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/

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

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 presents a comprehensive coverage of the five fundamental yet intertwined pillars paving the road towards the future of connected autonomous electric vehicles and smart cities. The connectivity pillar covers all the latest advancements and various technologies on vehicle-to-everything (V2X) communications/networking and vehicular cloud computing, with special emphasis on their role towards vehicle autonomy and smart cities applications. On the other hand, the autonomy track focuses on the different efforts to improve vehicle spatiotemporal perception of its surroundings using multiple sensors and different perception technologies. Since most of CAVs are expected to run on electric power, studies on their electrification technologies, satisfaction of their charging demands, interactions with the grid, and the reliance of these components on their connectivity and autonomy, is the third pillar that this book covers. On the smart services side, the book highlights the game-changing roles CAV will play in future mobility services and intelligent transportation systems. The book also details the ground-breaking directions exploiting CAVs in broad spectrum of smart cities applications. Example of such revolutionary applications are autonomous mobility on-demand services with integration to public transit, smart homes, and buildings. The fifth and final pillar involves the illustration of security mechanisms, innovative business models, market opportunities, and societal/economic impacts resulting from the soon-to-be-deployed CAVs. This book contains an archival collection of top quality, cutting-edge and multidisciplinary research on connected autonomous electric vehicles and smart cities. The book is an authoritative reference for smart city decision makers, automotive manufacturers, utility operators, smart-mobility service providers, telecom operators, communications engineers, power engineers, vehicle charging providers, university professors, researchers, and students who would like to learn more about the advances in CAEVs connectivity, autonomy, electrification, security, and integration into smart cities and intelligent transportation systems.

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.

Rapid technological advances in the field of robotics and autonomous systems (RAS) are transforming the international security environment and the conduct of contemporary conflict. Bringing together leading experts from across the globe, this book provides timely analysis on the current and future challenges associated with greater utilization of RAS by states, their militaries, and a host of non-state actors. Technologically driven change in the international security environment can come about through the development of one significant technology, such as the atomic bomb. At other times, it results from several technologies maturing at roughly the same pace. This second image better reflects the rapid technological change that is taking us into the robotics age. Many of the chapters in this edited volume explore unresolved ethical, legal, and operational challenges that are only likely to become more complex as RAS technology matures. Though the precise ways in which the impact of autonomous systems - both physical and non-physical - will be felt in the long-run is hidden from us, attempting to anticipate the direction of travel remains an important undertaking and one that this book makes a critical effort to contend with. The chapters in this book were originally published as a special issue of the journal Small Wars & Insurgencies.

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.

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.

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.

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.

Optimal Power Flow Using FACTS Devices: Soft Computing Techniques develops intelligent algorithms to analyze optimal power flow (OPF) and to enhance the power transfer capability of the transmission line with reduced congestion. By providing elaborate studies on FACTS devices and by using soft computing metaheuristics algorithms such as Firefly, Cuckoo, Flower Pollination, and others., this book enables readers to know about algorithms in real-time power system applications and damping of subsynchronous resonance (SSR) oscillations. Key features of this book include: Offers comprehensive review of FACTS devices and the importance of soft computing techniques for solving OPF. Describes the various problems associated with power system operation and control. Addresses issues of SSR in power systems and proposes soft techniques for SSR analysis in power systems. Demonstrates of the importance of SSR and congestion management using intelligent FACTS devices as part of OPF. Covers power systems' reliability, quality, cost-effectiveness, effects on customer goodwill, and pollution limits, including the deregulation of markets and different intelligent controllers. Optimal Power Flow Using FACTS Devices: Soft Computing Techniques is aimed at researchers and professionals in the field of power systems.

Advances in high spatial resolution mapping capabilities and the new rules established by the Federal Aviation Administration in the United States for the operation of Small Unmanned Aircraft Systems (sUAS) have provided new opportunities to acquire aerial data at a lower cost and more safely versus other methods. A similar opening of the skies for sUAS applications is being allowed in countries across the world. Also, sUAS can access hazardous or inaccessible areas during disaster events and provide rapid response when needed. Applications of Small Unmanned Aircraft systems: Best Practices and Case Studies is the first book that brings together the best practices of sUAS applied to a broad range of issues in high spatial resolution mapping projects. Very few sUAS pilots have the knowledge of how the collected imagery is processed into value added mapping products that have commercial and/or academic import. Since the field of sUAS applications is just a few years old, this book covers the need for a compendium of case studies to guide the planning, data collection, and most importantly data processing and map error issues, with the range of sensors available to the user community. Written by experienced academics and professionals, this book serves as a guide on how to formulate sUAS based projects, from choice of a sUAS, flight planning for a particular application, sensors and data acquisition, data processing software, mapping software and use of the high spatial resolution maps produced for particular types of geospatial modeling. Features: Focus on sUAS based data acquisition and processing into map products Broad range of case studies by highly experienced academics Practical guidance on sUAS hardware, sensors, and software utilized Compilation of workflow insights from expert professors and professionals Relevant to academia, government, and industry Positional and thematic map accuracy, UAS curriculum development and workflow replicability issues This book would be an excellent text for upper-level undergraduate to graduate level sUAS mapping application courses. It is also invaluable as a reference for educators designing sUAS based curriculum as well as for potential sUAS users to assess the scope of mapping projects that can be done with this technology.

Transforming Management Using Artificial Intelligence Techniques redefines management practices using artificial intelligence (AI) by providing a new approach. It offers a detailed, well-illustrated treatment of each topic with examples and case studies, and brings the exciting field to life by presenting a substantial and robust introduction to AI in a clear and concise manner. It provides a deeper understanding of how the relevant aspects of AI impact each other's efficacy for better output. It's a reliable and accessible one-step resource that introduces AI; presents a full examination of applications; provides an understanding of the foundations; examines education powered by AI, entertainment, home and service robots, healthcare re-imagined, predictive policing, space exploration; and so much more, all within the realm of AI. This book will feature: Uncovering new and innovative features of AI and how it can help in raising economic efficiency at both micro- and macro levels Both the literature and practical aspects of AI and its uses This book summarizing key concepts at the end of each chapter to assist reader comprehension Case studies of tried and tested approaches to resolutions of typical problems Ideal for both teaching and general-knowledge purposes. This book will also simply provide the topic of AI for the readers, aspiring researchers and practitioners involved in management and computer science, so they can obtain a high-level of understanding of AI and managerial applications.

The term "mechatronics" was coined in 1969, merging "mecha" from mechanism and "tronics" from electronics, to reflect the original idea at the basis of this discipline, that is, the integration of electrical and mechanical systems into a single device. The spread of this term, and of mechatronics itself, has been growing in the years, including new aspects and disciplines, like control engineering, computer engineering and communication/information engineering. Nowadays mechatronics has a well-defined and fundamental role, in strict relation with robotics. Drawing a sharp border between mechatronics and robotics is impossible, as they share many technologies and objectives. Advanced robots could be defined as mechatronic devices equipped with a "smart brain", but there are also up-to-date mechatronic devices, used in tight interaction with humans, that are governed by smart architectures (for example, for safety purposes). Aim of this book is to offer a wide overview of new research trends and challenges for both mechatronics and robotics, through the contribution of researchers from different institutions, providing their view on specific subjects they consider as "hot topics" in both fields, with attention to new fields of application, new challenges to the research communities and new technologies available. The reader of this book will enjoy the various contributions, as they have been prepared with actual applications in mind, along a journey from advanced actuators and sensors to human-robot interaction, through robot control, navigation, planning and programming issues. The book presents several state-of-the-art solutions, like multiple-stage actuation to cope with conflicting specification of large motion-spans, ultra-high accuracy, model-based control for high-tech mechatronic systems, modern approaches of software systems engineering to robotics, aand humanoids for human assistance. The reader can also find new techniques in approaching the design of mechatronic systems in some possible industrial and service robotics scenarios, with a particular attention for the interaction between humans and mechanisms.

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.

Linear and Non-Linear System Theory focuses on the basics of linear and non-linear systems, optimal control and optimal estimation with an objective to understand the basics of state space approach linear and non-linear systems and its analysis thereof. Divided into eight chapters, materials cover an introduction to the advanced topics in the field of linear and non-linear systems, optimal control and estimation supported by mathematical tools, detailed case studies and numerical and exercise problems. This book is aimed at senior undergraduate and graduate students in electrical, instrumentation, electronics, chemical, control engineering and other allied branches of engineering. Features Covers both linear and non-linear system theory Explores state feedback control and state estimator concepts Discusses non-linear systems and phase plane analysis Includes non-linear system stability and bifurcation behaviour Elaborates optimal control and estimation

Businesses today are faced with a highly competitive market and fast-changing technologies. In order to meet demanding customers' needs, they rely on high quality software. A new field of study, soft computing techniques, is needed to estimate the efforts invested in component-based software. Component-Based Systems: Estimating Efforts Using Soft Computing Techniques is an important resource that uses computer-based models for estimating efforts of software. It provides an overview of component-based software engineering, while addressing uncertainty involved in effort estimation and expert opinions. This book will also instruct the reader how to develop mathematical models. This book is an excellent source of information for students and researchers to learn soft computing models, their applications in software management, and will help software developers, managers, and those in the industry to apply soft computing techniques to estimate efforts.

Human Factors in Intelligent Vehicles addresses issues related to the analysis of human factors in the design and evaluation of intelligent vehicles for a wide spectrum of applications and over different dimensions. To commemorate the 8th anniversary of the IEEE ITS Workshop on Human Factors (http://hfiv.net) some recent works of authors active in the automotive human factors community have been collected in this book. Enclosed here are extended versions of papers and tutorials that were presented at the IEEE ITSS Workshop on "Human Factors in Intelligent Vehicles" and also included is additional deeper analysis along with detailed experimental and simulation results. The contributors cover autonomous vehicles as well as the frameworks for analyzing automation, modelling and methods for road users' interaction such as intelligent user interfaces, including brain-computer interfaces and simulation and analysis tools related to human factors.

Covers ML applications across multiple application domains (reservoir characterization, drilling, production, reservoir modeling, preventative maintenance, and text mining) Details the emerging area of unstructured (text and image) data analysis applied across these multiple domains Offers multiple perspectives from authors representing operating companies, universities, and research organizations Provides an array of case studies illustrating the latest application of multiple ML techniques in each application domain Includes a literature review of each state-of-art application domain

In today's world, the use of technology is growing rapidly, and people need effective solutions for their real-life problems. This book discusses smart applications of associated technologies to develop cohesive and comprehensive solutions for the betterment of humankind. It comprehensively covers the effective use of the Internet of Things (IoT), wireless sensor network, wearable sensors, body area network, cloud computing, and distributed computing methodologies. The book comprehensively covers IoT and fog computing sensor supported technologies or protocols including web of things, near-field communication, 6LoWPAN, LoRAWAN, XMPP, DDS, LwM2M, Mesh Protocol, and radio-frequency identification. The book- Discusses smart applications to develop cohesive and comprehensive solutions for real-life problems. Covers analytical descriptions with appropriate simulation and prototype models. Examines the role of IoT and fog computing technologies during global emergency situations. Discusses key technologies including cloud computing, 5G communication, big data, artificial intelligence, control systems, and wearable sensors. The text is primarily written for graduate students, and academic researchers working in diverse fields of electrical engineering, biomedical engineering, electronics and communication engineering, computer engineering, and information technology.

Highlights developments, discoveries, and practical and advanced experiences related to responsive distributed computing and how it can support the deployment of trajectory-based applications in intelligent systems. Presents metamodeling with new trajectories patterns which are very useful for intelligent transportation systems. Examines the processing aspects of raw trajectories to develop other types of semantic and activity-type and space-time path type trajectories. Discusses Complex Event Processing (CEP), Internet of Things (IoT), Internet of Vehicle (IoV), V2X communication, Big Data Analytics, distributed processing frameworks, and Cloud Computing. Presents a number of case studies to demonstrate smart trajectories related to spatio-temporal events such as traffic congestion, viral contamination, and pedestrian accidents.

Discusses replacement, repair, and inspection Offers estimation and statistical tests Covers accelerated life testing Explores warranty analysis manufacturing Includes service reliability

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/

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.

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.