of work at all the key centers researching in this area. It will prove an invaluable asset to all researchers working in the area and to the libraries of organizations involved in imaging research.

This book offers readers a broad view of research in some Western and Eastern European countries on pattern and signal analysis, and on coding, handling and measurement of images. It is a selection of refereed papers from two sources: first, a satellite conference within the biannual International Conference on Pattern Recognition held in Rome, November 14-17, 1988, and second, work done at the International Basic Laboratory on Image Processing and Computer Graphics, Berlin, GDR. The papers are grouped into three sections. The first section contains new proposals for the specific computation of particular features of digital images and the second section is devoted to the introduction and testing of general approaches to the solution of problems met in digital geometry, image coding, feature extraction and object classification. The third section illustrates some recent practical results obtained on real images specifically in character and speech recognition as well as in biomedicine. All the techniques illustrated in this book will find direct application in the near future. This book should interest and stimulate the reader, provoke new thoughts and encourage further research in this widely appealing field.

In this volume the author gives an introduction to the theory of group representations and their applications in image science. The main feature of the presentation is a systematic treatment of the invariance principle in image processing and pattern recognition with the help of group theoretical methods. The invariance properties of a problem often largely define the solution to the problem. Invariance principles are well known in theoretical physics but their use in image processing is only a few years old. The reader will find that group theory provides a unifying framework for many problems in image science. The volume is based on graduate-level lectures given by the author, and the book is intended for students and researchers interested in theoretical aspects of computer vision.

In addition to its thorough coverage of DSP design and programming techniques, Smith also covers the operation and usage of DSP chips. He uses Analog Devices' popular DSP chip family as design examples. Also included on the companion website is technical info on DSP processors from the four major manufacturers (Analog Devices, Texas Instruments, Motorola, and Lucent) and other DSP software.
*Covers all major DSP topics
*Full of insider information and shortcuts
*Basic techniques and algorithms explained without complex numbers

This unique text helps make sense of big data in engineering applications using tools and techniques from signal processing. It presents fundamental signal processing theories and software implementations, reviews current research trends and challenges, and describes the techniques used for analysis, design and optimization. Readers will learn about key theoretical issues such as data modelling and representation, scalable and low-complexity information processing and optimization, tensor and sublinear algorithms, and deep learning and software architecture, and their application to a wide range of engineering scenarios. Applications discussed in detail include wireless networking, smart grid systems, and sensor networks and cloud computing. This is the ideal text for researchers and practising engineers wanting to solve practical problems involving large amounts of data, and for students looking to grasp the fundamentals of big data analytics.

Learn about the key technologies and understand the state of the art in research for full-duplex communication networks and systems with this comprehensive and interdisciplinary guide. Incorporating physical, MAC, network, and application layer perspectives, it explains the fundamental theories on which full-duplex communications are built, and lays out the techniques needed for network design, analysis and optimization. Techniques covered in detail include self-interference cancellation and signal processing algorithms, physical layer algorithms, methods for efficient resource allocation, and game theory. Potential applications and networking schemes are discussed, including full-duplex cognitive radio networks, cooperative networks, and heterogeneous networks. The first book to focus exclusively on full-duplex communications, this is an indispensable reference for both researchers and practitioners designing the next generation of wireless networks.

An introduction to a new design for nonlinear control systems—backstepping—written by its own architects.

This innovative book breaks new ground in nonlinear and adaptive control design for systems with uncertainties. Introducing the recursive backstepping methodology, it shows—for the first time—how uncertain systems with severe nonlinearities can be successfully controlled with this new powerful design tool.

Communicative and accessible at a level not usually present in research texts, Nonlinear and Adaptive Control Design can be used as either a stand-alone or a supplemental text in courses on nonlinear or adaptive control, as well as in control research and applications. It eases the reader into the subject matter, assuming only standard undergraduate knowledge of control theory, and provides a pedagogical presentation of the material, most of which is completely new and not available in other textbooks. Written by the creators of backstepping, the book:

• Introduces the basic design tools and demonstrates their effectiveness through worked examples
• Provides detailed proofs and application examples (active suspension, jet engine, induction motor, and many others)
• Develops adaptive backstepping, tuning functions, and modular designs with full state feedback
• Generalizes the methodology to nonlinear systems with output feedback
• Describes the advantages of the new adaptive nonlinear techniques over traditional methods
• Offers a systematic methodology for performance improvement
• Provides new designs for linear systems which can be used independently from therest of the book
• Is self-contained with an extensive summary of stability and passivity prerequisites
• Includes sixty illustrations and tables with design algorithms

Nonlinear and Adaptive Control Design is an absolute must for researchers and graduate students with an interest in nonlinear systems, adaptive control, stability and differential equations and for anyone who would like to find out about the new and exciting advances in these areas.

Recent advancements in signal processing and computerised methods are expected to underpin the future progress of biomedical research and technology, particularly in measuring and assessing signals and images from the human body. This book focuses on singular spectrum analysis (SSA), an effective approach for single channel signal analysis, and its bivariate, multivariate, tensor based, complex-valued, quaternion-valued and robust variants. SSA currently has numerous applications in detecting abnormalities in quasi-periodic biosignals, such as electrocardiograms, (ECGs or EKGs), oxygen levels, arterial pressure, and electroencephalograms (EEGs). Singular Spectrum Analysis of Biomedical Signals presents relatively newly applied concepts for biomedical applications of SSA, including: Signal source separation, extraction, decomposition, and factorization Physiological, biological, and biochemical signal processing A new SSA grouping algorithm for filtering and noise reduction of genetics data Prediction of various clinical events The book introduces a new mathematical and signal processing technique for the decomposition of widely available single channel biomedical data. It also provides illustrations of new signal processing results in the form of signals, graphs, images, and tables to reinforce understanding of the related concepts. Singular Spectrum Analysis of Biomedical Signals enhances current clinical knowledge and aids physicians in improving diagnosis, treatment and monitoring some clinical abnormalities. It also lays groundwork for progress in SSA by making suggestions for future research.

Exploring the interrelation between information theory and signal processing theory, the book contains a new algebraic approach to signal processing theory. Readers will learn this new approach to constructing the unified mathematical fundamentals of both information theory and signal processing theory in addition to new methods of evaluating quality indices of signal processing. The book discusses the methodology of synthesis and analysis of signal processing algorithms providing qualitative increase of signal processing efficiency under parametric and nonparametric prior uncertainty conditions. Examples are included throughout the book to further emphasize new material.

Now available in a three-volume set, this updated and expanded edition of the bestselling The Digital Signal Processing Handbook continues to provide the engineering community with authoritative coverage of the fundamental and specialized aspects of information-bearing signals in digital form. Encompassing essential background material, technical details, standards, and software, the second edition reflects cutting-edge information on signal processing algorithms and protocols related to speech, audio, multimedia, and video processing technology associated with standards ranging from WiMax to MP3 audio, low-power/high-performance DSPs, color image processing, and chips on video. Drawing on the experience of leading engineers, researchers, and scholars, the three-volume set contains 29 new chapters that address multimedia and Internet technologies, tomography, radar systems, architecture, standards, and future applications in speech, acoustics, video, radar, and telecommunications. This volume, Video, Speech, and Audio Signal Processing and Associated Standards, provides thorough coverage of the basic foundations of speech, audio, image, and video processing and associated applications to broadcast, storage, search and retrieval, and communications.

Within the healthcare domain, big data is defined as any ``high volume, high diversity biological, clinical, environmental, and lifestyle information collected from single individuals to large cohorts, in relation to their health and wellness status, at one or several time points.'' Such data is crucial because within it lies vast amounts of invaluable information that could potentially change a patient's life, opening doors to alternate therapies, drugs, and diagnostic tools. Signal Processing and Machine Learning for Biomedical Big Data thus discusses modalities; the numerous ways in which this data is captured via sensors; and various sample rates and dimensionalities. Capturing, analyzing, storing, and visualizing such massive data has required new shifts in signal processing paradigms and new ways of combining signal processing with machine learning tools. This book covers several of these aspects in two ways: firstly, through theoretical signal processing chapters where tools aimed at big data (be it biomedical or otherwise) are described; and, secondly, through application-driven chapters focusing on existing applications of signal processing and machine learning for big biomedical data. This text aimed at the curious researcher working in the field, as well as undergraduate and graduate students eager to learn how signal processing can help with big data analysis. It is the hope of Drs. Sejdic and Falk that this book will bring together signal processing and machine learning researchers to unlock existing bottlenecks within the healthcare field, thereby improving patient quality-of-life. Provides an overview of recent state-of-the-art signal processing and machine learning algorithms for biomedical big data, including applications in the neuroimaging, cardiac, retinal, genomic, sleep, patient outcome prediction, critical care, and rehabilitation domains. Provides contributed chapters from world leaders in the fields of big data and signal processing, covering topics such as data quality, data compression, statistical and graph signal processing techniques, and deep learning and their applications within the biomedical sphere. This book's material covers how expert domain knowledge can be used to advance signal processing and machine learning for biomedical big data applications.

Before you can build analytics tools to gain quick insights, you first need to know how to process data in real time. With this practical guide, developers familiar with Apache Spark will learn how to put this in-memory framework to use for streaming data. You'll discover how Spark enables you to write streaming jobs in almost the same way you write batch jobs. Authors Gerard Maas and Francois Garillot help you explore the theoretical underpinnings of Apache Spark. This comprehensive guide features two sections that compare and contrast the streaming APIs Spark now supports: the original Spark Streaming library and the newer Structured Streaming API. Learn fundamental stream processing concepts and examine different streaming architectures Explore Structured Streaming through practical examples; learn different aspects of stream processing in detail Create and operate streaming jobs and applications with Spark Streaming; integrate Spark Streaming with other Spark APIs Learn advanced Spark Streaming techniques, including approximation algorithms and machine learning algorithms Compare Apache Spark to other stream processing projects, including Apache Storm, Apache Flink, and Apache Kafka Streams

Gain a solid understanding of how information theoretic approaches can inform the design of more secure information systems and networks with this authoritative text. With a particular focus on theoretical models and analytical results, leading researchers show how techniques derived from the principles of source and channel coding can provide new ways of addressing issues of data security, embedded security, privacy, and authentication in modern information systems. A wide range of wireless and cyber-physical systems is considered, including 5G cellular networks, the Tactile Internet, biometric identification systems, online data repositories, and smart electricity grids. This is an invaluable guide for both researchers and graduate students working in communications engineering, and industry practitioners and regulators interested in improving security in the next generation of information systems.

The Handbook of Signal Processing in Acoustics brings together a wide range of perspectives from over 100 authors to reveal the interdisciplinary nature of the subject. It brings the key issues from both acoustics and signal processing into perspective and is a unique resource for experts and practitioners alike to find new ideas and techniques within the diversity of signal processing in acoustics.

Nowadays, many aspects of electrical and electronic engineering are essentially applications of DSP. This is due to the focus on processing information in the form of digital signals, using certain DSP hardware designed to execute software. Fundamental topics in digital signal processing are introduced with theory, analytical tables, and applications with simulation tools. The book provides a collection of solved problems on digital signal processing and statistical signal processing. The solutions are based directly on the math-formulas given in extensive tables throughout the book, so the reader can solve practical problems on signal processing quickly and efficiently.

FEATURES

Explains how applications of DSP can be implemented in certain programming environments designed for real time systems, ex. biomedical signal analysis and medical image processing.

Pairs theory with basic concepts and supporting analytical tables.

Includes an extensive collection of solved problems throughout the text.

Fosters the ability to solve practical problems on signal processing without focusing on extended theory.

Covers the modeling process and addresses broader fundamental issues.

Table of Contents

Part 1: Digital Signal Processing. Introduction. Discrete-time Signals and Systems. z-Transform. Implementation of Discrete Systems. Frequency Domain Analysis. Designing Digital Filters. Part 2: Statistical Signal Processing. Statistical Models. Fundamental Principles of Parametric Estimation. Linear Evaluation. Fundamentals of Signal Detection.