From the Foreword: "While large-scale machine learning and data mining have greatly impacted a range of commercial applications, their use in the field of Earth sciences is still in the early stages. This book, edited by Ashok Srivastava, Ramakrishna Nemani, and Karsten Steinhaeuser, serves as an outstanding resource for anyone interested in the opportunities and challenges for the machine learning community in analyzing these data sets to answer questions of urgent societal interest...I hope that this book will inspire more computer scientists to focus on environmental applications, and Earth scientists to seek collaborations with researchers in machine learning and data mining to advance the frontiers in Earth sciences." --Vipin Kumar, University of Minnesota Large-Scale Machine Learning in the Earth Sciences provides researchers and practitioners with a broad overview of some of the key challenges in the intersection of Earth science, computer science, statistics, and related fields. It explores a wide range of topics and provides a compilation of recent research in the application of machine learning in the field of Earth Science. Making predictions based on observational data is a theme of the book, and the book includes chapters on the use of network science to understand and discover teleconnections in extreme climate and weather events, as well as using structured estimation in high dimensions. The use of ensemble machine learning models to combine predictions of global climate models using information from spatial and temporal patterns is also explored. The second part of the book features a discussion on statistical downscaling in climate with state-of-the-art scalable machine learning, as well as an overview of methods to understand and predict the proliferation of biological species due to changes in environmental conditions. The problem of using large-scale machine learning to study the formation of tornadoes is also explored in depth. The last part of the book covers the use of deep learning algorithms to classify images that have very high resolution, as well as the unmixing of spectral signals in remote sensing images of land cover. The authors also apply long-tail distributions to geoscience resources, in the final chapter of the book.

This book will change the way you think about problems. It focuses on creating solutions to all sorts of complex problems by taking a practical, problem-solving approach. It discusses not only what needs to be done, but it also provides guidance and examples of how to do it. The book applies systems thinking to systems engineering and introduces several innovative concepts such as direct and indirect stakeholders and the Nine-System Model, which provides the context for the activities performed in the project, along with a framework for successful stakeholder management. A list of the figures and tables in this book is available at https://www.crcpress.com/9781138387935. FEATURES * Treats systems engineering as a problem-solving methodology * Describes what tools systems engineers use and how they use them in each state of the system lifecycle * Discusses the perennial problem of poor requirements, defines the grammar and structure of a requirement, and provides a template for a good imperative construction statement and the requirements for writing requirements * Provides examples of bad and questionable requirements and explains the reasons why they are bad and questionable * Introduces new concepts such as direct and indirect stakeholders and the Shmemp! * Includes the Nine-System Model and other unique tools for systems engineering

This book provides an in-depth overview of artificial intelligence and deep learning approaches with case studies to solve problems associated with biometric security such as authentication, indexing, template protection, spoofing attack detection, ROI detection, gender classification etc. This text highlights a showcase of cutting-edge research on the use of convolution neural networks, autoencoders, recurrent convolutional neural networks in face, hand, iris, gait, fingerprint, vein, and medical biometric traits. It also provides a step-by-step guide to understanding deep learning concepts for biometrics authentication approaches and presents an analysis of biometric images under various environmental conditions. This book is sure to catch the attention of scholars, researchers, practitioners, and technology aspirants who are willing to research in the field of AI and biometric security.

Recent developments in reliability engineering has become the most challenging and demanding area of research. Modeling and Simulation, along with System Reliability Engineering has become a greater issue because of high-tech industrial processes, using more complex systems today. This book gives the latest research advances in the field of modeling and simulation, based on analysis in engineering sciences. Features Focuses on the latest research in modeling and simulation based analysis in reliability engineering. Covers performance evaluation of complex engineering systems Identifies and fills the gaps of knowledge pertaining to engineering applications Provides insights on an international and transnational scale Modeling and Simulation Based Analysis in Reliability Engineering aims at providing a reference for applications of mathematics in engineering, offering a theoretical sound background with adequate case studies, and will be of interest to researchers, practitioners, and academics.

Sustainability is one of the most embraced topics nowadays. Everybody is affected by issues of sustainability. Every organization needs to pay attention to these issues. As long as more people and more organizations are engaging in business and industry activities, there will always be a need for sustainability. This book presents tools such as lean six sigma to help sustain results by using process focused decisions. This book covers tools and techniques of industrial engineering to promote sustainability. It discusses a systems approach, the evolution of new products, development of sustainability alliances, and highlights the role of sustainability in advancing organizational goals. The book also addresses sustainability as a coordinated project using a project management approach. It includes the interface of humans and technology and presents an integration of analytics. The book is ideal for all engineering, business, and management fields.

With the increasing demand of robots for industrial and domestic use, it becomes indispensable to ensure their safety, security, and reliability. Safety, Security and Reliability of Robotic Systems: Algorithms, Applications, and Technologies provides a broad and comprehensive coverage of the evolution of robotic systems, as well as industrial statistics and future forecasts. First, it analyzes the safety-related parameters of these systems. Then, it covers security attacks and related countermeasures, and how to establish reliability in these systems. The later sections of the book then discuss various applications of these systems in modern industrial and domestic settings. By the end of this book, you will be familiarized with the theoretical frameworks, algorithms, applications, technologies, and empirical research findings on the safety, security, and reliability of robotic systems, while the book's modular structure and comprehensive material will keep you interested and involved throughout. This book is an essential resource for students, professionals, and entrepreneurs who wish to understand the safe, secure, and reliable use of robotics in real-world applications. It is edited by two specialists in the field, with chapter contributions from an array of experts on robotics systems and applications.

Enabling Technologies for Next Generation Wireless Communications provides up-to-date information on emerging trends in wireless systems, their enabling technologies and their evolving application paradigms. This book includes the latest trends and developments toward next generation wireless communications. It highlights the requirements of next generation wireless systems, limitations of existing technologies in delivering those requirements and the need to develop radical new technologies. It focuses on bringing together information on various technological developments that are enablers vital to fulfilling the requirements of future wireless communication systems and their applications. Topics discussed include spectrum issues, network planning, signal processing, transmitter, receiver, antenna technologies, channel coding, security and application of machine learning and deep learning for wireless communication systems. The book also provides information on enabling business models for future wireless systems. This book is useful as a resource for researchers and practitioners worldwide, including industry practitioners, technologists, policy decision-makers, academicians, and graduate students.

This book applies the concepts and methods of psychoanalysis to the study of artificial intelligence (AI) and human-AI interaction. It develops a new, more fruitful approach for applying psychoanalysis to AI and machine behavior. It appeals to a broad range of scholars: philosophers working on psychoanalysis, technology, AI ethics, and cognitive sciences, psychoanalysts, psychologists, and computer scientists. The book is divided into four parts. The first part (Chapter 1) analyzes the concept of "machine behavior." The second part (Chapter 2) develops a reinterpretation of some fundamental Freudian and Lacanian concepts through Bruno Latour's actor-network theory. The third part (Chapters 3 and 4) focuses on the nature and structure of the algorithmic unconscious. The author claims that the unconscious roots of AI lie in a form of projective identification, i.e., an emotional and imaginative exchange between humans and machines. In the fourth part of the book (Chapter 5), the author advances the thesis that neuropsychoanalysis and the affective neurosciences can provide a new paradigm for research on artificial general intelligence. The Algorithmic Unconscious explores a completely new approach to AI, which can also be defined as a form of "therapy." Analyzing the projective identification processes that take place in groups of professional programmers and designers, as well as the "hidden" features of AI (errors, noise information, biases, etc.), represents an important tool to enable a healthy and positive relationship between humans and AI. Psychoanalysis is used as a critical space for reflection, innovation, and progress.

Innovative examination of augmentation technologies in terms of technical, social, and ethical considerations Usable as a supplemental text for a variety of courses, and also of interest to researchers and professionals in fields including: technical communication, digital communication, UX design, information technology, informatics, human factors, artificial intelligence, ethics, philosophy of technology, and sociology of technology First major work to combine technological, ethical, social, and rhetorical perspectives on human augmentation Additional cases and research material available at the authors' Fabric of Digital Life research database at https://fabricofdigitallife.com/

Human-Robot Interaction: Safety, Standardization, and Benchmarking provides a comprehensive introduction to the new scenarios emerging where humans and robots interact in various environments and applications on a daily basis. The focus is on the current status and foreseeable implications of robot safety, approaching these issues from the standardization and benchmarking perspectives. Featuring contributions from leading experts, the book presents state-of-the-art research, and includes real-world applications and use cases. It explores the key leading sectors-robotics, service robotics, and medical robotics-and elaborates on the safety approaches that are being developed for effective human-robot interaction, including physical robot-human contacts, collaboration in task execution, workspace sharing, human-aware motion planning, and exploring the landscape of relevant standards and guidelines. Features Presenting a comprehensive introduction to human-robot interaction in a number of domains, including industrial robotics, medical robotics, and service robotics Focusing on robot safety standards and benchmarking Providing insight into current developments in international standards Featuring contributions from leading experts, actively pursuing new robot development

This book serves three basic purposes: (1) a tutorial-type reference for complex systems engineering (CSE) concepts and associated terminology, (2) a recommendation of a proposed methodology showing how the evolving practice of CSE can lead to a more unified theory, and (3) a complex systems (CSs) initiative for organizations to invest some of their resources toward helping to make the world a better place. A wide variety of technical practitioners-e.g., developers of new or improved systems (particularly systems engineers), program and project managers, associated staff/workers, funders and overseers, government executives, military officers, systems acquisition personnel, contract specialists, owners of large and small businesses, professional society members, and CS researchers-may be interested in further exploring these topics. Readers will learn more about CS characteristics and behaviors and CSE principles and will therefore be able to focus on techniques that will better serve them in their everyday work environments in dealing with complexity. The fundamental observation is that many systems inherently involve a deeper complexity because stakeholders are engaged in the enterprise. This means that such CSs are more difficult to invent, create, or improve upon because no one can be in total control since people cannot be completely controlled. Therefore, one needs to concentrate on trying to influence progress, then wait a suitable amount of time to see what happens, iterating as necessary. With just three chapters in this book, it seems to make sense to provide a tutorial introduction that readers can peruse only as necessary, considering their background and understanding, then a chapter laying out the suggested artifacts and methodology, followed by a chapter emphasizing worthwhile areas of application.

This book supports readers in the development of a remotely operated vehicle (ROV) pilot training simulator by exploiting open-source or free gaming software and emphasizing the importance of using established and widely-available game design techniques to provide engaging scenarios for ROV training developers and trainees. There is no such book to guide the users to create an open-source virtual simulator for pilot training in the marine and offshore industry. This book can be used as a reference for undergraduate and postgraduate students, engineers, researchers, and lecturers in VR simulation using UnityTM as the leading software. Some of the key features of the book include: * Step-by-step procedures in development ROV pilot training simulator * Use of open-source software UnityTM that is freely available to all readers * The codes used in the book are self-sufficient as there are no codes hidden from readers

IoT is empowered by various technologies used to detect, gather, store, act, process, transmit, oversee, and examine information. The combination of emergent technologies for information processing and distributed security, such as Cloud computing, Artificial intelligence, and Blockchain, brings new challenges in addressing distributed security methods that form the foundation of improved and eventually entirely new products and services. As systems interact with each other, it is essential to have an agreed interoperability standard, which is safe and valid. This book aims at providing an introduction by illustrating state-of-the-art security challenges and threats in IoT and the latest developments in IoT with Cloud, AI, and Blockchain security challenges. Various application case studies from domains such as science, engineering, and healthcare are introduced, along with their architecture and how they leverage various technologies Cloud, AI, and Blockchain. This book provides a comprehensive guide to researchers and students to design IoT integrated AI, Cloud, and Blockchain projects and to have an overview of the next generation challenges that may arise in the coming years.

Content-Based Image Classification: Efficient Machine Learning Using Robust Feature Extraction Techniques is a comprehensive guide to research with invaluable image data. Social Science Research Network has revealed that 65% of people are visual learners. Research data provided by Hyerle (2000) has clearly shown 90% of information in the human brain is visual. Thus, it is no wonder that visual information processing in the brain is 60,000 times faster than text-based information (3M Corporation, 2001). Recently, we have witnessed a significant surge in conversing with images due to the popularity of social networking platforms. The other reason for embracing usage of image data is the mass availability of high-resolution cellphone cameras. Wide usage of image data in diversified application areas including medical science, media, sports, remote sensing, and so on, has spurred the need for further research in optimizing archival, maintenance, and retrieval of appropriate image content to leverage data-driven decision-making. This book demonstrates several techniques of image processing to represent image data in a desired format for information identification. It discusses the application of machine learning and deep learning for identifying and categorizing appropriate image data helpful in designing automated decision support systems. The book offers comprehensive coverage of the most essential topics, including: Image feature extraction with novel handcrafted techniques (traditional feature extraction) Image feature extraction with automated techniques (representation learning with CNNs) Significance of fusion-based approaches in enhancing classification accuracy MATLAB (R) codes for implementing the techniques Use of the Open Access data mining tool WEKA for multiple tasks The book is intended for budding researchers, technocrats, engineering students, and machine learning/deep learning enthusiasts who are willing to start their computer vision journey with content-based image recognition. The readers will get a clear picture of the essentials for transforming the image data into valuable means for insight generation. Readers will learn coding techniques necessary to propose novel mechanisms and disruptive approaches. The WEKA guide provided is beneficial for those uncomfortable coding for machine learning algorithms. The WEKA tool assists the learner in implementing machine learning algorithms with the click of a button. Thus, this book will be a stepping-stone for your machine learning journey. Please visit the author's website for any further guidance at https://www.rikdas.com/

Artificial Intelligence Techniques in IoT Sensor Networks is a technical book which can be read by researchers, academicians, students and professionals interested in artificial intelligence (AI), sensor networks and Internet of Things (IoT). This book is intended to develop a shared understanding of applications of AI techniques in the present and near term. The book maps the technical impacts of AI technologies, applications and their implications on the design of solutions for sensor networks. This text introduces researchers and aspiring academicians to the latest developments and trends in AI applications for sensor networks in a clear and well-organized manner. It is mainly useful for research scholars in sensor networks and AI techniques. In addition, professionals and practitioners working on the design of real-time applications for sensor networks may benefit directly from this book. Moreover, graduate and master's students of any departments related to AI, IoT and sensor networks can find this book fascinating for developing expert systems or real-time applications. This book is written in a simple and easy language, discussing the fundamentals, which relieves the requirement of having early backgrounds in the field. From this expectation and experience, many libraries will be interested in owning copies of this work.

Artificial Intelligence (AI) fascinates, challenges and disturbs us. There are many voices in society that predict drastic changes that may come as a consequence of AI - a possible apocalypse or Eden on earth. However, only a few people truly understand what AI is, what it can do and what its limitations are. Understanding Artificial Intelligence explains, through a straightforward narrative and amusing illustrations, how AI works. It is written for a non-specialist reader, adult or adolescent, who is interested in AI but is missing the key to understanding how it works. The author demystifies the creation of the so-called "intelligent" machine and explains the different methods that are used in AI. It presents new possibilities offered by algorithms and the difficulties that researchers, engineers and users face when building and using such algorithms. Each chapter allows the reader to discover a new aspect of AI and to become fully aware of the possibilities offered by this rich field.

Visual Perception and Control of Underwater Robots covers theories and applications from aquatic visual perception and underwater robotics. Within the framework of visual perception for underwater operations, image restoration, binocular measurement, and object detection are addressed. More specifically, the book includes adversarial critic learning for visual restoration, NSGA-II-based calibration for binocular measurement, prior knowledge refinement for object detection, analysis of temporal detection performance, as well as the effect of the aquatic data domain on object detection. With the aid of visual perception technologies, two up-to-date underwater robot systems are demonstrated. The first system focuses on underwater robotic operation for the task of object collection in the sea. The second is an untethered biomimetic robotic fish with a camera stabilizer, its control methods based on visual tracking. The authors provide a self-contained and comprehensive guide to understand underwater visual perception and control. Bridging the gap between theory and practice in underwater vision, the book features implementable algorithms, numerical examples, and tests, where codes are publicly available. Additionally, the mainstream technologies covered in the book include deep learning, adversarial learning, evolutionary computation, robust control, and underwater bionics. Researchers, senior undergraduate and graduate students, and engineers dealing with underwater visual perception and control will benefit from this work.

This book focuses on RFID (Radio Frequency Identification), IoT (Internet of Things), and WSN (Wireless Sensor Network). It includes contributions that discuss the security and privacy issues as well as the opportunities and applications that are tightly linked to sensitive infrastructures and strategic services. This book addresses the complete functional framework and workflow in IoT-enabled RFID systems and explores basic and high-level concepts. It is based on the latest technologies and covers the major challenges, issues, and advances in the field. It presents data acquisition and case studies related to data-intensive technologies in RFID-based IoT and includes WSN-based systems and their security. It can serve as a manual for those in the industry while also helping beginners to understand both the basic and advanced aspects of IoT-based RFID-related issues. This book can be a premier interdisciplinary platform for researchers, practitioners, and educators to present and discuss the most recent innovations, trends, and concerns as well as practical challenges encountered, and find solutions that have been adopted in the fields of IoT and analytics.

This book offers a survey of successful attributes of the systems engineer. It focuses on the key positive attributes of what today’s systems engineer should be and puts a model in place for achievement and behavior for future systems engineers. The book, in survey form, provides a description of how and why systems engineers can be, and have been, successful. It offers successful attributes, focuses on the key positive qualities, and drills down to the success features to aim for and the failure characteristics to avoid. The ending result is that it sets a model for achievement and behavior for future systems engineers to follow a successful path. This book will be helpful to systems engineers, industrial engineers, mechanical engineers, general engineers, and those in technical management.

Machine Learning for Healthcare: Handling and Managing Data provides in-depth information about handling and managing healthcare data through machine learning methods. This book expresses the long-standing challenges in healthcare informatics and provides rational explanations of how to deal with them. Machine Learning for Healthcare: Handling and Managing Data provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of machine learning applications. These are illustrated in a case study which examines how chronic disease is being redefined through patient-led data learning and the Internet of Things. This text offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare. Readers will discover the ethical implications of machine learning in healthcare and the future of machine learning in population and patient health optimization. This book can also help assist in the creation of a machine learning model, performance evaluation, and the operationalization of its outcomes within organizations. It may appeal to computer science/information technology professionals and researchers working in the area of machine learning, and is especially applicable to the healthcare sector. The features of this book include: A unique and complete focus on applications of machine learning in the healthcare sector. An examination of how data analysis can be done using healthcare data and bioinformatics. An investigation of how healthcare companies can leverage the tapestry of big data to discover new business values. An exploration of the concepts of machine learning, along with recent research developments in healthcare sectors.

This book introduces the principles and practices in automotive systems, including modern automotive systems that incorporate the latest trends in the automobile industry. The fifteen chapters present new and innovative methods to master the complexities of the vehicle of the future. Topics like vehicle classification, structure and layouts, engines, transmissions, braking, suspension and steering are illustrated with modern concepts, such as battery-electric, hybrid electric and fuel cell vehicles and vehicle maintenance practices. Each chapter is supported with examples, illustrative figures, multiple-choice questions and review questions. Aimed at senior undergraduate and graduate students in automotive/automobile engineering, mechanical engineering, electronics engineering, this book covers the following: Construction and working details of all modern as well as fundamental automotive systems Complexities of operation and assembly of various parts of automotive systems in a simplified manner Handling of automotive systems and integration of various components for smooth functioning of the vehicle Modern topics such as battery-electric, hybrid electric and fuel cell vehicles Illustrative examples, figures, multiple-choice questions and review questions at the end of each chapter

Provides a wide snapshot of building knowledge-based systems, inconsistency measures, methods for handling consistency, and methods for integrating knowledge bases. Provides the mathematical background to solve problems of restoring consistency and problems of integrating probabilistic knowledge bases in the integrating process. The research results presented in the book can be applied in decision support systems, semantic web systems, multimedia information retrieval systems, medical imaging systems, cooperative information systems, and more.

Whilst our outside world is modifying into a more complex and hybrid networked world, our most intimate dwelling, our home, is at risk of falling behind as for many it seems to have remained the same as it has been for many decades. This book explores what it means to have a home in such a networked world. It describes what architecture can, or perhaps should, contribute to enable a more participatory role for inhabitants. This forward-thinking book will try to answer the question - What is the role and position of technology in our most intimate locations both now and what could it be like in the future?

This book focuses on a specialized branch of the vast domain of software engineering: component-based software engineering (CBSE). Component-Based Software Engineering: Methods and Metrics enhances the basic understanding of components by defining categories, characteristics, repository, interaction, complexity, and composition. It divides the research domain of CBSE into three major sub-domains: (1) reusability issues, (2) interaction and integration issues, and (3) testing and reliability issues. This book covers the state-of-the-art literature survey of at least 20 years in the domain of reusability, interaction and integration complexities, and testing and reliability issues of component-based software engineering. The aim of this book is not only to review and analyze the previous works conducted by eminent researchers, academicians, and organizations in the context of CBSE, but also suggests innovative, efficient, and better solutions. A rigorous and critical survey of traditional and advanced paradigms of software engineering is provided in the book. Features: In-interactions and Out-Interactions both are covered to assess the complexity. In the context of CBSE both white-box and black-box testing methods and their metrics are described. This work covers reliability estimation using reusability which is an innovative method. Case studies and real-life software examples are used to explore the problems and their solutions. Students, research scholars, software developers, and software designers or individuals interested in software engineering, especially in component-based software engineering, can refer to this book to understand the concepts from scratch. These measures and metrics can be used to estimate the software before the actual coding commences.

Artificial intelligence (AI) stands out as a transformational technology of the digital age. Its practical applications are growing very rapidly. One of the chief reasons AI applications are attaining prominence, is in its design to learn continuously, from real-world use and experience, and its capability to improve its performance. It is no wonder that the applications of AI span from complex high-technology equipment manufacturing to personalized exclusive recommendations to end-users. Many deployments of AI software, given its continuous learning need, require computation platforms that are resource intense, and have sustained connectivity and perpetual power through central electrical grid. In order to harvest the benefits of AI revolution to all of humanity, traditional AI software development paradigms must be upgraded to function effectively in environments that have resource constraints, small form factor computational devices with limited power, devices with intermittent or no connectivity and/or powered by non-perpetual source or battery power. The aim this book is to prepare current and future software engineering teams with the skills and tools to fully utilize AI capabilities in resource-constrained devices. The book introduces essential AI concepts from the perspectives of full-scale software development with emphasis on creating niche Blue Ocean small form factored computational environment products.