Houbing Herbert Song – författare

Cyber-Physical Systems: Foundations, Principles and Applications explores the core system science perspective needed to design and build complex cyber-physical systems. Using Systems Science's underlying theories, such as probability theory, decision theory, game theory, organizational sociology, behavioral economics, and cognitive psychology, the book addresses foundational issues central across CPS applications, including System Design -- How to design CPS to be safe, secure, and resilient in rapidly evolving environments, System Verification -- How to develop effective metrics and methods to verify and certify large and complex CPS, Real-time Control and Adaptation -- How to achieve real-time dynamic control and behavior adaptation in a diverse environments, such as clouds and in network-challenged spaces, Manufacturing -- How to harness communication, computation, and control for developing new products, reducing product concepts to realizable designs, and producing integrated software-hardware systems at a pace far exceeding today's timeline. The book is part of the Intelligent Data-Centric Systems: Sensor-Collected Intelligence series edited by Fatos Xhafa, Technical University of Catalonia.

Indexing: The books of this series are submitted to EI-Compendex and SCOPUS

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Includes in-depth coverage of the latest models and theories that unify perspectives, expressing the interacting dynamics of the computational and physical components of a system in a dynamic environment Focuses on new design, analysis, and verification tools that embody the scientific principles of CPS and incorporate measurement, dynamics, and control Covers applications in numerous sectors, including agriculture, energy, transportation, building design and automation, healthcare, and manufacturing

Cyber-Physical Systems: Foundations, Principles and Applications explores the core system science perspective needed to design and build complex cyber-physical systems. Using Systems Science''s underlying theories, such as probability theory, decision theory, game theory, organizational sociology, behavioral economics, and cognitive psychology, the book addresses foundational issues central across CPS applications, including System Design -- How to design CPS to be safe, secure, and resilient in rapidly evolving environments, System Verification -- How to develop effective metrics and methods to verify and certify large and complex CPS, Real-time Control and Adaptation -- How to achieve real-time dynamic control and behavior adaptation in a diverse environments, such as clouds and in network-challenged spaces, Manufacturing -- How to harness communication, computation, and control for developing new products, reducing product concepts to realizable designs, and producing integrated software-hardware systems at a pace far exceeding today''s timeline. The book is part of the Intelligent Data-Centric Systems: Sensor-Collected Intelligence series edited by Fatos Xhafa, Technical University of Catalonia.Indexing: The books of this series are submitted to EI-Compendex and SCOPUS- Includes in-depth coverage of the latest models and theories that unify perspectives, expressing the interacting dynamics of the computational and physical components of a system in a dynamic environment- Focuses on new design, analysis, and verification tools that embody the scientific principles of CPS and incorporate measurement, dynamics, and control- Covers applications in numerous sectors, including agriculture, energy, transportation, building design and automation, healthcare, and manufacturing

Approx.374 pagesApprox.374 pages

Big Data Analytics in Cyber-Physical Systems: Machine Learning for the Internet of Things examines sensor signal processing, IoT gateways, optimization and decision-making, intelligent mobility, and implementation of machine learning algorithms in embedded systems. This book focuses on the interaction between IoT technology and the mathematical tools used to evaluate the extracted data of those systems. Each chapter provides the reader with a broad list of data analytics and machine learning methods for multiple IoT applications. Additionally, this volume addresses the educational transfer needed to incorporate these technologies into our society by examining new platforms for IoT in schools, new courses and concepts for universities and adult education on IoT and data science.- Bridges the gap between IoT, CPS, and mathematical modelling- Features numerous use cases that discuss how concepts are applied in different domains and applications- Provides "best practices", "winning stories" and "real-world examples" to complement innovation- Includes highlights of mathematical foundations of signal processing and machine learning in CPS and IoT

Federated Learning for Digital Healthcare Systems critically examines the key factors that contribute to the problem of applying machine learning in healthcare systems and investigates how federated learning can be employed to address the problem. The book discusses, examines, and compares the applications of federated learning solutions in emerging digital healthcare systems, providing a critical look in terms of the required resources, computational complexity, and system performance.In the first section, chapters examine how to address critical security and privacy concerns and how to revamp existing machine learning models. In subsequent chapters, the book''s authors review recent advances to tackle emerging efficient and lightweight algorithms and protocols to reduce computational overheads and communication costs in wireless healthcare systems. Consideration is also given to government and economic regulations as well as legal considerations when federated learning is applied to digital healthcare systems.- Provides insights into real-world scenarios of the design, development, deployment, application, management, and benefits of federated learning in emerging digital healthcare systems- Highlights the need to design efficient federated learning-based algorithms to tackle the proliferating security and patient privacy issues in digital healthcare systems- Reviews the latest research, along with practical solutions and applications developed by global experts from academia and industry

Federated Learning for Digital Healthcare Systems critically examines the key factors that contribute to the problem of applying machine learning in healthcare systems and investigates how federated learning can be employed to address the problem. The book discusses, examines, and compares the applications of federated learning solutions in emerging digital healthcare systems, providing a critical look in terms of the required resources, computational complexity, and system performance.In the first section, chapters examine how to address critical security and privacy concerns and how to revamp existing machine learning models. In subsequent chapters, the book's authors review recent advances to tackle emerging efficient and lightweight algorithms and protocols to reduce computational overheads and communication costs in wireless healthcare systems. Consideration is also given to government and economic regulations as well as legal considerations when federated learning is applied to digital healthcare systems.

Provides insights into real-world scenarios of the design, development, deployment, application, management, and benefits of federated learning in emerging digital healthcare systemsHighlights the need to design efficient federated learning-based algorithms to tackle the proliferating security and patient privacy issues in digital healthcare systemsReviews the latest research, along with practical solutions and applications developed by global experts from academia and industry

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

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 applications- Gives 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 languages- Covers 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 languages- Presents all the key tools and techniques as well as the foundations of graph theory, including mathematical concepts, research, and graph analytics

Cybersecurity Defensive Walls in Edge Computing dives into the creation of robust cybersecurity defenses for increasingly vulnerable edge devices. This book examines the unique security challenges of edge environments, including limited resources and potentially untrusted networks, providing fundamental concepts for real-time vulnerability detection and mitigation through novel system architectures, experimental frameworks, and AI/ML techniques. Researchers and industry professionals working in cybersecurity, edge computing, cloud computing, defensive technologies, and threat intelligence will find this to be a valuable resource that illuminates critical aspects of edge-based security to advance theoretical analysis, system design, and practical implementation of defensive walls. With a focus on fast-growing edge application scenarios, this book offers valuable insights into strengthening real-time security for the proliferation of interconnected edge devices.Provides researchers with insights into real-world scenarios of the design, development, deployment, application, management, and benefits of cybersecurity defensive walls in edge computingDiscusses critical cybersecurity defensive walls and their applications to resolve security and privacy issues which affect all parties in edge computing and provide practical learning-based solutions to solve these problemsPresents well-structured chapters from industry experts and global researchers who consider unique security challenges, including limited resources, diverse device types, and potentially untrusted network environments

Cybersecurity Defensive Walls in Edge Computing dives into the creation of robust cybersecurity defenses for increasingly vulnerable edge devices. This book examines the unique security challenges of edge environments, including limited resources and potentially untrusted networks, providing fundamental concepts for real-time vulnerability detection and mitigation through novel system architectures, experimental frameworks, and AI/ML techniques. Researchers and industry professionals working in cybersecurity, edge computing, cloud computing, defensive technologies, and threat intelligence will find this to be a valuable resource that illuminates critical aspects of edge-based security to advance theoretical analysis, system design, and practical implementation of defensive walls. With a focus on fast-growing edge application scenarios, this book offers valuable insights into strengthening real-time security for the proliferation of interconnected edge devices. - Provides researchers with insights into real-world scenarios of the design, development, deployment, application, management, and benefits of cybersecurity defensive walls in edge computing- Discusses critical cybersecurity defensive walls and their applications to resolve security and privacy issues which affect all parties in edge computing and provide practical learning-based solutions to solve these problems- Presents well-structured chapters from industry experts and global researchers who consider unique security challenges, including limited resources, diverse device types, and potentially untrusted network environments

Neuro-Symbolic AI: Integrating Neural Networks and Symbolic Reasoning explores the convergence of two historically distinct paradigms in artificial intelligence-data-driven neural networks and logic-based symbolic reasoning. This book presents a comprehensive roadmap of this emerging hybrid discipline, offering deep theoretical insights, practical methodologies, and transformative applications across diverse research sectors, including healthcare, finance, engineering, and autonomous systems. It is structured into four parts-Foundational Principles, Hybrid Models and Techniques, Real-World Applications, and Emerging Challenges, bringing together cutting-edge research and expert perspectives to highlight how Neuro-Symbolic AI enhances interpretability, reasoning capabilities, and trust in intelligent systems.While neural networks have achieved remarkable success in perception and pattern recognition tasks, they often lack the reasoning, transparency, and generalizability that symbolic systems excel at. Conversely, symbolic AI lacks the flexibility and scalability of deep learning. This handbook directly addresses these challenges by providing a structured approach to Neuro-symbolic AI, presenting rigorous theoretical foundations, state-of-the-art hybrid techniques (e.g., knowledge graphs, compositionality, category theory), and diverse real-world applications. This book consolidates research insights, methodological innovations, and practical use cases into a single, accessible volume.Presents theoretical advances, hybrid model architectures, and practical applications in a single volumeProvides comprehensive coverage across machine learning, symbolic AI, and cognitive computingIncludes real-world case studies from healthcare, autonomous systems, and scientific discovery that offer readers concrete insights into how Neuro-Symbolic AI is implementedFeatures a dedicated section on emerging challenges and future directions, such as scalability, energy efficiency, security, and AGI potential, giving readers a roadmap for future research, innovation, and responsible deployment of hybrid AI systems

Written by a team of experts at the forefront of the cyber-physical systems (CPS) revolution, this book provides an in-depth look at security and privacy, two of the most critical challenges facing both the CPS research and development community and ICT professionals. It explores, in depth, the key technical, social, and legal issues at stake, and it provides readers with the information they need to advance research and development in this exciting area.  Cyber-physical systems (CPS) are engineered systems that are built from, and depend upon the seamless integration of computational algorithms and physical components. Advances in CPS will enable capability, adaptability, scalability, resiliency, safety, security, and usability far in excess of what today’s simple embedded systems can provide. Just as the Internet revolutionized the way we interact with information, CPS technology has already begun to transform the way people interact with engineered systems. In the years ahead, smart CPS will drive innovation and competition across industry sectors, from agriculture, energy, and transportation, to architecture, healthcare, and manufacturing.  A priceless source of practical information and inspiration, Security and Privacy in Cyber-Physical Systems: Foundations, Principles and Applications is certain to have a profound impact on ongoing R&D and education at the confluence of security, privacy, and CPS.

Written by a team of experts at the forefront of the cyber-physical systems (CPS) revolution, this book provides an in-depth look at security and privacy, two of the most critical challenges facing both the CPS research and development community and ICT professionals. It explores, in depth, the key technical, social, and legal issues at stake, and it provides readers with the information they need to advance research and development in this exciting area.  

Cyber-physical systems (CPS) are engineered systems that are built from, and depend upon the seamless integration of computational algorithms and physical components. Advances in CPS will enable capability, adaptability, scalability, resiliency, safety, security, and usability far in excess of what today’s simple embedded systems can provide. Just as the Internet revolutionized the way we interact with information, CPS technology has already begun to transform the way people interact with engineered systems. In the years ahead, smart CPS will drive innovation and competition across industry sectors, from agriculture, energy, and transportation, to architecture, healthcare, and manufacturing.  A priceless source of practical information and inspiration, Security and Privacy in Cyber-Physical Systems: Foundations, Principles and Applications is certain to have a profound impact on ongoing R&D and education at the confluence of security, privacy, and CPS. 

Written by a team of experts at the forefront of the cyber-physical systems (CPS) revolution, this book provides an in-depth look at security and privacy, two of the most critical challenges facing both the CPS research and development community and ICT professionals. It explores, in depth, the key technical, social, and legal issues at stake, and it provides readers with the information they need to advance research and development in this exciting area.  

Cyber-physical systems (CPS) are engineered systems that are built from, and depend upon the seamless integration of computational algorithms and physical components. Advances in CPS will enable capability, adaptability, scalability, resiliency, safety, security, and usability far in excess of what today’s simple embedded systems can provide. Just as the Internet revolutionized the way we interact with information, CPS technology has already begun to transform the way people interact with engineered systems. In the years ahead, smart CPS will drive innovation and competition across industry sectors, from agriculture, energy, and transportation, to architecture, healthcare, and manufacturing.  A priceless source of practical information and inspiration, Security and Privacy in Cyber-Physical Systems: Foundations, Principles and Applications is certain to have a profound impact on ongoing R&D and education at the confluence of security, privacy, and CPS. 

Provides the foundations and principles needed for addressing the various challenges of developing smart citiesSmart cities are emerging as a priority for research and development across the world. They open up significant opportunities in several areas, such as economic growth, health, wellness, energy efficiency, and transportation, to promote the sustainable development of cities. This book provides the basics of smart cities, and it examines the possible future trends of this technology. Smart Cities: Foundations, Principles, and Applications provides a systems science perspective in presenting the foundations and principles that span multiple disciplines for the development of smart cities.Divided into three parts—foundations, principles, and applications—Smart Cities addresses the various challenges and opportunities of creating smart cities and all that they have to offer. It also covers smart city theory modeling and simulation, and examines case studies of existing smart cities from all around the world. In addition, the book: Addresses how to develop a smart city and how to present the state of the art and practice of them all over the worldFocuses on the foundations and principles needed for advancing the science, engineering, and technology of smart cities—including system design, system verification, real-time control and adaptation, Internet of Things, and test bedsCovers applications of smart cities as they relate to smart transportation/connected vehicle (CV) and Intelligent Transportation Systems (ITS) for improved mobility, safety, and environmental protectionSmart Cities: Foundations, Principles, and Applications is a welcome reference for the many researchers and professionals working on the development of smart cities and smart city-related industries.

Provides the foundations and principles needed for addressing the various challenges of developing smart cities

Smart cities are emerging as a priority for research and development across the world. They open up significant opportunities in several areas, such as economic growth, health, wellness, energy efficiency, and transportation, to promote the sustainable development of cities. This book provides the basics of smart cities, and it examines the possible future trends of this technology. Smart Cities: Foundations, Principles, and Applications provides a systems science perspective in presenting the foundations and principles that span multiple disciplines for the development of smart cities.

Divided into three parts—foundations, principles, and applications—Smart Cities addresses the various challenges and opportunities of creating smart cities and all that they have to offer. It also covers smart city theory modeling and simulation, and examines case studies of existing smart cities from all around the world. In addition, the book:

Addresses how to develop a smart city and how to present the state of the art and practice of them all over the world Focuses on the foundations and principles needed for advancing the science, engineering, and technology of smart cities—including system design, system verification, real-time control and adaptation, Internet of Things, and test beds Covers applications of smart cities as they relate to smart transportation/connected vehicle (CV) and Intelligent Transportation Systems (ITS) for improved mobility, safety, and environmental protection

Smart Cities: Foundations, Principles, and Applications is a welcome reference for the many researchers and professionals working on the development of smart cities and smart city-related industries.

Provides the foundations and principles needed for addressing the various challenges of developing smart cities

Smart cities are emerging as a priority for research and development across the world. They open up significant opportunities in several areas, such as economic growth, health, wellness, energy efficiency, and transportation, to promote the sustainable development of cities. This book provides the basics of smart cities, and it examines the possible future trends of this technology. Smart Cities: Foundations, Principles, and Applications provides a systems science perspective in presenting the foundations and principles that span multiple disciplines for the development of smart cities.

Divided into three parts—foundations, principles, and applications—Smart Cities addresses the various challenges and opportunities of creating smart cities and all that they have to offer. It also covers smart city theory modeling and simulation, and examines case studies of existing smart cities from all around the world. In addition, the book:

Addresses how to develop a smart city and how to present the state of the art and practice of them all over the world Focuses on the foundations and principles needed for advancing the science, engineering, and technology of smart cities—including system design, system verification, real-time control and adaptation, Internet of Things, and test beds Covers applications of smart cities as they relate to smart transportation/connected vehicle (CV) and Intelligent Transportation Systems (ITS) for improved mobility, safety, and environmental protection

Smart Cities: Foundations, Principles, and Applications is a welcome reference for the many researchers and professionals working on the development of smart cities and smart city-related industries.

Informative reference on the state of the art in cybersecurity and how to achieve a more secure cyberspace AI for Cybersecurity presents the state of the art and practice in AI for cybersecurity with a focus on four interrelated defensive capabilities of deter, protect, detect, and respond. The book examines the fundamentals of AI for cybersecurity as a multidisciplinary subject, describes how to design, build, and operate AI technologies and strategies to achieve a more secure cyberspace, and provides why-what-how of each AI technique-cybersecurity task pair to enable researchers and practitioners to make contributions to the field of AI for cybersecurity. This book is aligned with the National Science and Technology Council’s (NSTC) 2023 Federal Cybersecurity Research and Development Strategic Plan (RDSP) and President Biden’s Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence. Learning objectives and 200 illustrations are included throughout the text. Written by a team of highly qualified experts in the field, AI for Cybersecurity discusses topics including: Robustness and risks of the methods covered, including adversarial ML threats in model training, deployment, and reusePrivacy risks including model inversion, membership inference, attribute inference, re-identification, and deanonymizationForensic and formal methods for analyzing, auditing, and verifying security- and privacy-related aspects of AI componentsUse of generative AI systems for improving security and the risks of generative AI systems to securityTransparency and interpretability/explainability of models and algorithms and associated issues of fairness and biasAI for Cybersecurity is an excellent reference for practitioners in AI for cybersecurity related industries such as commerce, education, energy, financial services, healthcare, manufacturing, and defense. Fourth year undergraduates and postgraduates in computer science and related programs of study will also find it valuable.

Informative reference on the state of the art in cybersecurity and how to achieve a more secure cyberspace AI for Cybersecurity presents the state of the art and practice in AI for cybersecurity with a focus on four interrelated defensive capabilities of deter, protect, detect, and respond. The book examines the fundamentals of AI for cybersecurity as a multidisciplinary subject, describes how to design, build, and operate AI technologies and strategies to achieve a more secure cyberspace, and provides why-what-how of each AI technique-cybersecurity task pair to enable researchers and practitioners to make contributions to the field of AI for cybersecurity. This book is aligned with the National Science and Technology Council s (NSTC) 2023 Federal Cybersecurity Research and Development Strategic Plan (RDSP) and President Biden s Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence. Learning objectives and 200 illustrations are included throughout the text. Written by a team of highly qualified experts in the field, AI for Cybersecurity discusses topics including: Robustness and risks of the methods covered, including adversarial ML threats in model training, deployment, and reusePrivacy risks including model inversion, membership inference, attribute inference, re-identification, and deanonymizationForensic and formal methods for analyzing, auditing, and verifying security- and privacy-related aspects of AI componentsUse of generative AI systems for improving security and the risks of generative AI systems to securityTransparency and interpretability/explainability of models and algorithms and associated issues of fairness and bias AI for Cybersecurity is an excellent reference for practitioners in AI for cybersecurity related industries such as commerce, education, energy, financial services, healthcare, manufacturing, and defense. Fourth year undergraduates and postgraduates in computer science and related programs of study will also find it valuable.

Informative reference on the state of the art in cybersecurity and how to achieve a more secure cyberspace AI for Cybersecurity presents the state of the art and practice in AI for cybersecurity with a focus on four interrelated defensive capabilities of deter, protect, detect, and respond. The book examines the fundamentals of AI for cybersecurity as a multidisciplinary subject, describes how to design, build, and operate AI technologies and strategies to achieve a more secure cyberspace, and provides why-what-how of each AI technique-cybersecurity task pair to enable researchers and practitioners to make contributions to the field of AI for cybersecurity. This book is aligned with the National Science and Technology Council s (NSTC) 2023 Federal Cybersecurity Research and Development Strategic Plan (RDSP) and President Biden s Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence. Learning objectives and 200 illustrations are included throughout the text. Written by a team of highly qualified experts in the field, AI for Cybersecurity discusses topics including: Robustness and risks of the methods covered, including adversarial ML threats in model training, deployment, and reusePrivacy risks including model inversion, membership inference, attribute inference, re-identification, and deanonymizationForensic and formal methods for analyzing, auditing, and verifying security- and privacy-related aspects of AI componentsUse of generative AI systems for improving security and the risks of generative AI systems to securityTransparency and interpretability/explainability of models and algorithms and associated issues of fairness and bias AI for Cybersecurity is an excellent reference for practitioners in AI for cybersecurity related industries such as commerce, education, energy, financial services, healthcare, manufacturing, and defense. Fourth year undergraduates and postgraduates in computer science and related programs of study will also find it valuable.

An expert compilation of on-device training techniques, regulatory frameworks, and ethical considerations of TinyML design and development In Tiny Machine Learning: Design Principles and Applications, a team of distinguished researchers delivers a comprehensive discussion of the critical concepts, design principles, applications, and relevant issues in Tiny Machine Learning (TinyML). Expert contributors introduce a new low power resource, offering vast applications in IoT devices with system-algorithm co-design. Tiny Machine Learning explores TinyML paradigms and enablers, TinyML for anomaly detection, and the learning panorama under TinyML. Readers will find explanations of TinyML devices and tools, power consumption and memory in IoT microcontrollers, and lightweight frameworks for TinyML. The book also describes TinyML techniques for real-time and environmental applications. Additional topics covered in the book include: A thorough introduction to security and privacy techniques for TinyML devices, including the implementation of novel security schemesIncisive explorations of power consumption and memory in IoT MCUs, including ultralow-power smart IoT devices with embedded TinyMLPractical discussions of TinyML research targeting microcontrollers for data extraction and synthesisPerfect for industry and academic researchers, scientists, and engineers, Tiny Machine Learning will also benefit lecturers and graduate students interested in machine learning.

An expert compilation of on-device training techniques, regulatory frameworks, and ethical considerations of TinyML design and development In Tiny Machine Learning: Design Principles and Applications, a team of distinguished researchers delivers a comprehensive discussion of the critical concepts, design principles, applications, and relevant issues in Tiny Machine Learning (TinyML). Expert contributors introduce a new low power resource, offering vast applications in IoT devices with system-algorithm co-design. Tiny Machine Learning explores TinyML paradigms and enablers, TinyML for anomaly detection, and the learning panorama under TinyML. Readers will find explanations of TinyML devices and tools, power consumption and memory in IoT microcontrollers, and lightweight frameworks for TinyML. The book also describes TinyML techniques for real-time and environmental applications. Additional topics covered in the book include: A thorough introduction to security and privacy techniques for TinyML devices, including the implementation of novel security schemesIncisive explorations of power consumption and memory in IoT MCUs, including ultralow-power smart IoT devices with embedded TinyMLPractical discussions of TinyML research targeting microcontrollers for data extraction and synthesis Perfect for industry and academic researchers, scientists, and engineers, Tiny Machine Learning will also benefit lecturers and graduate students interested in machine learning.

An expert compilation of on-device training techniques, regulatory frameworks, and ethical considerations of TinyML design and development In Tiny Machine Learning: Design Principles and Applications, a team of distinguished researchers delivers a comprehensive discussion of the critical concepts, design principles, applications, and relevant issues in Tiny Machine Learning (TinyML). Expert contributors introduce a new low power resource, offering vast applications in IoT devices with system-algorithm co-design. Tiny Machine Learning explores TinyML paradigms and enablers, TinyML for anomaly detection, and the learning panorama under TinyML. Readers will find explanations of TinyML devices and tools, power consumption and memory in IoT microcontrollers, and lightweight frameworks for TinyML. The book also describes TinyML techniques for real-time and environmental applications. Additional topics covered in the book include: A thorough introduction to security and privacy techniques for TinyML devices, including the implementation of novel security schemesIncisive explorations of power consumption and memory in IoT MCUs, including ultralow-power smart IoT devices with embedded TinyMLPractical discussions of TinyML research targeting microcontrollers for data extraction and synthesis Perfect for industry and academic researchers, scientists, and engineers, Tiny Machine Learning will also benefit lecturers and graduate students interested in machine learning.

An up-to-date and expert discussion of neuro-symbolic artificial intelligence development In Neuro-symbolic AI: Foundations and Applications, a team of distinguished researchers delivers a comprehensive overview of the emerging field of neuro-symbolic artificial intelligence. Expert contributors explain the integration of symbolic representations with neural networks, demonstrating state-of-the-art practices in the field. The book fosters collaboration amongst diverse disciplines and promotes a deeper understanding of the challenges posed by deep learning, including generalizability, explainability, and robustness. It is an authoritative, self-contained reference text that provides a solid foundation for newcomers to the field as well as seasoned researchers and developers. Readers will find: A systematic perspective on the foundations of neuro-development AI system developmentComprehensive explorations of key concepts in neuro-symbolic artificial intelligenceDiscussions of real-world applications of neuro-symbolic AI in fields such as healthcare, finance, autonomous driving, and the militaryComplete treatments of the foundations of neuro-symbolic AI from multiple disciplinary perspectives, including computer science, software engineering, and academic researchPerfect for researchers and professionals in artificial intelligence involved industries, including autonomous driving, military, healthcare, and finance, Neuro-symbolic AI: Foundations and Applications will also benefit students of computer science, software engineering, data science, and machine learning.

An up-to-date and expert discussion of neuro-symbolic artificial intelligence development In Neuro-symbolic AI: Foundations and Applications, a team of distinguished researchers delivers a comprehensive overview of the emerging field of neuro-symbolic artificial intelligence. Expert contributors explain the integration of symbolic representations with neural networks, demonstrating state-of-the-art practices in the field. The book fosters collaboration amongst diverse disciplines and promotes a deeper understanding of the challenges posed by deep learning, including generalizability, explainability, and robustness. It is an authoritative, self-contained reference text that provides a solid foundation for newcomers to the field as well as seasoned researchers and developers. Readers will find: A systematic perspective on the foundations of neuro-development AI system developmentComprehensive explorations of key concepts in neuro-symbolic artificial intelligenceDiscussions of real-world applications of neuro-symbolic AI in fields such as healthcare, finance, autonomous driving, and the militaryComplete treatments of the foundations of neuro-symbolic AI from multiple disciplinary perspectives, including computer science, software engineering, and academic research Perfect for researchers and professionals in artificial intelligence involved industries, including autonomous driving, military, healthcare, and finance, Neuro-symbolic AI: Foundations and Applications will also benefit students of computer science, software engineering, data science, and machine learning.

An up-to-date and expert discussion of neuro-symbolic artificial intelligence development In Neuro-symbolic AI: Foundations and Applications, a team of distinguished researchers delivers a comprehensive overview of the emerging field of neuro-symbolic artificial intelligence. Expert contributors explain the integration of symbolic representations with neural networks, demonstrating state-of-the-art practices in the field. The book fosters collaboration amongst diverse disciplines and promotes a deeper understanding of the challenges posed by deep learning, including generalizability, explainability, and robustness. It is an authoritative, self-contained reference text that provides a solid foundation for newcomers to the field as well as seasoned researchers and developers. Readers will find: A systematic perspective on the foundations of neuro-development AI system developmentComprehensive explorations of key concepts in neuro-symbolic artificial intelligenceDiscussions of real-world applications of neuro-symbolic AI in fields such as healthcare, finance, autonomous driving, and the militaryComplete treatments of the foundations of neuro-symbolic AI from multiple disciplinary perspectives, including computer science, software engineering, and academic research Perfect for researchers and professionals in artificial intelligence involved industries, including autonomous driving, military, healthcare, and finance, Neuro-symbolic AI: Foundations and Applications will also benefit students of computer science, software engineering, data science, and machine learning.

In the era of big data and multi-connectivity via IoTs, protecting and securing multimedia data has become a real necessity and priority for organizations and businesses, but this can be a rather difficult task due to the heterogeneous nature of platforms and data sets. It is therefore essential to improve the security level of multimedia information by developing core technologies to prevent the loss and damage of information during processing and transmission.This book covers innovations and future perspectives in access control and security monitoring of multimedia Information processing and transmission. The authors present cybersecurity, privacy and control methods and technologies integrated with blockchain and multimedia AI, including encryption and watermarking techniques, wearable-based IoT security methods, multimedia data forensics and deepfake video security monitoring.This will be a useful reference for researchers, engineers and scientists in both academia and industry as well as lecturers and advanced students for developing efficient methods, frameworks and techniques for multimedia information processing security and privacy. It will also be of interest to multimedia platform and system developers and designers.

Billions of wireless applications are proliferating in the global space. The novel wireless systems to support these applications need to satisfy several user-defined metrics to ensure quality of physical experience, quality of service, low latency and ultra-high reliability. Enabling technologies are being developed in order to meet these rate-hungry requirements.Managing these complex wireless and computing resources requires a proactive analysis of the core wireless systems, especially in the metaverse, which is a virtual model of the physical world systems. The metaverse concept is intended to be self-sustainable and facilitates the seamless operation of wireless systems at minimal operational costs for the efficient management of wireless system resources. It is gaining widespread popularity using technologies such as advanced optimization theory, digital twins, virtual reality, machine learning, and blockchain.This book presents a comprehensive overview of advanced metaverse wireless communication systems. The authors highlight the technology enablers and critical design aspects, emphasize how the metaverse can support wireless systems but also show how wireless systems can empower the metaverse. They address critical concerns covering theoretical analysis, novel system architecture design and new implementation methodologies, emerging application scenarios, experimental frameworks, and reliability, security, privacy, technical, and ethical considerations, viable solutions, as well as open challenges, the social impacts of the metaverse concept and future perspectives.Offering a range of international perspectives from researchers in the field, this book is intended for researchers from academia and industry, and scientists, engineers, lecturers and advanced students in the fields of wireless and mobile communications, networking, computing, data science, AI/ML/DL, multimedia, haptics, sensing, CPS and immersive technologies.

Quantum computing has shown a potential to tackle specific types of problems, especially those involving a daunting number of variables, at an exponentially faster rate compared to classical computers. This volume focuses on quantum variants of machine learning algorithms, such as quantum neural networks, quantum reinforcement learning, quantum principal component analysis, quantum support vectors, quantum Boltzmann machines, and many more.