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Two-Dimensional Materials for Electromagnetic Shielding
Two-Dimensional Materials for Electromagnetic Shielding Discover a cutting-edge reference on 2D EMI shielding materials for both industrial and academic audiences Two-Dimensional Materials for Electromagnetic Shielding delivers a thorough and comprehensive examination of all aspects of electromagnetic interference (EMI) shielding and microwave absorption, including fundamentals and applications, as well as emerging 2D materials in the field, like graphene, and MXenes. The book covers basic knowledge on shielding mechanisms and the demanding physical, chemical, and mechanical properties of the 2D materials against betrayed electromagnetic waves. The benefits of novel 2D materials over existin...
MXenes
Since their discovery in 2011, MXenes (2D carbides, nitrides, and carbonitrides of early transition metals) have developed into one of the largest and most intensively studied families of 2D materials. They offer unique properties and are being explored in a large variety of applications. This book compiles the most important research from a pioneer of the field, Professor Yury Gogotsi, and his interdisciplinary research team, as well as numerous collaborators worldwide. It reports on the discovery and rise of MXenes and describes their synthesis and processing, properties, and incorporation into polymer, ceramic, and metal matrices to produce composites. It also discusses the potential of MXenes for use in energy storage, optics, electronics, and sensing, as well as biomedical, environmental, and electrocatalysis applications. The book will appeal to anyone interested in nanomaterials and their synthesis, properties, and applications.
Riemannian Geometric Statistics in Medical Image Analysis
Over the past 15 years, there has been a growing need in the medical image computing community for principled methods to process nonlinear geometric data. Riemannian geometry has emerged as one of the most powerful mathematical and computational frameworks for analyzing such data. Riemannian Geometric Statistics in Medical Image Analysis is a complete reference on statistics on Riemannian manifolds and more general nonlinear spaces with applications in medical image analysis. It provides an introduction to the core methodology followed by a presentation of state-of-the-art methods. Beyond medical image computing, the methods described in this book may also apply to other domains such as sign...
Statistical Shape and Deformation Analysis
Statistical Shape and Deformation Analysis: Methods, Implementation and Applications contributes enormously to solving different problems in patient care and physical anthropology, ranging from improved automatic registration and segmentation in medical image computing to the study of genetics, evolution and comparative form in physical anthropology and biology. This book gives a clear description of the concepts, methods, algorithms and techniques developed over the last three decades that is followed by examples of their implementation using open source software. Applications of statistical shape and deformation analysis are given for a wide variety of fields, including biometry, anthropology, medical image analysis and clinical practice. - Presents an accessible introduction to the basic concepts, methods, algorithms and techniques in statistical shape and deformation analysis - Includes implementation examples using open source software - Covers real-life applications of statistical shape and deformation analysis methods
Shape in Medical Imaging
This book constitutes the proceedings of the Workshop on Shape in Medical Imaging, ShapeMI 2018, held in conjunction with the 21st International Conference on Medical Image Computing, MICCAI 2018, in Granada, Spain, in September 2018. The 26 full papers and 2 short papers presented were carefully reviewed and selected for inclusion in this volume. The papers discuss novel approaches and applications in shape and geometry processing and their use in research and clinical studies and explore novel, cutting-edge theoretical methods and their usefulness for medical applications, e.g., from the fields of geometric learning or spectral shape analysis.
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