This book is used at the graduate or advanced undergraduate level and many others. Manned and unmanned ground, aerial and marine vehicles enable many promising and revolutionary civilian and military applications that will change our life in the near future. These applications include, but are not limited to, surveillance, search and rescue, environment monitoring, infrastructure monitoring, self-driving cars, contactless last-mile delivery vehicles, autonomous ships, precision agriculture and transmission line inspection to name just a few. These vehicles will benefit from advances of deep learning as a subfield of machine learning able to endow these vehicles with different capability such as perception, situation awareness, planning and intelligent control. Deep learning models also have the ability to generate actionable insights into the complex structures of large data sets. In recent years, deep learning research has received an increasing amount of attention from researchers in academia, government laboratories and industry. These research activities have borne some fruit in tackling some of the challenging problems of manned and unmanned ground, aerial and marine vehicles that are still open. Moreover, deep learning methods have been recently actively developed in other areas of machine learning, including reinforcement training and transfer/meta-learning, whereas standard, deep learning methods such as recent neural network (RNN) and coevolutionary neural networks (CNN). The book is primarily meant for researchers from academia and industry, who are working on in the research areas such as engineering, control engineering, robotics, mechatronics, biomedical engineering, mechanical engineering and computer science. The book chapters deal with the recent research problems in the areas of reinforcement learning-based control of UAVs and deep learning for unmanned aerial systems (UAS) The book chapters present various techniques of deep learning for robotic applications. The book chapters contain a good literature survey with a long list of references. The book chapters are well written with a good exposition of the research problem, methodology, block diagrams and mathematical techniques. The book chapters are lucidly illustrated with numerical examples and simulations. The book chapters discuss details of applications and future research areas.

Highlights the contributions of different optimization techniques, decision analytics (predictive, prescriptive, and descriptive), multi-criteria decision making "Helps develop intelligent machines to provide solutions to real-world problems, which are not modelled or are too difficult to model mathematically in hospital management systems " Discusses machine learning-based analytics such as GAN networks, autoencoders, computational imaging, quantum computing will be rigorously applied to smart cloud computing Explores evolutionary algorithms that demonstrate their ability as robust approaches to cope with the fundamental steps of image processing, image analysis, and computer vision pipeline (e.g., restoration, segmentation, registration, classification, reconstruction, or tracking), Creates a bridge between Industrial Engineering concepts and Computational Intelligence for designing complex and convoluted hospital management problems

Most of the real-life signals are non-stationary in nature. The examples of such signals include biomedical signals, communication signals, speech, earthquake signals, vibration signals, etc. Time-frequency analysis plays an important role for extracting the meaningful information from these signals. The book presents time-frequency analysis methods together with their various applications. The basic concepts of signals and different ways of representing signals have been provided. The various time-frequency analysis techniques namely, short-time Fourier transform, wavelet transform, quadratic time-frequency transforms, advanced wavelet transforms, and adaptive time-frequency transforms have been explained. The fundamentals related to these methods are included. The various examples have been included in the book to explain the presented concepts effectively. The recently developed time-frequency analysis techniques such as, Fourier-Bessel series expansion-based methods, synchrosqueezed wavelet transform, tunable-Q wavelet transform, iterative eigenvalue decomposition of Hankel matrix, variational mode decomposition, Fourier decomposition method, etc. have been explained in the book. The numerous applications of time-frequency analysis techniques in various research areas have been demonstrated. This book covers basic concepts of signals, time-frequency analysis, and various conventional and advanced time-frequency analysis methods along with their applications. The set of problems included in the book will be helpful to gain an expertise in time-frequency analysis. The material presented in this book will be useful for students, academicians, and researchers to understand the fundamentals and applications related to time-frequency analysis.

Unique selling point: • Complete coverage of intelligent vehicle networks with deep learning tools. Core audience: • Researchers and postgraduates studying UAV communication systems, as well as industry engineers and technicians Place in the market: • Many advanced UAV communication protocols and modeling details will be provided. Thus, it is more like an engineering/science design reference instead of a common introduction book.

Explores text mining and IoT applications for monitoring and controlling smart industrial systems Describes the key principles and techniques for Big-data analytics, security, and optimization for industrial applications. Provides context-aware insights, human-centric industry, smart computing for next-generation industry

This book is about the simulation and modeling of novel chaotic systems within the frame of fractal-fractional operators. The methods used, their convergence, stability, and error analysis are given, and this is the first book to offer mathematical modeling and simulations of chaotic problems with a wide range of fractal-fractional operators, to find solutions. Numerical Methods for Fractal-Fractional Differential Equations and Engineering: Simulations and Modeling provides details for stability, convergence, and analysis along with numerical methods and their solution procedures for fractal-fractional operators. The book offers applications to chaotic problems and simulations using multiple fractal-fractional operators and concentrates on models that display chaos. The book details how these systems can be predictable for a while and then can appear to become random. Practitioners, engineers, researchers, and senior undergraduate and graduate students from mathematics and engineering disciplines will find this book of interest._

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.

This volume presents selected aspects of non-integer, or fractional order systems, whose analysis, synthesis and applications have increasingly become a real challenge for various research communities, ranging from science to engineering. The spectrum of applications of the fractional order calculus has incredibly expanded, in fact it would be hard to find a science/engineering-related subject area where the fractional calculus had not been incorporated. The content of the fractional calculus is ranged from pure mathematics to engineering implementations and so is the content of this volume. The volume is subdivided into six parts, reflecting particular aspects of the fractional order calculus. The first part contains a single invited paper on a new formulation of fractional-order descriptor observers for fractional-order descriptor continous LTI systems. The second part provides new elements to the mathematical theory of fractional-order systems. In the third part of this volume, a bunch of new results in approximation, modeling and simulations of fractional-order systems is given. The fourth part presents new solutions to some problems in controllability and control of non-integer order systems, in particular fractional PID-like control. The fifth part analyzes the stability of non-integer order systems and some new results are offered in this important respect, in particular for discrete-time systems. The final, sixth part of this volume presents a spectrum of applications of the noninteger order calculus, ranging from bi-fractional filtering, in particular of electromyographic signals, through the thermal diffusion and advection diffusion processes to the SIEMENS platform implementation. This volume's papers were all subjected to stimulating comments and discussions from the active audience of the RRNR'2014, the 6th Conference on Non-integer Order Calculus and Its Applications that was organized by the Department of Electrical, Control and Computer Engineering, Opole University of Technology, Opole, Poland.

This book systematically describes the concepts and principles for multi-satellite relative motion, passive and near passive formation designs, trajectory planning and control for fuel optimal formation maneuvers, and formation flying maintenance control design. As such, it provides a sound foundation for researchers and engineers in this field to develop further theories and pursue their implementations. Though satellite formation flying is widely considered to be a major advance in space technology, there are few systematic treatments of the topic in the literature. Addressing that gap, the book offers a valuable resource for academics, researchers, postgraduate students and practitioners in the field of satellite science and engineering.

This book is a compendium of various applications and current progress in a powerful technology known as the Internet of Things (IoT). IoT provides a system of interconnecting things such as vehicles, electrical equipment, agriculture devices, etc. Such items are allocated with the computing device so that they can use a network to transfer data to one another and automate their actions on certain events. Internet of Things: Applications for Sustainable Development will throw light on recent developments in the latest field and will be of great interest to know various application areas for sustainable development. This book mainly focuses on the current state of the art, including protocol design and low-cost sensor design, for the sustainable development of society using IoT. The sustainable development areas include climate, healthcare systems, electrical systems, and energy that can meet present and next-generation advancement using IoT. Sustainable development faces various issues, challenges, opportunities, and future enhancements with the latest technologies, hardware, and software. Features: A real-world problem-solving approach for diversified problems Potential contributors from industries/academia have been given the opportunity to publish their work Identification of various challenges in IoT for future contributions Diversified coverage of the book, including applications, securities, industrialization, automation, etc IoT for the sustainable development areas This book will offer strong support as a reference book for students, practitioners, researchers, and scientific investigators worldwide, as well as anyone who wants to set up IoT-enabled industries. It provides pertinent industries with new ideas and innovations to visionaries.

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/

Distributed Artificial Intelligence (DAI) came to existence as an approach for solving complex learning, planning, and decision-making problems. When we talk about decision making, there may be some meta-heuristic methods where the problem solving may resemble like operation research. But exactly, it is not related completely to management research. The text examines representing and using organizational knowledge in DAI systems, dynamics of computational ecosystems, and communication-free interactions among rational agents. This publication takes a look at conflict-resolution strategies for nonhierarchical distributed agents, constraint-directed negotiation of resource allocations, and plans for multiple agents. Topics included plan verification, generation, and execution, negotiation operators, representation, network management problem, and conflict-resolution paradigms. The manuscript elaborates on negotiating task decomposition and allocation using partial global planning and mechanisms for assessing nonlocal impact of local decisions in distributed planning. The book will attract researchers and practitioners who are working in management and computer science, and industry persons in need of a beginner to advanced understanding of the basic and advanced concepts.

Highly automated production and logistics facilities require mechatronic drive solutions. This book describes in which way the industrial production and logistics work and shows the structure of the drive solutions required for this purpose.

The functionality of the mechanical and electronic elements of a drive system including the software is described, and their basic dimensioning principles are explained. Furthermore the authors outline the engineering, reliability, and important aspects of the life cycle.

The great number of applications within the fields of conveying and material handling technology in continuous and cycled production lines and for machining processes are divided into twelve drive solutions. They comprise the motor, the inverter with its software, the gearbox, and mechanical drive elements. The specific requirements and the functionality of these twelve solutions are presented. The authors focus on the energy conversion with controlled electric drive systems.

This book is the first research collection by the Malaysian Society for Automatic Control Engineers (MACE). Numerous applications of control engineering, sensor, and instrumentation technology in robotics, industrial automation, and other mechatronic systems are presented in this book. The book begins by introducing control engineering in robotics and industrial automation. It progresses through a series of chapters, discussing the application of control engineering in various areas such as: brake-by-wire technology; web scrubber systems; robot localization; and, autonomous navigation systems. Coverage of swarm robotics behaviors and applications of sensor technology in the field of music, biomedical technology, and structural analysis takes the book beyond its core of mechatronic systems and demonstrates a more diverse application of the ideas it presents. Each chapter provides comprehensive and detailed coverage of the main ideas, design methods, and practical needs of its chosen topic, making this book accessible and useful to researchers, engineers, postgraduates, and undergraduate students.

The manufacturing industries remain the foundation of local, regional, and global economies. Manufacturing plants operate in dynamic markets that demand upgrading with transformational technologies for maintaining profitability, competitiveness, and business sustainability. Yet most manufacturing plants currently use technologies that are no longer competitive and industry leaders face an overwhelming array of operational challenges that require agile and enhanced transformational solutions. This book offers manufacturers effective strategies and tools for the adoption and implementation of advanced operational technologies to ensure long-term innovation, efficiency, and profitability. The book covers advanced automation integration in manufacturing, including digitization, AI, machine learning, IIoT, and cybersecurity. It describes innovation, development, and integration of control technologies for sustainable manufacturing. It explains how to upgrade existing manufacturing plants for the global market. It shows how to apply emerging technologies including asset optimization and process integration for product lifecycle improvements, plant operation and maintenance enhancement, and supply chain integration. This book serves as a strategic guide to applying advanced operational technologies for engineers, industry professionals, and management in the manufacturing sector.

Explores the history and significance of interplanetary space missions. Features detailed explanations and mathematical methods for trajectory optimization. Includes detailed explanations and mathematical methods for mission analysis for interplanetary missions. Covers the introduction, mathematical methods, and applications of the N-body problem (N>2). Discusses navigation and targeting for interplanetary mission.

This book presents the state of the art, challenges and future trends in automotive software engineering. The amount of automotive software has grown from just a few lines of code in the 1970s to millions of lines in today's cars. And this trend seems destined to continue in the years to come, considering all the innovations in electric/hybrid, autonomous, and connected cars. Yet there are also concerns related to onboard software, such as security, robustness, and trust. This book covers all essential aspects of the field. After a general introduction to the topic, it addresses automotive software development, automotive software reuse, E/E architectures and safety, C-ITS and security, and future trends. The specific topics discussed include requirements engineering for embedded software systems, tools and methods used in the automotive industry, software product lines, architectural frameworks, various related ISO standards, functional safety and safety cases, cooperative intelligent transportation systems, autonomous vehicles, and security and privacy issues. The intended audience includes researchers from academia who want to learn what the fundamental challenges are and how they are being tackled in the industry, and practitioners looking for cutting-edge academic findings. Although the book is not written as lecture notes, it can also be used in advanced master's-level courses on software and system engineering. The book also includes a number of case studies that can be used for student projects.

Provides background material needed to understand digital twin technology Presents computational facet of digital twin Includes physics based and surrogate model representations Addresses the problem of uncertainty in measurements and modeling Discusses practical case studies of implementation of digital twins addressing additive manufacturing, server farms, predictive maintenance, and smart cities

This book elaborates fuzzy machine and deep learning models for single class mapping from multi-sensor, multi-temporal remote sensing images while handling mixed pixels and noise. It also covers the ways of pre-processing and spectral dimensionality reduction of temporal data. Further, it discusses the ‘individual sample as mean’ training approach to handle heterogeneity within a class. The appendix section of the book includes case studies such as mapping crop type, forest species, and stubble burnt paddy fields. Key features: Focuses on use of multi-sensor, multi-temporal data while handling spectral overlap between classes Discusses range of fuzzy/deep learning models capable to extract specific single class and separates noise Describes pre-processing while using spectral, textural, CBSI indices, and back scatter coefficient/Radar Vegetation Index (RVI) Discusses the role of training data to handle the heterogeneity within a class Supports multi-sensor and multi-temporal data processing through in-house SMIC software Includes case studies and practical applications for single class mapping This book is intended for graduate/postgraduate students, research scholars, and professionals working in environmental, geography, computer sciences, remote sensing, geoinformatics, forestry, agriculture, post-disaster, urban transition studies, and other related areas.

1. This book gives the reader a comprehensive and in-depth introduction to the variable gain control and details a series of related innovative technologies. Instead of reading various relevant books, this book enables readers to establish an overall understanding of variable gain control methods for different systems. 2. The goal of this book is to interpret complex control knowledge in simple terms. It is easy to understand and needs little background knowledge. 3. It highlights the practical applications of variable gain control in energy conversation. The designed variable gain control has a simple form and is convenient for engineering applications. 4. Discussion remarks are provided in each chapter with new contributions to emphasize the novelty of the presented design and analysis methods. Moreover, a number of interesting remaining problems in the related topics, which are worthy of further investigation, are discussed in this book. 5. Simulation results are given in each chapter, sometimes in a comparative manner, to show the effectiveness of these methods.

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.

This book presents recent advanced techniques in sliding mode control and observer design for industrial power systems, focusing on their applications in polymer electrolyte membrane fuel cells and power converters. Readers will find not only valuable new fault detection and isolation techniques based on sliding mode control and observers, but also a number of robust control and estimation methodologies combined with fuzzy neural networks and extended state observer methods. The book also provides necessary experimental and simulation examples for proton exchange membrane fuel cell systems and power converter systems. Given its scope, it offers a valuable resource for undergraduate and graduate students, academics, scientists and engineers who are working in the field.

Includes several emerging required standardization and interoperability initiatives Offers various AI and Machine Learning algorithms Discusses how health technology can face the challenge of improving the quality of life regardless of social and financial consideration, gender, age, and residence Presents real-time applications and case studies in the field of engineering, computer science, IoT, Smart Cities with modern tools and technologies used in healthcare Focuses on many examples of successful IoT projects from various industries

Video has rich information including meta-data, visual, audio, spatial and temporal data which can be analysed to extract a variety of low and high-level features to build predictive computational models using machine-learning algorithms to discover interesting patterns, concepts, relations, and associations. This book includes a review of essential topics and discussion of emerging methods and potential applications of video data mining and analytics. It integrates areas like intelligent systems, data mining and knowledge discovery, big data analytics, machine learning, neural network, and deep learning with focus on multimodality video analytics and recent advances in research/applications. Features: Provides up-to-date coverage of the state-of-the-art techniques in intelligent video analytics. Explores important applications that require techniques from both artificial intelligence and computer vision. Describes multimodality video analytics for different applications. Examines issues related to multimodality data fusion and highlights research challenges. Integrates various techniques from video processing, data mining and machine learning which has many emerging indoors and outdoors applications of smart cameras in smart environments, smart homes, and smart cities. This book aims at researchers, professionals and graduate students in image processing, video analytics, computer science and engineering, signal processing, machine learning, and electrical engineering.

• Utilizes case studies from different disciplines and sectors within engineering and other related technical areas to demonstrate how to go from data, to insight, and to decision making • Introduces various approaches to build models that exploits different algorithms • Discusses predictive models that can be built through machine learning and used to mine patterns from large datasets • Explores the augmentation of technical and mathematical materials with explanatory worked examples • Includes a glossary, lecture notes, self-assessments, and worked-out practice exercises