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  • image of ReRAM-based Machine Learning
    ReRAM-based Machine Learning
    • Author(s): Hao Yu ; Leibin Ni ; Sai Manoj Pudukotai Dinakarrao
    • Publication Year: 2021
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      The transition towards exascale computing has resulted in major transformations in computing paradigms. The need to analyze and respond to such large amounts of data sets has led to the adoption of machine learning (ML) and deep learning (DL) methods in a wide range of applications. One of the major challenges is the fetching of data from computing memory and writing it back without experiencing a memory-wall bottleneck. To address such concerns, in-memory computing (IMC) and supporting frameworks have been introduced. In-memory computing methods have ultra-low power and high-density embedded storage. Resistive Random-Access Memory (ReRAM) technology seems the most promising IMC solution due to its minimized leakage power, reduced power consumption and smaller hardware footprint, as well as its compatibility with CMOS technology, which is widely used in industry. In this book, the authors introduce ReRAM techniques for performing distributed computing using IMC accelerators, present ReRAM-based IMC architectures that can perform computations of ML and data-intensive applications, as well as strategies to map ML designs onto hardware accelerators. The book serves as a bridge between researchers in the computing domain (algorithm designers for ML and DL) and computing hardware designers.

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  • image of E-learning Methodologies: Fundamentals, technologies and applications
    E-learning Methodologies: Fundamentals, technologies and applications
    • Editors: Mukta Goyal; Rajalakshmi Krishnamurthi; Divakar Yadav
    • Publication Year: 2021
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      E-learning has become an important part of our educational life with the development of e-learning systems and platforms and the need for online and remote learning. ICT and computational intelligence techniques are being used to design more intelligent and adaptive systems. However, the art of designing good real-time e-learning systems is difficult as different aspects of learning need to be considered including challenges such as learning rates, involvement, knowledge, qualifications, as well as networking and security issues. The earlier concepts of standalone integrated virtual e-learning systems have been greatly enhanced with emerging technologies such as cloud computing, mobile computing, big data, Internet of Things (IoT), AI and machine learning, and AR/VT technologies. With this book, the editors and authors wish to help researchers, scholars, professionals, lecturers, instructors, developers, and designers understand the fundamental concepts, challenges, methodologies and technologies for the design of performant and reliable intelligent and adaptive real time e-learning systems and platforms. This edited volume covers state of the art topics including user modeling for e-learning systems and cloud, IOT, and mobile-based frameworks. It also considers security challenges and ethical conduct using Blockchain technology.

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  • image of Advances in Communications Satellite Systems: Proceedings of the 37th International Communications Satellite Systems Conference (ICSSC-2019)
    Advances in Communications Satellite Systems: Proceedings of the 37th International Communications Satellite Systems Conference (ICSSC-2019)
    • Editors: Ifiok Otung; Thomas Butash; Tetsushi Ikegami
    • Publication Year: 2021
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      The International Communications Satellite Systems Conference (ICSSC) is the oldest and one of the most influential technical conferences in the field. The 37th edition was held from 29 Oct - 1 Nov 2019 in Okinawa, Japan. These proceedings present a broad spectrum of space communications contributions from the conference, with highlights including high speed optical communications and feeder links, advanced digital payloads, broadband satellite communication architectures and applications. Subjects covered include integrated applications and architectures for vessels and IoT; DTN and HTS technologies; new satellite system architectures and components; high speed optical communications and feeder links; advanced digital payloads and components; satellite antenna technologies; propagation and modelling for satellite communications; future technologies for 5G and beyond; flexible HTS systems and advanced digital payloads; satellite networks design challenges and applications; new satellite components and transmitter and modem technologies; NGSO constellations and 5G integration; and NGSO and GSO system issues and interference mitigation techniques. Offering a wide range of expert perspectives on communications satellite systems, these proceedings will be of interest to engineers and researchers in academia and industry working on satellite, digital, and wireless communications and networks, as well as advanced students, policy makers and stakeholders in the field.

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  • image of Patient-Centered Digital Healthcare Technology: Novel applications for next generation healthcare systems
    Patient-Centered Digital Healthcare Technology: Novel applications for next generation healthcare systems
    • Editors: Leonard Goldschmidt; Rona Margaret Relova
    • Publication Year: 2020
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      Patient-Centered Digital Healthcare Technology explores the creative intersection of novel, emerging technologies and medicine. This convergence is transforming the landscape of healthcare with the overarching objectives of improving clinical outcomes and advocating wellness. The concept of encountering or treating a medical condition when it has already become disturbingly manifest is being replaced by earlier awareness, diagnosis, and proactive intervention enabled by technologies. This book features a range of innovations in health information systems and big data, artificial intelligence and machine learning, real-time home monitoring tools, smartphone apps, medical robotics and intelligent machines, virtual and augmented realities, genome sequencing, blockchain and gamification in healthcare. Intuitive digital health solutions motivate end-users to become active partners in their care, thus enhancing patient engagement and empowerment. This may perhaps lead to yet the most gratifying consequence of novel and emerging technologies: the democratization of healthcare. This book offers a valuable resource for healthcare providers, engineers, data and computer scientists, researchers, practitioners in academia, biomedical industry and multi-disciplinary clinical settings. Subjects include informatics, computing, networking, wireless and mobile applications, and robotic sensing that collectively improve healthcare access and delivery.

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  • image of Traffic Information and Control
    Traffic Information and Control
    • Editors: Ruimin Li; Zhengbing He
    • Publication Year: 2020
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      Written by an international team of researchers, this book focuses on traffic information processing and signal control using emerging types of traffic data. It conveys advanced methods to estimate and predict traffic flows at different levels, including macroscopic, mesoscopic and microscopic. The aim of these predictions is to optimize traffic signal control for intersections and to mitigate ever-growing traffic congestion. The book begins with an introduction to the topic, its fundamental principles and recent developments. The first part of the book then covers the estimation and prediction of the traffic flow state based on emerging detailed data sources. Coverage in this section includes traffic analytics with online web data; macroscopic traffic performance indicators based on floating car data; short-term travel time prediction by deep learning a comparison of different LSTM-DNN models; short-term traffic prediction under disruptions using deep leaning; real time demand based traffic diversion; game theoretic lane change strategy for cooperative vehicles under perfect information; and cooperative driving and a lane change-free road transportation system. The second part focuses on traffic signal control optimization, explaining how to use improved data and advanced tools for better signal control. Chapters include urban traffic control systems; algorithms and models for signal coordination; emerging technologies to enhance traffic signal coordination practices; control for short-distance intersections; and multi-day evaluation of adaptive traffic signal system based on license plate recognition detector data. A valuable resource for researchers and engineers working in the field of traffic information and control, and intelligent transport systems, Traffic Information and Control offers an overview of recent research and practical approaches to optimising traffic signal control.

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  • image of Signal Processing for Fault Detection and Diagnosis in Electric Machines and Systems
    Signal Processing for Fault Detection and Diagnosis in Electric Machines and Systems
    • Editor: Mohamed Benbouzid
    • Publication Year: 2020
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      Over the last three decades, the search for competitiveness and growth gains has driven the evolution of machine maintenance policies, and the industry has moved from passive maintenance to active maintenance with the aim of improving productivity. Active maintenance requires continuous monitoring of industrial systems in order to increase reliability, availability rates and guarantee the safety of people and property. This book presents the main advanced signal processing techniques for fault detection and diagnosis in electromechanical systems. It focuses on presenting these advanced tools from time-frequency representation and time-scale analysis to demodulation techniques, including innovative and recently developed options. Each technique is evaluated and compared, and its advantages and drawbacks highlighted. Parametric spectral analysis, which aims to handle some of the main drawbacks of these approaches, is introduced as a potential solution. Signal Processing for Fault Detection and Diagnosis in Electric Machines and Systems offers thorough, analytical coverage of the following topics: parametric signal processing approach; the signal demodulation techniques; Kullback-Leibler divergence for incipient fault diagnosis; high-order spectra (HOS); and fault detection and diagnosis based on principal component analysis. Finally, a brief conclusion suggests some possibilities for the future direction of the field. The book is a useful resource for researchers and engineers whose work involves electrical machines or fault detection specifically, and also of value to postgraduate students with an interest in entering this field.

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  • image of Integrated Optics Volume 1: Modeling, material platforms and fabrication techniques
    Integrated Optics Volume 1: Modeling, material platforms and fabrication techniques
    • Editors: Giancarlo C. Righini; Maurizio Ferrari
    • Publication Year: 2020
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      Edited by two recognised experts, this book in two volumes provides a comprehensive overview of integrated optics, from modelling to fabrication, materials to integration platforms, and characterization techniques to applications. The technology is explored in detail, and set in a broad context that addresses a range of current and potential future research and development trends. Volume 1 begins with introductory chapters on the history of integrated optics technology, design tools, and modelling techniques. The next section of the book goes on to discuss the range of materials used for integrated optics, their deposition techniques, and their specific applications, including glasses, plasmonic nanostructures, SOI and SOS, and III-V and II-VI semiconductors. Volume 2 addresses characterization techniques, integrated optical waveguides and devices. A range of applications are also discussed, including devices for sensing, telecommunications, optical amplifiers and lasers, and quantum computing. The introductory chapters are intended to be of use to newcomers to the field, but its depth and breadth of coverage means that this book is also appropriate reading for early-career and senior researchers wishing to refresh their knowledge or keep up to date with recent developments in integrated optics.

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  • image of Integrated Optics Volume 2: Characterization, devices and applications
    Integrated Optics Volume 2: Characterization, devices and applications
    • Editors: Giancarlo C. Righini; Maurizio Ferrari
    • Publication Year: 2020
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      Edited by two recognised experts, this book in two volumes provides a comprehensive overview of integrated optics, from modelling to fabrication, materials to integration platforms, and characterization techniques to applications. The technology is explored in detail, and set in a broad context that addresses a range of current and potential future research and development trends. Volume 1 begins with introductory chapters on the history of integrated optics technology, design tools, and modelling techniques. The next section of the book goes on to discuss the range of materials used for integrated optics, their deposition techniques, and their specific applications, including glasses, plasmonic nanostructures, SOI and SOS, and III-V and II-VI semiconductors. Volume 2 addresses characterization techniques, integrated optical waveguides and devices. A range of applications are also discussed, including devices for sensing, telecommunications, optical amplifiers and lasers, and quantum computing. The introductory chapters are intended to be of use to newcomers to the field, but its depth and breadth of coverage means that this book is also appropriate reading for early-career and senior researchers wishing to refresh their knowledge or keep up to date with recent developments in integrated optics.

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  • image of Advances in Mathematical Methods for Electromagnetics
    Advances in Mathematical Methods for Electromagnetics
    • Editors: Kazuya Kobayashi; Paul Denis Smith
    • Publication Year: 2020
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      This book covers recent achievements in the area of advanced analytical and associated numerical methods as applied to various problems arising in all branches of electromagnetics. The unifying theme is the application of advanced or novel mathematical techniques to produce analytical solutions or effective analytical-numerical methods for computational electromagnetics addressing more general problems. Each chapter contains an outline of its topic, discusses its scientific context and importance, describes approaches to date, gives an exposition of the author's approach to the problem tackled in the chapter, describes the results, and concludes with a discussion of the range or class of problems where the techniques described work most appropriately and effectively. Intended primarily for researchers in the fields of electrical engineering, mathematics, physics and related disciplines, the book offers systematic and thorough coverage of this complex topic. It is hoped that the book will help to stimulate further investigation and discussion of the important problems in electromagnetics within this research community.

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  • image of Radio Wave Propagation in Vehicular Environments
    Radio Wave Propagation in Vehicular Environments
    • Author(s): Leyre Azpilicueta ; Cesar Vargas-Rosales ; Francisco Falcone ; Ana Vazquez Alejos
    • Publication Year: 2020
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      Providing a unified treatment of radio wave propagation in vehicular environments, this book offers a thorough discussion of their theoretical background and fundamental limits, and paves the way to a better understanding of more advanced topics. Further, the book introduces the newest challenges and problems posed by the ever growing need to communicate in mobile networks whilst in a vehicle. The book provides an exhaustive survey of current propagation models, setting a theoretical path that will allow practical engineers and researchers to understand and improve existing wireless mobile networks through better understanding of propagation modelling as applied to the vehicular environment. Chapters are included on wireless channel properties for vehicular environments; modelling and simulation of vehicular communications; intra-vehicle and inter-vehicle short-range wireless channel characterization; vehicular communications channel modelling; and wireless communication system optimization. The book concludes with a chapter exploring applications and case studies. Radio Wave Propagation in Vehicular Environments is suitable for master's students and above in signal processing and mobile communications courses, as well as researchers working in vehicular communications.

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  • image of Electromagnetic Reverberation Chambers: Recent advances and innovative applications
    Electromagnetic Reverberation Chambers: Recent advances and innovative applications
    • Editor: Guillaume Andrieu
    • Publication Year: 2020
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      This book offers state of the art information about a novel range of applications for electromagnetic reverberation chambers. It is written by international experts in electromagnetic theory, electromagnetic compatibility (EMC), and antenna design and measurement. For each application area a renowned researcher introduces the general concepts and then describes the recent advances in that field. The following topics are thus addressed in the context of reverberation chambers in the 9 chapters of the book: Calibration of reverberation chambers with the "well stirred condition" method; Performance assessment of the most popular stirring techniques for broadband tests; Probabilistic model in the context of radiated susceptibility tests; Over-the-air testing of wireless devices; Material absorption and dosimetry for animal exposure; Characterization of antenna efficiency and radiation pattern; Radar cross section estimation. Finally, a concluding chapter identifies and explores some of the exciting developments and open issues that represent emerging trends in reverberation chamber research. The book is aimed at advanced students and researchers in signal processing, antenna design or EMC who need to know about new applications of reverberation chambers.

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  • image of Control of Prosthetic Hands: Challenges and emerging avenues
    Control of Prosthetic Hands: Challenges and emerging avenues
    • Editor: Kianoush Nazarpour
    • Publication Year: 2020
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      This edited book brings together research from laboratories across the world, in order to offer a global perspective on advances in prosthetic hand control. State-of-the-art control of prosthetics in the laboratory and clinical spaces are presented and the challenges discussed, and the effect of user training on control of prosthetics to evaluate the translational efficacy and value for the end-user is highlighted. The book begins with a chapter introducing the fundamental principles, engineering challenges and control solutions for prosthetic hands. Further chapters address methods to design bespoke sockets, magnetomyography, implantable technologies for closed-loop control of prostheses, direct neural control of prostheses via nerve implants as well as user-prosthesis co-adaptation, and two chapters on prosthetics for children. The book concludes with a chapter by Dr Nazarpour on the future of myoelectric prosthetics control, with particular focus on the successful translation of research advances into real clinical gains. The book is essential reading for anyone involved in research or undertaking advanced courses in prosthetic design and control. It provides an in-depth exploration of this rewarding topic, by exploring technologies with the potential to improve the quality of life of upper-limb prosthetic users.

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  • image of Deep Neural Network Design for Radar Applications
    Deep Neural Network Design for Radar Applications
    • Editor: Sevgi Zubeyde Gurbuz
    • Publication Year: 2020
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      Novel deep learning approaches are achieving state-of-the-art accuracy in the area of radar target recognition, enabling applications beyond the scope of human-level performance. This book provides an introduction to the unique aspects of machine learning for radar signal processing that any scientist or engineer seeking to apply these technologies ought to be aware of. The book begins with three introductory chapters on radar systems and phenomenology, machine learning principles, and optimization for training common deep neural network (DNN) architectures. Subsequently, the book summarizes radar-specific issues relating to the different domain representations in which radar data may be presented to DNNs and synthetic data generation for training dataset augmentation. Further chapters focus on specific radar applications, which relate to DNN design for micro-Doppler analysis, SAR-based automatic target recognition, radar remote sensing, and emerging fields, such as data fusion and image reconstruction. Edited by an acknowledged expert, and with contributions from an international team of authors, this book provides a solid introduction to the fundamentals of radar and machine learning, and then goes on to explore a range of technologies, applications and challenges in this developing field. This book is also a valuable resource for both radar engineers seeking to learn more about deep learning, as well as computer scientists who are seeking to explore novel applications of machine learning. In an era where the applications of RF sensing are multiplying by the day, this book serves as an easily accessible primer on the nuances of deep learning for radar applications.

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  • image of Advanced Sparsity-Driven Models and Methods for Radar Applications
    Advanced Sparsity-Driven Models and Methods for Radar Applications
    • Author(s): Gang Li
    • Publication Year: 2020
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      This book introduces advanced sparsity-driven models and methods and their applications in radar tasks such as detection, imaging and classification. Compressed sensing (CS) is one of the most active topics in the signal processing area. By exploiting and promoting the sparsity of the signals of interest, CS offers a new framework for reducing data without compromising the performance of signal recovery, or for enhancing resolution without increasing measurements. An introductory chapter outlines the fundamentals of sparse signal recovery. The following topics are then systematically and comprehensively addressed: hybrid greedy pursuit algorithms for enhancing radar imaging quality; two-level block sparsity model for multichannel radar signals; parametric sparse representation for radar imaging with model uncertainty; Poisson-disk sampling for high-resolution and wide-swath SAR imaging; when advanced sparse models meet coarsely quantized radar data; sparsity-aware micro-Doppler analysis for radar target classification; and distributed detection of sparse signals in radar networks via locally most powerful test. Finally, a concluding chapter summarises key points from the preceding chapters and offers concise perspectives. The book focuses on how to apply the CS-based models and algorithms to solve practical problems in radar, for the radar and signal processing research communities.

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  • image of The Nine Pillars of Technologies for Industry 4.0
    The Nine Pillars of Technologies for Industry 4.0
    • Editors: Wai Yie Leong; Joon Huang Chuah; Boon Tuan Tee
    • Publication Year: 2020
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      Industry 4.0 refers to automation and data exchange in manufacturing technologies. From innovative research, challenges, solutions and strategies to real-world case studies, the aim of this edited book is to focus on the nine pillars of technology that are supporting the transition to Industry 4.0 and smart manufacturing. The nine pillars include the internet of things, cloud computing, autonomous and robotics systems, big data analytics, augmented reality, cyber security, simulation, system integration, and additive manufacturing. A key role is played by the industrial IoTs and state-of-the-art technologies such as fog and edge computing, advanced data analytics, innovative data exchange models, artificial intelligence, machine learning, mobile and network technologies, robotics and sensors. This book is a useful resource for an audience of academic and industry researchers and engineers, as well as advanced students in the fields of information and communication technologies, robotics and automation, big data analytics and data mining, machine learning, artificial intelligence, AR/VR/ER, cybersecurity, cyber physical systems, sensing and robotics with a focus on Industry 4.0, and smart manufacturing.

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  • image of Modeling and Simulation of HVDC Transmission
    Modeling and Simulation of HVDC Transmission
    • Editors: Minxiao Han; Aniruddha M. Gole
    • Publication Year: 2020
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      The development of large-scale renewable generation and load electrification call for highly efficient and flexible electric power integration, transmission and interconnection. High Voltage DC (HVDC) transmission technology has been recognized as the key technology for this scenario. HVDC transmissions, including both the line commutated converter (LCC) HVDC and voltage source converter (VSC) HVDC have played an important role in the modern electric power system. However, with the inclusion of power electronic devices, HVDC introduces the characteristics of nonlinearity and different timescales into the traditional electromechanical system and thus careful modeling and simulation of HVDC transmission are essential for power system design, commissioning, operation and maintenance. This book focuses on the modeling and simulation of HVDC transmission systems. The development of HVDC technologies is briefly introduced, and then the role of modeling and simulation in the research and development of HVDC systems is discussed. The chapters cover the general practice of HVDC modeling and simulation; electromagnetic modeling of LCC HVDC; VSC HVDC system modeling and stability analysis; electromagnetic modeling of DC grids; electromagnetic simulation of HVDC transmission; electromechanical transient simulation of LCC HVDC; electromechanical simulation of VSC HVDC; dynamic phasor modeling of HVDC; small-signal modeling of HVDC systems; hybrid simulation for HVDC; and real-time modeling and simulation for HVDC systems. The simulation algorithms are explained for each model and case studies and application examples are included. This book is essential reading for engineers and researchers involved with transmission grid construction, as well as advanced students of electrical engineering.

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  • image of AI for Emerging Verticals: Human-robot computing, sensing and networking
    AI for Emerging Verticals: Human-robot computing, sensing and networking
    • Editors: Muhammad Zeeshan Shakir; Naeem Ramzan
    • Publication Year: 2020
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      By specializing in a vertical market, companies can better understand their customers and bring more insight to clients in order to become an integral part of their businesses. This approach requires dedicated tools, which is where artificial intelligence (AI) and machine learning (ML) will play a major role. By adopting AI software and services, businesses can create predictive strategies, enhance their capabilities, better interact with customers, and streamline their business processes. This edited book explores novel concepts and cutting-edge research and developments towards designing these fully automated advanced digital systems. Fostered by technological advances in artificial intelligence and machine learning, such systems potentially have a wide range of applications in robotics, human computing, sensing and networking. The chapters focus on models and theoretical approaches to guarantee automation in large multi-scale implementations of AI and ML systems; protocol designs to ensure AI systems meet key requirements for future services such as latency; and optimisation algorithms to leverage the trusted distributed and efficient complex architectures. The book is of interest to researchers, scientists, and engineers working in the fields of ICTs, networking, AI, ML, signal processing, HCI, robotics and sensing. It could also be used as supplementary material for courses on AI, machine and deep learning, ICTs, networking signal processing, robotics and sensing.

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  • image of Power Grids with Renewable Energy: Storage, integration and digitalization
    Power Grids with Renewable Energy: Storage, integration and digitalization
    • Author(s): Abdelhay A. Sallam  and  Om P. Malik
    • Publication Year: 2020
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      Generation of electricity from renewable sources has become a necessity, particularly due to environmental concerns. In order for renewable sources to provide reliable power, their sporadic availability under certain conditions and the lack of control over the resource must be addressed. Different renewable energy sources and storage technologies bring various properties to the table, and power systems must be adapted and constructed to accommodate these. Power electronics and micro-grids play key roles in enabling the use of renewable energy in the evolving smarter grids. This book, written by well-known researchers with broad expertise and successful publication records, provides a systematic overview of modern power systems with integrated renewable energy. Chapters provide concise coverage of renewable energy generation, of storage technologies including chemical, electrostatic and thermal storage systems, and of energy integration, power conditioning systems, economic dispatch and scheduling, EV integration, as well as communications and cyber-security in power systems. This work is a highly valuable resource for researchers in industry and academia involved with renewable energy technology and power systems, for advanced students of related subjects, and for utilities engineers and professionals.

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  • image of Smart Sensing for Traffic Monitoring
    Smart Sensing for Traffic Monitoring
    • Editor: Nobuyuki Ozaki Dr.
    • Publication Year: 2020
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      Growth in urbanisation, particularly in emerging economies, is causing increased traffic congestion and affecting environmental conditions in cities. Cities need to manage this growth in traffic in an efficient way. Intelligent infrastructure for traffic monitoring and sensing offers a potential solution, and so this book explores the prospective role of this approach in managing congestion, the established and emerging related technologies, and routes to effective implementation. Intelligent infrastructure will also play an important role in the future automation of vehicles. Onboard sensor technology is well-established, but higher levels of vehicle automation are difficult to achieve without additional sensing technology on the infrastructure side. Thanks to recent advances in device and software technology, new and innovative approaches to intelligent sensing infrastructure, both fixed and onboard, can be implemented. The development and deployment of automated vehicles is a hot topic, with early versions potentially entering the market within 5-8 years, so as an overview of developments in the field this book is timely and relevant. The book systematically covers the key elements of intelligent infrastructure. It begins with the architectures and projects in key regions, and then continues with coverage of novel technologies for vehicle, bicycle and pedestrian detection using different kinds of recognition technology. The third part describes technology for detecting traffic conditions such as incidents, illegal parking and adverse weather. Smart Sensing for Traffic Monitoring offers methodically presented information for researchers, practitioners, and advanced students with an interest in the technologies behind intelligent infrastructure and traffic monitoring.

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  • image of Blockchain and Machine Learning for e-Healthcare Systems
    Blockchain and Machine Learning for e-Healthcare Systems
    • Editors: Balamurugan Balusamy; Naveen Chilamkurti; Lucia Agnes Beena; T. Poongodi
    • Publication Year: 2020
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      Blockchain and machine learning technologies can mitigate healthcare issues such as slow access to medical data, poor system interoperability, lack of patient agency, and data quality and quantity for medical research. Blockchain technology facilitates and secures the storage of information in such a way that doctors can see a patient's entire medical history, but researchers see only statistical data instead of any personal information. Machine learning can make use of this data to notice patterns and give accurate predictions, providing more support for the patients and also in research related fields where there is a need for accurate data to predict credible results. This book examines the application of blockchain technology and machine learning algorithms in various healthcare settings, covering the basics of the technologies and exploring how they can be used to improve clinical outcomes and improving the patient's experience. These topics are illustrated with reference to issues around the supply chain, drug verification, reimbursement, control access and clinical trials. Case studies are given for applications in the analysis of breast cancer, hepatitis C, and COVID-19.

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