This book introduces the fundamentals of computer vision (CV), with a focus on extracting useful information from digital images and videos. Including a wealth of methods used in detecting and classifying image objects and their shapes, it is the first book to apply a trio of tools (computational geometry, topology and algorithms) in solving CV problems, shape tracking in image object recognition and detecting the repetition of shapes in single images and video frames. Computational geometry provides a visualization of topological structures such as neighborhoods of points embedded in images, while image topology supplies us with structures useful in the analysis and classification of image regions. Algorithms provide a practical, step-by-step means of viewing image structures. The implementations of CV methods in Matlab and Mathematica, classification of chapter problems with the symbols (easily solved) and (challenging) and its extensive glossary of key words, examples and connections with the fabric of CV make the book an invaluable resource for advanced undergraduate and first year graduate students in Engineering, Computer Science or Applied Mathematics. It offers insights into the design of CV experiments, inclusion of image processing methods in CV projects, as well as the reconstruction and interpretation of recorded natural scenes.

The universe is a massive system of systems - for example, ecological systems, social systems, commodity and stock markets. These systems are complex, constantly adapting to their environment, and many are essential to the very existence of human beings. To fully understand these systems, complex adaptive systems research uses systemic inquiry to build multi-level and multidisciplinary representations of reality to study these systems.""Applications of Complex Adaptive Systems"" provides a global view of the most up-to-date research on the strategies, applications, practice, and implications of complex adaptive systems, to better understand the various critical systems that surround human life. Researchers working in the field of complex adaptive systems and related fields such as machine learning and artificial intelligence, multi-agent systems, and data mining, as well as professionals in related applications such as defense, bioinformatics, and sociology will find this book an indispensable, state-of-the-art reference.

This book shows how common operation management methods and algorithms can be extended to deal with vague or imprecise information in decision-making problems. It describes how to combine decision trees, clustering, multi-attribute decision-making algorithms and Monte Carlo Simulation with the mathematical description of imprecise or vague information, and how to visualize such information. Moreover, it discusses a broad spectrum of real-life management problems including forecasting the apparent consumption of steel products, planning and scheduling of production processes, project portfolio selection and economic-risk estimation. It is a concise, yet comprehensive, reference source for researchers in decision-making and decision-makers in business organizations alike.

The present book includes a set of selected extended papers from the sixth International Joint Conference on Computational Intelligence (IJCCI 2014), held in Rome, Italy, from 22 to 24 October 2014. The conference was composed by three co-located conferences: The International Conference on Evolutionary Computation Theory and Applications (ECTA), the International Conference on Fuzzy Computation Theory and Applications (FCTA), and the International Conference on Neural Computation Theory and Applications (NCTA). Recent progresses in scientific developments and applications in these three areas are reported in this book. IJCCI received 210 submissions, from 51 countries, in all continents. After a double blind paper review performed by the Program Committee, 15% were accepted as full papers and thus selected for oral presentation. Additional papers were accepted as short papers and posters. A further selection was made after the Conference, based also on the assessment of presentation quality and audience interest, so that this book includes the extended and revised versions of the very best papers of IJCCI 2014. Commitment to high quality standards is a major concern of IJCCI that will be maintained in the next editions, considering not only the stringent paper acceptance ratios but also the quality of the program committee, keynote lectures, participation level and logistics.

This thesis takes an empirical approach to understanding of the behavior and interactions between the two main components of reinforcement learning: the learning algorithm and the functional representation of learned knowledge. The author approaches these entities using design of experiments not commonly employed to study machine learning methods. The results outlined in this work provide insight as to what enables and what has an effect on successful reinforcement learning implementations so that this learning method can be applied to more challenging problems.

This book presents novel classification algorithms for four challenging prediction tasks, namely learning from imbalanced, semi-supervised, multi-instance and multi-label data. The methods are based on fuzzy rough set theory, a mathematical framework used to model uncertainty in data. The book makes two main contributions: helping readers gain a deeper understanding of the underlying mathematical theory; and developing new, intuitive and well-performing classification approaches. The authors bridge the gap between the theoretical proposals of the mathematical model and important challenges in machine learning. The intended readership of this book includes anyone interested in learning more about fuzzy rough set theory and how to use it in practical machine learning contexts. Although the core audience chiefly consists of mathematicians, computer scientists and engineers, the content will also be interesting and accessible to students and professionals from a range of other fields.

This book presents the scientific outcomes of the 6th International Conference on Applied Computing and Information Technology (ACIT 2018), which was held in Kunming, China on June 13-15, 2018. The aim of this conference was to bring together researchers and scientists, businessmen and entrepreneurs, teachers, engineers, computer users, and students to discuss the numerous fields of computer science and to share their experiences and exchange new ideas and information in a meaningful way. The book includes research findings on all aspects (theory, applications and tools) of computer and information science and discusses the practical challenges encountered and the solutions adopted to address them. The book features 13 of the conference's most promising papers.

This monograph bridges the gap between the nonlinear predictor as a concept and as a practical tool, presenting a complete theory of the application of predictor feedback to time-invariant, uncertain systems with constant input delays and/or measurement delays. It supplies several methods for generating the necessary real-time solutions to the systems' nonlinear differential equations, which the authors refer to as approximate predictors. Predictor feedback for linear time-invariant (LTI) systems is presented in Part I to provide a solid foundation on the necessary concepts, as LTI systems pose fewer technical difficulties than nonlinear systems. Part II extends all of the concepts to nonlinear time-invariant systems. Finally, Part III explores extensions of predictor feedback to systems described by integral delay equations and to discrete-time systems. The book's core is the design of control and observer algorithms with which global stabilization, guaranteed in the previous literature with idealized (but non-implementable) predictors, is preserved with approximate predictors developed in the book. An applications-driven engineer will find a large number of explicit formulae, which are given throughout the book to assist in the application of the theory to a variety of control problems. A mathematician will find sophisticated new proof techniques, which are developed for the purpose of providing global stability guarantees for the nonlinear infinite-dimensional delay system under feedback laws employing practically implementable approximate predictors. Researchers working on global stabilization problems for time-delay systems will find this monograph to be a helpful summary of the state of the art, while graduate students in the broad field of systems and control will advance their skills in nonlinear control design and the analysis of nonlinear delay systems.

This book covers the fundamental principles, new theories and methodologies, and potential applications of hybrid intelligent networks. Chapters focus on hybrid neural networks and networked multi-agent networks, including their communication, control and optimization synthesis. This text also provides a succinct but useful guideline for designing neural network-based hybrid artificial intelligence for brain-inspired computation systems and applications in the Internet of Things. Artificial Intelligence has developed into a deep research field targeting robots with more brain-inspired perception, learning, decision-making abilities, etc. This text devoted to a tutorial on hybrid intelligent networks that have been identified in nature and engineering, especially in the brain, modeled by hybrid dynamical systems and complex networks, and have shown potential application to brain-inspired intelligence. Included in this text are impulsive neural networks, neurodynamics, multiagent networks, hybrid dynamics analysis, collective dynamics, as well as hybrid communication, control and optimization methods. Graduate students who are interested in artificial intelligence and hybrid intelligence, as well as professors and graduate students who are interested in neural networks and multiagent networks will find this textbook a valuable resource. AI engineers and consultants who are working in wireless communications and networking will want to buy this book. Also, professional and academic institutions in universities and Mobile vehicle companies and engineers and managers who concern humans in the loop of IoT will also be interested in this book.

This book provides fresh insights into the cutting edge of multimedia data mining, reflecting how the research focus has shifted towards networked social communities, mobile devices and sensors. The work describes how the history of multimedia data processing can be viewed as a sequence of disruptive innovations. Across the chapters, the discussion covers the practical frameworks, libraries, and open source software that enable the development of ground-breaking research into practical applications. Features: reviews how innovations in mobile, social, cognitive, cloud and organic based computing impacts upon the development of multimedia data mining; provides practical details on implementing the technology for solving real-world problems; includes chapters devoted to privacy issues in multimedia social environments and large-scale biometric data processing; covers content and concept based multimedia search and advanced algorithms for multimedia data representation, processing and visualization.

This book presents recent research in the recognition of vulnerabilities of national systems and assets which gained special attention for the Critical Infrastructures in the last two decades. The book concentrates on R&D activities in the relation of Critical Infrastructures focusing on enhancing the performance of services as well as the level of security. The objectives of the book are based on a project entitled "Critical Infrastructure Protection Researches" (TAMOP-4.2.1.B-11/2/KMR-2011-0001) which concentrated on innovative UAV solutions, robotics, cybersecurity, surface engineering, and mechatornics and technologies providing safe operations of essential assets. This report is summarizing the methodologies and efforts taken to fulfill the goals defined. The project has been performed by the consortium of the Obuda University and the National University of Public Service.

This book aims to provide important information about adaptivity in computer-based and/or web-based educational systems. In order to make the student modeling process clear, a literature review concerning student modeling techniques and approaches during the past decade is presented in a special chapter. A novel student modeling approach including fuzzy logic techniques is presented. Fuzzy logic is used to automatically model the learning or forgetting process of a student. The presented novel student model is responsible for tracking cognitive state transitions of learners with respect to their progress or non-progress. It maximizes the effectiveness of learning and contributes, significantly, to the adaptation of the learning process to the learning pace of each individual learner. Therefore the book provides important information to researchers, educators and software developers of computer-based educational software ranging from e-learning and mobile learning systems to educational games including stand alone educational applications and intelligent tutoring systems.

This book highlights recent research advances in unsupervised learning using natural computing techniques such as artificial neural networks, evolutionary algorithms, swarm intelligence, artificial immune systems, artificial life, quantum computing, DNA computing, and others. The book also includes information on the use of natural computing techniques for unsupervised learning tasks. It features several trending topics, such as big data scalability, wireless network analysis, engineering optimization, social media, and complex network analytics. It shows how these applications have triggered a number of new natural computing techniques to improve the performance of unsupervised learning methods. With this book, the readers can easily capture new advances in this area with systematic understanding of the scope in depth. Readers can rapidly explore new methods and new applications at the junction between natural computing and unsupervised learning. Includes advances on unsupervised learning using natural computing techniques Reports on topics in emerging areas such as evolutionary multi-objective unsupervised learning Features natural computing techniques such as evolutionary multi-objective algorithms and many-objective swarm intelligence algorithms

This book examines the introduction of smart technologies into public administrations and the organizational issues caused by these implementations, and the potential of information and communication technologies (ICTs) to rationalize and improve government, transform governance and organizational issues, and address economic, social, and environmental challenges. Cities are increasingly using new technologies in the delivery of public sector services and in the improvement of government transparency, business-led urban development, and urban sustainability. The book will examine specific smart projects that cities are embracing to improve transparency, efficiency, sustainability, mobility, and whether all cities are prepared to implement smart technologies and the incentives for promoting implementation. This focus on the smart technologies applied to public sector entities will be of interest to academics, researchers, policy-makers, public managers, international organizations and technical experts involved in and responsible for the governance, development and design of Smart Cities.

We are now entering an era where the human world assumes recognition of itself as data. Much of humanity's basis for existence is becoming subordinate to software processes that tabulate, index, and sort the relations that comprise what we perceive as reality. The acceleration of data collection threatens to relinquish ephemeral modes of representation to ceaseless processes of computation. This situation compels the human world to form relations with non-human agencies, to establish exchanges with software processes in order to allow a profound upgrade of our own ontological understanding. By mediating with a higher intelligence, we may be able to rediscover the inner logic of the age of intelligent machines. In The End of the Future, Stephanie Polsky conceives an understanding of the digital through its dynamic intersection with the advent and development of the nation-state, race, colonization, navigational warfare, mercantilism, and capitalism, and the mathematical sciences over the past five centuries, the era during which the world became "modern." The book animates the twenty-first century as an era in which the screen has split off from itself and proliferated onto multiple surfaces, allowing an inverted image of totalitarianism to flash up and be altered to support our present condition of binary apperception. It progresses through a recognition of atomized political power, whose authority lies in the control not of the means of production, but of information, and in which digital media now serves to legitimize and promote a customized micropolitics of identity management. On this new apostolate plane, humanity may be able to shape a new world in which each human soul is captured and reproduced as an autonomous individual bearing affects and identities. The digital infrastructure of the twenty-first century makes it possible for power to operate through an esoteric mathematical means, and for factual material to be manipulated in the interest of advancing the means of control. This volume travels a course from Elizabethan England, to North American slavery, through cybernetic Social Engineering, Cold War counterinsurgency, and the (neo)libertarianism of Silicon Valley in order to arrive at a place where an organizing intelligence that started from an ambition to resourcefully manipulate physical bodies has ended with their profound neutralization.

There is widespread interest in the way that smart energy control systems, such as assessment and monitoring techniques for low carbon, nearly-zero energy and net positive buildings can contribute to a Sustainable future, for current and future generations. There is a turning point on the horizon for the supply of energy from finite resources such as natural gas and oil become less reliable in economic terms and extraction become more challenging, and more unacceptable socially, such as adverse public reaction to 'fracking'. Thus, in 2016 these challenges are having a major influence on the design, optimisation, performance measurements, operation and preservation of: buildings, neighbourhoods, cities, regions, countries and continents. The source and nature of energy, the security of supply and the equity of distribution, the environmental impact of its supply and utilization, are all crucial matters to be addressed by suppliers, consumers, governments, industry, academia, and financial institutions. This book entitled 'Smart Energy Control Systems for Sustainable Buildings' contains eleven chapters written by international experts based on enhanced conference papers presented at the Sustainability and Energy in Buildings International conference series. This book will be of interest to University staff and students; and also industry practioners.

Highlights key research currently being undertaken within the field of telepresence, providing the most detailed account of the field to date, advancing our understanding of a fundamental property of all media - the illusion of presence; the sense of "being there" inside a virtual environment, with actual or virtual others. This collection has been put together by leading international scholars from America, Europe, and Asia. Together, they describe the state-of-the-art in presence theory, research and technology design for an advanced academic audience. Immersed in Media provides research that can help designers optimize presence for users of advanced media technologies such as virtual and augmented reality, collaborative social media, robotics, and artificial intelligence and lead us to better understand human cognition, emotion and behaviour.

The first book of its kind devoted to this topic, this comprehensive text/reference presents state-of-the-art research and reviews current challenges in the application of computer vision to problems in sports. Opening with a detailed introduction to the use of computer vision across the entire life-cycle of a sports event, the text then progresses to examine cutting-edge techniques for tracking the ball, obtaining the whereabouts and pose of the players, and identifying the sport being played from video footage. The work concludes by investigating a selection of systems for the automatic analysis and classification of sports play. The insights provided by this pioneering collection will be of great interest to researchers and practitioners involved in computer vision, sports analysis and media production.

This book gathers a selection of the best papers presented during the 14th International Conference on Location Based Services, which was held in Zurich (Switzerland) between the 15th and 17th January 2018. It presents a general overview of recent research activities related to location based services. Such activities have grown in importance over the past several years, especially those concerning outdoor/indoor positioning, smart environments, spatial modeling, personalization and context-awareness, cartographic communication, novel user interfaces, crowdsourcing, social media, big data analysis, usability and privacy.

Local Area Networks (LANs) have a high potential for alleviating many of the problems associated with stand-alone microcomputers. Networking microcomputers to share information, software and hardware, as well as facilite electronic mail is not only feasible and desirable, but also logical. Harry Kibirige's issue-oriented study explores microcomputer networking systems with particular emphasis on LANs. Although his analysis emphasizes issues from an information scientist's perspective, readers who want to gain an understanding of LAN technology and its applications should find it useful. Written with a minimum of jargon, the book can be used in academic, corporate, library, federal and state agency, and not-for-profit organizational settings. The author begins with an introduction to the general concepts surrounding LANs. He discusses LANs as structures for processing information and compares and contrasts them with other structures such as time-sharing systems. Also considered are salient factors concerned with LAN design and implementation. In a chapter devoted to choosing an LAN, Kibirige explains in detail the fundamental problems of choice as well as steps which should be taken in making a final selection. Other issues covered are the relationship of LANs to other existing automation programs, significant management issues, currently implemented alternatives to LANS, technology trends which will impact the future of LANs, and social issues concerned with LANs. Finally, Kibirige summarizes the results of the CUNY study of microcomputer networking systems, a report that emphasized information center/libraries.

The publication is attempted to address emerging trends in machine learning applications. Recent trends in information identification have identified huge scope in applying machine learning techniques for gaining meaningful insights. Random growth of unstructured data poses new research challenges to handle this huge source of information. Efficient designing of machine learning techniques is the need of the hour. Recent literature in machine learning has emphasized on single technique of information identification. Huge scope exists in developing hybrid machine learning models with reduced computational complexity for enhanced accuracy of information identification. This book will focus on techniques to reduce feature dimension for designing light weight techniques for real time identification and decision fusion. Key Findings of the book will be the use of machine learning in daily lives and the applications of it to improve livelihood. However, it will not be able to cover the entire domain in machine learning in its limited scope. This book is going to benefit the research scholars, entrepreneurs and interdisciplinary approaches to find new ways of applications in machine learning and thus will have novel research contributions. The lightweight techniques can be well used in real time which will add value to practice.

Medical images are at the base of many routine clinical decisions and their influence continues to increase in many fields of medicine. Since the last decade, computers have become an invaluable tool for supporting medical image acquisition, processing, organization and analysis. Biomedical Image Analysis and Machine Learning Technologies: Applications and Techniques provides a panorama of the current boundary between biomedical complexity coming from the medical image context and the multiple techniques which have been used for solving many of these problems. This innovative publication serves as a leading industry reference as well as a source of creative ideas for applications of medical issues.