Autonomous mobile systems (AMS) are systems capable of some mobility and equipped with advanced sensor devices in order to flexibly respond to changing environmental situations, thus achieving some degree of autonomy. The purpose of this book is to contribute to some essential topics in this broad research area related to sensing and control, but not to present a complete design of an AMS. Subjects conceming knowledge based control and decision, such as moving around obstacles, task planning and diagnosis are left for future publications in this series. Research in the area of AMS has grown rapidly during the last decade, see e.g. WAXMAN et al. 87], DICKMANNS, ZAPP 87]. The requirements of an AMS strongly depends on the desired tasks the system should execute, its operational environment and the expected speed of the AMS. For instance, road vehicles obtain velocities of 10 m/s and more, therefore the processing of sensor data such as video image sequences has to be very fast and simple, while indoor mobile robots deal with shorter distances and lower speeds, thus more sophistcated techniques are applicable and -as is done in our approach- additional sensors can be integrated to allow for multi sensor processing.

This volume contains the papers selected for presentation at the Second International Conference on Parallel Image Analysis (ICPIA '92), held in Ube, Japan, December 21-23, 1992. The conference topics are data structures, parallel algorithms and architectures, neural networks, computational vision, syntactic generation and recognition, and multidimensional models. The first meeting with these topics was theInternational Colloquium on Parallel Image Processing, which took place in Paris in June 1991. The aim of the meetings is to bring together specialistsfrom various countries who are interested in the topics and to stimulatetheoretical and practical research in the field of parallel image processingand analysis. The volume contains three invited papers, a summary of a tutorial lecture, and twenty selected and refereed communications.

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.

This book contains the 61 papers that were accepted for presenta tion at the 1992 British Machine Vision Conference. Together they provide a snapshot of current machine vision research throughout the UK in 24 different institutions. There are also several papers from vision groups in the rest of Europe, North America and Australia. At the start of the book is an invited paper from the first keynote speaker, Robert Haralick. The quality of papers submitted to the conference was very high and the programme committee had a hard task selecting around half for presentation at the meeting and inclusion in these proceedings. It is a positive feature of the annual BMV A conference that the entire process from the submission deadline through to the conference itself and publication of the proceedings is completed in under 5 months. My thanks to members of the programme committee for their essential contribution to the success of the conference and to Roger Boyle, Charlie Brown, Nick Efford and Sue Nemes for their excellent local organisation and administration of the conference at the University of Leeds."

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.

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.

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.

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.

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.

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.

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.

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.

This textbook presents an introduction to communication systems with an emphasis on signal design and modulation. The book carefully develops the mathematical principles upon which such systems are based, using examples from a wide variety of current communications systems wherever possible. These range from commercial broadcasting and telephone systems to satellite telemetry and radar.

An important working resource for engineers and researchers involved in the design, development, and implementation of signal processing systems The last decade has seen a rapid expansion of the use of field programmable gate arrays (FPGAs) for a wide range of applications beyond traditional digital signal processing (DSP) systems. Written by a team of experts working at the leading edge of FPGA research and development, this second edition of FPGA-based Implementation of Signal Processing Systems has been extensively updated and revised to reflect the latest iterations of FPGA theory, applications, and technology. Written from a system-level perspective, it features expert discussions of contemporary methods and tools used in the design, optimization and implementation of DSP systems using programmable FPGA hardware. And it provides a wealth of practical insights along with illustrative case studies and timely real-world examples of critical concern to engineers working in the design and development of DSP systems for radio, telecommunications, audio-visual, and security applications, as well as bioinformatics, Big Data applications, and more. Inside you will find up-to-date coverage of: * FPGA solutions for Big Data Applications, especially as they apply to huge data sets * The use of ARM processors in FPGAs and the transfer of FPGAs towards heterogeneous computing platforms * The evolution of High Level Synthesis tools including new sections on Xilinx's HLS Vivado tool flow and Altera's OpenCL approach * Developments in Graphical Processing Units (GPUs), which are rapidly replacing more traditional DSP systems FPGA-based Implementation of Signal Processing Systems, 2nd Edition is an indispensable guide for engineers and researchers involved in the design and development of both traditional and cutting-edge data and signal processing systems. Senior-level electrical and computer engineering graduates studying signal processing or digital signal processing also will find this volume of great interest.

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 text presents a comprehensive treatment of signal processing and linear systems suitable for undergraduate students in electrical engineering, It is based on Lathi's widely used book, Linear Systems and Signals, with additional applications to communications, controls, and filtering as well as new chapters on analog and digital filters and digital signal processing.This volume's organization is different from the earlier book. Here, the Laplace transform follows Fourier, rather than the reverse; continuous-time and discrete-time systems are treated sequentially, rather than interwoven. Additionally, the text contains enough material in discrete-time systems to be used not only for a traditional course in signals and systems but also for an introductory course in digital signal processing. In Signal Processing and Linear Systems Lathi emphasizes the physical appreciation of concepts rather than the mere mathematical manipulation of symbols. Avoiding the tendency to treat engineering as a branch of applied mathematics, he uses mathematics not so much to prove an axiomatic theory as to enhance physical and intuitive understanding of concepts. Wherever possible, theoretical results are supported by carefully chosen examples and analogies, allowing students to intuitively discover meaning for themselves.

Discover the exciting world of software-defined radio (SDR) through this hands-on, beginner-friendly introduction. Software-defined radio (SDR) is transforming wireless communications through flexible, inexpensive devices that can be programmed to receive AM and FM broadcasts, transmit signals over Wi-Fi, monitor GPS location data, communicate with the International Space Station, and more. This book provides a beginner-friendly introduction to this revolutionary technology. Its learn-by-doing approach will take you from total beginner to confident SDR practitioner, without confusing math or technical jargon. Working with intuitive, graphical software, you’ll explore how SDRs work, discover how to demodulate, filter, tune, and transmit analog radio signals—and get hooked on an exciting new hobby!

"In 1971 Dr. Paul C. Lauterbur pioneered spatial information encoding principles that made image formation possible by using magnetic resonance signals. Now Lauterbur, ""father of the MRI,"" and Dr. Zhi-Pei Liang have co-authored the first engineering textbook on magnetic resonance imaging. This long-awaited, definitive text will help undergraduate and graduate students of biomedical engineering, biomedical imaging scientists, radiologists, and electrical engineers gain an in-depth understanding of MRI principles. The authors use a signal processing approach to describe the fundamentals of magnetic resonance imaging. You will find a clear and rigorous discussion of these carefully selected essential topics: * Mathematical fundamentals Signal generation and detection principles* Signal characteristics* Signal localization principles* Image reconstruction techniques* Image contrast mechanisms Image resolution, noise, and artifacts* Fast-scan imaging* Constrained reconstruction. Complete with a comprehensive set of examples and homework problems, PRINCIPLES OF MAGNETIC RESONANCE IMAGING is the must-read book to improve your knowledge of this revolutionary technique. For more information on the IEEE Press Series in Biomedical Engineering edited by Metin Akay, go to http://www caip.rutgers.edu/ per cent7Eakay/book/ Professors: To request an examination copy simply e-mail [email protected]." Sponsored by: IEEE Engineering in Medicine and Biology Society.

Image Segmentation Summarizes and improves new theory, methods, and applications of current image segmentation approaches, written by leaders in the field The process of image segmentation divides an image into different regions based on the characteristics of pixels, resulting in a simplified image that can be more efficiently analyzed. Image segmentation has wide applications in numerous fields ranging from industry detection and bio-medicine to intelligent transportation and architecture. Image Segmentation: Principles, Techniques, and Applications is an up-to-date collection of recent techniques and methods devoted to the field of computer vision. Covering fundamental concepts, new theories and approaches, and a variety of practical applications including medical imaging, remote sensing, fuzzy clustering, and watershed transform. In-depth chapters present innovative methods developed by the authors--such as convolutional neural networks, graph convolutional networks, deformable convolution, and model compression--to assist graduate students and researchers apply and improve image segmentation in their work. Describes basic principles of image segmentation and related mathematical methods such as clustering, neural networks, and mathematical morphology. Introduces new methods for achieving rapid and accurate image segmentation based on classic image processing and machine learning theory. Presents techniques for improved convolutional neural networks for scene segmentation, object recognition, and change detection, etc. Highlights the effect of image segmentation in various application scenarios such as traffic image analysis, medical image analysis, remote sensing applications, and material analysis, etc. Image Segmentation: Principles, Techniques, and Applications is an essential resource for undergraduate and graduate courses such as image and video processing, computer vision, and digital signal processing, as well as researchers working in computer vision and image analysis looking to improve their techniques and methods.

An intuitive and accessible text explaining the fundamentals and applications of graph signal processing. Requiring only an elementary understanding of linear algebra, it covers both basic and advanced topics, including node domain processing, graph signal frequency, sampling, and graph signal representations, as well as how to choose a graph. Understand the basic insights behind key concepts and learn how graphs can be associated to a range of specific applications across physical, biological and social networks, distributed sensor networks, image and video processing, and machine learning. With numerous exercises and Matlab examples to help put knowledge into practice, and a solutions manual available online for instructors, this unique text is essential reading for graduate and senior undergraduate students taking courses on graph signal processing, signal processing, information processing, and data analysis, as well as researchers and industry professionals.

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.