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At least 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Sub-10 second fly-scan nano-tomography using machine learning

Abstract X-ray computed tomography is a versatile technique for 3D structure characterization. However, conventional reconstruction algorithms require that the sample not change throughout the scan, and the timescale of sample dynamics must be longer than the data acquisition time to fulfill the stable sample requirement. Meanwhile, concerns about X-ray-induced parasite reaction and sample damage have driven research efforts to reduce beam dosage. Here, we report a machine-learning-based image processing method that can significantly reduce data acquisition time and X-ray dose, outperforming conventional approaches like Filtered-Back Projection, maximum-likelihood, and model-based maximum-a-posteriori probability. Applying machine learning, we achieve ultrafast nano-tomography with sub-10 second data acquisition time and sub-50 nm pixel resolution in a transmission X-ray microscope. We apply our algorithm to study dynamic morphology changes in a lithium-ion battery cathode under a heating rate of 50 o C min −1 , revealing crack self-healing during thermal annealing. The proposed method can be applied to various tomography modalities.

36 MATERIALS SCIENCE↗
NASA NTRS1991

Test equipment data package for the KC-135 fiber pulling apparatus

The Fiber Pulling Apparatus (FPA) is a device designed to produce continuous glass fibers from simulated lunar soil, and to determine the effects of reduced gravity, specifically 1/6 g on fiber formation and resultant properties. Briefly, pre-melt simulated lunar soil will be placed in a pint crucible and heated to 1200 C or higher, up to a maximum temperature of 1400 C. At a given temperature, a quartz fiber will be immersed into the melt and then pulled through a chill block and wound onto a cylindrical bobbin using a servo motor control. A high resolution video camera will record the fiber as it is being pulled. This assembly wil be enclosed in Plexiglas. Before fiber pulling commences, the apparatus will be backfilled with dry nitrogen. A separate data acquisition system will support this apparatus. This system will contain a personal computer, video recorder, and monitor. Temperature, acceleration, winding speed, and video images will be controlled and recorded using the data acquisition system. Thus, the FPA will consist of two hardware packages, the fiber production assembly and the data acquisition rack. The primary objective of this test is to determine the effects of 1/6 g on the formation of continuous glass fiber made from simulated lunar soil. Baseline studies using the FPA on the ground will provide a reference for the 1/6 g studies. Of particular interest will be the effect of 1/6 g on the free fluid zone where the fiber exits the crucible. In the fiber spinning parlance this zone is known as the upper jet region, where the boundary slope is greater than one tenth. The properties of the resulting glass fiber will depend on the jet shape as well as distributions of velocity, temperature and tension within the jet. It is unknown at this time how 1/6 g will effect these parameters.

Kosten, Sue↗
NASA NTRS1997

Alaskan Auroral All-Sky Images on the World Wide Web

In response to a 1995 NASA SPDS announcement of support for preservation and distribution of important data sets online, the Geophysical Institute, University of Alaska Fairbanks, Alaska, proposed to provide World Wide Web access to the Poker Flat Auroral All-sky Camera images in real time. The Poker auroral all-sky camera is located in the Davis Science Operation Center at Poker Flat Rocket Range about 30 miles north-east of Fairbanks, Alaska, and is connected, through a microwave link, with the Geophysical Institute where we maintain the data base linked to the Web. To protect the low light-level all-sky TV camera from damage due to excessive light, we only operate during the winter season when the moon is down. The camera and data acquisition is now fully computer controlled. Digital images are transmitted each minute to the Web linked data base where the data are available in a number of different presentations: (1) Individual JPEG compressed images (1 minute resolution); (2) Time lapse MPEG movie of the stored images; and (3) A meridional plot of the entire night activity.

Stenbaek-Nielsen, H. C.↗
DOE OSTI2025

Distributed Cross-Channel Hierarchical Aggregation for Foundation Models

Vision-based scientific foundation models hold significant promise for advancing scientific discovery and innovation. This potential stems from their ability to aggregate images from diverse sources—such as varying physical groundings or data acquisition systems—and to learn spatio-temporal correlations using transformer architectures. However, tokenizing and aggregating images can be compute-intensive, a challenge not fully addressed by current distributed methods. In this work, we introduce the Distributed Cross-Channel Hierarchical Aggregation (D-CHAG) approach designed for datasets with a large number of channels across image modalities. Our method is compatible with any model-parallel strategy and any type of vision transformer architecture, significantly improving computational efficiency. We evaluated D-CHAG on hyperspectral imaging and weather forecasting tasks. When integrated with tensor parallelism and model sharding, our approach achieved up to a 75% reduction in memory usage and more than doubled sustained throughput on up to 1,024 AMD GPUs on the Frontier Supercomputer.

Tsaris, Aristeidis (aris) [ORNL] (ORCID:0000000277↗
DOE OSTI2024

High‐speed 4‐dimensional scanning transmission electron microscopy using compressive sensing techniques

Abstract Here we show that compressive sensing allows 4‐dimensional (4‐D) STEM data to be obtained and accurately reconstructed with both high‐speed and reduced electron fluence. The methodology needed to achieve these results compared to conventional 4‐D approaches requires only that a random subset of probe locations is acquired from the typical regular scanning grid, which immediately generates both higher speed and the lower fluence experimentally. We also consider downsampling of the detector, showing that oversampling is inherent within convergent beam electron diffraction (CBED) patterns and that detector downsampling does not reduce precision but allows faster experimental data acquisition. Analysis of an experimental atomic resolution yttrium silicide dataset shows that it is possible to recover over 25 dB peak signal‐to‐noise ratio in the recovered phase using 0.3% of the total data. Lay abstract : Four‐dimensional scanning transmission electron microscopy (4‐D STEM) is a powerful technique for characterizing complex nanoscale structures. In this method, a convergent beam electron diffraction pattern (CBED) is acquired at each probe location during the scan of the sample. This means that a 2‐dimensional signal is acquired at each 2‐D probe location, equating to a 4‐D dataset. Despite the recent development of fast direct electron detectors, some capable of 100kHz frame rates, the limiting factor for 4‐D STEM is acquisition times in the majority of cases, where cameras will typically operate on the order of 2kHz. This means that a raster scan containing 256^2 probe locations can take on the order of 30s, approximately 100‐1000 times longer than a conventional STEM imaging technique using monolithic radial detectors. As a result, 4‐D STEM acquisitions can be subject to adverse effects such as drift, beam damage, and sample contamination. Recent advances in computational imaging techniques for STEM have allowed for faster acquisition speeds by way of acquiring only a random subset of probe locations from the field of view. By doing this, the acquisition time is significantly reduced, in some cases by a factor of 10‐100 times. The acquired data is then processed to fill‐in or inpaint the missing data, taking advantage of the inherently low‐complex signals which can be linearly combined to recover the information. In this work, similar methods are demonstrated for the acquisition of 4‐D STEM data, where only a random subset of CBED patterns are acquired over the raster scan. We simulate the compressive sensing acquisition method for 4‐D STEM and present our findings for a variety of analysis techniques such as ptychography and differential phase contrast. Our results show that acquisition times can be significantly reduced on the order of 100‐300 times, therefore improving existing frame rates, as well as further reducing the electron fluence beyond just using a faster camera.

Robinson, Alex W.↗
DOE OSTI2026

3D Deep Learning Joint Inversion of Active Seismic Full Waveform and Passive Seismic Traveltime Data for Reservoir Imaging and Uncertainty Quantification

Here, we present deep learning (DL) networks for three-dimensional (3D) joint inversion of active seismic full waveform and passive seismic traveltime data to image reservoirs and their properties and quantify imaging uncertainties. Active seismic full-waveform data can provide high-resolution monitoring images but are collected only intermittently because of their high acquisition cost. In contrast, passive seismic data can be gathered at relatively low cost between regular active surveys, although their imaging quality can be compromised by factors such as low signal-to-noise ratios and limited ray coverage of the target. Although these datasets are routinely acquired together at CO 2 storage sites, their combined inversion within a 3D DL framework has not been previously demonstrated. To our knowledge, this is the first study to address this gap, combining the strength of both data types. For efficient data storage and DL training with large 3D seismic datasets, we use a 3D data matrix in which a random number of passive seismic traveltime data are stored as parabolic envelopes using one-hot encoding and a 3D full-waveform data matrix in which multiple shot gathers are summed. Two network architectures are evaluated: a single-encoder U-Net for single-data type inversion and a dual-encoder U-Net for joint inversion of active and passive seismic data. We also evaluate the single-encoder U-Net for joint inversion by concatenating full-waveform data and traveltime data. We propose a systematic approach for selecting an optimal dropout rate that balances regularization during training and Monte Carlo dropout-based uncertainty quantification during prediction by examining the correlation coefficient between standard deviation and prediction error, along with the training misfit, across a range of dropout rates. 3D DL inversion experiments include five different network configurations, with evaluations under ideal, noisy and dropout-enabled conditions. Both model and data uncertainties are assessed, as well as their combined effects. Across all conditions, the networks consistently predict accurate CO 2 saturation models with low prediction errors, such as a structural similarity index of 0.993 and CO 2 difference of 1.1%. Uncertainty estimates show strong spatial correlation with prediction errors, confirming the effectiveness of the proposed dropout selection approach. The results demonstrate that our DL approach, utilizing compact data representations and appropriate uncertainty quantification, yields accurate subsurface images under various inversion conditions and provides valuable insights into the reliability of predictions.

Um, Evan Schankee [Lawrence Berkeley National Labo↗
DOE OSTI2022

Exploiting voxel-sparsity for bone imaging with sparse-view cone-beam computed tomography

An optimization-based image reconstruction frame work is developed specifically for bone imaging. This framework exploits voxel-sparsity by use of ℓ 1 -norm image regularization and it enables image reconstruction from sparse-view cone-beam computed tomography (CBCT) acquisition. The effectiveness of the voxel-sparsity regularization is enhanced by using a blurred image representation. Ramp-filtering is included in the data discrepancy term and it has the effect of acting as a preconditioner, reducing the necessary number of iterations. The bone image reconstruction framework is demonstrated on CBCT data taken from an equine metacarpal condyle specimen.

Bone imaging↗
NASA NTRS2000

Acousto-Optic Imaging Spectrometers for Mars Surface Science

NASA's long term plan for Mars sample collection and return requires a highly streamlined approach for spectrally characterizing a landing site, documenting the mineralogical make-up of the site and guiding the collections of samples which represent the diversity of the site. Ideally, image data should be acquired at hundreds of VIS and IR wavelengths, in order to separately distinguish numerous anticipated species, using principal component analysis and linear unmixing. Cameras with bore-sighted point spectrometers can acquire spectra of isolated scene elements, but it requires 10(exp 2) to 10(exp 2) successive motions and precise relative pointing knowledge in order to create a single data cube which qualifies as a spectral map. These and other competing science objectives have to be accomplished within very short lander/rover operational lifetime (a few sols). True, 2-D imaging spectroscopy greatly speeds up the data acquisition process, since the spectra of all pixels in the scene are collected at once. This task can be accomplished with cameras that use electronically tunable acousto-optic tunable filters (AOTFs) as the optical tuning element. AOTFs made from TeO2 are now a mature technology, and operate at wavelengths from near-UV to about 5 microns. Because of incremental improvements in the last few years, present generation devices are rugged, radiation-hard and operate at temperatures down to at least 150K so they can be safely integrated into the ambient temperature optics of in-situ instruments such as planetary or small-body landers. They have been used for ground-based astronomy, and were also baselined for the ST-4 Champollion IR comet lander experiment (CIRCLE), prior to cancellation of the ST-4 mission last year. AIMS (for Acousto-optic Imaging spectrometer), is a prototype lander instrument which is being built at GSFC with support by the NASA OSS Advanced Technologies and Mission Studies, Mars Instrument Definition and Development Program (MIDP). AIMS is capable of tunable spectroscopic imaging of surface mineralogy, ices and dust between 0.5 and 2.4 microns, at a resolving power (lambda/delta lambda) which is typically several hundred. The design spatial resolution, similar to IMP and SSI, will allow mapping at scales down to about 1 cm.

Glenar, D. A.↗
NASA NTRS2003

Solar System Observing with the Space Infrared Telescope Facility (SIRTF)

SIRTF is NASA's Space Infrared Telescope Facility. Currently planned for launch on 15 Apr 2003, it is the final element in NASA's Great Observatories Program. SIRTF has an 85 cm diameter f/12 lightweight beryllium telescope, cooled to lekss than 5.5K. It is diffraction-limited at 6.5 microns, and has wavelengthcoverage from 3-180 microns. Its estimated lifetime (limited by cryogen) is 2.5 years at minimum, with a goal of 5+ years. SIRTF has three instruments, IRAC, IRS, and MIPS. IRAC (InfraRed Array Camera) provides simultaneous images at wavelengths of 3.6, 4.5, 5.8, and 8.0 microns. IRS (InfraRed Spectrograph) has 4 modules providing low-resolution (R=60-120) spectra from 5.3 to 40 microns, high-resolution (R=600) spectra from 10 to 37 microns, and an autonomous target acquisition system (PeakUp) which includes small-field imaging at 15 microns. MIPS (Multiband Imaging Photometer for SIRTF)} does imaging photometry at 24, 70, and 160 m and low-resolution (R=15-25) spectroscopy (SED) between 55 and 96 microns. The SIRTF Guaranteed Time Observers (GTOs) are planning to observe Outer Solar System satellites and planets, extinct comets and low-albedo asteroids, Centaurs and Kuiper Belt Objects, cometary dust trails, and a few active short-period comets. The GTO programs are listed in detail in the SIRTF Reserved Observations Catalog (ROC). We would like to emphasize that there remain many interesting subjects for the General Observers (GO). Proposal success for the planetary observer community in the first SIRTF GO proposal cycle (GO-1) determines expectations for future GO calls and Solar System use of SIRTF, so we would like promote a strong set of planetary GO-1 proposals. Towards that end, we present this poster, and we will convene a Solar System GO workshop 3.5 months after launch.

Cleve, J. Van↗
NASA NTRS2004

First Solar System Results of the Spitzer Space Telescope

The Spitzer Space Telescope, formerly known as SIRTF, is now operational and delivers unprecedented sensitivity for the observation of Solar System targets. Spitzer's capabilities and first general results were presented at the January 2004 AAS meeting. In this poster, we focus on Spitzer's performance for moving targets, and the first Solar System results. Spitzer has three instruments, IRAC, IRS, and MIPS. IRAC (InfraRed Array Camera) provides simultaneous images at wavelengths of 3.6, 4.5, 5.8, and 8.0 microns. IRS (InfraRed Spectrograph) has 4 modules providing low-resolution (R=60-120) spectra from 5.3 to 40 microns, high-resolution (R=600) spectra from 10 to 37 m, and an autonomous target acquisition system (PeakUp) which includes small-field imaging at 15 m. MIPS (Multiband Imaging Photometer for SIRTF) does imaging photometry at 24, 70, and 160 m and low-resolution (R=15-25) spectroscopy (SED) between 55 and 96 microns. Guaranteed Time Observer (GTO) programs include the moons of the outer Solar System, Pluto, Centaurs, Kuiper Belt Objects, and comets

VanCleve, J.↗
NASA NTRS1992

An airborne system for collecting polarization imagery

This paper describes a recently completed electrooptical camera flying onboard the NASA ER-2 high altitude aircraft. The device includes a six-position filter wheel which can be fitted with a combination of polarizing and/or spectral filters. An alternate configuration will include a polarizing filter which can be rotated to any angle under computer control. The camera mount in the nose of the ER-2 can tilt forward or aft up to 40 degrees, both for bidirectional reflectance studies and for image motion compensation (the aircraft moves 34 meters between frame acquisitions). The ground resolution is nominally 5 meters from and altitude of 20 km. Spectral responsivity is that of the silicon imaging array (Kodak KAF-1400). Initial data sets were acquired in support of the International Satellite Cloud Climatology Program Regional Experiment of November, 1991, and will be used to study cirrus cloud properties.

Hildum, Edward A.↗
NASA NTRSDate not supplied

The Mars 2020 Watson Imaging Subsystem of the Sherloc Investigation and Anticpated Early Results

In February 2021, the Mars 2020 Perseverance rover is anticipated to touch down in Jezero crater, Mars. Perseverance is unique in that it will conduct in situ science as well as cache samples for eventual return to Earth for analysis in terrestrial laboratories. It will explore the geologic setting within Jezero over a range of scales in order to address fundamental questions about the evolution of Mars and assess whether there is evidence of past or present Martian life. The Wide Angle Topographic Sensor for Operations and eNgineering (WATSON), one of two imaging subsystems within the Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals (SHERLOC) instrument[1,2], acquires images that support scientific study of Jezero crater and sample acquisition, as well as rover and instrument operations(Fig. 1).WATSON serves a number of roles; it 1) provides color context imaging of SHERLOC and Planetary Instrument for X-Ray Litho chemistry (PIXL)analysis locations, placing the spatial distribution of organics and mineralogy detected by SHERLOC and the elemental maps generated by PIXL within the context of rock texture and structure; 2)acquires stand-alone observations of rock structures and textures from the outcrop to the grain scale; and 3)images rover components and other instruments to monitor their health and condition. We plan to present images acquired within the first ~30 sols of operations.

M E Minitti↗
DOE OSTI2023

Improving robustness for model discerning synthesis process of uranium oxide with unsupervised domain adaptation

The quantitative characterization of surface structures captured in scanning electron microscopy (SEM) images has proven to be effective for discerning provenance of an unknown nuclear material. Recently, many works have taken advantage of the powerful performance of convolutional neural networks (CNNs) to provide faster and more consistent characterization of surface structures. However, one inherent limitation of CNNs is their degradation in performance when encountering discrepancy between training and test datasets, which limits their use widely. The common discrepancy in an SEM image dataset occurs at low-level image information due to user-bias in selecting acquisition parameters and microscopes from different manufacturers. Therefore, in this study, we present a domain adaptation framework to improve robustness of CNNs against the discrepancy in low-level image information. Furthermore, our proposed approach makes use of only unlabeled test samples to adapt a pretrained model, which is more suitable for nuclear forensics application for which obtaining both training and test datasets simultaneously is a challenge due to data sensitivity. Through extensive experiments, we demonstrate that our proposed approach effectively improves the performance of a model by at least 18% when encountering domain discrepancy, and can be deployed in many CNN architectures.

scanning electron microscopy↗
DOE OSTI2024

Tractometry of the Human Connectome Project: resources and insights

The Human Connectome Project (HCP) has become a keystone dataset in human neuroscience, with a plethora of important applications in advancing brain imaging methods and an understanding of the human brain. We focused on tractometry of HCP diffusion-weighted MRI (dMRI) data. We used an open-source software library (pyAFQ; https://yeatmanlab.github.io/pyAFQ) to perform probabilistic tractography and delineate the major white matter pathways in the HCP subjects that have a complete dMRI acquisition (n = 1,041). We used diffusion kurtosis imaging (DKI) to model white matter microstructure in each voxel of the white matter, and extracted tract profiles of DKI-derived tissue properties along the length of the tracts. We explored the empirical properties of the data: first, we assessed the heritability of DKI tissue properties using the known genetic linkage of the large number of twin pairs sampled in HCP. Second, we tested the ability of tractometry to serve as the basis for predictive models of individual characteristics (e.g., age, crystallized/fluid intelligence, reading ability, etc.), compared to local connectome features. To facilitate the exploration of the dataset we created a new web-based visualization tool and use this tool to visualize the data in the HCP tractometry dataset. Finally, we used the HCP dataset as a test-bed for a new technological innovation: the TRX file-format for representation of dMRI-based streamlines. We released the processing outputs and tract profiles as a publicly available data resource through the AWS Open Data program's Open Neurodata repository. We found heritability as high as 0.9 for DKI-based metrics in some brain pathways. We also found that tractometry extracts as much useful information about individual differences as the local connectome method. We released a new web-based visualization tool for tractometry—“Tractoscope” (https://nrdg.github.io/tractoscope). We found that the TRX files require considerably less disk space-a crucial attribute for large datasets like HCP. In addition, TRX incorporates a specification for grouping streamlines, further simplifying tractometry analysis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Depth-resolved Laue microdiffraction with coded apertures

A rapid data acquisition and reconstruction method is introduced to image the crystalline structure of materials and the associated strain and orientations at micrometre resolution using Laue diffraction. The method relies on scanning a coded aperture across the diffracted X-ray beam from broadband illumination and a reconstruction algorithm to resolve Laue microdiffraction patterns as a function of depth along the incident illumination path. It provides rapid access to full diffraction information for sub-micrometre volume elements in bulk materials. Both the theory and the experimental validation of this imaging approach are presented in this report.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Transient superconductivity at nano- and meso-scales

High-T c superconductivity is surpassed by few, if any, other unsolved problems in contemporary physics in terms of its richness, complexity, impact on other fields, and potential technological importance in energy applications. Recent discoveries reveal that the highest transition temperatures emerge under extraordinary experimental conditions such as the strong perturbation of a single atomic layer material by the underlying substrate, application of ~200 GPa pressure or ~MV/cm static electric fields, or irradiation by intense femtosecond optical fields. Research in superconductivity under extreme experimental conditions is challenging and requires the development of novel experimental methods and new theoretical tools suitable to tackle the problem. Our team has approached the challenges of transient superconductivity under intense optical fields by rethinking the types of experimental observables that are best suited to elucidate the physics of light-induced effects. This exercise has identified the need for: i) ultra-fast spatio-temporal probes enabling access to plasmonic properties and also nano-scale inhomogeneities that are ubiquitous in unconventional superconductors; ii) meso-scale structures and hybrid meta-surfaces imperative for strong enhancement of optical fields and iii) theoretical approaches suitable to explain and predict the response of superconductors under ultra-fast photo-excitation. Co-PIs have already made major advances with developing capabilities i)-iii). Therefore, our team is poised to make significant progress in transient superconductivity by exploring novel spatio-temporal effects that currently remain unattainable. Co-investigators will search for light-induced superconductivity in the high-T c cuprates to build upon spectacular recent results still lacking independent confirmations. Novel data acquisition methods combined with nano-plasmonic imaging will allow our team to test the hypothesis of photo-induced superconducting pairing. Spatio-temporal experiments will provide insights into the interplay between superconductivity and competing orders in the cuprates. Co-PIs will also investigate strong light-matter interaction and superconductivity in FeSe monolayers. Finally, we will study spatio-temporal electrodynamics of planar Josephson junctions. This latter direction will allow our team to thoroughly characterize one of the most fundamental examples of inhomogeneity in all of unconventional superconductivity. The proposed experiments in combination with theoretical analysis will provide information that is difficult to obtain using alternative methods. Basov and Averitt will carry out pump-probe spectroscopy and nano-imaging experiments. Averitt and Hone will design and fabricate state-of-the-art meta-surfaces. Theoretical and computational studies of ultrafast transient phenomena will be carried out by Millis and Fogler. Modeling of time-resolved nano-optical effects will be done by Fogler. The bulk of the requested budget will be used to support graduate students at Columbia & UCSD that will be co-supervised by co-PIs. The proposed program is transformative, since it will enable an entire suite of experiments previously either impossible or technically implausible. It will deliver critically important insights not only into mechanisms of unconventional superconductivity but also to many other correlated quantum materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Deep Learning Model Segmentations on Computed Tomography 3D Reconstructions of Coffee Beans to Determine Void Ratio (U-Net) and Roast Level (LinkNet)

This project will evaluate the functionality of Object Research Dragonfly software’s user produced deep learning (DL) models on Computed Tomography (CT) scanned coffee beans from green (unroasted) through a dark roast. DL models will be expected to identify voids within the coffee beans, and identify the level of roast of the bean from the CT reconstruction. The scope of this project is intended to meet the Capstone Project requirements of University of California San Diego (UCSD) Structural Engineering master’s degree and offer useful insight on Dragonfly’s DL capability for LANL’s Non-Destructive Evaluation (NDE) CT team. The results of this project will be presented to the E-6 NDE group within LANL. Data acquisition was completed with a North Star Imaging (NSI) X-25 CT Cabinet.

97 MATHEMATICS AND COMPUTING↗