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  • image of Upgrading Urban Power Grids
    Upgrading Urban Power Grids
    • Author(s): Juan M. Gers
    • Publication Year: 2024
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      Today, more than half the global population lives in cities, and two thirds are expected to do so by 2050. Cities need technologies to reduce greenhouse gas emissions from energy, transport and heating, which requires distributed renewable electricity, from rooftop solar, small wind and other sources, in order to cleanly generate enough electricity to meet cities' needs. However, most urban electricity grids were not designed to manage distributed energy generation and the benefits of smart grids. Distribution systems therefore need to be strengthened and updated with the new technology offered by the hardware and software available, in order to maintain power quality and meet the requirements of reliability requested by regulatory bodies.

      The book opens with an introductory chapter, outlining the topic and explaining the importance of and motivations for upgrading urban power grids. Further chapters cover modelling, load management and demand response, reliability and fault location isolation and service restoration (FLISR) schemes for urban grids, renewable generation, integration and microgrids for cities, protection, automation and control, regulatory issues, volt/var control, as well as open challenges and future developments. Case studies help readers understand and apply the concepts discussed.

      Covering the key technologies needed for the urban energy transition, Upgrading Urban Power Grids is a valuable reference for consulting engineers, researchers and advanced students at universities and at utilities involved with electric power systems.

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  • image of Metaheuristic Optimization in Power Engineering. Volume 1: Algorithms and power dispatch using MATLAB®-based software
    Metaheuristic Optimization in Power Engineering. Volume 1: Algorithms and power dispatch using MATLAB®-based software
    • Author(s): Jordan Radosavljević
    • Publication Year: 2024
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      A metaheuristic is a higher-level procedure designed to find, generate, or select a heuristic or partial search algorithm that may provide a sufficiently good solution to an optimization problem with incomplete or imperfect information or limited computation capacity. Metaheuristics can often find good solutions with less computational effort than other algorithms. Modern and emerging power systems, with the growing complexity of distributed and intermittent generation and EV charging, are an application for such methods.

      The new edition of Metaheuristic Optimization in Power Engineering in two volumes uses a MATLAB-based software package for testing and comparing methods, and includes several new and substantially revised and updated chapters.

      Volume 1 covers principles and key algorithms, such as genetic and swarm algorithms, gravitational and metaheuristic algorithms, power flow and power dispatch under consideration of renewable generation.

      Volume 2 focuses on power distribution networks, including power flow, voltage control and regulation, optimisation of generation placement and sizing, and state estimation analysis.

      This reference for researchers and advanced students working on power system analysis and optimization offers an overview of metaheuristic optimization approaches to solving problems in modern power systems.

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  • image of Metaheuristic Optimization in Power Engineering. Volume 2: Distribution systems using MATLAB®-based software
    Metaheuristic Optimization in Power Engineering. Volume 2: Distribution systems using MATLAB®-based software
    • Author(s): Jordan Radosavljević
    • Publication Year: 2024
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      A metaheuristic is a higher-level procedure designed to find, generate, or select a heuristic or partial search algorithm that may provide a sufficiently good solution to an optimization problem with incomplete or imperfect information or limited computation capacity. Metaheuristics can often find good solutions with less computational effort than other algorithms. Modern and emerging power systems, with the growing complexity of distributed and intermittent generation and EV charging, are an application for such methods.

      The new edition of Metaheuristic Optimization in Power Engineering in two volumes uses a MATLAB-based software package for testing and comparing methods, and includes several new and substantially revised and updated chapters.

      Volume 1 covers principles and key algorithms, such as genetic and swarm algorithms, gravitational and metaheuristic algorithms, power flow and power dispatch under consideration of renewable generation.

      Volume 2 focuses on power distribution networks, including power flow, voltage control and regulation, optimisation of generation placement and sizing, and state estimation analysis.

      This reference for researchers and advanced students working on power system analysis and optimization offers an overview of metaheuristic optimization approaches to solving problems in modern power systems.

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  • image of Split Federated Learning for Secure IoT Applications: Concepts, frameworks, applications and case studies
    Split Federated Learning for Secure IoT Applications: Concepts, frameworks, applications and case studies
    • Editors: Gururaj Harinahalli Lokesh; Geetabai S. Hukkeri; N.Z. Jhanjhi; Hong Lin
    • Publication Year: 2024
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      New approaches in federated learning and split learning have the potential to significantly improve ubiquitous intelligence in internet of things (IoT) applications. In split federated learning, the machine learning model is divided into smaller network segments, with each segment trained independently on a server using distributed local client data.

      The split learning method mitigates two fundamental drawbacks of federated learning: affordability, and privacy and security. When running machine learning computation on devices with limited resources, assigning only a portion of the network to train at the client-side minimizes the processing burden, compared to running a complete network as in federated learning. In addition, neither client nor server has full access to the other, which is more secure.

      This book reviews cutting edge technologies and advanced research in split federated learning. Coverage includes approaches to realizing and evaluating the effectiveness and advantages of federated learning and split-fed learning, the role of this technology in advancing and securing IoTs, advanced research on emerging AI models for preserving the privacy of the data owned by the clients, and the analysis and development of AI mechanisms in IoT architectures and applications. The use of split federated learning in natural language processing, recommendation systems, healthcare systems, emotion detection, smart agriculture, smart transportation and smart cities is discussed.

      Split Federated Learning for Secure IoT Applications: Concepts, frameworks, applications and case studies offers useful insights to the latest developments in the field for researchers, engineers and scientists in academia and industry, who are working in computing, AI, data science and cybersecurity with a focus on federated learning, machine learning and deep learning.

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  • image of ESG Innovation for Sustainable Manufacturing Technology: Applications, designs and standards
    ESG Innovation for Sustainable Manufacturing Technology: Applications, designs and standards
    • Editor: Wai Yie Leong
    • Publication Year: 2024
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      In an era defined by unprecedented environmental challenges, social inequalities, and corporate responsibility, the concept of Environmental, Social, and Governance (ESG) has emerged as a transformative force. It is a set of practices used to evaluate the operational performance of a company or industry sector as it relates to social and environmental impact.

      Manufacturing plays a key role in many areas of human living, and it is both part of the problem and of the solution. As manufacturing organizations grapple with the imperative to foster sustainability, ESG principles have evolved into a guiding framework. At the heart of this evolution lies technology, acting as a catalyst for ESG innovation and driving systemic change across manufacturing. This book seeks to explore the intersection of ESG and manufacturing technology.

      The environmental criterion looks at whether a company pollutes or implements sustainable practices. The social criterion considers how the company works with and impacts its employees, customers, suppliers and the community as a whole. Finally, the governance criterion looks at the company's leadership, its internal controls and audits, board governance oversight and executive pay.

      This ground-breaking collection will be a comprehensive educational tool, promoting awareness, guiding investment strategies, helping to manage risks, facilitating stakeholder engagement, ensuring regulatory compliance, measuring impacts, driving innovation, and promoting ethical leadership - all of which are essential for addressing pressing global challenges and building a more sustainable future.

      ESG Innovation for Sustainable Manufacturing Technology: Applications, designs and standards offers insights and diverse perspectives on this cross-disciplinary topic for an audience of researchers, advanced students and professionals in sustainable manufacturing.

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  • image of Exploring Intelligent Healthcare with Quantum Computing
    Exploring Intelligent Healthcare with Quantum Computing
    • Editors: Abhishek Kumar; Ashutosh Kumar Dubey; Vincenzo Piuri; Joel J.P.C. Rodrigues
    • Publication Year: 2024
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      Classical computers encode information in binary bits that can either be 0s or 1s. In a quantum computer, the basic unit of memory is a quantum bit or qubit. These qubits play a similar role in terms of storing information, but use physical systems, such as the spin of an electron or the orientation of a photon, to do so. In situations where there are a large number of possible feature combinations, quantum computers can consider them simultaneously, speeding up the data processing time.

      In healthcare, where there are often large numbers of possible factors to consider, quantum computers can address them simultaneously, thereby allowing doctors to compare much, much more data, and all permutations of that data, in parallel to discover the best patterns that describe it, and therefore predict the best treatment options.

      This edited book explores the field of quantum computing and machine learning for medical data processing, and is a useful resource for computer engineers, researchers, healthcare technologists and scientists specialising in quantum computing, quantum AI, data processing, deep learning, machine learning, smart healthcare, and medical data systems.

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  • image of Applied Building Performance Simulation
    Applied Building Performance Simulation
    • Author(s): Joe Clarke ; Jeremy Cockroft ; Jon Hand ; Raheal McGhee
    • Publication Year: 2024
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      Good building performance is contingent on complex, interacting factors. The application of performance simulation tools allows practitioners to adopt a virtual prototype and test approach in selecting design solutions that balance performance and cost.

      The aim of this book is to provide guidance on approaches to the modelling and simulation of energy systems in the built environment at various scales and complexities to address the myriad challenges of the clean energy transition. The intention is to equip the reader with the understanding required to compose high integrity models, commission realistic simulations, and interpret predictions to assess life cycle performance and ensure operational resilience.

      Initial chapters introduce the impressive capabilities of building energy systems simulation and the new features that are likely to be added in future. The authors then describe the sustainable energy challenge and how modelling and simulation can be used to scrutinise proposals in a cost-effectively manner. The remainder of the book then covers different technology options including electrification of heating, net zero energy housing, community energy systems, active demand management, and urban energy action planning. In each case the nature of the problem is described, the construction of a high-fidelity computer model elaborated, and principal simulation outcomes demonstrated.

      Some of the problem domains addressed in the book are accompanied by a high-fidelity digital model available externally. These models are compatible with the ESP-r performance simulation program, which is freely available under an open-source licence.

      Applied Building Performance Simulation provides a thorough grounding and practical guidance in the role of software simulation tools for building performance, for an audience of researchers, industry professionals, advanced students, and policy makers, regulators and standards bodies operating in the field.

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  • image of 6G: Evolution or Revolution?: A converged view of cellular, Wi-Fi, computing and communication
    6G: Evolution or Revolution?: A converged view of cellular, Wi-Fi, computing and communication
    • Author(s): Peter Smyth ; Peter Willis ; David Wisely
    • Publication Year: 2024
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      There are numerous potential paths toward achieving the 6G network and various factors that could define it. Written by three telecom industry experts, this book explores the opportunities and challenges surrounding the next generation of wireless communication technology. Will 6G build solely on the future evolution success of the 5G network, or will the delivery of 6G facilitate a revolution in technology and infrastructure with the convergence of fixed/mobile networks and collaboration with hyperscalers to help fund it?

      The book presents a compelling vision of how 6G addresses the challenges that 5G faces for the broader digitalisation of society and industry. It defines new types of wireless communications and refines the smartphone and its use, revolutionising our digital landscape. While the introduction of mmWave phased arrays offers optical fibre-like bandwidths, many of the applications for this technology will not be telecoms-based. The authors also define a revolutionary 6G with new ideas for spectrum management, creating GHz of additional spectrum, convergence, privacy and core network infrastructure. They also review the evolution of 5G and Wi-Fi towards the end of the decade as a platform for 6G and the current worldwide 6G standards activities. Meanwhile, inside-out coverage could define how mobile networks are built.

      6G: Evolution or Revolution? explores what the internet could be in 2030 and how it will influence 6G, particularly its intelligence layers. It covers the internet and the mobile core network and how these will change for the age of machines, with Wi-Fi and mmWave communications, AI, and the new uses of the smartphone.

      This book provides an informative and thought-provoking perspective for researchers, strategists, regulators, scientists, engineers, technology professionals, and academia interested in the future of communications technology.

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  • image of High-Level Synthesis based Methodologies for Hardware Security, Trust and IP Protection
    High-Level Synthesis based Methodologies for Hardware Security, Trust and IP Protection
    • Author(s): Anirban Sengupta
    • Publication Year: 2024
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      High-Level Synthesis based Methodologies for Hardware Security, Trust and IP Protection presents state-of-the art high-level synthesis methodologies for hardware security and trust, including IP protection through synthesis-based watermarking and structural obfuscation.

      All modern electronic gadgets have complex system-on-chips (SoCs) that rely heavily on data intensive application specific processors, for digital signal processing (DSP), machine learning, and image processing applications. These data-intensive cores, in the form of intellectual property (IP), form an integral part of various modern equipment and consumer applications, such as smart phones, smart watches, and tablets.

      High level synthesis (HLS) frameworks play a pivotal role in designing these application specific processors. However, the design of such processors can be exposed to several trust issues and hardware security threats, such as IP piracy, fraud IP ownership, and reverse engineering.

      Written by an expert author, this book is a source of information for readers on HLS solutions for hardware security. It covers topics such as HLS-based watermarking using retinal biometrics, HLS-based structural obfuscation, and detective countermeasure against HLS-based hardware Trojan attacks.

      This book is a useful resource for researchers, graduate students, and practising engineers working in electronics and chip design.

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  • image of Cybersecurity in Emerging Healthcare Systems
    Cybersecurity in Emerging Healthcare Systems
    • Editors: Agbotiname Lucky Imoize; Chandrashekhar Meshram; Joseph Bamidele Awotunde; Yousef Farhaoui; Dinh-Thuan Do
    • Publication Year: 2024
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      Emerging healthcare networks are interconnected physical systems that use cyber technologies for interaction and functionality. The proliferation of massive internet-of-things (IoT) devices enables remote and distributed access to cutting-edge diagnostics and treatment options in modern healthcare systems. New security vulnerabilities are emerging due to the increasing complexity of the healthcare architecture, in particular, threats to medical devices and critical infrastructure pose significant concerns owing to their potential risks to patient health and safety. In recent times, patients have been exposed to high risks from attacks capable of disrupting critical medical infrastructure, communications facilities, and services, interfering with medical devices, or compromising sensitive user data.

      This book seeks to present cyber risk and vulnerability models, considering a number of threats and examining how effective regulations could help guarantee medical device fidelity and trust. The book discusses the application of artificial intelligence and machine learning to provide practical learning-based solutions to address cyberattacks in emerging healthcare systems. The book focuses on the technical considerations, potential opportunities, critical cybersecurity challenges, the prospects and potential benefits of cybersecurity in emerging healthcare systems. Finally, the book presents case studies, highlighting critical lessons, and providing recommendations for designing AI-based cybersecurity architectures for emerging healthcare systems.

      Written by an international team of authors, this book is suitable for an audience of industry-based and academic researchers, scientists, and computer engineers working in data science, cybersecurity and wireless communications particularly those specialising in healthcare data science and those in related fields.

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  • image of Clean Energy for Low-Income Communities: Technology, deployment and challenges
    Clean Energy for Low-Income Communities: Technology, deployment and challenges
    • Editors: David S-K. Ting; Jacqueline A. Stagner
    • Publication Year: 2024
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      Energy provision for low income or remote communities is a difficult challenge, with many still depending on polluting and costly fossil fuels. Transporting energy is a further problem since some communities are only accessible during brief periods of the year. Local energy generation is a key solution, but technical challenges need to be overcome.

      This hands-on treatise explores technologies and approaches for accelerating deployment of accessible clean energy and discusses obstacles hindering that deployment. The primary focus is on engineering topics, although contributions from non-engineering application experts are also included. Chapters cover principal aspects of energy provision for low-income communities, low cost and energy-efficient housing designs, solar energy for low-income communities, solar PV integration in residential buildings, rural electrification in low-income communities and in remote communities using wind and solar energy, advances in biofuels production, and as a case study, modelling and forecasting energy mix scenarios for Turkey.

      Clean Energy for Low Income Communities: Technology, deployment and challenges offers in-depth discussion of this multidisciplinary topic for an audience of researchers in academia, renewable energy and utilities experts in industry, technology manufacturers and advanced students, as well as energy experts in think tanks and development banks.

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  • image of Fuzzy Logic Control in Energy Systems: With design examples in MATLAB<sup>®</sup>/Simulink<sup>®</sup>
    Fuzzy Logic Control in Energy Systems: With design examples in MATLAB®/Simulink®
    • Author(s): İsmail Hakkı Altaş
    • Publication Year: 2024
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      Fuzzy models have the capability of recognising, representing, and working with data that is vague or lacks certainty, making them suitable for managing electrical energy systems involving intermittent distributed generation and varying distributed loads.

      The second edition of Fuzzy Logic Control in Energy Systems has been revised and updated with new topics including shade and integration issues in PV, microgrids, cybersecurity in power systems, and day-ahead estimation of sun and wind data. Starting with a review of fuzzy logic, chapters cover fuzzy sets and partitioning, fuzzy decision making, fuzzy logic controllers (FLC), and system modelling and control. The application of FLC in power systems, wind energy, PV energy systems and microgrids is then explored.

      Several examples of fuzzy logic control and management are discussed including DC motor control, multi area load-frequency control, and wind and PV energy management. The book presents a fuzzy logic controller design in Matlab-Simulink enabling the reader to study every step and to develop their own fuzzy processor library and toolbox.

      Providing thorough and logical coverage of fuzzy logic control and its use in energy systems, this book provides valuable insights for researchers and practicing engineers involved with power system design and control, grid operators, PV and wind turbine manufacturers, as well as advanced students in EE.

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  • image of Antenna Booster Technology for Wireless Communications
    Antenna Booster Technology for Wireless Communications
    • Author(s): Jaume Anguera  and  Aurora Andújar
    • Publication Year: 2024
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      Being surface-mounted and chip-like in nature, the antenna booster fits seamlessly in an electronic printed circuit board the same way as any other electronic component such as an amplifier, filter or switch. It can be assembled with a conventional pick-and-place machine, making the manufacture and design of the new generation of IoT and mobile or wireless devices much simpler, faster and more effective.

      Starting with the theory and fundamentals behind the design of antenna boosters and matching networks, the authors present several architectures and show the readers how to put the theory into practice to design antenna systems on wireless platforms and multiband design for wireless device operations with optimized matching networks either passive or active.

      Written by two experts in the field, Antenna Booster Technology for Wireless Communications offers key insights into the principles and applications of antenna boosters for researchers from academia and industry, as well as lecturers and advanced students, engineers involved in antenna and electronics design, and developers of antenna, radio frequency, wireless and microwave technologies.

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  • image of AI for Power Electronics and Renewable Energy Systems
    AI for Power Electronics and Renewable Energy Systems
    • Author(s): Weihao Hu ; Guozhou Zhang ; Zhenyuan Zhang ; Sayed Abulanwar ; Frede Blaabjerg
    • Publication Year: 2024
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      Rising shares of renewable energy are needed to stave off catastrophic climate change, but also bring about the challenge of intermittency, jeopardizing power quality. Instead of large central generation units, many distributed generators and loads need to be managed in order to integrate renewable energy with power systems.

      Artificial intelligence (AI) can meet this challenge with adaptive control and demand side management. When managing distributed and changing network components, AI can give control computers human-level performance, helping to solve key issues with intermittency, power quality and distributed generation and loads including EV. Use of AI for power systems has therefore become a research hotspot.

      This reference book systematically treats the applications of AI in power electronics and renewable energy systems. The book begins with an introduction to AI in power systems, then subsequent chapters cover the use of AI for electric machine fault diagnosis, for power electronic reliability, design, and control, in dual-active-bridge converters; AI for distribution network voltage control, signal stability control, and energy management of hybrid systems as well as for renewable energy systems with AI. The book ends with conclusions and an outlook for AI in power systems. Numerous worked examples throughout the text help readers understand the operating and controlling guidelines.

      Written by a team of well-known scientists and power system experts, AI for Power Electronics and Renewable Energy Systems is a valuable resource for researchers and PhD students, as well as experts in industry and utilities involved with electric power systems.

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  • image of Agrivoltaics: Technical, ecological, commercial and legal aspects
    Agrivoltaics: Technical, ecological, commercial and legal aspects
    • Editors: Constantin Klyk; Stephan Schindele
    • Publication Year: 2024
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      Agrivoltaics, also called agricultural-photovoltaics (Agri-PV or APV), integrates solar power generation into an agricultural activity on farmland.

      The PV modules not only generate clean energy, but also shield crops from intense sun, drought or wind erosion. The market potential in EU-27, UK, and Switzerland alone is estimated to be 968 GWp if only 1 % of the utilized agricultural area is used for Agri-PV. Interest is swiftly growing amongst scientists, policy makers, and within the farming and energy industries. The challenges lie in the construction of the PV system, choice and ecology of crops, and sowing and harvesting techniques.

      Agrivoltaics: Technical, ecological, commercial and legal aspects provides an overview of agrivoltaics, covering existing technical solutions both on system level as well as on the module level. Chapters cover the principles and definition, technological aspects of the PV and the agricultural system, yield prediction, light management, operations and management, ecological and social aspects, commercial, and legal considerations. Legal frameworks in different countries are explained. A short outlook describes how the future of Agri-PV could develop.

      The book provides systematic coverage of this emerging topic for researchers, scientists, and engineers involved with PV, farmers, decision makers in PV and agricultural sector, as well as policy makers.

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  • image of Self-Organizing Dynamic Agents for the Operation of Decentralized Smart Grids
    Self-Organizing Dynamic Agents for the Operation of Decentralized Smart Grids
    • Author(s): Alfredo Vaccaro
    • Publication Year: 2024
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      Integrating intermittent distributed generation, distributed storage systems, electric vehicles, and flexible loads will present security, stability, and power quality challenges in future smart grids. The amount of data to be processed to face these issues can overwhelm grid operation tools and conventional IT-based applications, limiting situational awareness and decision support. Decentralized and self-organizing technologies can help with that problem. In a self-organizing system, information processing is based on local interactions of its elementary parts (dynamic agents), enabling the cooperative solution of complex decision-making problems by only requiring local information exchange without needing a fusion center for data collection and processing.

      Self-Organizing Dynamic Agents for the Operation of Decentralized Smart Grids describes the technology of cooperative sensor networks for smart grid computing, which allows for solving the fundamental power system operation problems by enabling the cooperation of dynamic agents. The resulting computing architecture is highly scalable, flexible, robust against perturbation, and able to self-repair.

      Chapters cover the needs and challenges in smart grids, cooperative and self-organizing sensor networks, self-organizing wide area measurement systems, decentralized voltage regulation and economic dispatch of distributed generators, grid monitoring estimation and control, and dynamic thermal rating assessment of overhead lines.

      Written with graduate students, researchers, and power system engineers in mind, this book offers a concise but thorough overview of the role of decentralized and self-organizing sensors in smart grids.

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  • image of Large Scale Grid Integration of Renewable Energy Sources: Solutions and technologies
    Large Scale Grid Integration of Renewable Energy Sources: Solutions and technologies
    • Editor: Antonio Moreno-Muñoz
    • Publication Year: 2024
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      Key to the further growth of shares of renewables is their integration with the existing grid system including EV and sector coupling. This in turn requires technical developments to enable flexibility and dispatch. Approaches to meeting these challenges include hybridization of different distributed energy resources, forecasting and control, storage and demand response, and modelling. Technological advances are also being accompanied by changes in business models.

      The first edition of this book for researchers in academia, power industry and at grid operators presented an overview of the steps on the way toward 100% clean power, covering systematically the different challenges and technologies. The 2nd edition has been substantially revised and updated.

      The book begins with an overview of the role of the power grid in a sustainable energy system. Chapters cover recent developments and future challenges for integration of renewable energy, wind energy forecasting, wind and PV integration, energy resources integration and demand response, DC distribution, distributed micro-storage and hydrogen energy systems.

      Providing an up-to-date and thorough overview of trends and developments on this essential renewable energy topic, the book is suitable for researchers in academia and industry as well as at utilities involved with renewables deployment and power grids.

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  • image of Digital Twins for 6G: Fundamental theory, technology and applications
    Digital Twins for 6G: Fundamental theory, technology and applications
    • Editors: Hamed Ahmadi; Trung Q. Duong; Avishek Nag; Vishal Sharma; Berk Canberk; Octavia A. Dobre
    • Publication Year: 2024
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      Digital twin (DT) technology is a real-time evolving digital duplicate of a physical object or process that contains all its history. It is enabled by massive real-time multi-source data collection and analysis. While 6G is considered as an enabler of digital twins, DT can also be a facilitator for integrating AI and 6G towards reliable, pervasive and efficient intelligent technologies.

      While the DT concept is familiar among aerospace and industrial engineers, it is a relatively new topic among electronic, electrical, computer, communications and networking engineers. For future massive-scale industrial internet-of-things (IoT) applications facilitated by DTs, a 6G network will be much more advantageous than its 5G counterpart.

      Digital Twins for 6G: Fundamental theory, technology and applications aims to bring together knowledge from industrial practitioners and researchers, and to introduce novel concepts that can help address the challenges associated with this interdisciplinary topic. The authors will cover fundamentals, enabling technologies, standards and advanced topics of DT and 6G to demystify the DT concept and its networking requirements and benefits, support a broader understanding of DT and its relationship with 6G to a larger audience, support learning and understanding for researchers and professionals working on 5G and 6G, and create a foundation on DT and 6G for the international research community.

      This book is intended to be both a tutorial of the important topics around digital twin and advanced wireless communications technologies, including 6G, as well as an advanced overview for technical professionals in the communications industry, technical managers, and researchers in both academia and industry.

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  • image of Affective Computing Applications using Artificial Intelligence in Healthcare: Methods, approaches and challenges in system design
    Affective Computing Applications using Artificial Intelligence in Healthcare: Methods, approaches and challenges in system design
    • Editor: M. Murugappan
    • Publication Year: 2024
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      Affective computing is the study and development of systems and devices that can recognise human emotions. This can be done using sensing technologies and AI algorithms to process biological signals or facial images to identify the different affective states, such as happiness, anger, fear, surprise, sadness and disgust. This non-invasive technique has applications in healthcare such as emotional impairment detection, mental health assessment, emotional stress assessment, cognitive decline detection, attention deficit disorders, neurodegenerative diseases, neurological disorders, autism spectrum disorder, stress, anxiety or other behavioural assessment.

      This edited book provides an overview of the ongoing research on affective computing applications in healthcare using AI and IoT. This book covers recent advancements in computing technology, modelling methods, frameworks, and algorithms used for human affect detection using bio-signal and image processing methods.

      The book explores the use of EEG signals, thermal imaging, eye-movement patterns, gesture recognition systems and IoT systems to gather information and discusses the use of deep learning, CNN and RNN-LSTM models of how this information can be usefully processed to detect emotional states.

      Discussing the latest trends and developments in research in the field, this book is a useful resource for researchers in affective computing, affective neuroscience, cognitive neuroscience, computer vision, signal and image processing, cybersecurity, AI/ML/DL, data science, HCI, sensing and robotics.

      Practicing physicians, clinical experts or researchers in experimental and applied cognitive psychology who wish to understand the emotional wellbeing (pain and stress level analysis) of patients may also find this book of interest.

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  • image of Managing Internet of Things Applications across Edge and Cloud Data Centres
    Managing Internet of Things Applications across Edge and Cloud Data Centres
    • Editors: Rajiv Ranjan; Karan Mitra; Prem Prakash Jayaraman; Albert Y. Zomaya
    • Publication Year: 2024
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      Cloud computing has been a game changer for internet-based applications such as content delivery networks, social networking and multi-tier enterprise applications. However, the requirements for low-latency data access, security, bandwidth, mobility, and cost have challenged centralized data center-based cloud computing models, which is driving the need for the novel computing paradigms of edge and fog computing. The internet of things (IoT) focuses on discovery, aggregation, management, and acting on data originating from internet-connected devices via programmable sensors, actuators, mobile phones, surveillance cameras, routers, gateways and switches. But the aggregation of this data is expensive and can be time consuming.

      Traditional cloud-centric resource management models need to move towards more distributed and decentralized models so that they can cope with the challenges posed by the evolution of IoT smart devices and network solutions. However, supporting IoT data processing across cloud and edge data centers is not a trivial challenge. IoT sensing devices must be configured as a collection of data-analytics driven workflows where each node in the process can essentially run on multiple heterogeneous cloud and edge data centers.

      This book presents state-of-the-art interdisciplinary computing research in the application lifecycle management for internet of things in edge and cloud computing. The book addresses challenges from a distributed system perspective that includes both cyber and physical aspects. The authors aim to bring together the four paradigms of cloud and edge computing, cyber physical systems, internet of things and big data for future ICT systems.

      Written and edited by an international team of experts in the field, this book offers key insights to researchers, engineers, IT professionals, advanced students, postgraduate students and lecturers working in the fields of parallel and distributed computing, data mining, information retrieval, cloud, edge and fog computing, and the IoT.

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