Search NASA⌕ Search

SEARCH · Search NASA

Results for “Image Comparison”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Ultimate FY23 Vacuum Creep Testing Facility Operation Report

This facility operation report covers the refurbishment and initial testing of a vacuum creep frame used to conduct 1300°C tensile and creep tests on Mo- and Nb-based alloys. The report details the refurbishment of the frame and initial operation including 100 h creep experiments compared to similar tests in argon. Unlike the 25 mm dogbone specimens tested in argon, the vacuum load train requires 76 mm dog bone specimens. Testing in vacuum has eliminated concern of O embrittlement in argon and Nb alloy CB103 showed >120% tensile elongation at 1300°C and 1450°C. Also, a window in the vacuum chamber allows the use of digital image correlation (DIC) to measure strain rather than the load train displacement used in the argon frames. Comparison of the displacement and DIC results in the vacuum frame showed significant variation in primary creep deformation, likely due to the machine compliance, but similar secondary creep rates. Recently, ULTIMATE teams have submitted specimens for testing in vacuum. With the relatively low volume of creep testing, the facility performance metric of testing within 4 weeks of receiving specimens has been met.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Seismic Elastic Double-Beam Characterization of Faults and Fractures for CO₂ Storage Site Selection

Site characterization for underground injection and storage of gigatonne-scale CO₂ requires reliable and cost-effective methods to detect and characterize faults and fractures and to assess their stress state and fault activation potential. This is critical, as wastewater injection and disposal have been shown to activate faults and induce earthquakes, and CO₂ leakage remains a key concern for long-term storage. In this project, we developed seismic methods to detect and characterize large-scale sedimentary and crystalline basement faults and associated small-scale fractures below conventional seismic imaging resolution using multicomponent (9C) surface seismic data. Machine learning was used to automatically interpret large-scale faults, providing key information for estimating the maximum magnitude of potential induced earthquakes. High-fidelity imaging was achieved by exploiting redundancy across multiple elastic wave modes, where independent images from different modes and frequencies cross-validate each other. We also used our nonlinear signal comparison (NLSC) method for ground roll removal, improving data quality in complex near-surface conditions. The methods were validated using field data acquired in central Montana. Results show that basement faults extend into the sedimentary section and that small-scale fractures are widespread above the basement. The inferred stress orientation is consistent with regional stress data, and the estimated maximum induced earthquake magnitude is small (Mw ~2.3). The developed workflow provides a practical approach for fault and fracture characterization and for assessing induced seismicity and leakage risk. It is directly applicable to CO₂ storage site selection and to other subsurface systems.

02 PETROLEUM↗
DOE OSTI2023

AutoSourceID-Classifier: Star-galaxy classification using a convolutional neural network with spatial information

Aims.Traditional star-galaxy classification techniques often rely on feature estimation from catalogs, a process susceptible to introducing inaccuracies, thereby potentially jeopardizing the classification’s reliability. Certain galaxies, especially those not manifesting as extended sources, can be misclassified when their shape parameters and flux solely drive the inference. We aim to create a robust and accurate classification network for identifying stars and galaxies directly from astronomical images. Methods.The AutoSourceID-Classifier (ASID-C) algorithm developed for this work uses 32x32 pixel single filter band source cutouts generated by the previously developed AutoSourceID-Light (ASID-L) code. By leveraging convolutional neural networks (CNN) and additional information about the source position within the full-field image, ASID-C aims to accurately classify all stars and galaxies within a survey. Subsequently, we employed a modified Platt scaling calibration for the output of the CNN, ensuring that the derived probabilities were effectively calibrated, delivering precise and reliable results. Results.We show that ASID-C, trained on MeerLICHT telescope images and using the Dark Energy Camera Legacy Survey (DECaLS) morphological classification, is a robust classifier and outperforms similar codes such as SourceExtractor. To facilitate a rigorous comparison, we also trained an eXtreme Gradient Boosting (XGBoost) model on tabular features extracted by SourceExtractor. While this XGBoost model approaches ASID-C in performance metrics, it does not offer the computational efficiency and reduced error propagation inherent in ASID-C’s direct image-based classification approach. ASID-C excels in low signal-to-noise ratio and crowded scenarios, potentially aiding in transient host identification and advancing deep-sky astronomy.

Astronomy & Astrophysics↗
DOE OSTI2021

Evaluation of sequence tracking methods for Compton cameras based on CdZnTe arrays

In this study, the performance of sequence tracking methods for multiple interaction events in specific CdZnTe Compton imagers was evaluated using Monte Carlo simulations. The Compton imager consisted of a 6 × 6 array of virtual Frisch-grid CZT crystals, where the dimensions of each crystal were 5 × 5 × 12 mm 3 . The sequence tracking methods for another Compton imager that consists of two identical CZT crystals arrays were also evaluated. When 662 keV radiation was incident on the detectors, the percentages of the correct sequences determined by the simple comparison and deterministic methods for two sequential interactions were identical (~80 %), while those evaluated using the minimum squared difference method (55-59 %) and Three Compton method (45-55 %) for three sequential interactions, differed from each other. The reconstructed images of a 662 keV point source detected using single and double arrays were evaluated based on their angular resolution and signal-to-noise ratio, and the results showed that the double arrays outperformed single arrays.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Electrical Resistivity Tomography Monitoring of In Situ Soil Flushing at the Hanford 100-K East Area: 100KE Soil Flushing Monitoring

Former operations in the 100-KE Area at the Hanford Site resulted in vadose zone hexavalent chromium that provides a source of groundwater contamination. Previous efforts to remediate vadose zone chromium involved excavation and offsite treatment of contaminated soils. Although much of the chromium was removed, contamination still exists in native soil between the water table and the bottom of the pit, which has since been backfilled. In-situ soil flushing was tested at the 100-KE Area to accelerate the removal of the remaining hexavalent chromium in the vadose zone. Soil flushing works by applying clean flush water at the surface, which mobilizes and transports chromium as it migrates downward to the water table. Once the chromium reaches the water table, it is hydraulically contained, extracted, and treated through pump and treat operations. The efficacy of soil flushing is directly related to the volume of clean water that infiltrates through contaminated soils. Therefore, performance monitoring requires and understanding of flush water migration through the vadose zone. It is challenging to comprehensively assess soil flushing performance via borehole access due to the limited volume of investigation afforded by a given wellbore. As an alternative, 3D time-lapse electrical resistivity tomography (ERT) was tested as a method of monitoring the distribution of flush water over time within the vadose zone. ERT is a method of imaging the bulk electrical conductivity of the subsurface, which is highly dependent on water saturation levels in unconsolidated and unsaturated sediments. Thus, the timing and location of changes in bulk conductivity caused by soil flushing can be used to infer flush water migration pathways. In this report we present results of 3D time-lapse ERT imaging during two separate soil flushing campaigns conducted during the spring and summer months of 2022 and 2023. Results show generally that: 1) Pit backfill materials appear to nominally have larger permeability than native soils. Consequently, the boundary between pit backfill and native soil had a significant impact on flush water migration, causing some flush water to migrate along the pit boundary to the bottom of the pit. 2) Non-uniform flows, likely caused by variations in hydrogeologic properties, developed in the pit backfill materials, resulting in uneven flush water distribution on the southern margin of the soil flushing zone. 3) Redistribution of water at the interface between backfill materials and Hanford formation materials likely facilitated elevated and uniform distribution of flush water within native soils beneath the deeper parts of the pit boundary beneath the center of the flush zone, which presumably overlie soils with elevated chromium contamination. These areas appear to have been infiltrated by higher volumes of flush water than the northern and southern margins of the flush zone. 4) In comparison to 2022, high flush water application rates significantly improved flush water distribution throughout the target flushing zone. 5) Imaging resolution was limited to a depth of approximate 20 meters below the land surface, due primarily to limitations on the lateral extent of the surface ERT array. The ~10 m region of the vadose zone between approximately 130 meters elevation and the water table at approximately 120 meters elevation was unresolved.

100 Area↗
DOE OSTI2022

Introduction to Special Section: Machine Learning for Image-based Geologic Interpretation

Image-based geological interpretation has been a labor-intensive and time-consuming process because it requires well-trained geoscientists to identify geological structures, features, and textures from various types of images. These images include scanning electron microscopic images, optical microscopic images, optical photos, resistivity images, seismic volumes, remote-sensing images, etc. With fast-evolving machine learning (ML) technology and computing power in recent decades, computers can achieve nearhuman-level to super-human-level performance with scalable high efficiency in the computer vision field. These technological revolutions facilitated image-based geological interpretation in petroleum exploration and production. For example, a fault picking method applied to 3-D seismic volume data using deep learning can achieve superior performance in comparison to conventional auto-picking methods. In addition, under the new normal of low oil prices, the petroleum industry seeks cost-effective strategies such as automating traditionally labor-intensive processes. Nevertheless, the potential of applying ML to geological image interpretation is still facing a few key challenges including data scarcity, data distribution, poor data and/or label quality, data leakage, learning algorithms, model architecture, training methodologies, testing and evaluation metrics, hyper-parameters optimization, model drift, production deployment, and the like.

58 GEOSCIENCES↗
DOE OSTI2021

Enabling Particulate Materials Processing Science for High-Consequence, Small-Lot Precision Manufacturing

This Laboratory Directed Research and Development project developed and applied closely coupled experimental and computational tools to investigate powder compaction across multiple length scales. The primary motivation for this work is to provide connections between powder feedstock characteristics, processing conditions, and powder pellet properties in the context of powder-based energetic components manufacturing. We have focused our efforts on multicrystalline cellulose, a molecular crystalline surrogate material that is mechanically similar to several energetic materials of interest, but provides several advantages for fundamental investigations. We report extensive experimental characterization ranging in length scale from nanometers to macroscopic, bulk behavior. Experiments included nanoindentation of well-controlled, micron-scale pillar geometries milled into the surface of individual particles, single-particle crushing experiments, in-situ optical and computed tomography imaging of the compaction of multiple particles in different geometries, and bulk powder compaction. In order to capture the large plastic deformation and fracture of particles in computational models, we have advanced two distinct meshfree Lagrangian simulation techniques: 1.) bonded particle methods, which extend existing discrete element method capabilities in the Sandia-developed , open-source LAMMPS code to capture particle deformation and fracture and 2.) extensions of peridynamics for application to mesoscale powder compaction, including a novel material model that includes plasticity and creep. We have demonstrated both methods for simulations of single-particle crushing as well as mesoscale multi-particle compaction, with favorable comparisons to experimental data. We have used small-scale, mechanical characterization data to inform material models, and in-situ imaging of mesoscale particle structures to provide initial conditions for simulations. Both mesostructure porosity characteristics and overall stress-strain behavior were found to be in good agreement between simulations and experiments. We have thus demonstrated a novel multi-scale, closely coupled experimental and computational approach to the study of powder compaction. This enables a wide range of possible investigations into feedstock-process-structure relationships in powder-based materials, with immediate applications in energetic component manufacturing, as well as other particle-based components and processes.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A Review of Doses for Dental Imaging in 2010–2020 and Development of a Web Dose Calculator

Dental imaging is one of the most common types of diagnostic radiological procedures in modern medicine. We introduce a comprehensive table of organ doses received by patients in dental imaging procedures extracted from literature and a new web application to visualize the summarized dose information. We analyzed articles, published after 2010, from PubMed on organ and effective doses delivered by dental imaging procedures, including intraoral radiography, panoramic radiography, and cone-beam computed tomography (CBCT), and summarized doses by dosimetry method, machine model, patient age, and technical parameters. Mean effective doses delivered by intraoral, 1.32 (0.60–2.56) μSv, and panoramic, 17.93 (3.47–75.00) μSv, procedures were found to be about1% and 15% of that delivered by CBCT, 121.09 (17.10–392.20) μSv, respectively. In CBCT imaging, child phantoms received about 29% more effective dose than the adult phantoms received. The effective dose of a large field of view (FOV) (>150 cm2) was about 1.6 times greater than that of a small FOV (<50 cm2). The maximum CBCT effective dose with a large FOV for children, 392.2 μSv, was about 13% of theeffective dose that a person receives on average every year from natural radiation, 3110 μSv. Monte Carlo simulations of representative cases of the three dental imaging procedures were then conducted to estimate and visualize the dose distribution within the head. The user-friendly interactive web application (available at http://dentaldose.org) receives user input, such as the number of intraoral radiographs taken, and displays total organ and effective doses, dose distribution maps, and a comparison with other medical and natural sources of radiation. The web dose calculator provides a practical resource for patients interested in understanding the radiation doses delivered by dental imaging procedures.

Lee, Hawon↗
DOE OSTI2026

Understanding the structural and morphological effects of synthesis route on NpO 2

The availability of actinide standard materials for use in nuclear safeguard applications is critical, as is thorough characterization thereof. Although accurate trace element compositions and isotopic considerations are paramount for deployment of reference standards, structural characterization is also essential towards accurately describing the chemical form and potential matrix effects in candidate materials. Here, to this end, samples of NpO 2 were synthesized via a direct denitration (DD) method and probed with powder X-ray diffraction (PXRD), Raman spectroscopy, and scanning electron microscopy (SEM) for structural and morphological characterization and comparison with NpO 2 materials produced via modified direct denitration (MDD). PXRD confirmed the bulk identity of NpO 2 , and no additional phases were identified using this method. Analysis of Raman data collected using a 532 nm excitation wavelength indicates that samples are mostly phase pure; however, some variability in spectral features is observed. Analysis of additional spectroscopic data collected with a 785 nm excitation wavelength revealed variability in the relative intensity of spectral features. Raman spectroscopy indicates that the sample is primarily NpO 2 ; however, additional signals indicate possible structural disorder, oxidized species, or potential contributions from other Np phases. To further investigate the possibility of additional phase contributions within the sample of NpO 2 , Raman spectroscopic mapping was employed to examine the homogeneity of the sample produced via DD. From this analysis, we determined that despite variability in the intensity of Raman-active vibrational modes, consistent spectra are obtained throughout the area of the sample investigated. SEM images show aggregates with variable sizes and shapes, with rounded, primary particles possessing an average diameter of approximately 100 nm. Comparison of the results of these multimodal analyses to the literature indicates that the crystal chemical, spectroscopic, and microstructural properties of NpO 2 vary based on synthesis method, even if X-ray diffraction data indicate that the bulk phase is NpO 2 .

Direct denitration↗
DOE OSTI2020

Time-Sequenced Flow Field Prediction in an Optima Spark-Ignition Direct-Injection Engine Using Bidirectional Recurrent Neural Network (bi-RNN) with Long Short-Term Memory

To further improve the energy conversion efficiency of internal combustion engine, the transient and complex air flow movement inside the cylinder needs to be better understood and controlled. Although the in-cylinder flow fields are highly stochastic with strong cycle-to-cycle fluctuations, machine learning can still provide an efficient way to learn and regress the complex flow movement process inside the cylinder. In this work, a bidirectional recurrent neural network (bi-RNN) model with long short-term memory was applied to predict the in-cylinder flow fields at different time steps using training data from mull-cycle particle image velocimetry (PIV) measurements. To evaluate the agreement between the true and predicted flow fields, structure and magnitude comparison indices are calculated both globally and locally. The comparison results show that the bi-RNN model can accurately predict the bulk flow and vortex motions from early intake stroke to compression stroke. This work demonstrates that the machine learning model has the potential to predict the underlying dynamics of the interaction between in-cylinder flows and provides a reliable way to improve temporal resolution in PIV flow data to better reveal transient in-cylinder flow features.

Bi-RNN model↗
DOE OSTI2020

3D xRAGE simulation of inertial confinement fusion implosion with imposed mode 2 laser drive asymmetry

Low-mode asymmetries represent an important obstacle to achieving high-gain inertial confinement fusion implosions. As a step in learning how to control such effects, an OMEGA experiment with imposed mode 2 laser drive asymmetries was done to study the expected signatures of this type of asymmetry [M. Gatu Johnson et al., PRE 2018]. In the present work, a 3D xRAGE simulation including the stalk mount has been brought to bear on the data from that experiment. Furthermore, comprehensive comparisons between simulated and measured observables are made. Good agreement between simulated and measured x-ray image-inferred shell trajectories, bang times and neutron emission widths are seen, showing that the hydrodynamics are well captured in the simulation. Asymmetries seen in simulated and measured time-resolved and time-integrated x-ray images and areal densities also compare well, showing impact of both stalk and mode 2. On the other hand, important differences in measured and simulated neutron emission histories, yield, and ion temperature (T ion ) asymmetries are seen, suggesting that the simulation is overestimating shock yield. Overall, the results clearly demonstrate the importance of considering all asymmetry sources when interpreting measured signatures of asymmetry.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Compositional screening of Ce-doped (Gd,Lu,Y) 3 (Al,Ga) 5 O 12 ceramics prepared by quenching from melt and their luminescence properties

Quenching from the melt using an Optical Floating Zone furnace was investigated as a possible high-throughput preparation method in order to screen novel scintillating materials. To validate this method, polycrystalline rare-earth aluminum garnets and yttrium gallium aluminum garnets were synthesized and characterized by X-ray diffraction, photoluminescence and radioluminescence emission spectra as well as radioluminescence imaging and compared to the previously reported properties of those materials. In order to achieve rapid, but quantitative comparison of the materials, pellets fabricated from the synthesized powders were sintered; two different sintering conditions were investigated and compared. Therefore, a simplistic energy deposition and light absorption model for brightness of opaque samples under X-ray irradiation was developed to estimate the relative scintillation efficiency of the materials. Based on the results, Y 3 Al 5 O 12 , LuY 2 Al 5 O 12 and GdY 2 Al 5 O 12 seem to have the highest scintillation efficiency among the prepared samples.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Neural Image Compression: Generalization, Robustness, and Spectral Biases

Recent advances in neural image compression (NIC) have resulted in models which are starting to outperform traditional codecs. While this has led to growing excitement about using these methods in real-world applications, the successful adoption of any machine learning system (including NIC) in the wild requires it to generalize (and be robust) to unseen distribution shifts at deployment time. Unfortunately, current research lacks comprehensive datasets and informative tools to evaluate and understand compression performance in real-world settings. To bridge this crucial gap, first, this paper presents a comprehensive benchmark suite to evaluate the out-of-distribution (OOD) performance of image compression methods. Specifically, we design CLIC-C and Kodak-C by introducing 15 common corruptions to popular CLIC and Kodak benchmarks. Next, we propose spectrally inspired introspection tools to gain a deeper understanding of errors introduced by image compression methods as well as their OOD performance. To this end, we carry out a detailed performance comparison of the classical codec with various variants of NIC (e.g., original, variable rate, pruned), revealing intriguing findings that challenge our current understanding of the strengths and limitations of NIC. Finally, we corroborate our empirical findings with theoretical analysis, providing an in-depth view of the OOD performance of NIC. Our benchmarks, spectral introspection tools, and findings provide a crucial bridge to the real-world adoption of NIC. We hope that our work will propel future efforts in designing more robust and generalizable NIC methods.

neural networks, Variational Autoencoder, robustne↗
DOE OSTI2023

Bridging Time-series Image Phenotyping and Functional–Structural Plant Modeling to Predict Adventitious Root System Architecture

Root system architecture (RSA) is an important measure of how plants navigate and interact with the soil environment. However, current methods in studying RSA must make tradeoffs between precision of data and proximity to natural conditions, with root growth in germination papers providing accessibility and high data resolution. Functional–structural plant models (FSPMs) can overcome this tradeoff, though parameterization and evaluation of FSPMs are traditionally based in manual measurements and visual comparison. Here, we applied a germination paper system to study the adventitious RSA and root phenology of Populus trichocarpa stem cuttings using time-series image-based phenotyping augmented by FSPM. We found a significant correlation between timing of root initiation and thermal time at cutting collection (P value = 0.0061, R 2 = 0.875), but little correlation with RSA. We also present a use of RhizoVision [1] for automatically extracting FSPM parameters from time series images and evaluating FSPM simulations. A high accuracy of the parameterization was achieved in predicting 2D growth with a sensitivity rate of 83.5%. This accuracy was lost when predicting 3D growth with sensitivity rates of 38.5% to 48.7%, while overall accuracy varied with phenotyping methods. Despite this loss in accuracy, the new method is amenable to high throughput FSPM parameterization and bridges the gap between advances in time-series phenotyping and FSPMs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Detailed Characterization of CZT Detector Response for Improved Coded-Aperture Imaging Performance

Gamma-ray imaging is a powerful method for locating and quantifying sources of radiation. The coded-aperture technique demonstrates superior angular resolution in comparison to other methods (e.g., Compton reconstruction). In this method, a mask constructed of highly attenuating material encodes the scene as a shadow pattern on a position-sensitive detector; this pattern can then be used to recreate the origin(s) of incident radiation. This is typically done through convolution of the mask and shadow patterns. Iterative methods which attempt to reconstruct the observed shadow pattern using a weighted combination of simulated patterns may also be employed. In either case, errors in event position reconstruction due to detector imperfections alter the shadow pattern and will therefore degrade system performance and may introduce imaging artifacts. These effects can be mitigated with a detailed understanding of such errors – allowing for the generation of representative simulations that include the errors and/or correction of raw imager data to remove the errors. We present a calibration process for a commercially available cadmium zinc telluride (CZT) gamma imager which provides a comprehensive characterization of the spatial and energy dependence of event reconstruction. By illuminating a mask featuring a regular grid of pinholes with a calibration source, the localized response of the detector can be measured with fine granularity. These local responses are combined to generate a full detector response map which can be used to distort simulations in a manner that is representative of the observed detector data. Details of the calibration procedure and an assessment of the impact of its end products on the performance of iterative imaging methods will be presented.

Ziock, Klaus-Peter↗
DOE OSTI2022

Computational models of direct and indirect X‐ray breast imaging detectors for in silico trials

Abstract Background To facilitate in silico studies that investigate digital mammography (DM) and breast tomosynthesis (DBT), models replicating the variety in imaging performance of the DM and DBT systems, observed across manufacturers are needed. Purpose The main purpose of this work is to develop generic physics models for direct and indirect detector technology used in commercially available systems, with the goal of making them available open source to manufacturers to further tweak and develop the exact in silico replicas of their systems. Methods We recently reported on an in silico version of the SIEMENS Mammomat Inspiration DM/DBT system using an open‐source GPU‐accelerated Monte Carlo x‐ray imaging simulation code (MC‐GPU). We build on the previous version of the MC‐GPU codes to mimic the imaging performances of two other Food and Drug Administration (FDA)‐approved DM/DBT systems, such as Hologic Selenia Dimensions (HSD) and the General Electric Senographe Pristina (GSP) systems. In this work, we developed a hybrid technique to model the optical spread and signal crosstalk observed in the GSP and HSD systems. MC simulations are used to track each x‐ray photon till its first interaction within the x‐ray detector. On the other hand, the signal spread in the x‐ray detectors is modeled using previously developed analytical equations. This approach allows us to preserve the modeling accuracy offered by MC methods in the patient body, while speeding up secondary carrier transport (either electron–hole pairs or optical photons) using analytical equations in the detector. The analytical optical spread model for the indirect detector includes the depth‐dependent spread and collection of optical photons and relies on a pre‐computed set of point response functions that describe the optical spread as a function of depth. To understand the capabilities of the computational x‐ray detector models, we compared image quality metrics like modulation transfer function (MTF), normalized noise power spectrum (NNPS), and detective quantum efficiency (DQE), simulated with our models against measured data. Please note that the purpose of these comparisons with measured data would be to gauge if the model developed as part of this work could replicate commercially used direct and indirect technology in general and not to achieve perfect fits with measured data. Results We found that the simulated image quality metrics such as MTF, NNPS, and DQE were in reasonable agreement with experimental data. To demonstrate the imaging performance of the three DM/DBT systems, we integrated the detector models with the VICTRE pipeline and simulated DM images of a fatty breast model containing a spiculated mass and a calcium oxalate cluster. In general, we found that the images generated using the indirect model appeared more blurred with a different noise texture and contrast as compared to the systems with direct detectors. Conclusions We have presented computational models of three commercially available FDA‐approved DM/DBT systems, which implement both direct and indirect detector technology. The updated versions of the MC‐GPU codes that can be used to replicate three systems are available in open source format through GitHub.

Sengupta, Aunnasha↗
DOE OSTI2020

Comparison of thermionic filament and carbon nanotube field emitter-based electron ionization sources in cycloidal coded aperture mass analyzers

This report compares the coded aperture imaging performance of thermionic filament and carbon nanotube (CNT) field emitter-based electron sources in cycloidal-coded aperture mass spectrometers. The use of spatially coded apertures in mass spectrometry enables miniaturization by improving throughput without sacrificing resolution. CNT-based electron ionization sources for mass spectrometers provide several potential benefits over conventional thermionic emitters, including low voltage and low power consumption, room temperature operation, long lifetime, and ability to emit electrons in a pulsed mode. However, spatiotemporal variation in electron emission from CNTs is a major disadvantage. In this study, electron emission stability and spatiotemporal stability of the coded aperture image were compared for coded aperture cycloidal mass analyzers with either a CNT-based ion source or a thermionic filament-based ion source. We found that the thermionic filament-based ion source produced a significantly more stable coded aperture image than the CNT based ion source. The aperture image fluctuations in the CNT-based source are likely a result of adsorption and desorption of molecules on the CNT surface that cause local work function changes and induce spatiotemporal variation in electron emission and subsequent ion generation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Optimizing cloud motion estimation on the edge with phase correlation and optical flow

Abstract. Phase correlation (PC) is a well-known method for estimating cloud motion vectors (CMVs) from infrared and visible spectrum images. Commonly, phase shift is computed in the small blocks of the images using the fast Fourier transform. In this study, we investigate the performance and the stability of the blockwise PC method by changing the block size, the frame interval, and combinations of red, green, and blue (RGB) channels from the total sky imager (TSI) at the United States Atmospheric Radiation Measurement user facility's Southern Great Plains site. We find that shorter frame intervals, followed by larger block sizes, are responsible for stable estimates of the CMV, as suggested by the higher autocorrelations. The choice of RGB channels has a limited effect on the quality of CMVs, and the red and the grayscale images are marginally more reliable than the other combinations during rapidly evolving low-level clouds. The stability of CMVs was tested at different image resolutions with an implementation of the optimized algorithm on the Sage cyberinfrastructure test bed. We find that doubling the frame rate outperforms quadrupling the image resolution in achieving CMV stability. The correlations of CMVs with the wind data are significant in the range of 0.38–0.59 with a 95 % confidence interval, despite the uncertainties and limitations of both datasets. A comparison of the PC method with constructed data and the optical flow method suggests that the post-processing of the vector field has a significant effect on the quality of the CMV. The raindrop-contaminated images can be identified by the rotation of the TSI mirror in the motion field. The results of this study are critical to optimizing algorithms for edge-computing sensor systems.

54 ENVIRONMENTAL SCIENCES↗