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mirrors based on Wolter optics, development of denoising and segmentation algorithms using machine learning [3], neutron energy-selective imaging, neutron phase grating imaging, polarized neutron imaging
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parallel algorithms, execution of algorithms in the computer cloud, to delivering on-demand measurements over the Web. key words Image processing; Machine learning, Computer vision; Statistical methods
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local disorder as probed by X-ray Absorption Spectroscopy result in emergent material properties Machine-learning driven electrochemical deposition of coatings controlling surface properties
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multiple 2D images and multiple channels, (d) optimizing 2D projection viewpoints (dose reduction and time savings), (e) applying artificial intelligence and traditional machine learning models to noise
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to acquire different kinds of images on large numbers of iPS cells in culture; machine learning algorithms and other image analysis strategies may be used to extract and test image features as predictors
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diffraction, x-ray absorption spectroscopy for the quantification of chemical short range order, and automated microstructural image analysis. The simulation approaches of interest include machine learning
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increase throughput and provide rich datasets that can be exploited by machine learning and artificial intelligence. Current advanced mechanical testing activities involve three-dimensional surface digital
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intelligence and machine learning tools. 1. S. M. Chavali, J. Roller, M. Dagenais and B. H. Hamadani, Sol. Eng. Mater. Sol. Cells, 236, 111543 (2022). 2. B. H. Hamadani, Appl. Phys. Lett., 117, 043904 (2020
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301.975.3507 Description Recent developments in Artificial Intelligence (AI) have allowed machine learning models to solve certain complex problems in natural language processing and other areas at large scales
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functionalities, from crossbar-based machine learning to race-logic-based computing. Opportunities exist for experimental work in device fabrication and measurement using NIST’s state-of-the-art NanoFab and