Intelligent Autonomy of UAVs: Advanced Missions and Future Use provides an approach to the formulation of the fundamental task typical to any mission and provides guidelines of how this task can be solved by different generic robotic problems. As such, this book aims to provide a systems engineering approach to UAV projects, discovering the real problems that need to be resolved independently of the application. After an introduction to the rapidly evolving field of aerial robotics, the book presents topics such as autonomy, mission analysis, human-UAV teams, homogeneous and heterogeneous UAV teams, and finally, UAV-UGV teams. It then covers generic robotic problems such as orienteering and coverage. The book next introduces deployment, patrolling, and foraging, while the last part of the book tackles an important application: aerial search, tracking, and surveillance. This book is meant for both scientists and practitioners. For practitioners, it presents existing solutions that are categorized according to various missions: surveillance and reconnaissance, 3D mapping, urban monitoring, precision agriculture, forestry, disaster assessment and monitoring, security, industrial plant inspection, etc. For scientists, it provides an overview of generic robotic problems such as coverage and orienteering; deployment, patrolling and foraging; search, tracking, and surveillance. The design and analysis of algorithms raise a unique combination of questions from many fields, including robotics, operational research, control theory, and computer science.

Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics presents a new approach to numerical analysis for modern computer scientists. Using examples from a broad base of computational tasks, including data processing, computational photography, and animation, the textbook introduces numerical modeling and algorithmic design from a practical standpoint and provides insight into the theoretical tools needed to support these skills. The book covers a wide range of topics-from numerical linear algebra to optimization and differential equations-focusing on real-world motivation and unifying themes. It incorporates cases from computer science research and practice, accompanied by highlights from in-depth literature on each subtopic. Comprehensive end-of-chapter exercises encourage critical thinking and build students' intuition while introducing extensions of the basic material. The text is designed for advanced undergraduate and beginning graduate students in computer science and related fields with experience in calculus and linear algebra. For students with a background in discrete mathematics, the book includes some reminders of relevant continuous mathematical background.

The Future of Digital Data, Heritage and Curation critiques digital cultural heritage concepts and their application to data, developing new theories, curatorial practices and a more-than-human museology for a contemporary and future world. Presenting a diverse range of case examples from around the globe, Cameron offers a critical and philosophical reflection on the ways in which digital cultural heritage is currently framed as societal data worth passing on to future generations in two distinct forms: digitally born and digitizations. Demonstrating that most perceptions of digital cultural heritage are distinctly western in nature, the book also examines the complicity of such heritage in climate change, and environmental destruction and injustice. Going further still, the book theorizes the future of digital data, heritage, curation and the notion of the human in the context of the profusion of new types of societal data and production processes driven by the intensification of data economies and through the emergence of new technologies. In so doing, the book makes a case for the development of new types of heritage that comprise AI, automated systems, biological entities, infrastructures, minerals and chemicals - all of which have their own forms of agency, intelligence and cognition. The Future of Digital Data, Heritage and Curation is essential reading for academics and students engaged in the study of museums, archives, libraries, galleries, archaeology, cultural heritage management, information management, curatorial studies and digital humanities.

Driving automation and autonomy are already upon us and the problems that were predicted twenty years ago are beginning to appear. These problems include shortfalls in expected benefits, equipment unreliability, driver skill fade, and error-inducing equipment designs. Designing Interaction and Interfaces for Automated Vehicles: User-Centred Ecological Design and Testing investigates the difficult problem of how to interface drivers with automated vehicles by offering an inclusive, human-centred design process that focusses on human variability and capability in interaction with interfaces. This book introduces a novel method that combines both systems thinking and inclusive user-centred design. It models driver interaction, provides design specifications, concept designs, and the results of studies in simulators on the test track, and in road going vehicles. This book is for designers of systems interfaces, interactions, UX, Human Factors and Ergonomics researchers and practitioners involved with systems engineering and automotive academics._ "In this book, Prof Stanton and colleagues show how Human Factors methods can be applied to the tricky problem of interfacing human drivers with vehicle automation. They have developed an approach to designing the human-automation interaction for the handovers between the driver and the vehicle. This approach has been tested in driving simulators and, most interestingly, in real vehicles on British motorways. The approach, called User-Centred Ecological Interface Design, has been validated against driver behaviour and used to support their ongoing work on vehicle automation. I highly recommend this book for anyone interested, or involved, in designing human-automation interaction in vehicles and beyond." Professor Michael A. Regan, University of NSW Sydney, AUSTRALIA

This book provides knowledge, skills, and strategies an engineer requires to effectively integrate Internet of Things (IoT) into the field of mechanical engineering. Divided into three sections named IoT Strategies, IoT Foundation topics, and IoT system development, the volume covers introduction to IoT framework, its components, advantages, challenges, and practical process for effective implementation of IoT from mechanical engineering perspective. Further, it explains IoT systems and hands-on training modules, implementation, and execution of IoT Systems. Features: Presents exclusive material on application of IoT in mechanical engineering. Combines theory and practice including relevant terminologies and hands-on. Emphasis on use of IoT to streamline operations, reduce costs, and increased profits. Focusses on development and implementation of Raspberry Pi and Arduino based IoT systems. Illustrates use IoT data to improve performance of robots, machines, and systems. This book aims at Researchers, Graduate students in Mechanical Engineering, Computer Programming, Automobile, Robotics, and Industry 4.0/automation.

Innovations in Artificial Intelligence and Human Computer Interaction in the Digital Era investigates the interaction and growing interdependency of the HCI and AI fields, which are not usually addressed in traditional approaches. Chapters explore how well AI can interact with users based on linguistics and user-centered design processes, especially with the advances of AI and the hype around many applications. Other sections investigate how HCI and AI can mutually benefit from a closer association and the how the AI community can improve their usage of HCI methods like “Wizard of Oz” prototyping and “Thinking aloud” protocols. Moreover, HCI can further augment human capabilities using new technologies. This book demonstrates how an interdisciplinary team of HCI and AI researchers can develop extraordinary applications, such as improved education systems, smart homes, smart healthcare and map Human Computer Interaction (HCI) for a multidisciplinary field that focuses on the design of computer technology and the interaction between users and computers in different domains.

With the considerable increase of AI applications, AI is being increasingly used to solve optimization problems in engineering. In the past two decades, the applications of artificial intelligence in power systems have attracted much research. This book covers the current level of applications of artificial intelligence to the optimization problems in power systems. This book serves as a textbook for graduate students in electric power system management and is also be useful for those who are interested in using artificial intelligence in power system optimization.

Belief-based Stability in Coalition Formation with Uncertainty An Intelligent Agents' Perspective discusses coalition stability. It extends the classic stability concept of the non-transferable utility core by proposing new belief-based stability criteria under uncertainty, and illustrates how the new concept can be used to analyze the stability of a new type of belief-based coalition formation game. The book is intended for graduate students, engineers, and researchers in the field of artificial intelligence and computer science. Chi-kong Chan is a laboratory manager and a visiting lecturer at the Hong Kong Polytechnic University, China; Ho-fung Leung is a Professor and the Chairman of Department of Computer Science and Engineering, The Chinese University of Hong Kong, China.

Diagnostic Biomedical Signal and Image Processing Applications: With Deep Learning Methods presents comprehensive research on both medical imaging and medical signals analysis. The book discusses classification, segmentation, detection, tracking and retrieval applications of non-invasive methods such as EEG, ECG, EMG, MRI, fMRI, CT and X-RAY, amongst others. These image and signal modalities include real challenges, which are the main themes that medical imaging and medical signal processing researchers focus on today. The book also emphasizes removing noise and specifying dataset key properties, with each chapter containing details of one of the medical imaging or medical signal modalities. Focusing on solving real medical problems using new deep learning and CNN approaches, this book will appeal to research scholars, graduate students, faculty members, R&D engineers, and biomedical engineers who want to learn how medical signals and images play an important role in the early diagnosis and treatment of diseases.

This book endeavours to highlight the untapped potential of Smart Agriculture for the innovation and expansion of the agriculture sector. The sector shall make incremental progress as it learns from associations between data over time through Artificial Intelligence, deep learning and Internet of Things applications. The farming industry and Smart agriculture develop from the stringent limits imposed by a farm's location, which in turn has a series of related effects with respect to supply chain management, food availability, biodiversity, farmers' decision-making and insurance, and environmental concerns among others. All of the above-mentioned aspects will derive substantial benefits from the implementation of a data-driven approach under the condition that the systems, tools and techniques to be used have been designed to handle the volume and variety of the data to be gathered. Contributions to this book have been solicited with the goal of uncovering the possibilities of engaging agriculture with equipped and effective profound learning algorithms. Most agricultural research centres are already adopting Internet of Things for the monitoring of a wide range of farm services, and there are significant opportunities for agriculture administration through the effective implementation of Machine Learning, Deep Learning, Big Data and IoT structures.

Behavior Trees (BTs) provide a way to structure the behavior of an artificial agent such as a robot or a non-player character in a computer game. Traditional design methods, such as finite state machines, are known to produce brittle behaviors when complexity increases, making it very hard to add features without breaking existing functionality. BTs were created to address this very problem, and enables the creation of systems that are both modular and reactive. Behavior Trees in Robotics and AI: An Introduction provides a broad introduction as well as an in-depth exploration of the topic, and is the first comprehensive book on the use of BTs. This book introduces the subject of BTs from simple topics, such as semantics and design principles, to complex topics, such as learning and task planning. For each topic, the authors provide a set of examples, ranging from simple illustrations to realistic complex behaviors, to enable the reader to successfully combine theory with practice. Starting with an introduction to BTs, the book then describes how BTs relate to, and in many cases, generalize earlier switching structures, or control architectures. These ideas are then used as a foundation for a set of efficient and easy to use design principles. The book then presents a set of important extensions and provides a set of tools for formally analyzing these extensions using a state space formulation of BTs. With the new analysis tools, the book then formalizes the descriptions of how BTs generalize earlier approaches and shows how BTs can be automatically generated using planning and learning. The final part of the book provides an extended set of tools to capture the behavior of Stochastic BTs, where the outcomes of actions are described by probabilities. These tools enable the computation of both success probabilities and time to completion. This book targets a broad audience, including both students and professionals interested in modeling complex behaviors for robots, game characters, or other AI agents. Readers can choose at which depth and pace they want to learn the subject, depending on their needs and background.

Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions presents an accessible treatment of the two main components of support vector machines (SVMs) classification problems and regression problems. The book emphasizes the close connection between optimization theory and SVMs since optimization is one of the pillars on which SVMs are built.

The authors share insight on many of their research achievements. They give a precise interpretation of statistical leaning theory for C-support vector classification. They also discuss regularized twin SVMs for binary classification problems, SVMs for solving multi-classification problems based on ordinal regression, SVMs for semi-supervised problems, and SVMs for problems with perturbations.

To improve readability, concepts, methods, and results are introduced graphically and with clear explanations. For important concepts and algorithms, such as the Crammer-Singer SVM for multi-class classification problems, the text provides geometric interpretations that are not depicted in current literature.

Enabling a sound understanding of SVMs, this book gives beginners as well as more experienced researchers and engineers the tools to solve real-world problems using SVMs.

Despite increasing scholarly attention to artificial intelligence (AI), studies at the intersection of AI and communication remain ripe for exploration, including investigations of the social, political, cultural, and ethical aspects of machine intelligence, interactions among agents, and social artifacts. This book tackles these unexplored research areas with special emphasis on conditions, components, and consequences of cognitive, attitudinal, affective, and behavioural dimensions toward communication and AI. In doing so, this book epitomizes communication, journalism and media scholarship on AI and its social, political, cultural, and ethical perspectives. Topics vary widely from interactions between humans and robots through news representation of AI and AI-based news credibility to privacy and value toward AI in the public sphere. Contributors from such countries as Brazil, Netherland, South Korea, Spain, and United States discuss important issues and challenges in AI and communication studies. The collection of chapters in the book considers implications for not only theoretical and methodological approaches, but policymakers and practitioners alike. The chapters in this book were originally published as a special issue of Communication Studies.

With the rise of advanced computerized data collection systems, monitoring devices, and instrumentation technologies, large and complex datasets accrue as an inevitable part of biomedical enterprise. The availability of these massive amounts of data offers unprecedented opportunities to advance our understanding of underlying biological and physiological functions, structures, and dynamics.

Biosignal Processing: Principles and Practices provides state-of-the-art coverage of contemporary methods in biosignal processing with an emphasis on brain signal analysis. After introducing the fundamentals, it presents emerging methods for brain signal processing, focusing on specific non-invasive imaging techniques such as electroencephalography (EEG), magnetoencephalography (MEG), magnetic resonance imaging (MRI), and functional near-infrared spectroscopy (fNIR). In addition, the book presents recent advances, reflecting the evolution of biosignal processing.

As biomedical datasets grow larger and more complicated, the development and use of signal processing methods to analyze and interpret these data has become a matter of course. This book is one step in the development of biosignal analysis and is designed to stimulate new ideas and opportunities in the development of cutting-edge computational methods for biosignal processing.

Introduces Machine Learning Techniques and Tools and Provides Guidance on How to Implement Machine Learning Into Chemical Safety and Health-related Model Development There is a growing interest in the application of machine learning algorithms in chemical safety and health-related model development, with applications in areas including property and toxicity prediction, consequence prediction, and fault detection. This book is the first to review the current status of machine learning implementation in chemical safety and health research and to provide guidance for implementing machine learning techniques and algorithms into chemical safety and health research. Written by an international team of authors and edited by renowned experts in the areas of process safety and occupational and environmental health, sample topics covered within the work include: An introduction to the fundamentals of machine learning, including regression, classification and cross-validation, and an overview of software and tools Detailed reviews of various applications in the areas of chemical safety and health, including flammability prediction, consequence prediction, asset integrity management, predictive nanotoxicity and environmental exposure assessment, and more Perspective on the possible future development of this field Machine Learning in Chemical Safety and Health serves as an essential guide on both the fundamentals and applications of machine learning for industry professionals and researchers in the fields of process safety, chemical safety, occupational and environmental health, and industrial hygiene.

Provides a clear and concise overview of how AI is used in game development Gives a brief overview of the history of AI in games Considers the different models, techniques and algorithms that are used in creating game AI

Originally published in 1992, this title reviews seven major subareas in artificial intelligence at that time: knowledge acquisition; logic programming and representation; machine learning; natural language; vision; the design of an AI programming environment; and medicine, a major application area of AI. This volume was an attempt primarily to inform fellow AI workers of recent European work in AI. It was hoped that researchers in 'sister' disciplines, such as computer science and linguistics would gain a deeper understanding of the assumptions, techniques and tools of contemporary AI.

With continuous advancements and an increase in user popularity, data mining technologies serve as an invaluable resource for researchers across a wide range of disciplines in the humanities and social sciences. In this comprehensive guide, author and research scientist Kalev Leetaru introduces the approaches, strategies, and methodologies of current data mining techniques, offering insights for new and experienced users alike.

Designed as an instructive reference to computer-based analysis approaches, each chapter of this resource explains a set of core concepts and analytical data mining strategies, along with detailed examples and steps relating to current data mining practices. Every technique is considered with regard to context, theory of operation and methodological concerns, and focuses on the capabilities and strengths relating to these technologies. In addressing critical methodologies and approaches to automated analytical techniques, this work provides an essential overview to a broad innovative field.

There are many myths and mistakes which make the topics of artificial intelligence complex and confusing. But the truth is that the foundations of AI are not rocket science. People do not need a PhD to understand how a basic neural network works. In fact, one does not even need computer skills to learn this. Cunning Machines: Your Pocket Guide to the World of Artificial Intelligence explains the main concepts: what does AI really mean, where do we find it, how do scientists try to evaluate it, what are its main limitations and what future we can expect with it? It also describes the most popular AI techniques in an easy-to-digest form: Artificial neural networks Genetic algorithms The Monte Carlo method Natural language processing Ontologies and their applications This book is for everyone. Still, it may be especially valuable to teachers who wish to enrich their classes with some interesting and popular topics, sales managers and business analysts who wish to better understand the IT world, and finally politicians and journalists who take part in debates on the latest technologies. Jedrzej Osinski earned a PhD in artificial intelligence, has worked on government grants and has published 14 scientific papers to date. He is also the co-author of two books. At the same time, he has over ten years of experience working in IT companies of different sizes, domains (the web, telecoms, banking, e-learning), organisation structures and locations (Poland, Ireland and the UK). He is also involved in various initiatives promoting AI, science and modern technologies including blog posts, invited talks and TV and radio appearances

For the Love of Cybernetics: Personal Narratives by Cyberneticians is a collection of personal accounts that offer unique insights into cybernetics via the personal journeys of nine individuals. For the authors in this collection, cybernetics is not their "area of interest"-it is how they think about what they do, and it is their practice. Ray Ison, Bruce Clarke, Frank Galuzska, Paul Pangaro, Klaus Krippendorff, Peter Tuddenham, Lucas Pawlik, Bernard Scott, and Jocelyn Chapman differ in their lineage, emphasis, and engagement with cybernetics. What they have in common is that they share the belief that cybernetics is not a tool to apply here and there, but a unifying way of seeing the world that transforms how we behave, thus increasing possibilities for positive systemic change. This book was originally published as a special issue of the journal, World Futures.

What is artificial intelligence (AI)? How does AI affect death matters and the digital beyond? How are death and dying handled in our digital age? AI for Dying and Death covers a broad range of literature, research and challenges around this topic. It explores ethical memorisation, digital legacies and bereavement, post death avatars and AI and the digital beyond. It also analyzes religious perspectives on AI for death and dying, and planning for death in a digital age.

This book presents the Proceedings of the Tenth International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, focusing on the theoretical aspects of intelligent systems research as well as extensions of theory of intelligent thinking machines.

This book will raise awareness on emerging challenges of AIempowered cyber arms used in weapon systems and stockpiled in the global cyber arms race. Based on real life events, it provides a comprehensive analysis of cyber offensive and defensive landscape, analyses the cyber arms evolution from prank malicious codes into lethal weapons of mass destruction, reveals the scale of cyber offensive conflicts, explores cyber warfare mutation, warns about cyber arms race escalation and use of Artificial Intelligence (AI) for military purposes. It provides an expert insight into the current and future malicious and destructive use of the evolved cyber arms, AI and robotics, with emphasis on cyber threats to CBRNe and critical infrastructure. The book highlights international efforts in regulating the cyber environment, reviews the best practices of the leading cyber powers and their controversial approaches, recommends responsible state behaviour. It also proposes information security and cyber defence solutions and provides definitions for selected conflicting cyber terms. The disruptive potential of cyber tools merging with military weapons is examined from the technical point of view, as well as legal, ethical, and political perspectives.

Brain-Computer Interfaces Handbook: Technological and Theoretical Advances provides a tutorial and an overview of the rich and multi-faceted world of Brain-Computer Interfaces (BCIs). The authors supply readers with a contemporary presentation of fundamentals, theories, and diverse applications of BCI, creating a valuable resource for anyone involved with the improvement of people's lives by replacing, restoring, improving, supplementing or enhancing natural output from the central nervous system. It is a useful guide for readers interested in understanding how neural bases for cognitive and sensory functions, such as seeing, hearing, and remembering, relate to real-world technologies. More precisely, this handbook details clinical, therapeutic and human-computer interfaces applications of BCI and various aspects of human cognition and behavior such as perception, affect, and action. It overviews the different methods and techniques used in acquiring and pre-processing brain signals, extracting features, and classifying users' mental states and intentions. Various theories, models, and empirical findings regarding the ways in which the human brain interfaces with external systems and environments using BCI are also explored. The handbook concludes by engaging ethical considerations, open questions, and challenges that continue to face brain-computer interface research. Features an in-depth look at the different methods and techniques used in acquiring and pre-processing brain signals, extracting features, and classifying the user's intention Covers various theories, models, and empirical findings regarding ways in which the human brain can interface with the systems or external environments Presents applications of BCI technology to understand various aspects of human cognition and behavior such as perception, affect, action, and more Includes clinical trials and individual case studies of the experimental therapeutic applications of BCI Provides human factors and human-computer interface concerns in the design, development, and evaluation of BCIs Overall, this handbook provides a synopsis of key technological and theoretical advances that are directly applicable to brain-computer interfacing technologies and can be readily understood and applied by individuals with no formal training in BCI research and development.

The book's core argument is that an artificial intelligence that could equal or exceed human intelligence-sometimes called artificial general intelligence (AGI)-is for mathematical reasons impossible. It offers two specific reasons for this claim: Human intelligence is a capability of a complex dynamic system-the human brain and central nervous system. Systems of this sort cannot be modelled mathematically in a way that allows them to operate inside a computer. In supporting their claim, the authors, Jobst Landgrebe and Barry Smith, marshal evidence from mathematics, physics, computer science, philosophy, linguistics, and biology, setting up their book around three central questions: What are the essential marks of human intelligence? What is it that researchers try to do when they attempt to achieve "artificial intelligence" (AI)? And why, after more than 50 years, are our most common interactions with AI, for example with our bank's computers, still so unsatisfactory? Landgrebe and Smith show how a widespread fear about AI's potential to bring about radical changes in the nature of human beings and in the human social order is founded on an error. There is still, as they demonstrate in a final chapter, a great deal that AI can achieve which will benefit humanity. But these benefits will be achieved without the aid of systems that are more powerful than humans, which are as impossible as AI systems that are intrinsically "evil" or able to "will" a takeover of human society.