Acquiring spatial data for geoinformation systems is still mainly done by human operators who analyze images using classical photogrammetric equipment or digitize maps, possibly assisted by some low level image processing. Automation of these tasks is difficult due to the complexity of the object, the topography, and the deficiency of current pattern recognition and image analysis tools for achieving a reliable transition from the data to the high level description of topographic objects. It appears that progress in automation only can be achieved by incorporating domain-specific semantic models into the analysis procedures. This volume collects papers which were presented at the Workshop "SMATI '97." The workshop focused on "Semantic Modeling for the Acquisition of Topographic Information from Images and Maps." This volume offers a comprehensive selection of high-quality and in-depth contributions by experts of the field coming from leading research institutes, treating both theoretical and implementation issues and integrating aspects of photogrammetry, cartography, computer vision, and image understanding.

This book reviews cutting-edge developments in neural signalling processing (NSP), systematically introducing readers to various models and methods in the context of NSP. Neuronal Signal Processing is a comparatively new field in computer sciences and neuroscience, and is rapidly establishing itself as an important tool, one that offers an ideal opportunity to forge stronger links between experimentalists and computer scientists. This new signal-processing tool can be used in conjunction with existing computational tools to analyse neural activity, which is monitored through different sensors such as spike trains, local filed potentials and EEG. The analysis of neural activity can yield vital insights into the function of the brain. This book highlights the contribution of signal processing in the area of computational neuroscience by providing a forum for researchers in this field to share their experiences to date.

This book treats essentials from neurophysiology (Hodgkin-Huxley equations, synaptic transmission, prototype networks of neurons) and related mathematical concepts (dimensionality reductions, equilibria, bifurcations, limit cycles and phase plane analysis). This is subsequently applied in a clinical context, focusing on EEG generation, ischaemia, epilepsy and neurostimulation. The book is based on a graduate course taught by clinicians and mathematicians at the Institute of Technical Medicine at the University of Twente. Throughout the text, the author presents examples of neurological disorders in relation to applied mathematics to assist in disclosing various fundamental properties of the clinical reality at hand. Exercises are provided at the end of each chapter; answers are included. Basic knowledge of calculus, linear algebra, differential equations and familiarity with MATLAB or Python is assumed. Also, students should have some understanding of essentials of (clinical) neurophysiology, although most concepts are summarized in the first chapters. The audience includes advanced undergraduate or graduate students in Biomedical Engineering, Technical Medicine and Biology. Applied mathematicians may find pleasure in learning about the neurophysiology and clinic essentials applications. In addition, clinicians with an interest in dynamics of neural networks may find this book useful, too.

In this book signals or images described by functions whose number of arguments varies from one to five are considered. This arguments can be time, spatial dimensions, or wavelength in a polychromatic signal. The book discusses the basics of mathematical models of signals, their transformations in technical pre-processing systems, and criteria of the systems quality. The models are used for the solution of practical tasks of system analysis, measurement and optimization, and signal restoration. Several examples are given.

This book describes an ECG processing architecture that guides biomedical SoC developers, from theory to implementation and testing. The authors provide complete coverage of the digital circuit implementation of an ultra-low power biomedical SoC, comprised of a detailed description of an ECG processor implemented and fabricated on chip. Coverage also includes the challenges and tradeoffs of designing ECG processors. Describes digital circuit architecture for implementing ECG processing algorithms on chip; Includes coverage of signal processing techniques for ECG processing; Features ultra-low power circuit design techniques; Enables design of ECG processing architectures and their respective on-chip implementation.

For courses in Probability and Random Processes. "Probability, Statistics, and Random Processes for Engineers, 4e "is a useful text for electrical and computer engineers. This book is a comprehensive treatment of probability and random processes that, more than any other available source, combines "rigor" with "accessibility." Beginning with the fundamentals of probability theory and requiring only college-level calculus, the book develops all the tools needed to understand more advanced topics such as random sequences, continuous-time random processes, and statistical signal processing. The book progresses at a leisurely pace, never assuming more knowledge than contained in the material already covered. Rigor is established by developing all results from the basic axioms and carefully defining and discussing such advanced notions as stochastic convergence, stochastic integrals and resolution of stochastic processes.

Dynamic logic (DL) recently had a highest impact on the development in several areas of modeling and algorithm design. The book discusses classical algorithms used for 30 to 50 years (where improvements are often measured by signal-to-clutter ratio), and also new areas, which did not previously exist. These achievements were recognized by National and International awards. Emerging areas include cognitive, emotional, intelligent systems, data mining, modeling of the mind, higher cognitive functions, evolution of languages and other. Classical areas include detection, recognition, tracking, fusion, prediction, inverse scattering, and financial prediction. All these classical areas are extended to using mixture models, which previously was considered unsolvable in most cases. Recent neuroimaging experiments proved that the brain-mind actually uses DL. Emotional Cognitive Neural Algorithms with Engineering Applications" is written for professional scientists and engineers developing computer and information systems, for professors teaching modeling and algorithms, and for students working on Masters and Ph.D. degrees in these areas. The book will be of interest to psychologists and neuroscientists interested in mathematical models of the brain and min das well.

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This book presents state-of-the-art techniques for radiation hardened high-resolution Time-to-Digital converters and low noise frequency synthesizers. Throughout the book, advanced degradation mechanisms and error sources are discussed and several ways to prevent such errors are presented. An overview of the prerequisite physics of nuclear interactions is given that has been compiled in an easy to understand chapter. The book is structured in a way that different hardening techniques and solutions are supported by theory and experimental data with their various tradeoffs. Based on leading-edge research, conducted in collaboration between KU Leuven and CERN, the European Center for Nuclear Research Describes in detail advanced techniques to harden circuits against ionizing radiation Provides a practical way to learn and understand radiation effects in time-based circuits Includes an introduction to the underlying physics, circuit design, and advanced techniques accompanied with experimental data

This new book from Richard Klemm, author of the highly successful Principles of Space-time Adaptive Processing (IEE,2002), examines the various applications of space-time adaptive processing including applications in OTH-radar, ground target tracking, STAP in real world clutter environments, jammer cancellation, superresolution, active sonar, seismics and communications. Including contributions from distinguished international authors, the book provides a unique overview of the field of space-time procesing. The book is divided in two parts; the first dealing with the classical adaptive suppression of airbourne and space based radar clutter and the second comprising of miscellaneous applications in other fields such as communications, underwater sound and seismics. The book will be of interest to those working in the field of sensor signal processing and in particular postgraduate students, research scientists, system engineers, university teachers and research project managers.

Partial Contents: Reliability Concepts; Device Reliability; Hazard Rates; Monitoring Reliability; Specific Device Information, and more. Appendixes. 60 illustrations.

Authored by engineers for engineers, this book is designed to be a practical and easy-to-understand solution sourcebook for real-world high-resolution and spot-light SAR image processing. Widely-used algorithms are presented for both system errors and propagation phenomena as well as numerous formerly-classified image examples. As well as providing the details of digital processor implementation, the text presents the polar format algorithm and two modern algorithms for spot-light image formation processing - the range migration algorithm and the chirp scaling algorithm. Bearing practical needs in mind, the authors have included an entire chapter devoted to SAR system performance including image quality metrics and image quality assessment. Another chapter contains image formation processor design examples for two operational fine-resolution SAR systems. This is a reference for radar engineers, managers, system developers, and for students in high-resolution microwave imaging courses. It includes 662 equations, 265 figures, and 55 tables.

Digitally enhanced analog and mixed signal techniques are increasingly important to current and future circuit and system design. This book discusses how digital enhancement can be used to address key challenges relevant to analog components in terms of shrinking CMOS technology, increasing user demand for higher flexibility and data traffic in communications networks, and the drive to reduce power consumption. The book opens with an introduction to the main trends in current digitally enhanced systems, emphasising the impact of shrinking technology, and provides an overview of the principles of non-linear models. Later chapters cover pre-distortion and post-distortion techniques, analog-to-digital and digital-to-analog converters, clock generation, fixed-point refinement and adaptive filtering. Key themes of the book are the implementation approaches common between digital enhancement techniques and the trade-offs between complexity and performance for digitally enhanced devices and circuits. The book will be of particular interest to academic researchers and engineers working in analog and mixed signal circuit and system design.

Document imaging is a new discipline in applied computer science. It is building bridges between computer graphics, the world of prepress and press, and the areas of color vision and color reproduction. The focus of this book is of special relevance to people learning how to utilize and integrate such available technology as digital printing or short run color, how to make use of CIM techniques for print products, and how to evaluate related technologies that will become relevant in the next few years. This book is the first to give a comprehensive overview of document imaging, the areas involved, and how they relate. For readers with a background in computer graphics it gives insight into all problems related to putting information in print, a field only very thinly covered in textbooks on computer graphics.

Fourier analysis is one of the most useful tools in many applied sciences. The recent developments of wavelet analysis indicate that in spite of its long history and well-established applications, the field is still one of active research. This text bridges the gap between engineering and mathematics, providing a rigorously mathematical introduction of Fourier analysis, wavelet analysis and related mathematical methods, while emphasizing their uses in signal processing and other applications in communications engineering. The interplay between Fourier series and Fourier transforms is at the heart of signal processing, which is couched most naturally in terms of the Dirac delta function and Lebesgue integrals. The exposition is organized into four parts. The first is a discussion of one-dimensional Fourier theory, including the classical results on convergence and the Poisson sum formula. The second part is devoted to the mathematical foundations of signal processing ¿ sampling, filtering, digital signal processing. Fourier analysis in Hilbert spaces is the focus of the third part, and the last part provides an introduction to wavelet analysis, time-frequency issues, and multiresolution analysis. An appendix provides the necessary background on Lebesgue integrals.

The aim of this volume is to bring together research directions in theoretical signal and imaging processing developed rather independently in electrical engineering, theoretical physics, mathematics and the computer sciences.

In particular, mathematically justified algorithms and methods, the mathematical analysis of these algorithms, and methods as well as the investigation of connections between methods from time series analysis and image processing are reviewed. An interdisciplinary comparison of these methods, drawing upon common sets of test problems from medicine and geophysical/environmental sciences, is also addressed.

This volume coherently summarizes work carried out in the field of theoretical signal and image processing. It focuses on non-linear and non-parametric models for time series as well as on adaptive methods in image processing.

Micro-electronics and so integrated circuit design are heavily driven by technology scaling. The main engine of scaling is an increased system performance at reduced manufacturing cost (per system). In most systems digital circuits dominate with respect to die area and functional complexity. Digital building blocks take full - vantage of reduced device geometries in terms of area, power per functionality, and switching speed. On the other hand, analog circuits rely not on the fast transition speed between a few discrete states but fairly on the actual shape of the trans- tor characteristic. Technology scaling continuously degrades these characteristics with respect to analog performance parameters like output resistance or intrinsic gain. Below the 100 nm technology node the design of analog and mixed-signal circuits becomes perceptibly more dif cult. This is particularly true for low supply voltages near to 1V or below. The result is not only an increased design effort but also a growing power consumption. The area shrinks considerably less than p- dicted by the digital scaling factor. Obviously, both effects are contradictory to the original goal of scaling. However, digital circuits become faster, smaller, and less power hungry. The fast switching transitions reduce the susceptibility to noise, e. g. icker noise in the transistors. There are also a few drawbacks like the generation of power supply noise or the lack of power supply rejection.

Methods of signal analysis represent a broad research topic with applications in many disciplines, including engineering, technology, biomedicine, seismography, eco nometrics, and many others based upon the processing of observed variables. Even though these applications are widely different, the mathematical background be hind them is similar and includes the use of the discrete Fourier transform and z-transform for signal analysis, and both linear and non-linear methods for signal identification, modelling, prediction, segmentation, and classification. These meth ods are in many cases closely related to optimization problems, statistical methods, and artificial neural networks. This book incorporates a collection of research papers based upon selected contri butions presented at the First European Conference on Signal Analysis and Predic tion (ECSAP-97) in Prague, Czech Republic, held June 24-27, 1997 at the Strahov Monastery. Even though the Conference was intended as a European Conference, at first initiated by the European Association for Signal Processing (EURASIP), it was very gratifying that it also drew significant support from other important scientific societies, including the lEE, Signal Processing Society of IEEE, and the Acoustical Society of America. The organizing committee was pleased that the re sponse from the academic community to participate at this Conference was very large; 128 summaries written by 242 authors from 36 countries were received. In addition, the Conference qualified under the Continuing Professional Development Scheme to provide PD units for participants and contributors.

Using the Bayesian inference framework, this book enables the reader to design and develop mathematically sound algorithms for dealing with tracking problems involving multiple targets, multiple sensors, and multiple platforms. It shows how non-linear Multiple Hypothesis Tracking and the Theory of United Tracking are successful methods when multiple target tracking must be performed without contacts or association. With detailed examples illustrating the developed concepts, algorithms, and approaches, the book helps the reader track when observations are non-linear functions of target site, when the target state distributions or measurements error distributions are not Gaussian, when notions of contact and association are merged or unresolved among more than one target, and in low data rate and low signal to noise ratio situations.

Provides a digest of the current developments, open questions and unsolved problems likely to determine a new frontier for future advanced study and research in the rapidly growing areas of wavelets, wavelet transforms, signal analysis, and signal and image processing. Ideal reference work for advanced students and practitioners in wavelets, and wavelet transforms, signal processing and time-frequency signal analysis. Professionals working in electrical and computer engineering, applied mathematics, computer science, biomedical engineering, physics, optics, and fluid mechanics will also find the book a valuable resource.

Provides a textbook treatment that is concise and practical introduction to the underlying foundations and important applications.

Through numerous examples and case studies from industry, it demonstrates both the potential and the limits of wavelet techniques, expanding the usual treatment beyond the discrete wavelet transform to the continuous transform.

Providing the basics of Fourier transforms and digital filters in the appendix, the text is supplemented with end-of-chapter exercises, MatLab code, and a short introduction to the MATLAB wavelet toolbox.

Telecommunication systems and human-machine interfaces start employing multiple microphones and loudspeakers in order to make conversations and interactions more lifelike, hence more efficient. This development gives rise to a variety of acoustic signal processing problems under multiple-input multiple-output (MIMO) scenarios, encompassing distant speech acquisition, sound source localization and tracking, echo and noise control, source separation and speech dereverberation, and many others.

Acoustic MIMO Signal Processing is divided into two major parts - the theoretical and the practical. The authors begin by introducing an acoustic MIMO paradigm, establishing the fundamental of the field, and linking acoustic MIMO signal processing with the concepts of classical signal processing and communication theories in terms of system identification, equalization, and adaptive algorithms. In the second part of the book, a novel and penetrating analysis of aforementioned acoustic applications is carried out in the paradigm to reinforce the fundamental concepts of acoustic MIMO signal processing.

Acoustic MIMO Signal Processing is a timely and important professional reference for researchers and practitioners from universities and a wide range of industries. It is also an excellent text for graduate students who are interested in this exciting field.

This book provides a Mathematical Theory of Distributed Sensor Networks. It introduces the Mathematical & Computational Structure by discussing what they are, their applications and how they differ from traditional systems. It also explains how mathematics are utilized to provide efficient techniques implementing effective coverage, deployment, transmission, data processing, signal processing, and data protection within distributed sensor networks. Finally, it discusses some important challenges facing mathematics to get more incite to the multidisciplinary area of distributed sensor networks. -This book will help design engineers to set up WSN-based applications providing better use of resources while optimizing processing costs. -This book is highly useful for graduate students starting their first steps in research to apprehend new approaches and understand the mathematics behind them and face promising challenges. -This book aims at presenting a formal framework allowing to show how mathematical theories can be used to provide distributed sensor modeling and to solve important problems such as coverage hole detection and repair. -This book aims at presenting the current state of the art in formal issues related to sensor networking. It can be used as a handbook for different classes at the graduate level and the undergraduate level. It is self contained and comprehensive, presenting a complete picture of the discipline of optical network engineering including modeling functions, controlling quality of service, allocation resources, monitoring traffic, protecting infrastructure, and conducting planning. This book addresses a large set of theoretical aspects. It is designed for specialists in ad hoc and wireless sensor networks and does not include discusses on very promising areas such as homotopy, computational geometry, and wavelet transforms.

After a slow and somewhat tentative beginning, machine vision systems are now finding widespread use in industry. So far, there have been four clearly discernible phases in their development, based upon the types of images processed and how that processing is performed: (1) Binary (two level) images, processing in software (2) Grey-scale images, processing in software (3) Binary or grey-scale images processed in fast, special-purpose hardware (4) Coloured/multi-spectral images Third-generation vision systems are now commonplace, although a large number of binary and software-based grey-scale processing systems are still being sold. At the moment, colour image processing is commercially much less significant than the other three and this situation may well remain for some time, since many industrial artifacts are nearly monochrome and the use of colour increases the cost of the equipment significantly. A great deal of colour image processing is a straightforward extension of standard grey-scale methods. Industrial applications of machine vision systems can also be sub divided, this time into two main areas, which have largely retained distinct identities: (i) Automated Visual Inspection (A VI) (ii) Robot Vision (RV) This book is about a fifth generation of industrial vision systems, in which this distinction, based on applications, is blurred and the processing is marked by being much smarter (i. e. more "intelligent") than in the other four generations."

With the rapid expansion of the Internet over the last 20 years, event-based distributed systems are playing an increasingly important role in a broad range of application domains, including enterprise management, environmental monitoring, information dissemination, finance, pervasive systems, autonomic computing, collaborative working and learning, and geo-spatial systems. Many different architectures, languages and technologies are being used for implementing event-based distributed systems, and much of the development has been undertaken independently by different communities. However, a common factor is an ever-increasing complexity. Users and developers expect that such systems are able not only to handle large volumes of simple events but also to detect complex patterns of events that may be spatially distributed and may span significant periods of time. Intelligent and logic-based approaches provide sound foundations for addressing many of the research challenges faced and this book covers a broad range of recent advances, contributed by leading experts in the field. It presents a comprehensive view of reasoning in event-based distributed systems, bringing together reviews of the state-of-the art, new research contributions, and an extensive set of references. It will serve as a valuable resource for students, faculty and researchers as well as industry practitioners responsible for new systems development.

World-class experts from academia and industry assembled at the sixth Biennial Workshop on Digital Signal Processing (DSP) for In-Vehicle Systems at Korea University, Seoul, Korea in 2013. The Workshop covered a wide spectrum of automotive fields, including in-vehicle signal processing and cutting-edge studies on safety, driver behavior, infrastructure, in-vehicle technologies. Contributors to this volume have expanded their contributions to the Workshop into full chapters with related works, methodology, experiments, and the analysis of the findings. Topics in this volume include: DSP technologies for in-vehicle systems Driver status and behavior monitoring In-Vehicle dialogue systems and human machine interfaces In-vehicle video and applications for safety Passive and active driver assistance technologies Ideas and systems for autonomous driving Transportation infrastructure