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.

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.