The notion of swarm intelligence was introduced for describing decentralized and self-organized behaviors of groups of animals. Then this idea was extrapolated to design groups of robots which interact locally to cumulate a collective reaction. Some natural examples of swarms are as follows: ant colonies, bee colonies, fish schooling, bird flocking, horse herding, bacterial colonies, multinucleated giant amoebae Physarum polycephalum, etc. In all these examples, individual agents behave locally with an emergence of their common effect. An intelligent behavior of swarm individuals is explained by the following biological reactions to attractants and repellents. Attractants are biologically active things, such as food pieces or sex pheromones, which attract individuals of swarm. Repellents are biologically active things, such as predators, which repel individuals of swarm. As a consequence, attractants and repellents stimulate the directed movement of swarms towards and away from the stimulus, respectively. It is worth noting that a group of people, such as pedestrians, follow some swarm patterns of flocking or schooling. For instance, humans prefer to avoid a person considered by them as a possible predator and if a substantial part of the group in the situation of escape panic (not less than 5%) changes the direction, then the rest follows the new direction, too. Some swarm patterns are observed among human beings under the conditions of their addictive behavior such as the behavior of alcoholics or gamers. The methodological framework of studying swarm intelligence is represented by unconventional computing, robotics, and cognitive science. In this book we aim to analyze new methodologies involved in studying swarm intelligence. We are going to bring together computer scientists and cognitive scientists dealing with swarm patterns from social bacteria to human beings. This book considers different models of simulating, controlling, and predicting the swarm behavior of different species from social bacteria to humans.

This book highlights the state-of-the-art research on data usage, security, and privacy in the scenarios of the Internet of Things (IoT), along with related applications using Machine Learning and Big Data technologies to design and make efficient Internet-compatible IoT systems. ICT and Data Sciences brings together IoT and Machine Learning and provides the careful integration of both, along with many examples and case studies. It illustrates the merging of two technologies while presenting basic to high-level concepts covering different fields and domains such as the Hospitality and Tourism industry, Smart Clothing, Cyber Crime, Programming, Communications, Business Intelligence, all in the context of the Internet of Things. The book is written for researchers and practitioners, working in Information Communication Technology and Computer Science.

1. Focuses on practical design and manufacturing process 2. Contains Industrial working experiences 3. Includes innovations in development of electric machines 4. Includes read-to-implement solutions in electric machine design 5. Discusses state-of-the-art technology in modern electric machine design

This book presents the entire body of thought of Norbert Wiener (1894-1964), knowledge of which is essential if one wishes to understand and correctly interpret the age in which we live. The focus is in particular on the philosophical and sociological aspects of Wiener's thought, but these aspects are carefully framed within the context of his scientific journey. Important biographical events, including some that were previously unknown, are also highlighted, but while the book has a biographical structure, it is not only a biography. The book is divided into four chronological sections, the first two of which explore Wiener's development as a philosopher and logician and his brilliant interwar career as a mathematician, supported by his philosophical background. The third section considers his research during World War II, which drew upon his previous scientific work and reflections and led to the birth of cybernetics. Finally, the radical post-war shift in Wiener's intellectual path is considered, examining how he came to abandon computer science projects and commenced ceaseless public reflections on the new sciences and technologies of information, their social effects, and the need for responsibility in science.

This proceeding includes original and peer-reviewed research papers from the 3rd International Conference on Control, Instrumentation and Mechatronics Engineering (CIM2022). The conference is a virtual conference held on 2-3 March 2022. The topics covered latest work and finding in the area of Control Engineering, Mechatronics, Robotics and Automation, Artificial Intelligence, Manufacturing, Sensor, Measurement and Instrumentation. Moreover, the latest applications of instrumentations, control and mechatronics are provided. Therefore, this proceeding is a valuable material for researchers, academicians, university students and engineers.

Metaheuristic algorithms are considered as generic optimization tools that can solve very complex problems characterized by having very large search spaces. Metaheuristic methods reduce the effective size of the search space through the use of effective search strategies. Book Features: Provides a unified view of the most popular metaheuristic methods currently in use Includes the necessary concepts to enable readers to implement and modify already known metaheuristic methods to solve problems Covers design aspects and implementation in MATLAB (R) Contains numerous examples of problems and solutions that demonstrate the power of these methods of optimization The material has been written from a teaching perspective and, for this reason, this book is primarily intended for undergraduate and postgraduate students of artificial intelligence, metaheuristic methods, and/or evolutionary computation. The objective is to bridge the gap between metaheuristic techniques and complex optimization problems that profit from the convenient properties of metaheuristic approaches. Therefore, engineer practitioners who are not familiar with metaheuristic computation will appreciate that the techniques discussed are beyond simple theoretical tools, since they have been adapted to solve significant problems that commonly arise in such areas.

The book, Transformation in Healthcare with Emerging Technologies, presents healthcare industrial revolution based on service aggregation and virtualisation that can transform the healthcare sector with the aid of technologies such as Artificial Intelligence (AI), Internet of Things (IoT), Bigdata and Blockchain. These technologies offer fast communication between doctors and patients, protected transactions, safe data storage and analysis, immutable data records, transparent data flow service, transaction validation process, and secure data exchanges between organizations. Features: * Discusses the Integration of AI, IoT, big data and blockchain in healthcare industry * Highlights the security and privacy aspect of AI, IoT, big data and blockchain in healthcare industry * Talks about challenges and issues of AI, IoT, big data and blockchain in healthcare industry * Includes several case studies It is primarily aimed at graduates and researchers in computer science and IT who are doing collaborative research with the medical industry. Industry professionals will also find it useful.

This reference text introduces the classical probabilistic model, deep learning, and big data techniques for improving medical imaging and detecting various diseases. The text addresses a wide variety of application areas in medical imaging where deep learning techniques provide solutions with lesser human intervention and reduced time. It comprehensively covers important machine learning for signal analysis, deep learning techniques for cancer detection, diabetic cases, skin image analysis, Alzheimer's disease detection, coronary disease detection, medical image forensic, fetal anomaly detection, and plant phytology. The text will serve as a useful text for graduate students and academic researchers in the fields of electronics engineering, computer science, biomedical engineering, and electrical engineering.

This book focuses on the design of efficient & dynamic methods to allocate divisible resources under various auction mechanisms, discussing their applications in power & microgrid systems and the V2G & EV charging coordination problems in smart grids. It describes the design of dynamic methods for single-sided and double-sided auction games and presents a number of simulation cases verifying the performances of the proposed algorithms in terms of efficiency, convergence and computational complexity. Further, it explores the performances of certain auction mechanisms in a hierarchical structure and with large-scale agents, as well as the auction mechanisms for the efficient allocation of multi-type resources. Lastly, it generalizes the main and demonstrates their application in smart grids. This book is a valuable resource for researchers, engineers, and graduate students in the fields of optimization, game theory, auction mechanisms and smart grids interested in designing dynamic auction mechanisms to implement optimal allocation of divisible resources, especially electricity and other types of energy in smart grids.

The categorisation of analytical projects could help to simplify complexity reasonably and, at the same time, clarify the critical aspects of analytical initiatives. But how can this complex work be categorized? What makes it so complex? Data Analytics Initiatives: Managing Analytics for Success emphasizes that each analytics project is different. At the same time, analytics projects have many common aspects, and these features make them unique compared to other projects. Describing these commonalities helps to develop a conceptual understanding of analytical work. However, features specific to each initiative affects the entire analytics project lifecycle. Neglecting them by trying to use general approaches without tailoring them to each project can lead to failure. In addition to examining typical characteristics of the analytics project and how to categorise them, the book looks at specific types of projects, provides a high-level assessment of their characteristics from a risk perspective, and comments on the most common problems or challenges. The book also presents examples of questions that could be asked of relevant people to analyse an analytics project. These questions help to position properly the project and to find commonalities and general project challenges.

Explains concepts of Internet of Everything problems, research challenge goals, and vision in smart cities Based on the most recent innovations, and covering the major challenges in smart cities, between IoT and Big Data Examines security issues and challenges related to data-intensive advances in IoT Addresses the total information science challenges in Internet of Everything enabled technologies Covers the exploring and creating IoT environment related self-adaptive systems

This essential textbook concerns analysis and control of engineering mechanisms, which includes almost any apparatus with moving parts used in daily life, from musical instruments to robots. A particular characteristic of this book is that it presents with considerable breadth and rigor both vibrations and controls. Many contemporary texts combine both of these topics in a single, one term course. This text supports the more favorable circumstance where the material is covered in a one year sequence contains enough material for a two semester sequence, but it can also be used in a single semester course combining two topics. "Mechanical Systems: A Unified Approach to Vibrations and Controls" presents a common notation and approach to these closely related areas. Examples from the both vibrations and controls components are integrated throughout this text.

This book is for anyone who wants to gain an understanding of Blockchain technology and its potential. The book is research-oriented and covers different verticals of Blockchain technology. It discusses the characteristics and features of Blockchain, includes techniques, challenges, and future trends, along with case studies for deeper understanding. Blockchain Technology: Exploring Opportunities, Challenges, and Applications covers the core concepts related to Blockchain technology starting from scratch. The algorithms, concepts, and application areas are discussed according to current market trends and industry needs. It presents different application areas of industry and academia and discusses the characteristics and features of this technology. It also explores the challenges and future trends and provides an understanding of new opportunities. This book is for anyone at the beginner to intermediate level that wants to learn about the core concepts related to Blockchain technology.

Exploring the interrelations between generalized metric spaces, lattice-ordered groups, and order statistics, the book contains a new algebraic approach to Signal Processing Theory. It describes mathematical concepts and results important in the development, analysis, and optimization of signal processing algorithms intended for various applications. The book offers a solution of large-scale Signal Processing Theory problems of increasing both signal processing efficiency under prior uncertainty conditions and signal processing rate that is provided by multiplication-free signal processing algorithms based on lattice-ordered group operations. From simple basic relationships to computer simulation, the text covers a wide range of new mathematical techniques essential for understanding the proposed signal processing algorithms developed for solving the following problems: signal parameter and spectral estimation, signal filtering, detection, classification, and resolution; array signal processing; demultiplexing and demodulation in multi-channel communication systems and multi-station networks; wavelet analysis of 1D/ 2D signals. Along with discussing mathematical aspects, each chapter presents examples illustrating operation of signal processing algorithms developed for various applications. The book helps readers understand relations between known classic and obtained results as well as recent research trends in Signal Processing Theory and its applications, providing all necessary mathematical background concerning lattice-ordered groups to prepare readers for independent work in the marked directions including more advanced research and development.

Takes the reader on a journey across three broad developments in safety science Focuses on the individual including human error, risk, and the role of cognition in human performance Includes research in safety science that uses organizations as the basic unit of analysis Discusses questions about organizational decision making and the characteristics that dispose towards or against organizational failure Introduces perspectives based on systems science that address issues that arise out of complexity and interdependence

The strategically sound combination of edge computing and artificial intelligence (AI) results in a series of distinct innovations and disruptions enabling worldwide enterprises to visualize and realize next-generation software products, solutions and services. Businesses, individuals, and innovators are all set to embrace and experience the sophisticated capabilities of Edge AI. With the faster maturity and stability of Edge AI technologies and tools, the world is destined to have a dazzling array of edge-native, people-centric, event-driven, real-time, service-oriented, process-aware, and insights-filled services. Further on, business workloads and IT services will become competent and cognitive with state-of-the-art Edge AI infrastructure modules, AI algorithms and models, enabling frameworks, integrated platforms, accelerators, high-performance processors, etc. The Edge AI paradigm will help enterprises evolve into real-time and intelligent digital organizations. Applied Edge AI: Concepts, Platforms, and Industry Use Cases focuses on the technologies, processes, systems, and applications that are driving this evolution. It examines the implementation technologies; the products, processes, platforms, patterns, and practices; and use cases. AI-enabled chips are exclusively used in edge devices to accelerate intelligent processing at the edge. This book examines AI toolkits and platforms for facilitating edge intelligence. It also covers chips, algorithms, and tools to implement Edge AI, as well as use cases. FEATURES The opportunities and benefits of intelligent edge computing Edge architecture and infrastructure AI-enhanced analytics in an edge environment Encryption for securing information An Edge AI system programmed with Tiny Machine learning algorithms for decision making An improved edge paradigm for addressing the big data movement in IoT implementations by integrating AI and caching to the edge Ambient intelligence in healthcare services and in development of consumer electronic systems Smart manufacturing of unmanned aerial vehicles (UAVs) AI, edge computing, and blockchain in systems for environmental protection Case studies presenting the potential of leveraging AI in 5G wireless communication

Takes the reader on a journey across three broad developments in safety science Focuses on the individual including human error, risk, and the role of cognition in human performance Includes research in safety science that uses organizations as the basic unit of analysis Discusses questions about organizational decision making and the characteristics that dispose towards or against organizational failure Introduces perspectives based on systems science that address issues that arise out of complexity and interdependence

This book investigates statistical observables for anomalous and nonergodic dynamics, focusing on the dynamical behaviors of particles modelled by non-Brownian stochastic processes in the complex real-world environment. Statistical observables are widely used for anomalous and nonergodic stochastic systems, thus serving as a key to uncover their dynamics. This study explores the cutting edge of anomalous and nonergodic diffusion from the perspectives of mathematics, computer science, statistical and biological physics, and chemistry. With this interdisciplinary approach, multiple physical applications and mathematical issues are discussed, including stochastic and deterministic modelling, analyses of (stochastic) partial differential equations (PDEs), scientific computations and stochastic analyses, etc. Through regularity analysis, numerical scheme design and numerical experiments, the book also derives the governing equations for the probability density function of statistical observables, linking stochastic processes with PDEs. The book will appeal to both researchers of electrical engineering expert in the niche area of statistical observables and stochastic systems and scientists in a broad range of fields interested in anomalous diffusion, especially applied mathematicians and statistical physicists.

The concept of a system as an entity in its own right has emerged with increasing force in the past few decades in, for example, the areas of electrical and control engineering, economics, ecology, urban structures, automaton theory, operational research and industry. The more definite concept of a large-scale system is implicit in these applications, but is particularly evident in fields such as the study of communication networks, computer networks and neural networks. The Wiley-Interscience Series in Systems and Optimization has been established to serve the needs of researchers in these rapidly developing fields. It is intended for works concerned with developments in quantitative systems theory, applications of such theory in areas of interest, or associated methodology.

This is the first book-length treatment of risk-sensitive control, with many new results. The quadratic cost function of the standard LQG (linear/quadratic/Gaussian) treatment is replaced by the exponential of a quadratic, giving the so-called LEQG formulation allowing for a degree of optimism or pessimism on the part of the optimiser. The author is the first to achieve formulation and proof of risk-sensitive versions of the certainty-equivalence and separation principles. Further analysis allows one to formulate the optimization as the extremization of a path integral and to characterize the solution in terms of canonical factorization. It is thus possible to achieve the long-sought goal of an operational stochastic maximum principle, valid for a higher-order model, and in fact only evident when the models are extended to the risk-sensitive class. Additional results include deduction of compact relations between value functions and canonical factors, the exploitation of the equivalence between policy improvement and Newton—Raphson methods and the direct relation of LEQG methods to the H??? and minimum-entropy methods. This book will prove essential reading for all graduate students, researchers and practitioners who have an interest in control theory including mathematicians, engineers, economists, physicists and psychologists. 1990 Stochastic Programming Peter Kall, University of Zürich, Switzerland and Stein W. Wallace, University of Trondheim, Norway Stochastic Programming is the first textbook to provide a thorough and self-contained introduction to the subject. Carefully written to cover all necessary background material from both linear and non-linear programming, as well as probability theory, the book draws together the methods and techniques previously described in disparate sources. After introducing the terms and modelling issues when randomness is introduced in a deterministic mathematical programming model, the authors cover decision trees and dynamic programming, recourse problems, probabilistic constraints, preprocessing and network problems. Exercises are provided at the end of each chapter. Throughout, the emphasis is on the appropriate use of the techniques, rather than on the underlying mathematical proofs and theories, making the book ideal for researchers and students in mathematical programming and operations research who wish to develop their skills in stochastic programming. 1994

Recent advances in wireless technology have led to the emergence of industry standards such as WirelessHART. These strategies minimise the need for cumbersome cabling, thereby reducing costs. However, applying them involves the challenge of handling stochastic network delays, which can degrade control performance. To address this problem, commonly used simple PID could be employed. However, PID suffers from gain range limitations when used in a delayed environment. Furthermore, model-based controllers are complex and require exact models of the process and systematic system identification for implementation. Therefore, to address these issues, the book proposes control strategies that retain the simplicity of PID in terms of ease of tuning and structure, while improving on the performance of the closed-loop system with regard to stochastic network delays and mismatches. Concretely, it proposes and discusses three strategies, namely: Setpoint Weighting (SW), Filtered Predictive PI (FPPI) and Optimal Fuzzy PID. In order to optimise some of these controllers, two novel hybrid optimisation algorithms combining the dynamism of the Bacterial Foraging Algorithm (BFA) and advantages of both the Spiral Dynamic Algorithm (SDA) and the Accelerated Particle Swarm Optimisation (APSO) have been used. The strategies proposed here can also be applied in stochastic control scenarios (not necessarily wireless) characterised by uncertainties. This book will be useful to engineers and researchers in both industry and academia. In industry, it will be particularly useful to research and development efforts where PID controllers and wireless sensor networks (WSNs) involving both short and long term stochastic network delay are employed. Thus, it can be used for real-time control design in these areas. In the academic setting, the book will be useful for researchers, undergraduate and graduate students of instrumentation and control. It can also be used as reference material for teaching courses on predictive and adaptive controls and their application.

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.

Cooperative Control of Multi-Agent Systems extends optimal control and adaptive control design methods to multi-agent systems on communication graphs. It develops Riccati design techniques for general linear dynamics for cooperative state feedback design, cooperative observer design, and cooperative dynamic output feedback design. Both continuous-time and discrete-time dynamical multi-agent systems are treated. Optimal cooperative control is introduced and neural adaptive design techniques for multi-agent nonlinear systems with unknown dynamics, which are rarely treated in literature are developed. Results spanning systems with first-, second- and on up to general high-order nonlinear dynamics are presented. Each control methodology proposed is developed by rigorous proofs. All algorithms are justified by simulation examples. The text is self-contained and will serve as an excellent comprehensive source of information for researchers and graduate students working with multi-agent systems.

The purpose of Numerical Linear Algebra in Signals, Systems and Control is to present an interdisciplinary book, blending linear and numerical linear algebra with three major areas of electrical engineering: Signal and Image Processing, and Control Systems and Circuit Theory. Numerical Linear Algebra in Signals, Systems and Control will contain articles, both the state-of-the-art surveys and technical papers, on theory, computations, and applications addressing significant new developments in these areas. The goal of the volume is to provide authoritative and accessible accounts of the fast-paced developments in computational mathematics, scientific computing, and computational engineering methods, applications, and algorithms. The state-of-the-art surveys will benefit, in particular, beginning researchers, graduate students, and those contemplating to start a new direction of research in these areas. A more general goal is to foster effective communications and exchange of information between various scientific and engineering communities with mutual interests in concepts, computations, and workable, reliable practices.

This graduate-level textbook elucidates low-risk and fail-safe systems in mathematical detail. It addresses, in particular, problems where mission-critical performance is paramount, such as in aircraft, missiles, nuclear reactors and weapons, submarines, and many other types of systems where "failure" can result in overwhelming loss of life and property. The book is divided into four parts: Fundamentals, Electronics, Software, and Dangerous Goods. The first part on Fundamentals addresses general concepts of system safety engineering that are applicable to any type of system. The second part, Electronics, addresses the detection and correction of electronic hazards. In particular, the Bent Pin Problem, Sneak Circuit Problem, and related electrical problems are discussed with mathematical precision. The third part on Software addresses predicting software failure rates as well as detecting and correcting deep software logical flaws (called defects). The fourth part on Dangerous Goods presents solutions to three typical industrial chemical problems faced by the system safety engineer during the design, storage, and disposal phases of a dangerous goods' life cycle.