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|a Statistical methods for materials science :
|b the data science of microstructure characterization /
|c edited by Jeffrey P. Simmons, Charles A. Bouman, Marc De Graef, Lawrence F. Drummy, Jr.
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|a Boca Raton, Florida :
|b CRC Press,
|c [2019]
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|c ©2019
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|a 1 online resource.
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|a computer
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|a online resource
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|a Includes bibliographical references and index.
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|a Description based on online resource; title from PDF title page (EBSCO, viewed February 15, 2019).
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|a Cover; Half Title; Title Page; Copyright Page; Contents; Preface; About the Editors; Contributors; I Introduction; 1 Materials Science vs. Data Science; II Emerging Data Science in Microstructure Characterization; 2 Emerging Digital Data Capabilities; 2.1 Introduction; 2.2 Benefits of Large Data Volumes; 2.3 Challenges of Large Data Volumes; 2.4 Emerging Techniques; 2.4.1 Multi-Instrument Coordination; 2.4.2 Upstream Data Analysis; 2.4.3 Data Mining; 2.4.4 Data Curation; 2.5 Conclusions; 3 Cultural Differences; 3.1 What Makes Modern Image Processing So Modern?
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|a 3.2 Language of Image Processing3.2.1 Notational Differences; 3.2.1.1 Sets; 3.2.1.2 Operations on Sets; 3.2.1.3 Computations on Sets; 3.2.2 Bayesian Probability and Image Processing; 3.2.2.1 Modern Probability and Sets; 3.2.2.2 Foundational Rules of Modern Probability; 3.2.2.3 Mathematical Constructs; 3.2.2.4 Bayesian Probability in Image Processing; 3.3 Language of Materials Science; 3.3.1 Thermodynamic Phases; 3.3.2 Free Energies; 3.4 Concluding Remarks; 4 Forward Modeling; 4.1 What Is Forward Modeling?; 4.1.1 What Are the Unknowns in Materials Characterization?
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|a 4.1.2 A Schematic Description of Forward Modeling4.2 A Brief Overview of Electron Scattering Modalities; 4.3 Case Studies; 4.3.1 Electron Backscatter Diffraction; 4.3.1.1 BSE Monte Carlo Simulations; 4.3.1.2 Dynamical Scattering Simulations; 4.3.1.3 Detector Parameters; 4.3.2 Lorentz Vector Field Electron Tomography; 4.3.2.1 Lorentz Forward Model; 4.3.2.2 Electron Wave Phase Shift Computations; 4.3.2.3 Example Lorentz Image Simulation; 4.4 Summary; 5 Inverse Problems and Sensing; 5.1 Introduction; 5.2 Traditional Approaches to Inversion; 5.3 Bayesian and Regularized Approaches to Inversion
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|a 5.4 Why Does Bayesian Estimation Work?5.5 Model-Based Reconstruction; 5.6 Successes and Opportunities of Bayesian Inversion; III Inverse Methods for Analysis of Data; 6 Model-Based Iterative Reconstruction for Electron Tomography; 6.1 Introduction; 6.2 Model-Based Iterative Reconstruction; 6.3 High-Angle Annular Dark-Field STEM Tomography; 6.3.1 HAADF-STEM Forward Model; 6.3.2 Prior Model; 6.3.3 Cost Function Formulation and Optimization Algorithm; 6.3.4 Experimental Results; 6.3.4.1 Simulated Dataset; 6.3.4.2 Experimental Dataset; 6.4 Bright-Field Electron Tomography
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|a 6.4.1 BF-TEM Forward Model and Cost Function Formulation6.4.1.1 Generalized Huber Functions for Anomaly Modeling; 6.4.1.2 MBIR Cost Formulation; 6.4.2 Results; 6.4.2.1 Simulated Dataset; 6.4.2.2 Real Dataset; 6.5 Future Directions; 6.6 Conclusion; 7 Statistical Reconstruction and Heterogeneity Characterization in 3-D Biological Macromolecular Complexes; 7.1 Introduction; 7.2 Statistical 3-D Signal Reconstruction of Macromolecular Complexes; 7.2.1 Introduction; 7.2.2 Statistical Model; 7.2.3 Relationship between the Moments of the Weights and the Moments of the Electron Scattering Intensity
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|a Jeffrey P. Simmons is a Scientist with the Materials and Manufacturing Directorate of the Air Force Research Laboratory (AFRL). He received the B.S. degree in metallurgical engineering from the New Mexico Institute of Mining and Technology, Socorro, NM, USA, and M.E. and Ph.D. degrees in Metallurgical Engineering and Materials Science and Materials Science and Engineering, respectively, from Carnegie Mellon University, Pittsburgh, PA, USA. After receiving the Ph.D. degree, he began work at AFRL as a post-doctoral research contractor. In 1998, he joined AFRL as a Research Scientist. His research interests are in computational imaging for microscopy and has developed advanced algorithms for analysis of large image datasets. Other research interests have included phase field (physics-based) modeling of microstructure formation, atomistic modeling of defect properties, and computational thermodynamics. He has lead teams developing tools for digital data analysis and computer resource integration and security. He has overseen execution of research contracts on computational materials science, particularly in prediction of machining distortion, materials behavior, and thermodynamic modeling. He has published in both the Materials Science and Signal Processing fields. He is a member of ACM and a senior member of IEEE. Charles A. Bouman received a B.S.E.E. degree from the University of Pennsylvania in 1981 and a MS degree from the University of California at Berkeley in 1982. From 1982 to 1985, he was a full staff member at MIT Lincoln Laboratory and in 1989 he received a Ph.D. in electrical engineering from Princeton University. He joined the faculty of Purdue University in 1989 where he is currently the Showalter Professor of Electrical and Computer Engineering and Biomedical Engineering. Professor Boumans research is in statistical signal and image processing in applications ranging from medical to scientific and consumer imaging. His research resulted in the first commercial model-based iterative reconstruction (MBIR) system for medical X-ray computed tom ography (CT), and he is co-inventor on over 50 issued patents that have been licensed and used in millions of consumer imaging products. Marc De Graef received his BS and MS degrees in physics from the University of Antwerp (Belgium) in 1983, and his Ph.D. in physics from the Catholic University of Leuven (Belgium) in 1989, with a thesis on copper-based shape memory alloys. He then spent three and a half years as a post-doctoral researcher in the Materials Department at the University of California at Santa Barbara before joining Carnegie Mellon in 1993 as an assistant professor. He is currently professor and codirector of the J. Earle and Mary Roberts Materials Characterization Laboratory. His research interests lie in the area of microstructural characterization of structural intermetallics and magnetic materials and include the development of numerical techniques to model a variety of materials characterization modalities. Prof. De Graef has published two text books and more than 280 publications. Lawrence F. Drummy Jr. is a senior materials engineer in the Soft Matter Materials Branch, Functional Materials Division, Materials and Manufacturing Directorate, Air Force Research Laboratory in Dayton, OH. Dr. Drummy received his BS in Physics at Rensselaer Polytechnic Institute while researching scanning tunneling microscopy and image processing of silicon growth on surfaces. In 2003 he received his PhD from the Department of Materials Science and Engineering at the University of Michigan while performing research on defect structures in organic molecular semiconductor thin films for flexible electronics. Dr. Drummys research interests include three dimensional morphology characterization of biological, polymeric and nanostructured materials, the structure of materials at interfaces, and data analytics for materials science applications such as microscopy.
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|a Electronic resource.
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| 650 |
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|a Materials science
|x Mathematical models.
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| 650 |
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|a Materials science
|x Statistical methods.
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| 655 |
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|a Electronic books.
|2 local
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| 700 |
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|a Simmons, Jeffrey P.,
|e editor.
|0 http://id.loc.gov/authorities/names/n2018070294
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| 700 |
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|a Bouman, Charles Addison,
|e editor.
|0 http://id.loc.gov/authorities/names/nr89010189
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| 700 |
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|a De Graef, Marc,
|e editor.
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| 700 |
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|a Drummy, Lawrence F.,
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|a Taylor & Francis.
|0 http://id.loc.gov/authorities/names/n84073022
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|b College Station
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