This book examines the nature of emergence in context of man-made (i.e. engineered) systems, in general, and system of systems engineering applications, specifically. It investigates emergence to interrogate or explore the domain space from a modeling and simulation perspective to facilitate understanding, detection, classification, prediction, control, and visualization of the phenomenon. Written by leading international experts, the text is the first to address emergence from an engineering perspective. "System engineering has a long and proud tradition of establishing the integrative view of systems. The field, however, has not always embraced and assimilated well the lessons and implications from research on complex adaptive systems. As the editors' note, there have been no texts on Engineering Emergence: Principles and Applications. It is therefore especially useful to have this new, edited book that pulls together so many of the key elements, ranging from the theoretical to the practical, and tapping into advances in methods, tools, and ways to study system complexity. Drs. Rainey and Jamshidi are to be congratulated both for their vision of the book and their success in recruiting contributors with so much to say. Most notable, however, is that this is a book with engineering at its core. It uses modeling and simulation as the language in which to express principles and insights in ways that include tight thinking and rigor despite dealing with notably untidy and often surprising phenomena." - Paul K. Davis, RAND and Frederick S. Pardee RAND Graduate School The first chapter is an introduction and overview to the text. The book provides 12 chapters that have a theoretical foundation for this subject. Includes 7 specific example chapters of how various modeling and simulation paradigms/techniques can be used to investigate emergence in an engineering context to facilitate understanding, detection, classification, prediction, control and visualization of emergent behavior. The final chapter offers lessons learned and the proposed way-ahead for this discipline.

Sparse models are particularly useful in scientific applications, such as biomarker discovery in genetic or neuroimaging data, where the interpretability of a predictive model is essential. Sparsity can also dramatically improve the cost efficiency of signal processing. Sparse Modeling: Theory, Algorithms, and Applications provides an introduction to the growing field of sparse modeling, including application examples, problem formulations that yield sparse solutions, algorithms for finding such solutions, and recent theoretical results on sparse recovery. The book gets you up to speed on the latest sparsity-related developments and will motivate you to continue learning about the field. The authors first present motivating examples and a high-level survey of key recent developments in sparse modeling. The book then describes optimization problems involving commonly used sparsity-enforcing tools, presents essential theoretical results, and discusses several state-of-the-art algorithms for finding sparse solutions. The authors go on to address a variety of sparse recovery problems that extend the basic formulation to more sophisticated forms of structured sparsity and to different loss functions. They also examine a particular class of sparse graphical models and cover dictionary learning and sparse matrix factorizations.

Human Action Recognition is a challenging area presently. The vigor of research effort directed towards this domain is self indicative of this. With the ever-increasing involvement of Computational Intelligence in our day to day applications, the necessity of human activity recognition has been able to make its presence felt to the concerned research community. The primary drive of such an effort is to equip the computing system capable of recognizing and interpreting human activities from posture, pose, gesture, facial expression etc. The intent of human activity recognition is a formidable component of cognitive science in which researchers are actively engaged of late. Features: A systematic overview of the state-of-the-art in computational intelligence techniques for human action recognition. Emphasized on different intelligent techniques to recognize different human actions. Discussed about the automation techniques to handle human action recognition. Recent research results and some pointers to future advancements in this arena. In the present endeavour the editors intend to come out with a compilation that reflects the concerns of relevant research community. The readers would be able to come across some of the latest findings of active researchers of the concerned field. It is anticipated that this treatise shall be useful to the readership encompassing students at undergraduate and postgraduate level, researchers active as well as aspiring, not to speak of the senior researchers.

Computational Intelligence Techniques and Their Applications to Software Engineering Problems focuses on computational intelligence approaches as applicable in varied areas of software engineering such as software requirement prioritization, cost estimation, reliability assessment, defect prediction, maintainability and quality prediction, size estimation, vulnerability prediction, test case selection and prioritization, and much more. The concepts of expert systems, case-based reasoning, fuzzy logic, genetic algorithms, swarm computing, and rough sets are introduced with their applications in software engineering. The field of knowledge discovery is explored using neural networks and data mining techniques by determining the underlying and hidden patterns in software data sets. Aimed at graduate students and researchers in computer science engineering, software engineering, information technology, this book: Covers various aspects of in-depth solutions of software engineering problems using computational intelligence techniques Discusses the latest evolutionary approaches to preliminary theory of different solve optimization problems under software engineering domain Covers heuristic as well as meta-heuristic algorithms designed to provide better and optimized solutions Illustrates applications including software requirement prioritization, software cost estimation, reliability assessment, software defect prediction, and more Highlights swarm intelligence-based optimization solutions for software testing and reliability problems

Offers fundamental theories and practical and more sophisticated applications of Evolutionary Computation in varied industries Provides insight into various platforms, paradigms, techniques, and tools used in Evolutionary Computation for diverse fields Presents an understanding related to optimization, performance tuning, virtualization, deployment models, and their applications Covers a variety of applications for social and essential models and is based on real life examples Useful for decision making based on optimized data through Evolutionary Computation in multi-dimensions

Risk is related to the magnitude and uncertainty of an output (consequence or outcome); outputs take on different identities in different disciplines and situations. Risk is peculiar to each stakeholder and the measurement scale for risk depends on the stakeholder's value system. Risk management provides a way of addressing the issues associated with the magnitude and uncertainty of outputs. This book provides a distinctively rational treatment of risk and risk management, based on a systems approach. The book's treatment applies to all disciplines and sets out the principles of risk and risk management as well as looking at a range of applications and more specialist tools and approaches. The book: Develops a risk framework through a systems approach Offers a challenging and fresh approach for infrastructure engineering, construction and project management in general The book will suit students and practitioners alike.

Provides detailed introduction to Internet of Healthcare Things (IoHT) and its applications Reviews underlying sensor and hardware technologies Includes recent advances in the IoHT such as remote healthcare monitoring and wearable devices Explores applications of Data Analytics/Data Mining in IoHT, including data management and data governance Focusses on regulatory and compliance issues in IoHT

Sport is an integral part of society, playing a key role in human health and well-being, and cultural, political and economic development. As sport is becoming more complex, competitive, diverse, and increasingly reliant on technology, HFE theories, methods, and principles are progressively being applied to help understand and optimize sports systems. Human Factors and Ergonomics in Sport: Applications and Future Directions showcases the latest in sports HFE research and practice. Including contributions from both HFE and sports science researchers, it provides a collection of state-of-the-art studies, reviews and commentaries covering a diverse set of sports and sporting issues. "This book is an excellent resource for all academics and students in general. It provides updated theoretical foundations and applications that conceive a world where everything is connected and embedded in technology that allows us to capture, process and visualise actions and interactions, also at transdisciplinary levels." Professor Jaime Sampaio, Head of the Research Center in Sports Sciences, Health and Human Development (CIDESD), University of Tras-os-Montes e Alto Douro, Portugal "With the changing nature of work comes an ever-greater focus on leisure. Sport is a major dimension of this crucial form of human activity. Now comes Salmon and his colleagues who have assembled a panoply of world leaders who each provide their own individual perspectives on this intriguing world. Their emphasis on the human factors and ergonomics of these activities brings us new and exciting insights. A great read for the specialist and generalist alike." Professor Peter Hancock, Pegasus Professor, Provost Distinguished Research Professor and Trustee Chair, University of Central Florida, USA. "Finally, the complexity of sports and health is being considered in full. This book challenges contemporary thinking toward the prevention of injuries in sports, and provides tangible solutions to help our field into a new decade." Professor Evert Verhagen, Amsterdam Collaboration on Health and Safety in Sports & Department of Public and Occupational Health, VU University Medical Center

Just Enough R! An Interactive Approach to Machine Learning and Analytics presents just enough of the R language, machine learning algorithms, statistical methodology, and analytics for the reader to learn how to find interesting structure in data. The approach might be called "seeing then doing" as it first gives step-by-step explanations using simple, understandable examples of how the various machine learning algorithms work independent of any programming language. This is followed by detailed scripts written in R that apply the algorithms to solve nontrivial problems with real data. The script code is provided, allowing the reader to execute the scripts as they study the explanations given in the text. Features Gets you quickly using R as a problem-solving tool Uses RStudio's integrated development environment Shows how to interface R with SQLite Includes examples using R's Rattle graphical user interface Requires no prior knowledge of R, machine learning, or computer programming Offers over 50 scripts written in R, including several problem-solving templates that, with slight modification, can be used again and again Covers the most popular machine learning techniques, including ensemble-based methods and logistic regression Includes end-of-chapter exercises, many of which can be solved by modifying existing scripts Includes datasets from several areas, including business, health and medicine, and science About the Author Richard J. Roiger is a professor emeritus at Minnesota State University, Mankato, where he taught and performed research in the Computer and Information Science Department for over 30 years.

This book presents practical guidelines for university research and administration. It uses a project management framework within a systems perspective to provide strategies for planning, scheduling, allocating resources, tracking, reporting, and controlling university-based research projects and programs. Project Management for Scholarly Researchers: Systems, Innovation, and Technologies covers the technical and human aspects of research management. It discusses federal requirements and compliance issues, in addition to offering advice on proper research lab management and faculty mentoring. It explains the hierarchy of needs of researchers to help readers identify their own needs for their research enterprises. This book provides rigorous treatment and guidance for all engineering fields and related business disciplines, as well as all management and humanities fields.

Examines the general nature of systems, systems modeling, and control Covers both linear graphing and bond graphing approaches to systems design and analysis Integrates computational software, using MATLAB and Simulink primarily Includes analytical examples and numerical examples, simulations, case studies, and chapter problems Presents key issues summaries in point form within chapters, for easy reference and recollection

This Newnes manual provides a practical introduction to the standard methods and techniques of assembly and wiring of electrical and electromechanical control panels and equipment. Electricians and technicians will find this a useful reference during training and a helpful memory aid at work. This is a highly illustrated guide, designed for ready use. The contents are presented in pictures and checklists. Each page has a series of 'how-to' instructions and illustrations. In this way the subject is covered in a manner which is easy to follow. Each step adds up to a comprehensive course in control panel wiring.This new edition includes extra underlying theory to help the technician plus application notes and limitations of use. Simple programmable logic controllers (PLCs) are covered, as well as new information about EMC/EMI regulations and their impact.

As today's organizations are capturing exponentially larger amounts of data than ever, now is the time for organizations to rethink how they digest that data. Through advanced algorithms and analytics techniques, organizations can harness this data, discover hidden patterns, and use the newly acquired knowledge to achieve competitive advantages. Presenting the contributions of leading experts in their respective fields, Big Data: Algorithms, Analytics, and Applications bridges the gap between the vastness of Big Data and the appropriate computational methods for scientific and social discovery. It covers fundamental issues about Big Data, including efficient algorithmic methods to process data, better analytical strategies to digest data, and representative applications in diverse fields, such as medicine, science, and engineering. The book is organized into five main sections: Big Data Management-considers the research issues related to the management of Big Data, including indexing and scalability aspects Big Data Processing-addresses the problem of processing Big Data across a wide range of resource-intensive computational settings Big Data Stream Techniques and Algorithms-explores research issues regarding the management and mining of Big Data in streaming environments Big Data Privacy-focuses on models, techniques, and algorithms for preserving Big Data privacy Big Data Applications-illustrates practical applications of Big Data across several domains, including finance, multimedia tools, biometrics, and satellite Big Data processing Overall, the book reports on state-of-the-art studies and achievements in algorithms, analytics, and applications of Big Data. It provides readers with the basis for further efforts in this challenging scientific field that will play a leading role in next-generation database, data warehousing, data mining, and cloud computing research. It also explores related applications in diverse sectors, covering technologies for media/data communication, elastic media/data storage, cross-network media/data fusion, and SaaS.

This book discusses Change Management Impact Analysis and how this method is used to analysis the risks and benefits of a change management initiative when it pertains to obtaining critical insight into how the change management program budget should be allotted. The process also offers useful indicators for what areas within the system should be monitored during the change management process. This book presents theoretical analysis of practical implications and surveys, along with analysis. It covers the functions aimed at identifying various stakeholders associated with the software such as requirement component, design component, and class component. The book talks about the interrelationship between the change and the effects on the rest of the system and dives deeper to include the critical role that the analysis places on the existing multiple functions such as estimating the development costs, the project overhead costs, cost for the modification of the system, and system strength or detecting errors in the system during the process. Case studies are also included to help researchers and practitioners to absorb the material presented. This book is useful to graduate students, researchers, academicians, institutions, and professionals that interested in exploring the areas of Impact Analysis.

Multi-Criteria Decision-Making (MCDM) includes methods and tools for modeling and solving complex problems. MCDM has become popular in the production and service sectors to improve the quality of service, reduce costs, and make people more prosperous. This book illustrates applications through case studies focused on disaster management. With a presentation of both Multi-Attribute Decision-Making (MADM) and Multi-Objective Decision-Making (MODM) models, this is the first book to merge these methods and tools with disaster management. This book raises awareness for society and decision-makers on how to measure readiness and what necessary preventive measures need to be taken. It offers models and case studies that can be easily adapted to solve complex problems and find solutions in other fields. Multi-Criteria Decision Analysis: Case Studies in Disaster Management will offer new insights to researchers working in the areas of industrial engineering, systems engineering, healthcare systems, operations research, mathematics, business, computer science, and disaster management, and, hopefully, the book will also stimulate further work in MCDM.

Control systems are pervasive in our lives. Our homes have environmental controls. The appliances we use, such as the washing machine, microwave, etc. carry embedded controllers in them. We fly in airplanes and drive automobiles that extensively use control systems. The industrial plants that produce consumer goods run on process control systems. The recent drive toward automation has increased our reliance on control systems technology. This book discusses control systems design from a model-based perspective for dynamic system models of single-input single-output type. The emphasis in this book is on understanding and applying the techniques that enable the design of effective control systems in multiple engineering disciplines. The book covers both time-domain and the frequency-domain design methods, as well as controller design for both continuous-time and discrete-time systems. MATLAB (c) and its Control Systems Toolbox are extensively used for design. Technical topics discussed in the book include: Mathematical models of physical systems Analysis of transfer function and state variable models Control systems design objectives Control system design with root locus Control system design in the state-space Control system design of sampled-data systems Compensator design with frequency response methods

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.

A microfluidic biochip is an engineered fluidic device that controls the flow of analytes, thereby enabling a variety of useful applications. According to recent studies, the fields that are best set to benefit from the microfluidics technology, also known as lab-on-chip technology, include forensic identification, clinical chemistry, point-of-care (PoC) diagnostics, and drug discovery. The growth in such fields has significantly amplified the impact of microfluidics technology, whose market value is forecast to grow from $4 billion in 2017 to $13.2 billion by 2023. The rapid evolution of lab-on-chip technologies opens up opportunities for new biological or chemical science areas that can be directly facilitated by sensor-based microfluidics control. For example, the digital microfluidics-based ePlex system from GenMarkDx enables automated disease diagnosis and can bring syndromic testing near patients everywhere. However, as the applications of molecular biology grow, the adoption of microfluidics in many applications has not grown at the same pace, despite the concerted effort of microfluidic systems engineers. Recent studies suggest that state-of-the-art design techniques for microfluidics have two major drawbacks that need to be addressed appropriately: (1) current lab-on-chip systems were only optimized as auxiliary components and are only suitable for sample-limited analyses; therefore, their capabilities may not cope with the requirements of contemporary molecular biology applications; (2) the integrity of these automated lab-on-chip systems and their biochemical operations are still an open question since no protection schemes were developed against adversarial contamination or result-manipulation attacks. Optimization of Trustworthy Biomolecular Quantitative Analysis Using Cyber-Physical Microfluidic Platforms provides solutions to these challenges by introducing a new design flow based on the realistic modeling of contemporary molecular biology protocols. It also presents a microfluidic security flow that provides a high-level of confidence in the integrity of such protocols. In summary, this book creates a new research field as it bridges the technical skills gap between microfluidic systems and molecular biology protocols but it is viewed from the perspective of an electronic/systems engineer.

As featured on CNN, Forbes and Inc - BookAuthority identifies and rates the best books in the world, based on recommendations by the world's most successful business leaders and experts. Winning the spot of #19 out of 26 on the 2020 Bookauthority Best New Industrial Management Books of All Time. Winning the spot of #3 out of 8 on the 2021 Bookauthority Best New Industrial Management Books to Read in 2021. Winning the spot of #5 out of 11 on the 2021 Bookauthority Best New Product Design Books to Read in 2021. 2020 Taylor & Francis Award Winner for Outstanding Professional Book! Product Lifecycle Management (PLM): A Digital Journey Using Industrial Internet of Things (IIot) provides a summary of the essential topics of Product Lifecycle Management (PLM) and the Industrial Internet of Things (IIoT) in the era of Industry 4.0. The book discusses emerging technologies, their contribution towards enhancing product design, development, and manufacturing. It also presents the integration of PLM, Enterprise Resource Planning (ERP), and Manufacturing Execution System (MES) along with IIoT as well the integration of mechanical, electronic components, embedded systems, firmware and software focusing on smart design, development, and manufacturing in the digital transformation journey. The book provides a high-level overview of how the smart product development through smart manufacturing materializes within the smart ecosystem. Manufacturing professionals, designers, mechanical, electrical, electronics, instrumentation and industrial engineers, information and communication technology consultants and those working in production planning, process control, and operations will find this book invaluable.

The author has spent approximately 50 years in the field of systems engineering. This Focus book provides a "looking back" at his 50-year run and the lessons he learned and would like to share with other engineers, so they can use these lessons in their day-to-day work in systems engineering and related fields. The book is written from a systems engineering perspective. It offers 50 lessons learned working for a variety of different companies, which can be used across many other engineering fields. The book will be of interested to students and engineers across many fields, as well as students and engineers working in business and management fields.

Medical image analysis using advanced fuzzy set theoretic techniques is an exciting and dynamic branch of image processing. Since the introduction of fuzzy set theory, there has been an explosion of interest in advanced fuzzy set theories-such as intuitionistic fuzzy and Type II fuzzy set-that represent uncertainty in a better way. Medical Image Processing: Advanced Fuzzy Set Theoretic Techniques deals with the application of intuitionistic fuzzy and Type II fuzzy set theories for medical image analysis. Designed for graduate and doctorate students, this higher-level text: Provides a brief introduction to advanced fuzzy set theory, fuzzy/intuitionistic fuzzy aggregation operators, and distance/similarity measures Covers medical image enhancement using advanced fuzzy sets, including MATLAB (R)-based examples to increase contrast of the images Describes intuitionistic fuzzy and Type II fuzzy thresholding techniques that separate different regions/leukocyte types/abnormal lesions Demonstrates the clustering of unwanted lesions/regions even in the presence of noise by applying intuitionistic fuzzy clustering Highlights the edges of poorly illuminated images and uses intuitionistic fuzzy edge detection to find the edges of different regions Defines fuzzy mathematical morphology and explores its application using the Lukasiewicz operator, t-norms, and t-conorms Medical Image Processing: Advanced Fuzzy Set Theoretic Techniques is useful not only for students, but also for teachers, engineers, scientists, and those interested in the field of medical image analysis. A basic knowledge of fuzzy set is required, along with a solid understanding of mathematics and image processing.

A social robot is a robot that interacts and communicates with humans or other autonomous physical agents by following social behaviors and rules attached to its role. We seem to accept the use of robots that perform dull, dirty, and dangerous jobs. But how far do we want to go with the automation of care for children and the elderly, or the killing of terrorists? Would we be setting humanity aside if we accepted such automation? Just Ordinary Robots: Automation from Love to War provides a socially involved, yet sober, view into the new age of robots. It supplies a cutting-edge look at robot technologies, including what these technologies are capable of and the ethical and regulatory questions they raise. The book surveys the various types of social robots and examines their social significance in homes, health care, traffic, the police, and the military. Considering the technical characteristics and societal expectations of robots in these areas, it explores what is possible right now in terms of robot technologies. It also looks into the social, ethical, and regulatory issues future robot technologies will create. The text provides authoritative insights into the social significance of robots for the medium and long term. Illustrating the political, administrative, and regulatory consequences related to each area, it highlights key points that need to be publicly discussed or put on the agenda by today's politicians and policy makers.

Text Mining and Visualization: Case Studies Using Open-Source Tools provides an introduction to text mining using some of the most popular and powerful open-source tools: KNIME, RapidMiner, Weka, R, and Python. The contributors-all highly experienced with text mining and open-source software-explain how text data are gathered and processed from a wide variety of sources, including books, server access logs, websites, social media sites, and message boards. Each chapter presents a case study that you can follow as part of a step-by-step, reproducible example. You can also easily apply and extend the techniques to other problems. All the examples are available on a supplementary website. The book shows you how to exploit your text data, offering successful application examples and blueprints for you to tackle your text mining tasks and benefit from open and freely available tools. It gets you up to date on the latest and most powerful tools, the data mining process, and specific text mining activities.

If the intelligence of artificial systems were to surpass that of humans, humanity would face significant risks. The time has come to consider these issues, and this consideration must include progress in artificial intelligence (AI) as much as insights from AI theory. Featuring contributions from leading experts and thinkers in artificial intelligence, Risks of Artificial Intelligence is the first volume of collected chapters dedicated to examining the risks of AI. The book evaluates predictions of the future of AI, proposes ways to ensure that AI systems will be beneficial to humans, and then critically evaluates such proposals. The book covers the latest research on the risks and future impacts of AI. It starts with an introduction to the problem of risk and the future of artificial intelligence, followed by a discussion (Armstrong/Sokala/OhEigeartaigh) on how predictions of its future have fared to date. Omohundro makes the point that even an innocuous artificial agent can easily turn into a serious threat for humans. T. Goertzel explains how to succeed in the design of artificial agents. But will these be a threat for humanity, or a useful tool? Ways to assure beneficial outcomes through 'machine ethics' and 'utility functions' are discussed by Brundage and Yampolskiy. B. Goertzel and Potapov/Rodionov propose 'learning' and 'empathy' as paths towards safer AI while Kornai explains how the impact of AI may be bounded. Sandberg explains the implications of human-like AI via the technique of brain emulation. Dewey discusses strategies to deal with the 'fast takeoff' of artificial intelligence and, finally, Bishop explains why there is no need to worry because computers will remain in a state of 'artificial stupidity'. Sharing insights from leading thinkers in artificial intelligence, this book provides you with an expert-level perspective of what is on the horizon for AI, whether it will be a threat for humanity, and how we might counteract this threat.

Automatic biometrics recognition techniques are increasingly important in corporate and public security systems and have increased in methods due to rapid field development. This book discusses classic behavioral biometrics as well as collects the latest advances in techniques, theoretical approaches, and dynamic applications. This future-looking book is an important reference tool for researchers, practitioners, academicians, and technologists. While there are existing books that focus on physiological biometrics or algorithmic approaches deployed in biometrics, this book addresses a gap in the existing literature for a text that is solely dedicated to the topic of behavioral biometrics.