Download or read book Seismic Diffraction written by Tijmen Jan Moser and published by SEG Books. This book was released on 2016-06-30 with total page 823 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of diffraction imaging to complement the seismic reflection method is rapidly gaining momentum in the oil and gas industry. As the industry moves toward exploiting smaller and more complex conventional reservoirs and extensive new unconventional resource plays, the application of the seismic diffraction method to image sub-wavelength features such as small-scale faults, fractures and stratigraphic pinchouts is expected to increase dramatically over the next few years. “Seismic Diffraction” covers seismic diffraction theory, modeling, observation, and imaging. Papers and discussion include an overview of seismic diffractions, including classic papers which introduced the potential of diffraction phenomena in seismic processing; papers on the forward modeling of seismic diffractions, with an emphasis on the theoretical principles; papers which describe techniques for diffraction mathematical modeling as well as laboratory experiments for the physical modeling of diffractions; key papers dealing with the observation of seismic diffractions, in near-surface-, reservoir-, as well as crustal studies; and key papers on diffraction imaging.

Download or read book Machine Learning Augmented Spectroscopies for Intelligent Materials Design written by Nina Andrejevic and published by Springer Nature. This book was released on 2022-10-06 with total page 106 pages. Available in PDF, EPUB and Kindle. Book excerpt: The thesis contains several pioneering results at the intersection of state-of-the-art materials characterization techniques and machine learning. The use of machine learning empowers the information extraction capability of neutron and photon spectroscopies. In particular, new knowledge and new physics insights to aid spectroscopic analysis may hold great promise for next-generation quantum technology. As a prominent example, the so-called proximity effect at topological material interfaces promises to enable spintronics without energy dissipation and quantum computing with fault tolerance, yet the characteristic spectral features to identify the proximity effect have long been elusive. The work presented within permits a fine resolution of its spectroscopic features and a determination of the proximity effect which could aid further experiments with improved interpretability. A few novel machine learning architectures are proposed in this thesis work which leverage the case when the data is scarce and utilize the internal symmetry of the system to improve the training quality. The work sheds light on future pathways to apply machine learning to augment experiments.

Download or read book Statistical Methods for Materials Science written by Jeffrey P. Simmons and published by CRC Press. This book was released on 2019-02-13 with total page 537 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data analytics has become an integral part of materials science. This book provides the practical tools and fundamentals needed for researchers in materials science to understand how to analyze large datasets using statistical methods, especially inverse methods applied to microstructure characterization. It contains valuable guidance on essential topics such as denoising and data modeling. Additionally, the analysis and applications section addresses compressed sensing methods, stochastic models, extreme estimation, and approaches to pattern detection.

Download or read book Molecules in Superfluid Helium Nanodroplets written by Alkwin Slenczka and published by Springer Nature. This book was released on 2022-05-28 with total page 590 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book covers recent advances in experiments using the ultra-cold, very weakly perturbing superfluid environment provided by helium nanodroplets for high resolution spectroscopic, structural and dynamic studies of molecules and synthetic clusters. The recent infra-red, UV-Vis studies of radicals, molecules, clusters, ions and biomolecules, as well as laser dynamical and laser orientational studies, are reviewed. The Coulomb explosion studies of the uniquely quantum structures of small helium clusters, X-ray imaging of large droplets and electron diffraction of embedded molecules are also described. Particular emphasis is given to the synthesis and detection of new species by mass spectrometry and deposition electron microscopy.

Download or read book Hyperspectral Image Analysis written by Saurabh Prasad and published by Springer Nature. This book was released on 2020-04-27 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reviews the state of the art in algorithmic approaches addressing the practical challenges that arise with hyperspectral image analysis tasks, with a focus on emerging trends in machine learning and image processing/understanding. It presents advances in deep learning, multiple instance learning, sparse representation based learning, low-dimensional manifold models, anomalous change detection, target recognition, sensor fusion and super-resolution for robust multispectral and hyperspectral image understanding. It presents research from leading international experts who have made foundational contributions in these areas. The book covers a diverse array of applications of multispectral/hyperspectral imagery in the context of these algorithms, including remote sensing, face recognition and biomedicine. This book would be particularly beneficial to graduate students and researchers who are taking advanced courses in (or are working in) the areas of image analysis, machine learning and remote sensing with multi-channel optical imagery. Researchers and professionals in academia and industry working in areas such as electrical engineering, civil and environmental engineering, geosciences and biomedical image processing, who work with multi-channel optical data will find this book useful.

Download or read book The International Conference on Image Vision and Intelligent Systems ICIVIS 2021 written by Jian Yao and published by Springer Nature. This book was released on 2022-03-03 with total page 1174 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collection of the papers accepted by the ICIVIS 2021—The International Conference on Image, Vision and Intelligent Systems held on June 15–17, 2021, in Changsha, China. The topics focus but are not limited to image, vision and intelligent systems. Each part can be used as an excellent reference by industry practitioners, university faculties, research fellows and undergraduates as well as graduate students who need to build a knowledge base of the most current advances and state-of-practice in the topics covered by this conference proceedings.

Download or read book In Situ Transmission Electron Microscopy Experiments written by Renu Sharma and published by John Wiley & Sons. This book was released on 2023-05-10 with total page 389 pages. Available in PDF, EPUB and Kindle. Book excerpt: In-Situ Transmission Electron Microscopy Experiments Design and execute cutting-edge experiments with transmission electron microscopy using this essential guide In-situ microscopy is a recently-discovered and rapidly-developing approach to transmission electron microscopy (TEM) that allows for the study of atomic and/or molecular changes and processes while they are in progress. Experimental specimens are subjected to stimuli that replicate near real-world conditions and their effects are observed at a previously unprecedented scale. Though in-situ microscopy is becoming an increasingly important approach to TEM, there are no current texts combining an up-to-date overview of this cutting-edge set of techniques with the experience of in-situ TEM professionals. In-Situ Transmission Electron Microscopy Experiments meets this need with a work that synthesizes the collective experience of myriad collaborators. It constitutes a comprehensive guide for planning and performing in-situ TEM measurements, incorporating both fundamental principles and novel techniques. Its combination of technical detail and practical how-to advice makes it an indispensable introduction to this area of research. In-Situ Transmission Electron Microscopy Experiments readers will also find: Coverage of the entire experimental process, from method selection to experiment design to measurement and data analysis Detailed treatment of multimodal and correlative microscopy, data processing and machine learning, and more Discussion of future challenges and opportunities facing this field of research In-Situ Transmission Electron Microscopy Experiments is essential for graduate students, post-doctoral fellows, and early career researchers entering the field of in-situ TEM.

Download or read book Modelling and Development of Intelligent Systems written by Dana Simian and published by Springer Nature. This book was released on 2023-02-25 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 8th International Conference on Modelling and Development of Intelligent Systems, MDIS 2022, held in Sibiu, Romania, during October 28–30, 2022. The 21 papers included in this book were carefully reviewed and selected from 48 submissions. They were organized in the following topical sections as follows: intelligent systems for decision support; machine learning; mathematical models for development of intelligent systems; and modelling and optimization of dynamic systems.

Download or read book Complex valued Neural Networks written by Akira Hirose and published by World Scientific. This book was released on 2003 with total page 387 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, complex-valued neural networks have widened the scope of application in optoelectronics, imaging, remote sensing, quantum neural devices and systems, spatiotemporal analysis of physiological neural systems, and artificial neural information processing. In this first-ever book on complex-valued neural networks, the most active scientists at the forefront of the field describe theories and applications from various points of view to provide academic and industrial researchers with a comprehensive understanding of the fundamentals, features and prospects of the powerful complex-valued networks.

Download or read book Deep Neural Networks for Multimodal Imaging and Biomedical Applications written by Suresh, Annamalai and published by IGI Global. This book was released on 2020-06-26 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: The field of healthcare is seeing a rapid expansion of technological advancement within current medical practices. The implementation of technologies including neural networks, multi-model imaging, genetic algorithms, and soft computing are assisting in predicting and identifying diseases, diagnosing cancer, and the examination of cells. Implementing these biomedical technologies remains a challenge for hospitals worldwide, creating a need for research on the specific applications of these computational techniques. Deep Neural Networks for Multimodal Imaging and Biomedical Applications provides research exploring the theoretical and practical aspects of emerging data computing methods and imaging techniques within healthcare and biomedicine. The publication provides a complete set of information in a single module starting from developing deep neural networks to predicting disease by employing multi-modal imaging. Featuring coverage on a broad range of topics such as prediction models, edge computing, and quantitative measurements, this book is ideally designed for researchers, academicians, physicians, IT consultants, medical software developers, practitioners, policymakers, scholars, and students seeking current research on biomedical advancements and developing computational methods in healthcare.

Download or read book Horizons in Materials written by Nicola Maria Pugno and published by Frontiers Media SA. This book was released on 2022-08-23 with total page 189 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Frontiers in Materials Editorial Office team are delighted to present the “Horizons in Materials” article collection, showcasing high-impact, authoritative, and accessible Review articles covering important topics at the forefront of the materials science and engineering field. All contributing authors were nominated by the Chief Editors and Editorial Office in recognition of their prominence and influence in their respective fields. The cutting-edge work presented in this article collection highlights the diversity of research performed across the entire breadth of the materials science and engineering field and reflects on the latest advances in theory, experiment, and methodology with applications to compelling problems. This Editorial features the corresponding author(s) of each paper published within this important collection, ordered by section alphabetically, highlighting them as the great researchers of the future. The Frontiers in Materials Chief Editors and Editorial Office team would like to thank each researcher who contributed their work to this collection. We are excited to see each article gain the deserved visibility and traction within the wider community, ensuring the collection’s truly global impact and success. Emily Young Journal Manager

Download or read book Guide to Convolutional Neural Networks written by Hamed Habibi Aghdam and published by Springer. This book was released on 2017-05-17 with total page 303 pages. Available in PDF, EPUB and Kindle. Book excerpt: This must-read text/reference introduces the fundamental concepts of convolutional neural networks (ConvNets), offering practical guidance on using libraries to implement ConvNets in applications of traffic sign detection and classification. The work presents techniques for optimizing the computational efficiency of ConvNets, as well as visualization techniques to better understand the underlying processes. The proposed models are also thoroughly evaluated from different perspectives, using exploratory and quantitative analysis. Topics and features: explains the fundamental concepts behind training linear classifiers and feature learning; discusses the wide range of loss functions for training binary and multi-class classifiers; illustrates how to derive ConvNets from fully connected neural networks, and reviews different techniques for evaluating neural networks; presents a practical library for implementing ConvNets, explaining how to use a Python interface for the library to create and assess neural networks; describes two real-world examples of the detection and classification of traffic signs using deep learning methods; examines a range of varied techniques for visualizing neural networks, using a Python interface; provides self-study exercises at the end of each chapter, in addition to a helpful glossary, with relevant Python scripts supplied at an associated website. This self-contained guide will benefit those who seek to both understand the theory behind deep learning, and to gain hands-on experience in implementing ConvNets in practice. As no prior background knowledge in the field is required to follow the material, the book is ideal for all students of computer vision and machine learning, and will also be of great interest to practitioners working on autonomous cars and advanced driver assistance systems.

Download or read book MATLAB Deep Learning written by Phil Kim and published by Apress. This book was released on 2017-06-15 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get started with MATLAB for deep learning and AI with this in-depth primer. In this book, you start with machine learning fundamentals, then move on to neural networks, deep learning, and then convolutional neural networks. In a blend of fundamentals and applications, MATLAB Deep Learning employs MATLAB as the underlying programming language and tool for the examples and case studies in this book. With this book, you'll be able to tackle some of today's real world big data, smart bots, and other complex data problems. You’ll see how deep learning is a complex and more intelligent aspect of machine learning for modern smart data analysis and usage. What You'll Learn Use MATLAB for deep learning Discover neural networks and multi-layer neural networks Work with convolution and pooling layers Build a MNIST example with these layers Who This Book Is For Those who want to learn deep learning using MATLAB. Some MATLAB experience may be useful.

Download or read book Theory of Seismic Diffractions written by Kamill Davydovich Klem-Musatov and published by SEG Books. This book was released on 1994 with total page 431 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presents a complete mathematical description of diffractions caused by seismic velocity discontinuities. Diffraction theory provides important physical insights into seismology and is a necessary part of describing the nature of a seismogram. The author describes elastic wave theory and relates it to the high-frequency approximations of ray theory.

Download or read book Deep Learning and Physics written by Akinori Tanaka and published by Springer Nature. This book was released on 2021-03-24 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: What is deep learning for those who study physics? Is it completely different from physics? Or is it similar? In recent years, machine learning, including deep learning, has begun to be used in various physics studies. Why is that? Is knowing physics useful in machine learning? Conversely, is knowing machine learning useful in physics? This book is devoted to answers of these questions. Starting with basic ideas of physics, neural networks are derived naturally. And you can learn the concepts of deep learning through the words of physics. In fact, the foundation of machine learning can be attributed to physical concepts. Hamiltonians that determine physical systems characterize various machine learning structures. Statistical physics given by Hamiltonians defines machine learning by neural networks. Furthermore, solving inverse problems in physics through machine learning and generalization essentially provides progress and even revolutions in physics. For these reasons, in recent years interdisciplinary research in machine learning and physics has been expanding dramatically. This book is written for anyone who wants to learn, understand, and apply the relationship between deep learning/machine learning and physics. All that is needed to read this book are the basic concepts in physics: energy and Hamiltonians. The concepts of statistical mechanics and the bracket notation of quantum mechanics, which are explained in columns, are used to explain deep learning frameworks. We encourage you to explore this new active field of machine learning and physics, with this book as a map of the continent to be explored.

Download or read book Electron Backscatter Diffraction in Materials Science written by Adam J. Schwartz and published by Springer Science & Business Media. This book was released on 2010-03-11 with total page 406 pages. Available in PDF, EPUB and Kindle. Book excerpt: Electron backscatter diffraction is a very powerful and relatively new materials characterization technique aimed at the determination of crystallographic texture, grain boundary character distributions, lattice strain, phase identification, and much more. The purpose of this book is to provide the fundamental basis for electron backscatter diffraction in materials science, the current state of both hardware and software, and illustrative examples of the applications of electron backscatter diffraction to a wide-range of materials including undeformed and deformed metals and alloys, ceramics, and superconductors. The text has been substantially revised from the first edition, and the authors have kept the format as close as possible to the first edition text. The new developments covered in this book include a more comphrensive coverage of the fundamentals not covered in the first edition or other books in the field, the advances in hardware and software since the first edition was published, and current examples of application of electron backscatter diffraction to solve challenging problems in materials science and condensed-matter physics.

Download or read book Computing and Data Science written by Weijia Cao and published by Springer Nature. This book was released on 2022-01-12 with total page 443 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume constitutes selected papers presented at the Third International Conference on Computing and Data Science, CONF-CDS 2021, held online in August 2021. The 22 full papers 9 short papers presented in this volume were thoroughly reviewed and selected from the 85 qualified submissions. They are organized in topical sections on advances in deep learning; algorithms in machine learning and statistics; advances in natural language processing.