This book presents a mathematical treatment of the radio resource allocation of modern cellular communications systems in contested environments. It focuses on fulfilling the quality of service requirements of the living applications on the user devices, which leverage the cellular system, and with attention to elevating the users' quality of experience. The authors also address the congestion of the spectrum by allowing sharing with the band incumbents while providing with a quality-of-service-minded resource allocation in the network. The content is of particular interest to telecommunications scheduler experts in industry, communications applications academia, and graduate students whose paramount research deals with resource allocation and quality of service.

This book presents an examination of the middleware that can be used to configure and operate heterogeneous node platforms and sensor networks. The middleware requirements for a range of application scenarios are compared and analysed. The text then defines middleware architecture that has been integrated in an approach demonstrated live in a refinery. Features: presents a thorough introduction to the major concepts behind wireless sensor networks (WSNs); reviews the various application scenarios and existing middleware solutions for WSNs; discusses the middleware mechanisms necessary for heterogeneous WSNs; provides a detailed examination of platform-agnostic middleware architecture, including important implementation details; investigates the programming paradigms for WSNs, and for heterogeneous sensor networks in general; describes the results of extensive experimentation and testing, demonstrating that the generic architecture is viable for implementation on multiple platforms.

The biggest challenges faced by the software industry are cost control and schedule control. As such, effective strategies for process improvement must be researched and implemented. Analyzing the Role of Risk Mitigation and Monitoring in Software Development is a critical scholarly resource that explores software risk and development as organizations continue to implement more applications across multiple technologies and a multi-tiered environment. Featuring coverage on a broad range of topics such as quantitative risk assessment, threat analysis, and software vulnerability management, this book is a vital resource for engineers, academicians, professionals, and researchers seeking current research on the importance of risk management in software development.

The papers gathered in this book were published over a period of more than twenty years in widely scattered journals. They led to the discovery of randomness in arithmetic which was presented in the recently published monograph on "Algorithmic Information Theory" by the author. There the strongest possible version of Goedel's incompleteness theorem, using an information-theoretic approach based on the size of computer programs, was discussed. The present book is intended as a companion volume to the monograph and it will serve as a stimulus for work on complexity, randomness and unpredictability, in physics and biology as well as in metamathematics.

This book bridges fundamental gaps between control theory and formal methods. Although it focuses on discrete-time linear and piecewise affine systems, it also provides general frameworks for abstraction, analysis, and control of more general models. The book is self-contained, and while some mathematical knowledge is necessary, readers are not expected to have a background in formal methods or control theory. It rigorously defines concepts from formal methods, such as transition systems, temporal logics, model checking and synthesis. It then links these to the infinite state dynamical systems through abstractions that are intuitive and only require basic convex-analysis and control-theory terminology, which is provided in the appendix. Several examples and illustrations help readers understand and visualize the concepts introduced throughout the book.

This book deals with an information-driven approach to plan materials discovery and design, iterative learning. The authors present contrasting but complementary approaches, such as those based on high throughput calculations, combinatorial experiments or data driven discovery, together with machine-learning methods. Similarly, statistical methods successfully applied in other fields, such as biosciences, are presented. The content spans from materials science to information science to reflect the cross-disciplinary nature of the field. A perspective is presented that offers a paradigm (codesign loop for materials design) to involve iteratively learning from experiments and calculations to develop materials with optimum properties. Such a loop requires the elements of incorporating domain materials knowledge, a database of descriptors (the genes), a surrogate or statistical model developed to predict a given property with uncertainties, performing adaptive experimental design to guide the next experiment or calculation and aspects of high throughput calculations as well as experiments. The book is about manufacturing with the aim to halving the time to discover and design new materials. Accelerating discovery relies on using large databases, computation, and mathematics in the material sciences in a manner similar to the way used to in the Human Genome Initiative. Novel approaches are therefore called to explore the enormous phase space presented by complex materials and processes. To achieve the desired performance gains, a predictive capability is needed to guide experiments and computations in the most fruitful directions by reducing not successful trials. Despite advances in computation and experimental techniques, generating vast arrays of data; without a clear way of linkage to models, the full value of data driven discovery cannot be realized. Hence, along with experimental, theoretical and computational materials science, we need to add a "fourth leg'' to our toolkit to make the "Materials Genome'' a reality, the science of Materials Informatics.

Since its first volume in 1960, Advances in Computers has presented detailed coverage of innovations in computer hardware, software, theory, design, and applications. It has also provided contributors with a medium in which they can explore their subjects in greater depth and breadth than journal articles usually allow. As a result, many articles have become standard references that continue to be of sugnificant, lasting value in this rapidly expanding field.
In-depth surveys and tutorials on new computer technologyWell-known authors and researchers in the fieldExtensive bibliographies with most chaptersMany of the volumes are devoted to single themes or subfields of computer science

This book presents cutting-edge developments in the advanced mathematical theories utilized in computer graphics research - fluid simulation, realistic image synthesis, and texture, visualization and digital fabrication. A spin-off book from the International Symposium on Mathematical Progress in Expressive Image Synthesis in 2016 and 2017 (MEIS2016/2017) held in Fukuoka, Japan, it includes lecture notes and an expert introduction to the latest research presented at the symposium. The book offers an overview of the emerging interdisciplinary themes between computer graphics and driven mathematic theories, such as discrete differential geometry. Further, it highlights open problems in those themes, making it a valuable resource not only for researchers, but also for graduate students interested in computer graphics and mathematics.

This book addresses the basics of interval/fuzzy set theory, artificial neural networks (ANN) and computational methods. It presents step-by-step modeling for application problems along with simulation and numerical solutions. In general, every science and engineering problem is inherently biased by uncertainty, and there is often a need to model, solve and interpret problems in the world of uncertainty. At the same time, exact information about models and parameters of practical applications is usually not known and precise values do not exist. This book discusses uncertainty in both data and models. It consists of seven chapters covering various aspects of fuzzy uncertainty in application problems, such as shallow water wave equations, static structural problems, robotics, radon diffusion in soil, risk of invasive alien species and air quality quantification. These problems are handled by means of advanced computational and fuzzy theory along with machine intelligence when the uncertainties involved are fuzzy. The proposed computational methods offer new fuzzy computing methods that help other areas of knowledge construction where inexact information is present.

Social networks have emerged as a major trend in computing and social paradigms in the past few years. The social network model helps to inform the study of community behavior, allowing qualitative and quantitative assessments of how people communicate and the rules that govern communication. Social Networking and Community Behavior Modeling: Qualitative and Quantitative Measures provides a clear and consolidated view of current social network models. This work explores new methods for modeling, characterizing, and constructing social networks. Chapters contained in this book study critical security issues confronting social networking, the emergence of new mobile social networking devices and applications, network robustness, and how social networks impact the business aspects of organizations.

The Semantic Web is characterized by the existence of a very large number of distributed semantic resources, which together define a network of ontologies. These ontologies in turn are interlinked through a variety of different meta-relationships such as versioning, inclusion, and many more. This scenario is radically different from the relatively narrow contexts in which ontologies have been traditionally developed and applied, and thus calls for new methods and tools to effectively support the development of novel network-oriented semantic applications.

This book by Suarez-Figueroa et al. provides the necessary methodological and technological support for the development and use of ontology networks, which ontology developers need in this distributed environment. After an introduction, in its second part the authors describe the NeOn Methodology framework. The book's third part details the key activities relevant to the ontology engineering life cycle. For each activity, a general introduction, methodological guidelines, and practical examples are provided. The fourth part then presents a detailed overview of the NeOn Toolkit and its plug-ins. Lastly, case studies from the pharmaceutical and the fishery domain round out the work.

The book primarily addresses two main audiences: students (and their lecturers) who need a textbook for advanced undergraduate or graduate courses on ontology engineering, and practitioners who need to develop ontologies in particular or Semantic Web-based applications in general. Its educational value is maximized by its structured approach to explaining guidelines and combining them with case studies and numerous examples. The description of the open source NeOn Toolkit provides an additional asset, as it allows readers to easily evaluate and apply the ideas presented."

The aim of this book is to give a complete overview of the classical electromagnetic theory together with detailed insight in modern numerical methods for analysis of the problems in electromagnetics. Classical electromagnetic theory was developed in the 19th century but due to a wide range of applications from electrical apparatus such as motors or heaters to telecommunications, this subject is still very interesting. This book explains basic postulates and laws of the theory and its specialization to static and time-dependent problems. Special attention is given to utilization of computers in application of the modern numerical methods to solution of electromagnetic field problems.

This is the first book devoted to the task of computing integrability structures by computer. The symbolic computation of integrability operator is a computationally hard problem and the book covers a huge number of situations through tutorials. The mathematical part of the book is a new approach to integrability structures that allows to treat all of them in a unified way. The software is an official package of Reduce. Reduce is free software, so everybody can download it and make experiments using the programs available at our website.

This book presents practical optimization techniques used in image processing and computer vision problems. Ill-posed problems are introduced and used as examples to show how each type of problem is related to typical image processing and computer vision problems. Unconstrained optimization gives the best solution based on numerical minimization of a single, scalar-valued objective function or cost function. Unconstrained optimization problems have been intensively studied, and many algorithms and tools have been developed to solve them. Most practical optimization problems, however, arise with a set of constraints. Typical examples of constraints include: (i) pre-specified pixel intensity range, (ii) smoothness or correlation with neighboring information, (iii) existence on a certain contour of lines or curves, and (iv) given statistical or spectral characteristics of the solution. Regularized optimization is a special method used to solve a class of constrained optimization problems. The term regularization refers to the transformation of an objective function with constraints into a different objective function, automatically reflecting constraints in the unconstrained minimization process. Because of its simplicity and efficiency, regularized optimization has many application areas, such as image restoration, image reconstruction, optical flow estimation, etc. Optimization plays a major role in a wide variety of theories for image processing and computer vision. Various optimization techniques are used at different levels for these problems, and this volume summarizes and explains these techniques as applied to image processing and computer vision.

This book focuses on the development of three novel approaches to build up a framework for the frequency domain analysis and design of nonlinear systems. The concepts are derived from Volterra series representation of nonlinear systems which are described by nonlinear difference or differential equations. Occupying the middle ground between traditional linear approaches and more complex nonlinear system theories, the book will help readers to have a good start to analyse and exploit the nonlinearities. Analysis and Design of Nonlinear Systems in the Frequency Domain provides clear illustrations and examples at the beginning and the end of each chapter, respectively, making it of interest to both academics and practicing engineers.

'Rough Computing' explores the application of rough set theory, which has attracted attention because of the ability to enhance databases by allowing for the management of uncertainty, a comparative analysis between rough sets, and other intelligent data analysis.

The variety and abundance of qualitative characteristics of agricultural products have been the main reasons for the development of different types of non-destructive methods (NDTs). Quality control of these products is one of the most important tasks in manufacturing processes. The use of control and automation has become more widespread, and new approaches provide opportunities for production competition through new technologies. Applications of Image Processing and Soft Computing Systems in Agriculture examines applications of artificial intelligence in agriculture and the main uses of shape analysis on agricultural products such as relationships between form and genetics, adaptation, product characteristics, and product sorting. Additionally, it provides insights developed through computer vision techniques. Highlighting such topics as deep learning, agribusiness, and augmented reality, it is designed for academicians, researchers, agricultural practitioners, and industry professionals.

Enabling information interoperability, fostering legal knowledge usability and reuse, enhancing legal information search, in short, formalizing the complexity of legal knowledge to enhance legal knowledge management are challenging tasks, for which different solutions and lines of research have been proposed.

During the last decade, research and applications based on the use of legal ontologies as a technique to represent legal knowledge has raised a very interesting debate about their capacity and limitations to represent conceptual structures in the legal domain. Making conceptual legal knowledge explicit would support the development of a web of legal knowledge, improve communication, create trust and enable and support open data, e-government and e-democracy activities. Moreover, this explicit knowledge is also relevant to the formalization of software agents and the shaping of virtual institutions and multi-agent systems or environments.

This book explores the use of ontologism in legal knowledge representation for semantically-enhanced legal knowledge systems or web-based applications. In it, current methodologies, tools and languages used for ontology development are revised, and the book includes an exhaustive revision of existing ontologies in the legal domain. The development of the Ontology of Professional Judicial Knowledge (OPJK) is presented as a case study.
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This book develops a coherent and quite general theoretical approach to algorithm design for iterative learning control based on the use of operator representations and quadratic optimization concepts including the related ideas of inverse model control and gradient-based design. Using detailed examples taken from linear, discrete and continuous-time systems, the author gives the reader access to theories based on either signal or parameter optimization. Although the two approaches are shown to be related in a formal mathematical sense, the text presents them separately as their relevant algorithm design issues are distinct and give rise to different performance capabilities. Together with algorithm design, the text demonstrates the underlying robustness of the paradigm and also includes new control laws that are capable of incorporating input and output constraints, enable the algorithm to reconfigure systematically in order to meet the requirements of different reference and auxiliary signals and also to support new properties such as spectral annihilation. Iterative Learning Control will interest academics and graduate students working in control who will find it a useful reference to the current status of a powerful and increasingly popular method of control. The depth of background theory and links to practical systems will be of use to engineers responsible for precision repetitive processes.

This book reveals the historical context and the evolution of the technically complex Allied Signals Intelligence (Sigint) activity against Japan from 1920 to 1945. It traces the all-important genesis and development of the cryptanalytic techniques used to break the main Japanese Navy code (JN-25) and the Japanese Army s Water Transport Code during WWII. This is the first book to describe, explain and analyze the code breaking techniques developed and used to provide this intelligence, thus closing the sole remaining gap in the published accounts of the Pacific War. The authors also explore the organization of cryptographic teams and issues of security, censorship, and leaks. Correcting gaps in previous research, this book illustrates how Sigint remained crucial to Allied planning throughout the war. It helped direct the advance to the Philippines from New Guinea, the sea battles and the submarine onslaught on merchant shipping. Written by well-known authorities on the history of cryptography and mathematics, Code Breaking in the Pacific is designed for cryptologists, mathematicians and researchers working in communications security. Advanced-level students interested in cryptology, the history of the Pacific War, mathematics or the history of computing will also find this book a valuable resource."

Technological advancements have become an integral part of life, impacting the way we work, communicate, make decisions, learn, and play. As technology continually progresses, humans are being outpaced by its capabilities, and it is important for businesses, organizations, and individuals to understand how to optimize data and to implement new methods for more efficient knowledge discovery and information management and retrieval. Innovative Applications of Knowledge Discovery and Information Resources Management offers in-depth coverage on the pervasiveness of technological change with a collection of material on topics such as the impact of permeable work-life boundaries, burnout and turnover, big data usage, and computer-based learning. It proves a worthy source for academicians, practitioners, IT leaders, IT professionals, and advanced-level students interested in examining the ways in which technology is changing the world.

Issues of matching and searching on elementary discrete structures arise pervasively in computer science and many of its applications, and their relevance is expected to grow as information is amassed and shared at an accelerating pace. Several algorithms were discovered as a result of these needs, which in turn created the subfield of Pattern Matching. This book provides an overview of the current state of Pattern Matching as seen by specialists who have devoted years of study to the field. It covers most of the basic principles and presents material advanced enough to faithfully portray the current frontier of research. As a result of these recent advances, this is the right time for a book that brings together information relevant to both graduate students and specialists in need of an in-depth reference.