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At least 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Improved Gas Plume Identification Using Nearest Neighbor Methods for Background Estimation

Longwave infrared (LWIR) hyperspectral imaging (HSI) can be used for many tasks in remote sensing, including detecting and identifying effluent gases by LWIR sensors on airborne platforms. Identification is used after detection to increase confidence in weakly detected plumes, reduce false positives from detection, and distinguish between similar and confounding material signatures. Background estimation is an important step used to reveal the unique spectral characteristics of the detected gas, allowing the identification model to determine what the gas is specifically. The importance of proper background estimation increases when dealing with weak signals, large libraries of gases of interest, and uncommon or heterogeneous backgrounds. In this article, we propose two methods for background estimation: a novel k-nearest segments (KNS) algorithm and the standard k-nearest neighbors (KNN) algorithm. We test our methods and three existing background estimation methods for comparison against global background estimation to determine which performs best at estimating the true background radiance under a plume and for increasing identification confidence using a neural network classification model. We compare the different methods using 640 simulated weak plumes in an urban environment. For identification, our KNS algorithm improves median neural network identification confidence by 53.2%. For background radiance estimation, the KNN algorithm provides a median of 49 times less RMSE than global background estimation. Furthermore, KNN is the easiest method to tune for different plumes, making it an excellent “out of the box” background estimator.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Semi-Automated, Object-Based Tomography of Dislocation Structures

The characterization of the three-dimensional arrangement of dislocations is important for many analyses in materials science. Dislocation tomography in transmission electron microscopy is conventionally accomplished through intensity-based reconstruction algorithms. Although such methods work successfully, a disadvantage is that they require many images to be collected over a large tilt range. Here, we present an alternative, semi-automated object-based approach that reduces the data collection requirements by drawing on the prior knowledge that dislocations are line objects. Our approach consists of three steps: (1) initial extraction of dislocation line objects from the individual frames, (2) alignment and matching of these objects across the frames in the tilt series, and (3) tomographic reconstruction to determine the full three-dimensional configuration of the dislocations. Drawing on innovations in graph theory, we employ a node-line segment representation for the dislocation lines and a novel arc-length mapping scheme to relate the dislocations to each other across the images in the tilt series. We demonstrate the method for a dataset collected from a dislocation network imaged by diffraction-contrast scanning transmission electron microscopy. Based on these results and a detailed uncertainty analysis for the algorithm, we discuss opportunities for optimizing data collection and further automating the method.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

FIRM image analysis: A machine learning workflow for quantifying extracellular matrix components from electron microscopy images

The extracellular matrix (ECM) is a complex network of biomolecules that plays an integral role in the structure, processes, and signaling mechanisms of cells and tissues. Identifying and quantifying changes in these matrix components provides insight into the mechanisms behind specific tissue remodeling processes; however, quantifying these changes is challenging due to difficult imaging conditions, complexity of the ECM, and the subtlety of these changes. Current imaging techniques allow us to visualize these critical remodeling events and developments in image analysis have employed a combination of analysis software and machine learning techniques to improve the efficiency and accuracy with which features are measured. Although image analysis has seen much improvement in recent years, there has been no technique developed to address ambiguity in feature edges in electron microscopy images. Presented here is a new machine learning-based workflow for the analysis of microscopy images named FIRM (Feature Identification from Raw Microscopy) that uses a random forest classifier to identify ECM features of interest and generate binary segmentation masks for quantification with ImageJ-FIJI. FIRM performed with an F1 score of 0.794 and greater than 80% accuracy for number and size of features detected. FIRM had similar deviation from the ground truth in the number of identified fibrils, fibril size, and size distributions when compared to human analyses. The results suggest that FIRM performs as well as manual analysis and requires a fraction of the time. This analysis technique is more efficient, eliminates user bias, and can be easily optimized to identify a variety of features, making it useful for any discipline requiring image analysis.

Science & Technology - Other Topics↗
DOE OSTI2025

Subject-specific modeling framework for particle deposition using computational fluid dynamics

Quantifying particle deposition and dose in the respiratory tract requires a physiologically realistic representation and reproducible computational workflows. However, existing modeling frameworks, such as the International Commission on Radiological Protection (ICRP) compartmental models and the Multiple Path Particle Dosimetry (MPPD) tool, lack detailed deposition profiles and subject-specific capabilities. The combination of advances in computer vision algorithms applied to the respiratory tract and Computational Fluid and Particle Dynamics (CFPD) allows high-fidelity simulations of particle behavior in anatomically accurate geometries derived from individual CT scans. The segmentation, preprocessing, and file preparation task for a CFPD simulation was often time-consuming, and no prior studies to-date have yet presented a fully automated framework. This work presents a fully automated workflow to obtain individualized particle deposition profiles in the human respiratory tract. The pipeline starts with segmenting upper and lower airway geometries using morphological and deep learning-based methods, generating three-dimensional (3D) models from CT imaging data. Next, a series of algorithms are presented to quality check and prepare the 3D geometry for a CFD or CFPD simulation. The preprocessing step includes correcting geometric artifacts, enforcing a physically consistent mesh, and automatically identifying and capping multiple outlets, which is required for CFD/CFPD simulations. These processed models are then input into open-source (OpenFOAM) or commercial (StarCCM+) CFD solvers, where flow and transient particle transport equations — including turbulence and particle–wall interactions are solved under realistic breathing conditions. Finally, the resulting particle deposition profiles can be integrated with Monte Carlo radiation transport codes and state-of-the-art computational phantoms to assess organ-specific absorbed doses in scenarios of radioactive aerosol inhalation. The presented work streamlines respiratory tract segmentation, preprocessing for CFD/CFPD simulations, and integration with dose assessment workflows, reducing manual intervention and improving access to high-fidelity, subject-specific modeling. The high precision in predicted particle deposition and dose distributions can improve personalized treatment strategies in respiratory medicine and refine dose estimates for radiation protection.

AI↗
NASA NTRS1993

A robust line extraction and matching algorithm

This paper presents an algorithm for extracting straight lines from intensity mages and describes a line matching algorithm for solving the line correspondence problem. The line extraction process begins by detecting edges in the intensity image. Next, line support regions are formed where image points (pixels) have similar gradient orientation. A line fitting algorithm is then used to fit a line to the points in the line support region based on a least means square fitting algorithm. Finally, line segments are linked together to form the final lines by using an adaptive line linking method; this results in much stronger lines and a smaller set of lines to be considered. Once the lines are detected in a sequence of images, a line matching algorithm is used to match lines in one image to the lines in the other image. The images are either from a motion or stereo sequence. The matched lines may then be used with the sensor position and orientation data to estimate range to objects corresponding to the lines. We present results based on applying the line extraction and line matching algorithms to a synthetic image and an outdoor scene captured by a camera on a helicopter.

Hussien, B.↗
DOE OSTI2020

Subdomain cryo-EM structure of nodaviral replication protein A crown complex provides mechanistic insights into RNA genome replication

For positive-strand RNA [(+)RNA] viruses, the major target for antiviral therapies is genomic RNA replication, which occurs at poorly understood membrane-bound viral RNA replication complexes. Recent cryoelectron microscopy (cryo-EM) of nodavirus RNA replication complexes revealed that the viral double-stranded RNA replication template is coiled inside a 30- to 90-nm invagination of the outer mitochondrial membrane, whose necked aperture to the cytoplasm is gated by a 12-fold symmetric, 35-nm diameter “crown” complex that contains multifunctional viral RNA replication protein A. Here we report optimizing cryo-EM tomography and image processing to improve crown resolution from 33 to 8.5 Å. This resolves the crown into 12 distinct vertical segments, each with 3 major subdomains: A membrane-connected basal lobe and an apical lobe that together comprise the ~19-nm-diameter central turret, and a leg emerging from the basal lobe that connects to the membrane at ~35-nm diameter. Despite widely varying replication vesicle diameters, the resulting two rings of membrane interaction sites constrain the vesicle neck to a highly uniform shape. Labeling protein A with a His-tag that binds 5-nm Ni-nanogold allowed cryo-EM tomography mapping of the C terminus of protein A to the apical lobe, which correlates well with the predicted structure of the C-proximal polymerase domain of protein A. These and other results indicate that the crown contains 12 copies of protein A arranged basally to apically in an N-to-C orientation. Moreover, the apical polymerase localization has significant mechanistic implications for template RNA recruitment and (-) and (+)RNA synthesis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

In-Situ Process Monitoring Evaluation and Demonstration using Advanced Characterization with Laser Powder Bed Systems

Oak Ridge National Laboratory’s (ORNL) Manufacturing Demonstration Facility (MDF) worked with EOS Group to evaluate the current in-situ sensor capabilities of an EOS M290 Laser Powder Bed Fusion machine. The M290 was fitted with a 1 Mega-Pixel (MP) grayscale visible-light camera and a 5 MP temporally integrated (TI) near-infrared (NIR) camera. One print from stainless steel (SS) 316 and two from Inconel 625 (IN625) were performed where data including in-situ imaging and a machine log file were captured. These data were subsequently analyzed using a Dynamic Multi-Scale Segmentation Convolutional Neural Network (DMSCNN) trained on user defined classes and correlated to as-printed flaws, in the form of porosity, discovered in X-Ray Computed Tomography (XCT). In Phase I, two indications were detected in-situ and spatially correlated to stochastic lack-of-fusion flaws discovered using XCT. In Phase II, using these links from in-situ signatures to XCT flaw populations, a second neural network (NN) was trained to create a Voxelized Property Prediction Model (VPPM) to predict porosity percentages within the part using only features garnered from the in-situ data from two IN625 complex geometries. The VPPM was able to accurately predict porosity values for IN625 parts with an R 2 value of 0.764.

36 MATERIALS SCIENCE↗
DOE OSTI2025

In-Situ Process Monitoring Evaluation and Demonstration using Advanced Characterization with Laser Powder Bed Systems

Oak Ridge National Laboratory’s (ORNL) Manufacturing Demonstration Facility (MDF) worked with EOS Group to evaluate the current in-situ sensor capabilities of an EOS M290 Laser Powder Bed Fusion machine. The M290 was fitted with a 1 Mega-Pixel (MP) grayscale visible-light camera and a 5 MP temporally integrated (TI) near-infrared (NIR) camera. One print from stainless steel (SS) 316 and two from Inconel 625 (IN625) were performed where data including in-situ imaging and a machine log file were captured. These data were subsequently analyzed using a Dynamic Multi-Scale Segmentation Convolutional Neural Network (DMSCNN) trained on user defined classes and correlated to as-printed flaws, in the form of porosity, discovered in X-Ray Computed Tomography (XCT). In Phase I, two indications were detected in-situ and spatially correlated to stochastic lack-of-fusion flaws discovered using XCT. In Phase II, using these links from in-situ signatures to XCT flaw populations, a second neural network (NN) was trained to create a Voxelized Property Prediction Model (VPPM) to predict porosity percentages within the part using only features garnered from the in-situ data from two IN625 complex geometries. The VPPM was able to accurately predict porosity values for IN625 parts with an R 2 value of 0.764.

36 MATERIALS SCIENCE↗
DOE OSTI2021

3D reconstruction identifies loci linked to variation in angle of individual sorghum leaves

Selection for yield at high planting density has reshaped the leaf canopy of maize, improving photosynthetic productivity in high density settings. Further optimization of canopy architecture may be possible. However, measuring leaf angles, the widely studied component trait of leaf canopy architecture, by hand is a labor and time intensive process. Here, we use multiple, calibrated, 2D images to reconstruct the 3D geometry of individual sorghum plants using a voxel carving based algorithm. Automatic skeletonization and segmentation of these 3D geometries enable quantification of the angle of each leaf for each plant. The resulting measurements are both heritable and correlated with manually collected leaf angles. This automated and scaleable reconstruction approach was employed to measure leaf-by-leaf angles for a population of 366 sorghum plants at multiple time points, resulting in 971 successful reconstructions and 3,376 leaf angle measurements from individual leaves. A genome wide association study conducted using aggregated leaf angle data identified a known large effect leaf angle gene, several previously identified leaf angle QTL from a sorghum NAM population, and novel signals. Genome wide association studies conducted separately for three individual sorghum leaves identified a number of the same signals, a previously unreported signal shared across multiple leaves, and signals near the sorghum orthologs of two maize genes known to influence leaf angle. Automated measurement of individual leaves and mapping variants associated with leaf angle reduce the barriers to engineering ideal canopy architectures in sorghum and other grain crops.

3D reconstruction↗
NASA NTRS1984

Nighttime observations of thunderstorm electrical activity from a high altitude airplane

Photographs from a NASA U-2 airplane flying over nocturnal thunderstorms show frequent lightning activity in the upper part of the cloud. In some cases, unobscured segments of lightning channels 1 km or longer are visible in clear air around and above the cloud. Multiple images of lightning channels indicate multiple discharges in the same channel. Photographs taken through a diffraction grating show that the lightning has a spectrum similar to that observed in the lower troposphere. Lightning spectra obtained with a slitless line-scan spectrometer show strong singly ionized nitrogen emissions at 463.0 and 500.5 nm. Field changes measured with an electric field-change meter correlate with pulses measured with a photocell optical system. Optical signals corresponding to dart leader, return stroke, and continuing current events are readily distinguished in the scattered light emerging from the cloud surface. The variation of light intensity with time in lightning events is consistent with predicted modification of optical lightning signals by clouds. It appears that satellite based optical sensor measurements cannot provide reliable information on current rise times in return strokes. On the other hand, discrimination between cloud-to-ground and intracloud flashes and the counting of ground strokes is possible using the optical pulse pairs which have been identified with leader, return-stroke events in the cloud-to-ground flashes studied.

Brook, M.↗
NASA NTRS1990

Advanced technology optical telescopes IV; Proceedings of the Meeting, Tucson, AZ, Feb. 12-16, 1990. Parts 1 & 2

The present conference on the current status of large, advanced-technology optical telescope development and construction projects discusses topics on such factors as their novel optical system designs, the use of phased arrays, seeing and site performance factors, mirror fabrication and testing, pointing and tracking techniques, mirror thermal control, structural design strategies, mirror supports and coatings, and the control of segmented mirrors. Attention is given to the proposed implementation of the VLT Interferometer, the first diffraction-limited astronomical images with adaptive optics, a fiber-optic telescope using a large cross-section image-transmitting bundle, the design of wide-field arrays, Hartmann test data reductions, liquid mirrors, inertial drives for telescope pointing, temperature control of large honeycomb mirrors, evaporative coatings for very large telescope mirrors, and the W. M. Keck telescope's primary mirror active control system software.

Barr, Lawrence D.↗
NASA NTRS1987

High-resolution CCD imagers using area-array CCD's for sensing spectral components of an optical line image

CCD imagers with a novel replicated-line-imager architecture are abutted to form an extended line sensor. The sensor is preceded by optics having a slit aperture and having an optical beam splitter or astigmatic lens for projecting multiple line images through an optical color-discriminating stripe filter to the CCD imagers. A very high resolution camera suitable for use in a satellite, for example, is thus provided. The replicated-line architecture of the imager comprises an area-array CCD, successive rows of which are illuminated by replications of the same line segment, as transmitted by respective color filter stripes. The charge packets formed by accumulation of photoresponsive charge in the area-array CCD are read out row by row. Each successive row of charge packets is then converted from parallel to serial format in a CCD line register and its amplitude sensed to generate a line of output signal.

Elabd, Hammam↗
NASA NTRS2021

Concept of Operations for OSIRIS-REx Optical Navigation Image Planning

Optical navigation (OpNav) is a critical subsystem of the OSIRIS-REx asteroid sample return mission, which operated in the vicinity of near-Earth asteroid (101955) Bennu from August 2018 through April 2021. A substantial amount of mission resources across multiple subsystems and institutions is required to ensure that the OpNav data are successfully acquired. The KinetX OpNav team, part of the Flight Dynamics System (FDS), is responsible for performing required analysis to develop the OpNav operations plans; requesting, reviewing and verifying the plans; and ultimately using the image data for critical navigation operations. The FDS team, responsible for the mission navigation, is operated by KinetX Aerospace with management and operations support from NASA’s Goddard Space Flight Center. The Science Processing and Operations Center (SPOC), located at the University of Arizona’s Lunar and Planetary Laboratory, is responsible for generating the planning products for all science and most OpNav data. These plans are integrated into the spacecraft sequences, tested, and commanded by the Mission Support Area (MSA) at Lockheed Martin Space. To ensure mission-critical navigation image data are successfully acquired, the plan is developed through a waterfall of planning cycles over the course of 3 months prior to onboard plan execution. During the initial strategic planning for a mission phase, detailed analysis is performed by the OpNav team to conceptualize the concept of operations (ConOps) for image data collection. This phase OpNav Narrative is included along with other strategic planning documents for the key ground segment stakeholders to review and provide feedback. The detailed OpNav plans get defined in the tactical planning cycle, which spans 8 to 3 weeks before the week-long integrated sequence is executed on-board the spacecraft. During the tactical cycle, the initial OpNav Request is submitted along with the science requests, kicking off development of the science and OpNav plans. Once the initial plan is drafted, interfaces are exercised so that the plan can be reviewed and iterated, if necessary. A rigorous schedule is followed by the planning teams during the implementation cycle, spanning the last 18 days before uplink, to ensure all the necessary integration, testing, and reviewing can occur on time. The development of the OpNav planning ConOps, including responsibilities, interfaces, timelines, and procedures, took extensive collaboration across mission elements and institutions. The process was robust throughout the 137 weeks of continuous Optical Navigation Operations at Bennu, which concluded on April 9th, 2021.

Coralie D. Adam↗
NASA NTRSDate not supplied

Introduction to Image-Based Wavefront Sensing

The James Webb Space Telescope (JWST) is easily one of the most advanced devices ever created by humans, bridging a gap between the macroscopic and quantum worlds in terms of how accurately we can align mirrors. It is the product of exhaustive testing, countless innovations, and the international collaboration between 1000’s of people working tirelessly for more than 20 years. In this talk we discuss the physics of image-based wavefront sensing: one of the core technologies that was utilized to complete the final alignment of the segmented mirror.

Wavefront Sensing↗
NASA NTRS2010

Wavefront Controls for a Large Submillimeter-Wave Observatory

The 25-m aperture Cornell Caltech Atacama Telescope (CCAT) will have a primary mirror that is divided into 162 individual segments, each of which is equipped with 3 positioning actuators. This paper presents a mathematical description of the telescope, its actuators and sensors, and uses it to derive control laws for figure maintenance. A Kalman Filter-based Optical State Estimator is used to continuously estimate the aberrations of the telescope; these are used in a state-feedback controller to maintain image quality. This approach provides the means to correct for the optical effects of errors that occur in unactuated degrees of freedom, such as lateral translations of the segments. The control laws are exercised in Monte Carlo and covariance analysis, to bound the closed-loop performance of the telescope and to conduct control design trades.

edge sensors↗
DOE OSTI2022

Panel-Segmentation: A Python Package for Automated Solar Array Metadata Extraction Using Satellite Imagery

The NREL Python Panel-Segmentation package is a toolkit that automates the process of extracting accurate and valuable metadata related to solar array installations, using publicly available Google Maps satellite imagery. Previously published work includes automated azimuth estimation for individual solar installations in satellite images. Our continued research focuses on automated detection and classification of solar installation mounting configuration (tracking or fixed-tilt; rooftop, ground, or carport). Specifically, a Faster-RCNN Resnet-50 feature pyramid network (FPN) model was trained and validated on 862 manually labeled satellite images. This model was used to perform object detection on satellite imagery, locating and classifying individual solar installations' mounting configuration and type. Model results showed a mean average precision score (mAP) of 77.79%, with the model strongest at detecting fixed-tilt ground mount and fixed-tilt carport installations. The object detection model and its outputs have been incorporated into the Panel-Segmentation package's automated metadata extraction pipeline, which returns the mounting configuration and azimuth for individual solar arrays in satellite imagery. The complete image data set with labels has been released on the U.S. Department of Energy (DOE) DuraMAT DataHub, to encourage further research in this area.

deep learning↗
DOE OSTI2023

An efficient instance segmentation approach for studying fission gas bubbles in irradiated metallic nuclear fuel

Abstract Gaseous fission products from nuclear fission reactions tend to form fission gas bubbles of various shapes and sizes inside nuclear fuel. The behavior of fission gas bubbles dictates nuclear fuel performances, such as fission gas release, grain growth, swelling, and fuel cladding mechanical interaction. Although mechanical understanding of the overall evolution behavior of fission gas bubbles is well known, lacking the quantitative data and high-level correlation between burnup/temperature and microstructure evolution blocks the development of predictive models and reduces the possibility of accelerating the qualification for new fuel forms. Historical characterization of fission gas bubbles in irradiated nuclear fuel relied on a simple threshold method working on low-resolution optical microscopy images. Advanced characterization of fission gas bubbles using scanning electron microscopic images reveals unprecedented details and extensive morphological data, which strains the effectiveness of conventional methods. This paper proposes a hybrid framework, based on digital image processing and deep learning models, to efficiently detect and classify fission gas bubbles from scanning electron microscopic images. The developed bubble annotation tool used a multitask deep learning network that integrates U-Net and ResNet to accomplish instance-level bubble segmentation. With limited annotated data, the model achieves a recall ratio of more than 90%, a leap forward compared to the threshold method. The model has the capability to identify fission gas bubbles with and without lanthanides to better understand the movement of lanthanide fission products and fuel cladding chemical interaction. Lastly, the deep learning model is versatile and applicable to the micro-structure segmentation of similar materials.

36 MATERIALS SCIENCE↗
NASA NTRS2002

Development of High Resolution Mirrors and Cd-Zn-Te Detectors for Hard X-ray Astronomy

We describe the fabrication and implementation of a high-resolution conical, grazing- incidence, hard X-ray (20-70 keV) telescope. When flown aboard stratospheric balloons, these mirrors are used to image cosmic sources such as supernovae, neutron stars, and quasars. The fabrication process involves generating super-polished mandrels, mirror shell electroforming, and mirror testing. The cylindrical mandrels consist of two conical segments; each segment is approximately 305 mm long. These mandrels are first, precision ground to within approx. 1.0 micron straightness along each conical segment and then lapped and polished to less than 0.5 micron straightness. Each mandrel segment is the super-polished to an average surface roughness of approx. 3.25 angstrom rms. By mirror shell replication, this combination of good figure and low surface roughness has enabled us to achieve 15 arcsec, confirmed by X-ray measurements in the Marshall Space Flight Center 102 meter test facility. To image the focused X-rays requires a focal plane detector with appropriate spatial resolution. For 15 arcsec optics of 6 meter focal length, this resolution must be around 200 microns. In addition, the detector must have a high efficiency, relatively high energy resolution, and low background. We are currently developing Cadmium-Zinc-Telluride fine-pixel detectors for this purpose. The detectors under study consist of a 16x16 pixel array with a pixel pitch of 300 microns and are 1 mm and 2 mm thick. At 60 keV, the measured energy resolution is around 2%.

Ramsey, Brian D.↗