Data Science For Nano Image Analysis
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Data Science for Nano Image Analysis
This book combines two distinctive topics: data science/image analysis and materials science. The purpose of this book is to show what type of nano material problems can be better solved by which set of data science methods. The majority of material science research is thus far carried out by domain-specific experts in material engineering, chemistry/chemical engineering, and mechanical & aerospace engineering. The book could benefit materials scientists and manufacturing engineers who were not exposed to systematic data science training while in schools, or data scientists in computer science or statistics disciplines who want to work on material image problems or contribute to materials discovery and optimization. This book provides in-depth discussions of how data science and operations research methods can help and improve nano image analysis, automating the otherwise manual and time-consuming operations for material engineering and enhancing decision making for nano material exploration. A broad set of data science methods are covered, including the representations of images, shape analysis, image pattern analysis, and analysis of streaming images, change points detection, graphical methods, and real-time dynamic modeling and object tracking. The data science methods are described in the context of nano image applications, with specific material science case studies.
In-Situ Transmission Electron Microscopy Experiments
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.
Handbook of Dynamic Data Driven Applications Systems
This Second Volume in the series Handbook of Dynamic Data Driven Applications Systems (DDDAS) expands the scope of the methods and the application areas presented in the first Volume and aims to provide additional and extended content of the increasing set of science and engineering advances for new capabilities enabled through DDDAS. The methods and examples of breakthroughs presented in the book series capture the DDDAS paradigm and its scientific and technological impact and benefits. The DDDAS paradigm and the ensuing DDDAS-based frameworks for systems’ analysis and design have been shown to engender new and advanced capabilities for understanding, analysis, and management of engineered, natural, and societal systems (“applications systems”), and for the commensurate wide set of scientific and engineering fields and applications, as well as foundational areas. The DDDAS book series aims to be a reference source of many of the important research and development efforts conducted under the rubric of DDDAS, and to also inspire the broader communities of researchers and developers about the potential in their respective areas of interest, of the application and the exploitation of the DDDAS paradigm and the ensuing frameworks, through the examples and case studies presented, either within their own field or other fields of study. As in the first volume, the chapters in this book reflect research work conducted over the years starting in the 1990’s to the present. Here, the theory and application content are considered for: Foundational Methods Materials Systems Structural Systems Energy Systems Environmental Systems: Domain Assessment & Adverse Conditions/Wildfires Surveillance Systems Space Awareness Systems Healthcare Systems Decision Support Systems Cyber Security Systems Design of Computer Systems The readers of this book series will benefit from DDDAS theory advances such as object estimation, information fusion, and sensor management. The increased interest in Artificial Intelligence (AI), Machine Learning and Neural Networks (NN) provides opportunities for DDDAS-based methods to show the key role DDDAS plays in enabling AI capabilities; address challenges that ML-alone does not, and also show how ML in combination with DDDAS-based methods can deliver the advanced capabilities sought; likewise, infusion of DDDAS-like approaches in NN-methods strengthens such methods. Moreover, the “DDDAS-based Digital Twin” or “Dynamic Digital Twin”, goes beyond the traditional DT notion where the model and the physical system are viewed side-by-side in a static way, to a paradigm where the model dynamically interacts with the physical system through its instrumentation, (per the DDDAS feed-back control loop between model and instrumentation).