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At least 1,315 records · Page 73
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Image Processing Pipeline for Fluoroelastomer Crystallite Detection in Atomic Force Microscopy Images

Phase transformations in materials systems can be tracked using atomic force microscopy (AFM), enabling the examination of surface properties and macroscale morphologies. In situ measurements investigating phase transformations generate large datasets of time-lapse image sequences. The interpretation of the resulting image sequences, guided by domain-knowledge, requires manual image processing using handcrafted masks. Here this approach is time-consuming and restricts the number of images that can be processed. Her in this study, we developed an automated image processing pipeline which integrates image detection and segmentation methods. We examine five time-series AFM videos of various fluoroelastomer phase transformations. The number of image sequences per video ranges from a hundred to a thousand image sequences. The resulting image processing pipeline aims to automatically classify and analyze images to enable batch processing. Using this pipeline, the growth of each individual fluoroelastomer crystallite can be tracked through time. We incorporated statistical analysis into the pipeline to investigate trends in phase transformations between different fluoroelastomer batches. Understanding these phase transformations is crucial, as it can provide valuable insights into manufacturing processes, improve product quality, and possibly lead to the development of more advanced fluoroelastomer formulations.

36 MATERIALS SCIENCE↗
NASA NTRS1992

Quantitative image processing in fluid mechanics

The current status of digital image processing in fluid flow research is reviewed. In particular, attention is given to a comprehensive approach to the extraction of quantitative data from multivariate databases and examples of recent developments. The discussion covers numerical simulations and experiments, data processing, generation and dissemination of knowledge, traditional image processing, hybrid processing, fluid flow vector field topology, and isosurface analysis using Marching Cubes.

Hesselink, Lambertus↗
NASA NTRS1981

Computer generated maps from digital satellite data - A case study in Florida

Ground cover maps are important tools to a wide array of users. Over the past three decades, much progress has been made in supplementing planimetric and topographic maps with ground cover details obtained from aerial photographs. The present investigation evaluates the feasibility of using computer maps of ground cover from satellite input tapes. Attention is given to the selection of test sites, a satellite data processing system, a multispectral image analyzer, general purpose computer-generated maps, the preliminary evaluation of computer maps, a test for areal correspondence, the preparation of overlays and acreage estimation of land cover types on the Landsat computer maps. There is every indication to suggest that digital multispectral image processing systems based on Landsat input data will play an increasingly important role in pattern recognition and mapping land cover in the years to come.

Arvanitis, L. G.↗
DOE OSTI2022

Current State of 44 Ti/ 44 Sc Radionuclide Generator Systems and Separation Chemistry

We report in recent years, there has been an increased interest in 44Ti/44Sc generators as an onsite source of 44Sc for medical applications without needing a proximal cyclotron. The relatively short half-life (3.97 hours) and high positron branching ratio (94.3%) of 44Sc make it a viable candidate for positron emission tomography (PET) imaging. This review discusses current 44Ti/44Sc generator designs, focusing on their chemistry, drawbacks, post-elution processing, and relevant preclinical studies of the 44Sc for potential PET radiopharmaceuticals.

43 PARTICLE ACCELERATORS↗
NASA NTRS2018

Deep Neural Network for Precision Multi-band Infrared Image Segmentation

Image segmentation is one of the fundamental steps in computer vision. Separating targets from background clutter with high precision is a challenging operation for both humans and computers. Currently, segmenting objects from IR images is done by tedious manual work. The implementation of a Deep Neural Network (DNN) to perform precision segmentation of multi-band IR video images is presented. A customized pix2pix DNN with multiple layers of generative encoder/decoder and discriminator architecture is used in the IR image segmentation process. Real and synthetic images and ground truths are employed to train the DNN. Iterative training is performed to achieve optimum accuracy of segmentation using a minimal number of training data. Special training images are created to enhance the missing features and to increase the segmentation accuracy of the objects. Retraining strategies are developed to minimize the DNN training time. Single pixel accuracy has been achieved in IR target boundary segmentation using DNNs. The segmentation accuracy between the customized pix2pix DNN and simple thresholding, GraphCut, simple neural network and ResNet models are compared.

Torres, Gil↗
DOE OSTI2022

Spiking retina microscope

A spiking retina microscope comprising microscope optics and a neuromorphic imaging sensor. The microscope optics are configured to direct a magnified image of a specimen onto the neuromorphic imaging sensor. The neuromorphic imaging sensor comprises a plurality of sensor elements that are configured to generate spike signals in response to integrated light from the magnified image reaching a threshold. The spike signals may be processed by a processor unit to generate a result, such as tracking biological particles in a specimen comprising biological material.

Severa, William Mark↗
NASA NTRS1991

Small convolution kernels for high-fidelity image restoration

An algorithm is developed for computing the mean-square-optimal values for small, image-restoration kernels. The algorithm is based on a comprehensive, end-to-end imaging system model that accounts for the important components of the imaging process: the statistics of the scene, the point-spread function of the image-gathering device, sampling effects, noise, and display reconstruction. Subject to constraints on the spatial support of the kernel, the algorithm generates the kernel values that restore the image with maximum fidelity, that is, the kernel minimizes the expected mean-square restoration error. The algorithm is consistent with the derivation of the spatially unconstrained Wiener filter, but leads to a small, spatially constrained kernel that, unlike the unconstrained filter, can be efficiently implemented by convolution. Simulation experiments demonstrate that for a wide range of imaging systems these small kernels can restore images with fidelity comparable to images restored with the unconstrained Wiener filter.

Reichenbach, Stephen E.↗
NASA NTRS2005

Phase-Imaging with a Sharpened Multi-Walled Carbon Nanotube AFM Tip: Investigation of Low-k Dielectric Polymer Hybrids

Phase shift tapping mode scanning force microscopy (TMSFM) has evolved into a very powerful technique for the nanoscale surface characterization of compositional variations in heterogeneous samples. Phase shift signal measures the difference between the phase angle of the excitation signal and the phase angle of the cantilever response. The signal correlates to the tip-sample inelastic interactions, identifying the different chemical and/or physical property of surfaces. In general, the resolution and quality of scanning probe microscopic images are highly dependent on the size of the scanning probe tip. In improving AFM tip technology, we recently developed a technique for sharpening the tip of a multi-walled carbon nanotube (CNT) AFM tip, reducing the radius of curvature of the CNT tip to less than 5 nm while still maintaining the inherent stability of multi-walled CNT tips. Herein we report the use of sharpened (CNT) AFM tips for phase-imaging of polymer hybrids, a precursor for generating nanoporous low-k dielectrics for on-chip interconnect applications. Using sharpened CNT tips, we obtained phase-contrast images having domains less than 10 nm. In contrast, conventional Si tips and unsharpened CNT tips (radius greater than 15 nm) were not able to resolve the nanoscale domains in the polymer hybrid films. C1early, the size of the CNT tip contributes significantly to the resolution of phase-contrast imaging. In addition, a study on the nonlinear tapping dynamics of the multi-walled CNT tip indicates that the multi-walled CNT tip is immune to conventional imaging instabilities related to the coexistence of attractive and repulsive tapping regimes. This factor may also contribute to the phase-contrast image quality of multi-walled CNT AFM tips. This presentation will also offer data in support of the stability of the CNT tip for phase shift TMSFM.

Nguyen, Cattien V.↗
NASA NTRS2006

Ice-Borehole Probe

An instrumentation system has been developed for studying interactions between a glacier or ice sheet and the underlying rock and/or soil. Prior borehole imaging systems have been used in well-drilling and mineral-exploration applications and for studying relatively thin valley glaciers, but have not been used for studying thick ice sheets like those of Antarctica. The system includes a cylindrical imaging probe that is lowered into a hole that has been bored through the ice to the ice/bedrock interface by use of an established hot-water-jet technique. The images acquired by the cameras yield information on the movement of the ice relative to the bedrock and on visible features of the lower structure of the ice sheet, including ice layers formed at different times, bubbles, and mineralogical inclusions. At the time of reporting the information for this article, the system was just deployed in two boreholes on the Amery ice shelf in East Antarctica and after successful 2000 2001 deployments in 4 boreholes at Ice Stream C, West Antarctica, and in 2002 at Black Rapids Glacier, Alaska. The probe is designed to operate at temperatures from 40 to +40 C and to withstand the cold, wet, high-pressure [130-atm (13.20-MPa)] environment at the bottom of a water-filled borehole in ice as deep as 1.6 km. A current version is being outfitted to service 2.4-km-deep boreholes at the Rutford Ice Stream in West Antarctica. The probe (see figure) contains a sidelooking charge-coupled-device (CCD) camera that generates both a real-time analog video signal and a sequence of still-image data, and contains a digital videotape recorder. The probe also contains a downward-looking CCD analog video camera, plus halogen lamps to illuminate the fields of view of both cameras. The analog video outputs of the cameras are converted to optical signals that are transmitted to a surface station via optical fibers in a cable. Electric power is supplied to the probe through wires in the cable at a potential of 170 VDC. A DC-to-DC converter steps the supply down to 12 VDC for the lights, cameras, and image-data-transmission circuitry. Heat generated by dissipation of electric power in the probe is removed simply by conduction through the probe housing to the visible features of the lower structure of the ice sheet, including ice layers formed at different times, bubbles, and mineralogical inclusions. At the time of reporting the information for this article, the system was just deployed in two boreholes on the Amery ice shelf in East Antarctica and after successful 2000 2001 deployments in 4 boreholes at Ice Stream C, West Antarctica, and in 2002 at Black Rapids Glacier, Alaska. The probe is designed to operate at temperatures from 40 to +40 C and to withstand the cold, wet, high-pressure [130-atm (13.20-MPa)] environment at the bottom of a water-filled borehole in ice as deep as 1.6 km. A current version is being outfitted to service 2.4-km-deep boreholes at the Rutford Ice Stream in West Antarctica. The probe (see figure) contains a sidelooking charge-coupled-device (CCD) camera that generates both a real-time analog video signal and a sequence of still-image data, and contains a digital videotape recorder. The probe also contains a downward-looking CCD analog video camera, plus halogen lamps to illuminate the fields of view of both cameras. The analog video outputs of the cameras are converted to optical signals that are transmitted to a surface station via optical fibers in a cable. Electric power is supplied to the probe through wires in the cable at a potential of 170 VDC. A DC-to-DC converter steps the supply down to 12 VDC for the lights, cameras, and image-datatransmission circuitry. Heat generated by dissipation of electric power in the probe is removed simply by conduction through the probe housing to the visible features of the lower structure of the ice sheet, including ice layers formed at different times, bubbles, and mineralogical inclusions. At thime of reporting the information for this article, the system was just deployed in two boreholes on the Amery ice shelf in East Antarctica and after successful 2000 2001 deployments in 4 boreholes at Ice Stream C, West Antarctica, and in 2002 at Black Rapids Glacier, Alaska. The probe is designed to operate at temperatures from 40 to +40 C and to withstand the cold, wet, high-pressure [130-atm (13.20-MPa)] environment at the bottom of a water-filled borehole in ice as deep as 1.6 km. A current version is being outfitted to service 2.4-km-deep boreholes at the Rutford Ice Stream in West Antarctica. The probe (see figure) contains a sidelooking charge-coupled-device (CCD) camera that generates both a real-time analog video signal and a sequence of still-image data, and contains a digital videotape recorder. The probe also contains a downward-looking CCD analog video camera, plus halogen lamps to illuminate the fields of view of both cameras. The analog video outputs of the cameras are converted to optical signals that are transmitted to a surface station via optical fibers in a cable. Electric power is supplied to the probe through wires in the cable at a potential of 170 VDC. A DC-to-DC converter steps the supply down to 12 VDC for the lights, cameras, and image-datatransmission circuitry. Heat generated by dissipation of electric power in the probe is removed simply by conduction through the probe housing to the adjacent water and ice.

Behar, Alberto↗
NASA NTRS2019

Quantitative Comparison of Proprietary and Open-Source Georeferencing Tools for Use with Astronaut Photography

The Crew Earth Observations (CEO) Facility within the Earth Science and Remote Sensing Unit at NASA’s Johnson Space Center supports the acquisition, analysis, and curation of astronaut photography of Earth’s surface and atmosphere. Astronauts on the International Space Station (ISS) respond to requests from CEO to acquire imagery of scientific and education targets, to include high profile targets in response to activations from the International Charter for Space & Major Disasters (also known as the International Disaster Charter, or IDC) and NASA’s Disasters Program. CEO facilitates the acquisition of astronaut photography in response to IDC events and delivers georeferenced data products to the United States Geological Survey (USGS) for distribution to the disaster community. Using GeoRef, an internal web-based tool developed in collaboration with NASA’s Ames Research Center, CEO generates data packages of georeferenced imagery, uncertainty images for assessing control and tie point accuracy, and metadata documenting raw and processed data. Operational experience with the Georef software identified vulnerabilities to internal code and server errors that can significantly increase time of data production. As such, CEO developed a backup procedure in case the GeoRef software experiences front-end or back-end errors. A system using OSGEO’s open-source QGIS software combined with a semi-automated pipeline using the object-oriented Python language and the Geospatial Abstract Library for generating metadata is quantitatively compared to GeoRef’s data package for quality and productivity. Root Mean Square Error (RMSE) provides a standard measurement of data quality as it relates to ground error. Assessing RMSE measurements generated from georeferenced astronaut photographs acquired with different obliquity and focal length offers a comprehensive accuracy assessment of the software’s transformation algorithms. This assessment will indicate the software's ability to produce data products with the least ground-error or highest data quality regarding ground accuracy. In addition, a comparison of the software’s efficiency in generating a data package that includes georeferenced images, metadata, and uncertainty images for measuring tie/ground point error was performed. Initial results, based on the comparison of three nadir-facing astronaut photographs acquired with a 95mm focal length, reveal the QGIS-based system's average RMSE is 2.36 (pixels) suggesting its georectification system produces data products that meet and perhaps improve upon Georef solution's average RMSE of 32.99 (pixels). However, the QGIS system was unable to reproduce two unique Georef data products, uncertainty images for measuring tie and control point errors and a translated unwrapped image. In addition, the Georef software is designed to accept handheld camera pose information from a hardware component (Geosens) scheduled for deployment on the ISS in late 2018; this information is intended to provide increased accuracy and auto-registration capability for astronaut photographs. Future work is expected to determine the QGIS-based georectification system’s potential as an open-source alternative (and operational backup) to Georef for georeferencing the full range of resolutions and viewing angles unique to handheld digital camera imagery in support of ISS disaster response activities.

Jagge, Amy M.↗
DOE OSTI2025

Broadband unidirectional visible imaging using wafer-scale nano-fabrication of multi-layer diffractive optical processors

We present a broadband and polarization-insensitive unidirectional imager that operates at the visible part of the spectrum, where image formation occurs in one direction, while in the opposite direction, it is blocked. This approach is enabled by deep learning-driven diffractive optical design with wafer-scale nano-fabrication using high-purity fused silica to ensure optical transparency and thermal stability. Our design achieves unidirectional imaging across three visible wavelengths (covering red, green, and blue parts of the spectrum), and we experimentally validated this broadband unidirectional imager by creating high-fidelity images in the forward direction and generating weak, distorted output patterns in the backward direction, in alignment with our numerical simulations. This work demonstrates wafer-scale production of diffractive optical processors, featuring 16 levels of nanoscale phase features distributed across two axially aligned diffractive layers for visible unidirectional imaging. This approach facilitates mass-scale production of ~0.5 billion nanoscale phase features per wafer, supporting high-throughput manufacturing of hundreds to thousands of multi-layer diffractive processors suitable for large apertures and parallel processing of multiple tasks. Beyond broadband unidirectional imaging in the visible spectrum, this study establishes a pathway for artificial-intelligence-enabled diffractive optics with versatile applications, signaling a new era in optical device functionality with industrial-level, massively scalable fabrication.

36 MATERIALS SCIENCE↗
NASA NTRS1983

Performance of an airborne imaging 92/183 GHz radiometer during the Bering Sea Marginal Ice Zone Experiment (MIZEX-WEST)

An airborne imaging 92/183 GHz radiometer was recently flown onboard NASA's Convair 990 research aircraft during the February 1983 Bering Sea Marginal Ice Zone Experiment (MIZEX-WEST). The 92 GHz portion of the radiometer was used to gather ice signature data and to generate real-time millimeter wave images of the marginal ice zone. Dry atmospheric conditions in the Arctic resulted in good surface ice signature data for the 183 GHz double sideband (DSB) channel situated + or - 8.75 GHz away from the water vapor absorption line. The radiometer's beam scanner imaged the marginal ice zone over a + or - 45 degrees swath angle about the aircraft nadir position. The aircraft altitude was 30,000 feet (9.20 km) maximum and 3,000 feet (0.92 km) minimum during the various data runs. Calculations of the minimum detectable target (ice) size for the radiometer as a function of aircraft altitude were performed. In addition, the change in the atmospheric attenuation at 92 GHz under varying weather conditions was incorporated into the target size calculations. A radiometric image of surface ice at 92 GHz in the marginal ice zone is included.

Gagliano, J. A.↗
DOE OSTI2023

DOLCE: A Model-Based Probabilistic Diffusion Framework for Limited-Angle CT Reconstruction

Limited-Angle Computed Tomography (LACT) is a non-destructive evaluation technique used in a variety of applications ranging from security to medicine. The limited angle coverage in LACT is often a dominant source of severe artifacts in the reconstructed images, making it a challenging inverse problem. We present DOLCE, a new deep model-based framework for LACT that uses a conditional diffusion model as an image prior. Diffusion models are a recent class of deep generative models that are relatively easy to train due to their implementation as image denoisers. DOLCE can form high-quality images from severely under-sampled data by integrating data-consistency updates with the sampling updates of a diffusion model, which is conditioned on the transformed limited-angle data. We show through extensive experimentation on several challenging real LACT datasets that, the same pre-trained DOLCE model achieves the SOTA performance on drastically different types of images. Additionally, we show that, unlike standard LACT reconstruction methods, DOLCE naturally enables the quantification of the reconstruction uncertainty by generating multiple samples consistent with the measured data.

Kim, Hyojin↗
DOE OSTI2023

Using Ultrasound Image Augmentation and Ensemble Predictions to Prevent Machine-Learning Model Overfitting

Deep learning predictive models have the potential to simplify and automate medical imaging diagnostics by lowering the skill threshold for image interpretation. However, this requires predictive models that are generalized to handle subject variability as seen clinically. Here, we highlight methods to improve test accuracy of an image classifier model for shrapnel identification using tissue phantom image sets. Using a previously developed image classifier neural network—termed ShrapML—blind test accuracy was less than 70% and was variable depending on the training/test data setup, as determined by a leave one subject out (LOSO) holdout methodology. Introduction of affine transformations for image augmentation or MixUp methodologies to generate additional training sets improved model performance and overall accuracy improved to 75%. Further improvements were made by aggregating predictions across five LOSO holdouts. This was done by bagging confidences or predictions from all LOSOs or the top-3 LOSO confidence models for each image prediction. Top-3 LOSO confidence bagging performed best, with test accuracy improved to greater than 85% accuracy for two different blind tissue phantoms. This was confirmed by gradient-weighted class activation mapping to highlight that the image classifier was tracking shrapnel in the image sets. Overall, data augmentation and ensemble prediction approaches were suitable for creating more generalized predictive models for ultrasound image analysis, a critical step for real-time diagnostic deployment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Experimental Evaluation of Geopolymer, Nano-Modified, and Neat Class H Cement by Using Diametrically Compressive Tests

Abstract Cement is the prime barrier material to maintain zonal isolation during wellbore life-cycle. Different types of the cement have different performances on sealability. The primary objective of this study is evaluation of three types of cement systems, neat Class H, nano-synthetic graphite, and rock-based geopolymer cements, on interfacial bond integrity. Diametrical compressive tests are used on casing–cement–formation systems. Cement performances are tested in a conventional configuration (Berea Sandstone). A universal testing system is utilized to analyze the relationship applied load–axial strain and the digital image correlation (DIC) technique is used to generate the strain field maps. Based on the relationship and DIC results, bond strengths in different cement systems are evaluated. Additionally, microscope images provide insight explanations of the bond from micro-view. Based on the experimental results, failures of all cases initiate and propagate from the casing–cement interface. Then, radial cracks develop through the formation. This is because the mechanical interlocking bonds are formed only at the cement–sandstone interface to enhance the bond strength. The comparison of geopolymer and nano-modified cements with the conventional cement (neat Class H) shows that the unconventional cements have a lower maximum load and higher axial strain before failure of the samples. It indicates that both the two kinds of cements are more ductile than conventional cement and can support more deformation before the initiation of cracks.

Energy & Fuels↗
NASA NTRS1995

Alternate physical formats for storing data in HDF

Since its inception HDF has evolved to meet new demands by the scientific community to support new kinds of data and data structures, larger data sets, and larger numbers of data sets. The first generation of HDF supported simple objects and simple storage schemes. These objects were used to build more complex objects such as raster images and scientific data sets. The second generation of HDF provided alternate methods of storing data elements, making it possible to do such things as store extendible objects with in HDF, to store data externally from HDF files, and support data compression effectively. As we look to the next generation of HDF, we are considering fundamental changes to HDF, including a redefinition of the basic HDF object from a simple object to a more general, higher-level scientific data object that has certain characteristics, such as dimensionality, a more general atomic number type, and attributes. These changes suggest corresponding changes to the HDF file format itself.

Folk, Mike↗
DOE OSTI2024

Deciphering Discrepancies: A Comparative Analysis of Docker Image Security

As the use of microservices continues to grow and become a foundational approach to architecting software solutions, ensuring the security of microservices is paramount. Docker images have emerged as the predominant solution to containerize microservices–and thus, Docker images are becoming a large attack surface. Thus, reducing vulnerabilities in Docker images will reduce microservice cyberattacks. A common way to find vulnerabilities in Docker images employs static analysis tools like Trivy and Grype. However, these tools frequently generate disparate vulnerability reports when analyzing the same Docker image, thus causing uncertainty in tool selection. We collected 927 Docker images, analyzed them with Trivy and Grype, and compared the vulnerabilities reported in each image. Among the 865 images found to have vulnerabilities, Trivy and Grype disagreed on both the number of vulnerabilities and the vulnerability IDs found therein. Since both tools interface with external vulnerability databases, some discrepancies can be attributed to how the tools interface with these external resources. The external vulnerability databases partially overlap and frequently contradict one another, thereby creating challenges for static analysis tool developers and end users alike. This New Ideas and Emerging Results (NIER) study contains new and critical information that practitioners need for selecting and using static analysis tools–given that increases in the use of Docker technologies means increases in the size of the attack surfaces.

Boles, Brittany [Montana State University]↗
DOE OSTI2023

From Images to Dark Matter: End-to-end Inference of Substructure from Hundreds of Strong Gravitational Lenses

Abstract Constraining the distribution of small-scale structure in our universe allows us to probe alternatives to the cold dark matter paradigm. Strong gravitational lensing offers a unique window into small dark matter halos (<10 10 M ⊙ ) because these halos impart a gravitational lensing signal even if they do not host luminous galaxies. We create large data sets of strong lensing images with realistic low-mass halos, Hubble Space Telescope (HST) observational effects, and galaxy light from HST’s COSMOS field. Using a simulation-based inference pipeline, we train a neural posterior estimator of the subhalo mass function (SHMF) and place constraints on populations of lenses generated using a separate set of galaxy sources. We find that by combining our network with a hierarchical inference framework, we can both reliably infer the SHMF across a variety of configurations and scale efficiently to populations with hundreds of lenses. By conducting precise inference on large and complex simulated data sets, our method lays a foundation for extracting dark matter constraints from the next generation of wide-field optical imaging surveys.

79 ASTRONOMY AND ASTROPHYSICS↗