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Harvey Award winner for Best Children or Young Adult Book! The year is 1946. Teenagers Roberta and Tommy Lee just moved with their parents from Chinatown to the centre of Metropolis, home to the famous hero, Superman. Tommy makes friends quickly, while Roberta pines for home. Then one night, the family awakens to find their house surrounded by the Klan of the Fiery Kross! Superman leaps into action, but his exposure to a mysterious green rock has left him weak. Can Roberta and Tommy help him smash the Klan? Inspired by the 1940s Superman radio serial Clan of the Fiery Cross, New York Times bestselling author Gene Luen Yang (American Born Chinese, Boxers and Saints, The Terrifics, New Super-Man) and artist Gurihiru (Avatar: The Last Airbender, The Unstoppable Wasp) bring us a personal retelling of two different immigrants finding ways to belong.

The Digital Twin Paradigm for Smarter Systems and Environments: The Industry Use Cases, Volume 117, the latest volume in the Advances in Computers series, presents detailed coverage of new advancements in computer hardware, software, theory, design and applications. Chapters vividly illustrate how the emerging discipline of digital twin is strategically contributing to various digital transformation initiatives. Specific chapters cover Demystifying the Digital Twin Paradigm, Digital Twin Technology for "Smarter Manufacturing", The Fog Computing/ Edge Computing to leverage Digital Twin, The industry use cases for the Digital Twin idea, Enabling Digital Twin at the Edge, The Industrial Internet of Things (IIOT), and much more.

Provides in-depth descriptions of digital transformation technologies and tools Covers various research accomplishments in this flourishing field of relevance Includes many detailed industry use cases with all the right information

Advances in Computers, Volume 127 presents innovations in computer hardware, software, theory, design and applications, with this updated volume including new chapters on Edge AI, Edge Computing, Edge Analytics, Edge Data Analytics, Edge Native Applications, Edge Platforms, Edge Computing, IoT, Internet of Things, etc.

Contains novel subject matter that is relevant to computer science Includes the expertise of contributing authors Presents an easy to comprehend writing style

Microservices architecture (MSA) is increasingly popular with software architects and engineers as it accelerates software solution design, development, and deployment in a risk-free manner. Placing a software system into a production environment is elegantly simplified and sped up with the use of MSA development platforms, runtime environments, acceleration engines, design patterns, integrated frameworks, and related tools. The MSA ecosystem is expanding with third-party products that automate as many tasks as possible. MSA is being positioned as the enterprise-grade and agile-application design method. This book covers in-depth the features and facilities that make up the MSA ecosystem. Beginning with an overview of Service-Oriented Architecture (SOA) that covers the Common Object Request Broker Architecture (CORBA), Distributed Component Object Model (DCOM), and Remote Method Invocation (RMI), the book explains the basic essentials of MSA and the continuous delivery of applications to customers. The book gives software developers insight into: Current and emerging communication modelsKey architectural elements of MSA-based applicationsDesigning efficient APIs for microservicesMSA middleware platforms such as REST, SOAP, Apache Thrift, and gRPCMicroservice discovery and the API gateway Service orchestration and choreography for composing individual services to achieve a useful business process Database transactions in MSA-centric applicationsDesign, composition, security, and deployment patternsMSA securityModernizing legacy applicationsThe book concludes with a chapter on composing and building powerful microservices. With the exponential growth of IoT devices, microservices are being developed and deployed on resource-constrained but resource-intensive devices in order to provide people-centric applications. The book discusses the challenges of these applications. Finally, the book looks at the role of microservices in smart environments and upcoming trends including ubiquitous yet disappearing microservices.

This book comprehensively conveys the theoretical and practical aspects of IoT and big data analytics with the solid contributions from practitioners as well as academicians. This book examines and expounds the unique capabilities of the big data analytics platforms in capturing, cleansing and crunching IoT device/sensor data in order to extricate actionable insights. A number of experimental case studies and real-world scenarios are incorporated in this book in order to instigate our book readers. This book Analyzes current research and development in the domains of IoT and big data analytics Gives an overview of latest trends and transitions happening in the IoT data analytics space Illustrates the various platforms, processes, patterns, and practices for simplifying and streamlining IoT data analytics The Internet of Things and Big Data Analytics: Integrated Platforms and Industry Use Cases examines and accentuates how the multiple challenges at the cusp of IoT and big data can be fully met. The device ecosystem is growing steadily. It is forecast that there will be billions of connected devices in the years to come. When these IoT devices, resource-constrained as well as resource-intensive, interact with one another locally and remotely, the amount of multi-structured data generated, collected, and stored is bound to grow exponentially. Another prominent trend is the integration of IoT devices with cloud-based applications, services, infrastructures, middleware solutions, and databases. This book examines the pioneering technologies and tools emerging and evolving in order to collect, pre-process, store, process and analyze data heaps in order to disentangle actionable insights.

As the number of Internet of Things (IoT) elements grows exponentially, their interactions can generate a massive amount of raw and multi-structured data. The challenge with this data explosion is to transform any raw data into information and knowledge, which can be used by people and systems to make intelligent decisions. Industrial IoT Application Architectures and Use Cases explores how artificial intelligence (AI), data analytics, and IoT technology combine to promote intelligent decision-making and automation in a range of industries. With faster, more stable AI algorithms and approaches, knowledge discovery and dissemination from IoT-device data can be simplified and streamlined. An era of powerful cognitive technology is beginning due to cloud-based cognitive systems that are forming the foundation of game-changing intelligent applications. This book presents next-generation use cases of IoT and IoT data analytics for a variety of industrial verticals as given below: An Intelligent IoT framework for smart water management An IoT-enabled smart traffic control system for congestion control and smart traffic management An intelligent airport system for airport management and security surveillance An IoT framework for healthcare to integrate and report patient information Fuzzy scheduling with IoT for tracking and monitoring hotel assets An IoT system for designing drainage systems and monitoring drainage pipes Predictive maintenance of plant equipment to decide the actual mean time to malfunction Integrated neural networks and IoT systems for predictive equipment maintenance IoT integration in blockchain for smart waste management This book also includes a chapter on the IoT paradigm and an overview of uses cases for personal, social, and industrial applications.

The Internet of Things (IoT) concept is defined as a flexible and futuristic network where all the different types of devices and smart objects can become seamlessly connected to each other and can actively participate in all types of processes which are happening around us. The grand objective of making physical, mechanical, electrical, and electronic devices to use the deeper and extreme connectivity and service-enablement techniques is to make them intelligent in their deeds, decisions, and deals. Cognitive IoT is the application of cognitive computing technologies to the data which is generated by the connected devices of the IoT ecosystem. Cognition means thinking; however, computers are not yet fully capable of mimicking human like thought. However, the present-day computer systems can perform some functions which are like the capability of human beings to think.Cognitive Internet of Things: Enabling Technologies, Platforms, and Use Cases explains the concepts surrounding Cognitive IoT. It also looks at the use cases and such supporting technologies as artificial intelligence and machine learning that act as key enablers of Cognitive IoT ecosystem. Different Cognitive IoT enabled platforms like IBM Watson and other product specific use cases like Amazon Alexa are covered in depth. Other highlights of the book include:Demystifying the cognitive computing paradigmDelineating the key capabilities of cognitive cloud environmentsDeep learning algorithms for cognitive IoT solutionsNatural language processing (NLP) methods for cognitive IoT systemsDesigning a secure infrastructure for cognitive IoT platforms and applications

Coud reliability engineering is a leading issue of cloud services. Cloud service providers guarantee computation, storage and applications through service-level agreements (SLAs) for promised levels of performance and uptime. Cloud Reliability Engineering: Technologies and Tools presents case studies examining cloud services, their challenges, and the reliability mechanisms used by cloud service providers. These case studies provide readers with techniques to harness cloud reliability and availability requirements in their own endeavors. Both conceptual and applied, the book explains reliability theory and the best practices used by cloud service companies to provide high availability. It also examines load balancing, and cloud security.Written by researchers and practitioners, the book’s chapters are a comprehensive study of cloud reliability and availability issues and solutions. Various reliability class distributions and their effects on cloud reliability are discussed. An important aspect of reliability block diagrams is used to categorize poor reliability of cloud infrastructures, where enhancement can be made to lower the failure rate of the system. This technique can be used in design and functional stages to determine poor reliability of a system and provide target improvements. Load balancing for reliability is examined as a migrating process or performed by using virtual machines. The approach employed to identify the lightly loaded destination node to which the processes/virtual machines migrate can be optimized by employing a genetic algorithm. To analyze security risk and reliability, a novel technique for minimizing the number of keys and the security system is presented. The book also provides an overview of testing methods for the cloud, and a case study discusses testing reliability, installability, and security. A comprehensive volume, Cloud Reliability Engineering: Technologies and Tools combines research, theory, and best practices used to engineer reliable cloud availability and performance.

Blockchain is emerging as a powerful technology, which has attracted the wider attention of all businesses across the globe. In addition to financial businesses, IT companies and business organizations are keenly analyzing and adapting this technology for improving business processes. Security is the primary enterprise application. There are other crucial applications that include creating decentralized applications and smart contracts, which are being touted as the key differentiator of this pioneering technology. The power of any technology lies in its ecosystem. Product and tool vendors are building and releasing a variety of versatile and robust toolsets and platforms in order to speed up and simplify blockchain application development, deployment and management. There are other infrastructure-related advancements in order to streamline blockchain adoption. Cloud computing, big data analytics, machine and deep learning algorithm, and connected and embedded devices all are driving blockchain application development and deployment.Blockchain Technology and Applications illustrates how blockchain is being sustained through a host of platforms, programming languages, and enabling tools. It examines:Data confidential, integrity, and authentication Distributed consensus protocols and algorithmsBlockchain systems design criteria and systems interoperability and scalabilityIntegration with other technologies including cloud and big dataIt also details how blockchain is being blended with cloud computing, big data analytics and IoT across all industry verticals. The book gives readers insight into how this path-breaking technology can be a value addition in several business domains ranging from healthcare, financial services, government, supply chain and retail.

Blockchain is emerging as a powerful technology, which has attracted the wider attention of all businesses across the globe. In addition to financial businesses, IT companies and business organizations are keenly analyzing and adapting this technology for improving business processes. Security is the primary enterprise application. There are other crucial applications that include creating decentralized applications and smart contracts, which are being touted as the key differentiator of this pioneering technology. The power of any technology lies in its ecosystem. Product and tool vendors are building and releasing a variety of versatile and robust toolsets and platforms in order to speed up and simplify blockchain application development, deployment and management. There are other infrastructure-related advancements in order to streamline blockchain adoption. Cloud computing, big data analytics, machine and deep learning algorithm, and connected and embedded devices all are driving blockchain application development and deployment.Blockchain Technology and Applications illustrates how blockchain is being sustained through a host of platforms, programming languages, and enabling tools. It examines:Data confidential, integrity, and authentication Distributed consensus protocols and algorithmsBlockchain systems design criteria and systems interoperability and scalabilityIntegration with other technologies including cloud and big dataIt also details how blockchain is being blended with cloud computing, big data analytics and IoT across all industry verticals. The book gives readers insight into how this path-breaking technology can be a value addition in several business domains ranging from healthcare, financial services, government, supply chain and retail.

Blockchain is a technology that has attracted the attention of all types of businesses. Cryptocurrency such as Bitcoin has gained the most attention, but now companies are applying Blockchain technology to develop solutions improving traditional applications and securing all types of transactions. Robust and innovative, this technology is being combined with other well-known technologies including Cloud Computing, Big Data, and IoT to revolutionize outcomes in all verticals.Unlike books focused on financial applications, Essential Enterprise Blockchain Concepts and Applications is for researchers and practitioners who are looking for secure, viable, low-cost, and workable applications to solve a broad range of business problems. The book presents research that rethinks how to incorporate Blockchain with existing technology. Chapters cover various applications based on Blockchain technology including: Digital votingSmart contractsSupply chain managementInternet securityLogistics managementIdentity managementSecuring medical devicesAsset managementBlockchain plays a significant role in providing security for data operations. It defines how trusted transactions can be carried out and addresses Internet vulnerability problems. Blockchain solves the security fault line between AI and IoT in smart systems as well as in other systems using devices connected to each other through public networks. Linear and permanent indexed records are maintained by Blockchain to face the vulnerability issues in a wide variety applications. In addition to applications, the book also covers consensus algorithms and protocols and performance of Blockchain algorithms.

Digital transformation (DT) has become a buzzword. Every industry segment across the globe is consciously jumping toward digital innovation and disruption to get ahead of their competitors. In other words, every aspect of running a business is being digitally empowered to reap all the benefits of the digital paradigm. All kinds of digitally enabled businesses across the globe are intrinsically capable of achieving bigger and better things for their constituents. Their consumers, clients, and customers will realize immense benefits with real digital transformation initiatives and implementations. The much-awaited business transformation can be easily and elegantly accomplished with a workable and winnable digital transformation strategy, plan, and execution. There are several enablers and accelerators for realizing the much-discussed digital transformation. There are a lot of digitization and digitalization technologies available to streamline and speed up the process of the required transformation. Industrial Internet of Things (IIoT) technologies in close association with decisive advancements in the artificial intelligence (AI) space can bring forth the desired transitions. The other prominent and dominant technologies toward forming digital organizations include cloud IT, edge/fog computing, real-time data analytics platforms, blockchain technology, digital twin paradigm, virtual and augmented reality (VR/AR) techniques, enterprise mobility, and 5G communication. These technological innovations are intrinsically competent and versatile enough to fulfill the varying requirements for establishing and sustaining digital enterprises. Enterprise Digital Transformation: Technology, Tools, and Use Cases features chapters on the evolving aspects of digital transformation and intelligence. It covers the unique competencies of digitally transformed enterprises, IIoT use cases, and applications. It explains promising technological solutions widely associated with digital innovation and disruption. The book focuses on setting up and sustaining smart factories that are fulfilling the Industry 4.0 vision that is realized through the IIoT and allied technologies.

This book vividly illustrates all the promising and potential machine learning (ML) and deep learning (DL) algorithms through a host of real-world and real-time business use cases. Machines and devices can be empowered to self-learn and exhibit intelligent behavior. Also, Big Data combined with real-time and runtime data can lead to personalized, prognostic, predictive, and prescriptive insights. This book examines the following topics: Cognitive machines and devices Cyber physical systems (CPS) The Internet of Things (IoT) and industrial use cases Industry 4.0 for smarter manufacturing Predictive and prescriptive insights for smarter systems Machine vision and intelligence Natural interfaces K-means clustering algorithm Support vector machine (SVM) algorithm A priori algorithms Linear and logistic regression Applied Learning Algorithms for Intelligent IoT clearly articulates ML and DL algorithms that can be used to unearth predictive and prescriptive insights out of Big Data. Transforming raw data into information and relevant knowledge is gaining prominence with the availability of data processing and mining, analytics algorithms, platforms, frameworks, and other accelerators discussed in the book. Now, with the emergence of machine learning algorithms, the field of data analytics is bound to reach new heights.This book will serve as a comprehensive guide for AI researchers, faculty members, and IT professionals. Every chapter will discuss one ML algorithm, its origin, challenges, and benefits, as well as a sample industry use case for explaining the algorithm in detail. The book’s detailed and deeper dive into ML and DL algorithms using a practical use case can foster innovative research.

Blockchain is a technology that has attracted the attention of all types of businesses. Cryptocurrency such as Bitcoin has gained the most attention, but now companies are applying Blockchain technology to develop solutions improving traditional applications and securing all types of transactions. Robust and innovative, this technology is being combined with other well-known technologies including Cloud Computing, Big Data, and IoT to revolutionize outcomes in all verticals.Unlike books focused on financial applications, Essential Enterprise Blockchain Concepts and Applications is for researchers and practitioners who are looking for secure, viable, low-cost, and workable applications to solve a broad range of business problems. The book presents research that rethinks how to incorporate Blockchain with existing technology. Chapters cover various applications based on Blockchain technology including: Digital votingSmart contractsSupply chain managementInternet securityLogistics managementIdentity managementSecuring medical devicesAsset managementBlockchain plays a significant role in providing security for data operations. It defines how trusted transactions can be carried out and addresses Internet vulnerability problems. Blockchain solves the security fault line between AI and IoT in smart systems as well as in other systems using devices connected to each other through public networks. Linear and permanent indexed records are maintained by Blockchain to face the vulnerability issues in a wide variety applications. In addition to applications, the book also covers consensus algorithms and protocols and performance of Blockchain algorithms.

The strategically sound combination of edge computing and artificial intelligence (AI) results in a series of distinct innovations and disruptions enabling worldwide enterprises to visualize and realize next-generation software products, solutions and services. Businesses, individuals, and innovators are all set to embrace and experience the sophisticated capabilities of Edge AI. With the faster maturity and stability of Edge AI technologies and tools, the world is destined to have a dazzling array of edge-native, people-centric, event-driven, real-time, service-oriented, process-aware, and insights-filled services. Further on, business workloads and IT services will become competent and cognitive with state-of-the-art Edge AI infrastructure modules, AI algorithms and models, enabling frameworks, integrated platforms, accelerators, high-performance processors, etc. The Edge AI paradigm will help enterprises evolve into real-time and intelligent digital organizations.Applied Edge AI: Concepts, Platforms, and Industry Use Cases focuses on the technologies, processes, systems, and applications that are driving this evolution. It examines the implementation technologies; the products, processes, platforms, patterns, and practices; and use cases. AI-enabled chips are exclusively used in edge devices to accelerate intelligent processing at the edge. This book examines AI toolkits and platforms for facilitating edge intelligence. It also covers chips, algorithms, and tools to implement Edge AI, as well as use cases. FEATURES The opportunities and benefits of intelligent edge computing Edge architecture and infrastructure AI-enhanced analytics in an edge environment Encryption for securing information An Edge AI system programmed with Tiny Machine learning algorithms for decision making An improved edge paradigm for addressing the big data movement in IoT implementations by integrating AI and caching to the edge Ambient intelligence in healthcare services and in development of consumer electronic systems Smart manufacturing of unmanned aerial vehicles (UAVs) AI, edge computing, and blockchain in systems for environmental protection Case studies presenting the potential of leveraging AI in 5G wireless communication

Coud reliability engineering is a leading issue of cloud services. Cloud service providers guarantee computation, storage and applications through service-level agreements (SLAs) for promised levels of performance and uptime. Cloud Reliability Engineering: Technologies and Tools presents case studies examining cloud services, their challenges, and the reliability mechanisms used by cloud service providers. These case studies provide readers with techniques to harness cloud reliability and availability requirements in their own endeavors. Both conceptual and applied, the book explains reliability theory and the best practices used by cloud service companies to provide high availability. It also examines load balancing, and cloud security.Written by researchers and practitioners, the book’s chapters are a comprehensive study of cloud reliability and availability issues and solutions. Various reliability class distributions and their effects on cloud reliability are discussed. An important aspect of reliability block diagrams is used to categorize poor reliability of cloud infrastructures, where enhancement can be made to lower the failure rate of the system. This technique can be used in design and functional stages to determine poor reliability of a system and provide target improvements. Load balancing for reliability is examined as a migrating process or performed by using virtual machines. The approach employed to identify the lightly loaded destination node to which the processes/virtual machines migrate can be optimized by employing a genetic algorithm. To analyze security risk and reliability, a novel technique for minimizing the number of keys and the security system is presented. The book also provides an overview of testing methods for the cloud, and a case study discusses testing reliability, installability, and security. A comprehensive volume, Cloud Reliability Engineering: Technologies and Tools combines research, theory, and best practices used to engineer reliable cloud availability and performance.

Quaternion-Sparse Image Processing: Advances in Multispectral Processing brings together the technologies, research, and managerial applications of quaternion-sparse based complex algebra in image processing. The book covers the entire range of complicated tasks performed on color images, including denoising, reconstruction, classification, hallucination, feature extraction, dimension reduction, and regularization. It provides easy understanding and smooth adaptability of basic and advanced concepts for graduate students, researchers, doctors, academics, and practitioners.Uncovers the innovative features of complex algebra, specifically the quaternion-sparse concept in image processing and how it can help in improving the computational efficiency of image processingDeals with the most common quaternion convolution neural network, quaternion wavelet, and sparse representation-based techniques in multispectral image processingFocuses on how evolution in algebraic concepts, i.e., quaternion and sparse, help in improving accuracy and efficiency of various color image restoration, reconstruction, and recognitionIllustrates how important features are extracted and complete information is stored in extracted features to help and process tasks in an easy and computationally efficient way

Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Use Cases delineates how graph data science significantly empowers the application of data science. The book discusses the emerging paradigm of graph data science in detail along with its practical research and real-world applications. Readers will be enriched with the knowledge of graph data science, graph analytics, algorithms, databases, platforms, and use cases across a variety of research and topics and applications. This book also presents how graphs are used as a programming language, especially demonstrating how Sleptsov Net Computing can contribute as an entirely graphical concurrent processing language for supercomputers. Graph data science is emerging as an expressive and illustrative data structure for optimally representing a variety of data types and their insightful relationships. These data structures include graph query languages, databases, algorithms, and platforms. From here, powerful analytics methods and machine learning/deep learning (ML/DL) algorithms are quickly evolving to analyze and make sense out of graph data. As a result, ground-breaking use cases across scientific research topics and industry verticals are being developed using graph data representation and manipulation. A wide range of complex business and scientific research requirements are efficiently represented and solved through graph data analysis, and Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Graph Data Science gives readers both the conceptual foundations and technical methods for applying these powerful techniques.Provides comprehensive coverage of the emerging paradigm of graph data science and its real-world applicationsGives readers practical guidance on how to approach and solve complex data analysis problems using graph data science, with an emphasis on deep analysis techniques including graph neural networks (GNNs), machine learning, algorithms, graph databases, and graph query languagesCovers extended graph models such as bipartite directed graphs of place-transition nets, graphs with dynamical processes defined on them - Petri and Sleptsov nets, and graphs as programming languagesPresents all the key tools and techniques as well as the foundations of graph theory, including mathematical concepts, research, and graph analytics

Signal Processing Roadmap: Technologies, Applications, and Future Directions explores cutting-edge and emerging signal-processing techniques across various measurement and monitoring applications, serving as an authoritative reference for engineers, researchers, and technologists. The book critically analyzes key signal processing considerations such as uncertainty modeling that enable more intelligent and reliable next-generation measurement systems, all of which are backed by real-world implementation examples in areas ranging from Internet of Things devices to complex biomedical equipment. In addition, sections provide an overview of the latest research in the hybrid information system modeling field, with a particular emphasis on practical applications in various fields. The book includes case studies and examples of how these models have been used to solve problems in finance, healthcare, engineering, and other related fields. Finally, the book reviews the theories and concepts related to non-linear optimization, fuzzy sets, and rough sets.Provides a comprehensive reference for signal processing techniques in modern measurement systemsHighlights the latest innovations and future directions that drive transformative capabilitiesOffers a roadmap for signal processing advances across application domains like 6G networks, pervasive health monitoring, and industry 4.0Discusses emerging trends in areas like photonic signal processing, virtual/augmented reality, additive manufacturing, and autonomous robotsBrings critical analysis of signal processing and uncertainty modeling for enabling next-generation smart measurement systems

This book focuses on environmental justice in African philosophy, highlighting important new perspectives which will be of significance to researchers with an interest in environmental ethics both within Africa and beyond. Drawing on African social and ethical conceptions of existence, the book makes suggestions for how to derive environmental justice from African philosophies such as communitarian ethics, relational ethics, unhu/ubuntu ethics, ecofeminist ethics and intergenerational ethics. Specifically, the book emphasises the ways in which African philosophies of existence seek to involve everyone in environmental policy and planning and to equitably distribute both environmental benefits (such as natural resources) and environmental burdens (such as pollution and the location of mining, industrial or dumping sites). This extends to fair distribution between global South and global North, rich and poor, urban and rural populations, men and women and adults and children. These principles of humaneness, relationships, equality, interconnectedness and teleologically oriented existence among all beings are important not only to African environmental justice but also to the environmental justice movement globally. The book will interest researchers and students working in the fields of environmental ethics, African philosophy and political philosophy in general.