It is useful to highlight how we rely on historical research on the potential of technology in order to problematize and make sense of the prominent role of technology in the UN's framework for sustainable development and the Sustainable Development Goals. The book follows an interdisciplinary approach wherein the contributors come from the disciplines of computer science, law, philosophy, political science, economy, sociology, amongst others.

It is useful to highlight how we rely on historical research on the potential of technology in order to problematize and make sense of the prominent role of technology in the UN's framework for sustainable development and the Sustainable Development Goals. The book follows an interdisciplinary approach wherein the contributors come from the disciplines of computer science, law, philosophy, political science, economy, sociology, amongst others.

Designed to offer an accessible set of case studies and analyses of ethical dilemmas in data science. This book will be suitable for technical readers in data science who want to understand diverse ethical approaches to AI.

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.

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

With the aim to better understand nature, mathematical tools are being used nowadays in many different fields. The concept of integral transforms, in particular, has been found to be a useful mathematical tool for solving a variety of problems not only in mathematics, but also in various other branches of science, engineering, and technology. Integral Transforms and Engineering: Theory, Methods, and Applications presents a mathematical analysis of integral transforms and their applications. The book illustrates the possibility of obtaining transfer functions using different integral transforms, especially when mapping any function into the frequency domain. Various differential operators, models, and applications are included such as classical derivative, Caputo derivative, Caputo-Fabrizio derivative, and Atangana-Baleanu derivative. This book is a useful reference for practitioners, engineers, researchers, and graduate students in mathematics, applied sciences, engineering, and technology fields.

Within a unified switched-control framework, this book investigates the high-performance control designs and theoretic analyses for teleoperation systems, including the joint space and task space teleoperation, the homogeneous and heterogeneous teleoperation, and the single-master single-slave and multi-master multi-slave teleoperation. The book begins with an introduction to the concepts and challenges of networked teleoperation systems. Then, it investigates a new adaptive control framework based on auxiliary switched filters for the bilateral teleoperation systems to handle the model uncertainty and non-passive external forces. To overcome the input constraints of robotic systems, this adaptive method is also extended to the anti-windup adaptive control case. Furthermore, to apply to multi-robot remote collaboration scenarios and heterogeneous teleoperation, two tele-coordination methods and an adaptive semi-autonomous control method are respectively developed. Finally, the authors examine two finite-time control schemes and two types of improved prescribed performance control for teleoperation systems to improve the transient-state and steady-state synchronization performances. This title will be an essential reference for researchers and engineers interested in teleoperation, robotic systems, and nonlinear control systems. It would also prove useful for graduate students in the fields of science, engineering, and computer science.

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.

One of the most difficult, yet important, questions regarding projects is "What advantages will this project create for the investors and key stakeholders?" Projects and programs should be treated as investments. This means that the focus of projects shifts from delivering within the triple constraints (time-cost-quality) towards some of the more fundamental questions: What is the purpose of this investment? What are the specific advantages expected? Are these benefits worth the investment? Implementing Project and Program Benefit Management is written for executives and practitioners within the portfolio, program, and project environment. It guides them through the important work that must be addressed as the investment progresses towards the realization of benefits. The processes discussed cover the strategic elements of benefits realization as well as the more detailed requirements, which are the domain of the program delivery teams and the operational users. Using real cases to explain complex situations, operational teams and wider groups of stakeholders, including communities affected by infrastructure projects, will be able to engage in the conversation with the sponsors and delivery teams. Covering an area of program and project management that is rapidly becoming more widely valued, this book blends theory with practical experience to present a clear process flow to managing the benefits life cycle. Best practices are defined, and pitfalls and traps are identified to enable practitioners to apply rigor and structure to this crucial discipline.

Software analysis patterns play an important role in reducing the overall cost and compressing the time of software project lifecycles. However, building reusable and stable software analysis patterns is still considered a major and delicate challenge. This book proposes a novel concept for building analysis patterns based on software stability and is a modern approach for building stable, highly reusable, and widely applicable analysis patterns. The book also aims to promote better understanding of problem spaces and discusses how to focus requirements analysis accurately. It demonstrates a new approach to discovering and creating stable analysis patterns (SAPs). This book presents a pragmatic approach to understanding problem domains, utilizing SAPs for any field of knowledge, and modeling stable software systems, components, and frameworks. It helps readers attain the basic knowledge that is needed to analyze and extract analysis patterns from any domain of interest. Readers also learn to master methods to document patterns in an effective, easy, and comprehensible manner. Bringing significant contributions to the field of computing, this book is a unique and comprehensive reference manual on SAPs. It provides insight on handling the understanding of problem spaces and supplies methods and processes to analyze user requirements accurately as well as ways to use SAPs in building myriad cost-effective and highly maintainable systems. The book also shows how to link SAPs to the design phase thereby ensuring a smooth transition between analysis and design.

The growing complexity of projects today, as well as the uncertainty inherent in innovative projects, is making obsolete traditional project management practices and procedures, which are based on the notion that much about a project is known at its start. The current high level of change and complexity confronting organizational leaders and managers requires a new approach to projects so they can be managed flexibly to embrace and exploit change. What once used to be considered extreme uncertainty is now the norm, and managing planned projects is being replaced by managing projects as they evolve. Successfully managing projects in extreme situations, such as polar and military expeditions, shows how to manage successfully projects in today's turbulent environment. Executed under the harshest and most unpredictable conditions, these projects are great sources for learning about how to manage unexpected and unforeseen situations as they occur. This book presents multiple case studies of managing extreme events as they happened during polar, mountain climbing, military, and rescue expeditions. A boat accident in the Artic is a lesson on how an effective project manager must be ambidextrous: on one hand able to follow plans and on the other hand able to abandon those plans when disaster strikes and improvise new ones in response. Polar expeditions also illustrate how a team can use "weak links" to go beyond its usual information network to acquire strategic information. Fire and rescues operations illustrate how one team member's knowledge can be transferred to the entire team. Military operations provide case material on how teams coordinate and make use of both individual and collective competencies. This groundbreaking work pushes the definitions of a project and project management to reveal new insight that benefits researchers, academics, and the practitioners managing projects in today's challenging and uncertain times.

• Introduces the principles of system dynamics, and demonstrates them with respect to problems in pavement engineering. • Provides useful models for readers to download and further modify and improve for their own use. • Serves as a practical guide for both students and practitioners to explore systems concepts, and develop insights for sustainable and resilient pavements.

Object detection is a basic visual identification problem in computer vision that has been explored extensively over the years. Visual object detection seeks to discover objects of specific target classes in a given image with pinpoint accuracy and apply a class label to each object instance. Object recognition strategies based on deep learning have been intensively investigated in recent years as a result of the remarkable success of deep learning-based image categorization. In this book, we go through in detail detector architectures, feature learning, proposal generation, sampling strategies, and other issues that affect detection performance. The book describes every newly proposed novel solution but skips through the fundamentals so that readers can see the field's cutting edge more rapidly. Moreover, unlike prior object detection publications, this project analyses deep learning-based object identification methods systematically and exhaustively, and also gives the most recent detection solutions and a collection of noteworthy research trends. The book focuses primarily on step-by-step discussion, an extensive literature review, detailed analysis and discussion, and rigorous experimentation results. Furthermore, a practical approach is displayed and encouraged.

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

Introduces different optimization algorithms together to solve complex combinatorial optimization problems related to hospital management system or healthcare Applies machine learning-based analytics such as GAN networks, autoencoders, computational imaging, and quantum computing to authentic hospital management problems Discusses metaheuristic algorithms such as evolutionary algorithms 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 Computational Intelligence and Industrial Engineering towards designing complex and convoluted hospital management problems

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

Despite major advances in healthcare over the past century, the successful treatment of cancer has remained a significant challenge, and cancers are the second leading cause of death worldwide behind cardiovascular disease. Early detection and survival are important issues to control cancer. The development of quantitative methods and computer technology has facilitated the formation of new models in medical and biological sciences. The application of mathematical modelling in solving many real-world problems in medicine and biology has yielded fruitful results. In spite of advancements in instrumentations technology and biomedical equipment, it is not always possible to perform experiments in medicine and biology for various reasons. Thus, mathematical modelling and simulation are viewed as viable alternatives in such situations, and are discussed in this book. The conventional diagnostic techniques of cancer are not always effective as they rely on the physical and morphological appearance of the tumour. Early stage prediction and diagnosis is very difficult with conventional techniques. It is well known that cancers are involved in genome level changes. As of now, the prognosis of various types of cancer depends upon findings related to the data generated through different experiments. Several machine learning techniques exist in analysing the data of expressed genes; however, the recent results related with deep learning algorithms are more accurate and accommodative, as they are effective in selecting and classifying informative genes. This book explores the probabilistic computational deep learning model for cancer classification and prediction.

First-hand technical expertise – torch has been developed in/by the author’s team at RStudio. Presupposes only a minimal background in mathematics. Presupposes no background in optimization and/or deep learning (but some general ideas about machine learning). Entertaining and fun to read (at least that’s the intention!). Concept-first approach

The development of computer software for nonlinear control systems has provided many benefits for teaching, research, and the development of control systems design. MATLAB is considered the dominant software platforms for linear and nonlinear control systems analysis. This book provides an easy way to learn nonlinear control systems such as feedback linearization technique and Sliding mode control (Structure variable control) which are one of the most used techniques in nonlinear control dynamical systems; therefore teachers-students and researchers are all in need to handle such techniques; and since they are too difficult for them to handle such nonlinear controllers especially for a more complicated systems such as induction motor, satellite, and vehicles dynamical models. Thus, this document it is an excellent resource for learning the principle of feedback linearization and sliding mode techniques in an easy and simple way: Provides a briefs description of the feedback linearization and sliding mode control strategies Includes a simple method on how to determine the right and appropriate controller (P-PI-PID) for feedback linearization control strategy. A Symbolic MATLAB Based function for finding the feedback linearization and sliding mode controllers are developed and tested using several examples. A simple method for finding the approximate sliding mode controller parameters is introduced Where the program used to construct the nonlinear controller uses symbolic computations; such that the user should provide the program with the necessary functions f(x), g(x) and h(x) using the symbolic library.

Derived from industry-training classes that the author teaches at the Embedded Systems Institute at Eindhoven, the Netherlands and at Buskerud University College at Kongsberg in Norway, Systems Architecting: A Business Perspective places the processes of systems architecting in a broader context by juxtaposing the relationship of the systems architect with enterprise and management. This practical, scenario-driven guide fills an important gap, providing systems architects insight into the business processes, and especially into the processes to which they actively contribute. The book uses a simple reference model to enable understanding of the inside of a system in relation to its context. It covers the impact of tool selection and brings balance to the application of the intellectual tools versus computer-aided tools. Stressing the importance of a clear strategy, the authors discuss methods and techniques that facilitate the architect's contribution to the strategy process. They also give insight into the needs and complications of harvesting synergy, insight that will help establish an effective synergy-harvesting strategy. The book also explores the often difficult relationship between managers and systems architects. Written in an approachable style, the book discusses the breadth of the human sciences and their relevance to systems architecting. It highlights the relevance of human aspects to systems architects, linking theory to practical experience when developing systems architecting competence.

If engineering is the art and science of technical problem solving, systems architecting happens when you don't yet know what the problem is. The third edition of a highly respected bestseller, The Art of Systems Architecting provides in-depth coverage of the least understood part of systems design: moving from a vague concept and limited resources to a satisfactory and feasible system concept and an executable program. The book provides a practical, heuristic approach to the "art" of systems architecting. It provides methods for embracing, and then taming, the growing complexity of modern systems. New in the Third Edition: Five major case studies illustrating successful and unsuccessful practices Information on architecture frameworks as standards for architecture descriptions New methods for integrating business strategy and architecture and the role of architecture as the technical embodiment of strategy Integration of process guidance for organizing and managing architecture projects Updates to the rapidly changing fields of software and systems-of-systems architecture Organization of heuristics around a simple and practical process model A Practical Heuristic Approach to the Art of Systems Architecting Extensively rewritten to reflect the latest developments, the text explains how to create a system from scratch, presenting invention/design rules together with clear explanations of how to use them. The author supplies practical guidelines for avoiding common systematic failures while implementing new mandates. He uses a heuristics-based approach that provides an organized attack on very ill-structured engineering problems. Examining architecture as more than a set of diagrams and documents, but as a set of decisions that either drive a system to success or doom it to failure, the book provide methods for integrating business strategy with technical architectural decision making.

Features In-depth coverage of the role of AI in smart healthcare. Research guideline for AI and data science researchers/practitioners interested in the healthcare sector. Comprehensive coverage on security and privacy issues for AI in smart healthcare.