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Welcome to IET Digital Library

The IET Digital Library holds more than 190,000 technical papers from 1994 onwards for all IET journals, magazines, books, conference publications and seminar digests, the IET's member magazine Engineering & Technology, plus seminar digests and conference publications. Find out more about the Digital Library.

Latest Books

image of Passive Radars on Moving Platforms
  • Passive Radars on Moving Platforms
  • This book collects, reviews and analyses recent research on passive radars on moving platforms. Due to the nature of the typical radar applications performed by moving platforms and the signals of opportunity typically exploited for passive radar purposes, which are not designed for reception while in motion, the special case of passive radar mounted on moving platforms is highly challenging.

    Passive Radars on Moving Platforms is intended for both passive radar experts and readers less familiar with the general topic of passive radar. The editors provide useful background information before fully exploring various research activities from a selection of working groups worldwide. An overview of operational systems is given, with considerations on multiple receiving channel calibration and hardware realization of radar systems based on the software defined radio (SDR) principle. The concluding chapter offers some outlook on what passive radar could look like in the near future, namely a component of a bigger architecture usually referred to as system of systems (SoS). Additionally, results of on-going activities related to new potential illuminators of opportunity for passive radar are covered.

    Providing a thorough overview of techniques, challenges and applications that are enabled when a passive radar is operated from a moving platform, this book will be of interest to radar engineers, researchers into radar design, and the wider radar signal processing community.

image of Physical Biometrics for Hardware Security of DSP and Machine Learning Coprocessors
  • Physical Biometrics for Hardware Security of DSP and Machine Learning Coprocessors
  • Physical Biometrics for Hardware Security of DSP and Machine Learning Coprocessors presents state-of-the art explanations for detective control-based security and protection of digital signal processing (DSP) and machine learning coprocessors against hardware threats. Such threats include intellectual property (IP) abuse and misuse, for example, fraudulent claims of IP ownership and IP piracy. DSP coprocessors such as finite impulse response filters, image processing filters, discrete Fourier transform, and JPEG compression hardware are extensively utilized in several real-life applications. Further, machine learning coprocessors such as convolutional neural network (CNN) hardware IP cores play a vital role in several applications such as face recognition, medical imaging, autonomous driving, and biometric authentication, amongst others.

    Written by an expert in the field, this book reviews recent advances in hardware security and IP protection of digital signal processing (DSP) and machine learning coprocessors using physical biometrics and DNA. It presents solutions for secured coprocessors for DSP, image processing applications and CNN, and where relevant chapters explores the advantages, disadvantages and security-cost trade-offs between different approaches and techniques to assist in the practical application of these methods.

    The interdisciplinary themes and topics covered are expected to be of interest to researchers in several areas of specialisation, encompassing the overlapping fields of hardware design security, VLSI design (high level synthesis, register transfer level, gate level synthesis), IP core, optimization using evolutionary computing, system-on-chip design, and biometrics. CAD/design engineers, system architects and students will also find this book to be a useful resource.

image of Applications of Deep Learning in Electromagnetics: Teaching Maxwell's equations to machines
  • Applications of Deep Learning in Electromagnetics: Teaching Maxwell's equations to machines
  • Deep learning has started to be applied to solving many electromagnetic problems, including the development of fast modelling solvers, accurate imaging algorithms, efficient design tools for antennas, as well as tools for wireless links/channels characterization. The contents of this book represent pioneer applications of deep learning techniques to electromagnetic engineering, where physical principles described by the Maxwell's equations dominate. With the development of deep learning techniques, improvement in learning capacity and generalization ability may allow machines to "learn" from properly collected data and "master" the physical laws in certain controlled boundary conditions. In the long run, a hybridization of fundamental physical principles with knowledge from training data could unleash numerous possibilities in electromagnetic theory and engineering that used to be impossible due to the limit of data information and ability of computation.

    Electromagnetic applications of deep learning covered in the book include electromagnetic forward modeling, free-space inverse scattering, non-destructive testing and evaluation, subsurface imaging, biomedical imaging, direction of arrival estimation, remote sensing, digital satellite communications, imaging and gesture recognition, metamaterials and metasurfaces design, as well as microwave circuit modeling.

    Applications of Deep Learning in Electromagnetics contains valuable information for researchers looking for new tools to solve Maxwell's equations, students of electromagnetic theory, and researchers in the field of deep learning with an interest in novel applications.

image of Charge Acceleration and the Spatial Distribution of Radiation Emitted by Antennas and Scatterers
  • Charge Acceleration and the Spatial Distribution of Radiation Emitted by Antennas and Scatterers
  • Given that charge acceleration is the cause of all electromagnetic radiation, the question arises about where such acceleration occurs on objects typically modelled and analysed by electromagnetic engineers. Charge acceleration, as the cause of radiation from these typical kinds of objects (antennas, radars etc) is examined in this book on a quantitative basis.

    The book describes new ways of modelling the actual distribution of EM radiation waves from its various sources. Unlike other books on EM it focuses on radiation, a fundamental property of electromagnetic fields, it does not follow the usual analytical kind of approach to be found in a book on electromagnetics. Rather than developing and presenting a formal theoretical foundation of electromagnetic theory, this book instead focuses on various aspects of EM radiation from a variety of perspectives.

    The goal is to provide the reader with computational tools for determining quantitatively why and where radiation is emitted by antennas and scatterers. This is a unique approach which is of wide interest to the EM theoretical community.

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image of Applications of Machine Learning in Digital Healthcare
  • Applications of Machine Learning in Digital Healthcare
  • Machine learning algorithms are increasingly finding applications in the healthcare sector. Whether assisting a clinician to process an individual patient's data or helping administrators view hospital bed turnover, the volume and complexity of healthcare data is a compelling reason for the development of machine learning based tools to aid in its interpretation and use.

    This edited book focuses on the applications of machine learning in the healthcare sector, both at the macro-level for guiding policy decisions, and at the granular level, showing how ML techniques can be applied to process an individual patient's medical data to swiftly aid diagnosis.

    Written by an international team of experts, the book presents several applications of machine learning in the healthcare sector, including health system planning, optimisation and preparedness, outlining the benefits and challenges of coordination and data sharing. Machine learning has many applications in processing patient data and topics such as arrhythmia detection, image-guided microsurgery and early detection of Alzheimer's disease are discussed in depth. The book also looks at machine learning applications exploiting wearable sensors for real-time analysis and concepts around enhancing physical performance.

    Suitable for an audience of computer scientists, healthcare engineers and those involved with digital medicine, this book brings together a plethora of machine learning applications from across the board of the healthcare services.

image of Technology-Enabled Motion Sensing and Activity Tracking for Rehabilitation
  • Technology-Enabled Motion Sensing and Activity Tracking for Rehabilitation
  • Documenting how technology has been increasingly facilitating rehabilitation both for physical and mental health, this book focuses on sensing and measurement technologies for rehabilitation applications.

    The author introduces various motion sensing technologies, such as inertial measurement units, pressure sensing, e-Textile, and vision-based motion sensing and discusses the applications in at-home rehabilitation scenarios. Common human motion recognition algorithms, ranging from simple single-parameter determination, such as the determination of range of motion in terms of angles, to sophisticated rule-based and machine-learning based activity recognition algorithms are explored, laying the foundation for adopting and understanding these technologies in rehabilitation.

    Interactive games illustrate how technology can help rehabilitation beyond assessment, invigorating the rehabilitation programs, and engaging patients in their own recovery journey via computer screens or virtual reality interfaces to provide real-time feedback on the quality and quantity of the physical activity performed. This serious game technology enables more accurate and consistent assessment of the quality of rehabilitation exercises done by the patients. The author looks at many patient populations (such as recovery from stroke, COPD, MS, or surgery) and many rehabilitation scenarios (such as upper extremity, lower extremity, posture, hand, gait and activities of daily living).

    Professionals and researchers in the field of rehabilitation technology engineering and related areas will find this book a valuable tool in navigating multidisciplinary work on healthcare technology and health science.

image of Digital Twin Technologies for Healthcare 4.0
  • Digital Twin Technologies for Healthcare 4.0
  • In healthcare, a digital twin is a digital representation of a patient or healthcare system using integrated simulations and service data. The digital twin tracks a patient's records, crosschecks them against registered patterns and analyses any diseases or contra indications. The digital twin uses adaptive analytics and algorithms to produce accurate prognoses and suggest appropriate interventions. A digital twin can run various medical scenarios before treatment is initiated on the patient, thus increasing patient safety as well as providing the most appropriate treatments to meet the patient's requirements.

    Digital Twin Technologies for Healthcare 4.0 discusses how the concept of the digital twin can be merged with other technologies, such as artificial intelligence (AI), machine learning (ML), big data analytics, IoT and cloud data management, for the improvement of healthcare systems and processes. The book also focuses on the various research perspectives and challenges in implementation of digital twin technology in terms of data analysis, cloud management and data privacy issues.

    With chapters on visualisation techniques, prognostics and health management, this book is a must-have for researchers, engineers and IT professionals in healthcare as well as those involved in using digital twin technology, AI, IoT and big data analytics for novel applications.

image of Blockchain Technology in e-Healthcare Management
  • Blockchain Technology in e-Healthcare Management
  • The healthcare arena has seen a shift in recent years, with more healthcare provisions being delivered or managed via electronic means. Healthcare providers can now provide patients with diagnosis, treatment, monitoring, or a prescription without ever sharing the same physical space. With so much more patient data now stored and accessed electronically, the security of this information is ever more critical to the delivery of effective and efficient healthcare services. As blockchains are resistant to modification of their data, blockchain technology therefore provides traceable and reliable security to e-Healthcare systems and services.

    Introducing the fundamentals of blockchain technology and discussing its applications in the e-Healthcare sphere, the editors highlight recent research and development in blockchain technology, specifically in healthcare environments such as e-healthcare records and data security, health insurance management and fraud detection, pharmaceutical supply chain management and drug traceability, and IoT enabled patient monitoring. Including a case study on managing e-Healthcare data the editors also explore the challenges and future directions of using blockchain technology in delivering and managing e-Healthcare provision.

    Written by a range of international experts, the book will be of interest to researchers and academics in information security, data scientists, and healthcare professionals/administrators with responsibility for e-Healthcare records.

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