Search NASA⌕ Search

SEARCH · Search NASA

Results for “computational imaging”

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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
NASA NTRS1999

Implementation of Multispectral Image Classification on a Remote Adaptive Computer

As the demand for higher performance computers for the processing of remote sensing science algorithms increases, the need to investigate new computing paradigms its justified. Field Programmable Gate Arrays enable the implementation of algorithms at the hardware gate level, leading to orders of m a,gnitude performance increase over microprocessor based systems. The automatic classification of spaceborne multispectral images is an example of a computation intensive application, that, can benefit from implementation on an FPGA - based custom computing machine (adaptive or reconfigurable computer). A probabilistic neural network is used here to classify pixels of of a multispectral LANDSAT-2 image. The implementation described utilizes Java client/server application programs to access the adaptive computer from a remote site. Results verify that a remote hardware version of the algorithm (implemented on an adaptive computer) is significantly faster than a local software version of the same algorithm implemented on a typical general - purpose computer).

Figueiredo, Marco A.↗
NASA NTRS2000

Reconstruction algorithm for polychromatic CT imaging: application to beam hardening correction

This paper presents a new reconstruction algorithm for both single- and dual-energy computed tomography (CT) imaging. By incorporating the polychromatic characteristics of the X-ray beam into the reconstruction process, the algorithm is capable of eliminating beam hardening artifacts. The single energy version of the algorithm assumes that each voxel in the scan field can be expressed as a mixture of two known substances, for example, a mixture of trabecular bone and marrow, or a mixture of fat and flesh. These assumptions are easily satisfied in a quantitative computed tomography (QCT) setting. We have compared our algorithm to three commonly used single-energy correction techniques. Experimental results show that our algorithm is much more robust and accurate. We have also shown that QCT measurements obtained using our algorithm are five times more accurate than that from current QCT systems (using calibration). The dual-energy mode does not require any prior knowledge of the object in the scan field, and can be used to estimate the attenuation coefficient function of unknown materials. We have tested the dual-energy setup to obtain an accurate estimate for the attenuation coefficient function of K2 HPO4 solution.

NASA Center ARC↗
DOE OSTI2026

AnisONet: A deep neural operator-based anisotropic permeability upscaler from pore to Darcy scale

Directional permeability variations, which govern directional fluid flow in porous media with anisotropy, are important to accurately predict flow behavior, reactive transport, and fluid–solid interactions for various processes such as enhanced geothermal systems, energy storage devices, and biological systems. However, the intricate architecture of porous media makes it difficult to predict directional permeabilities. In this work, we present a novel machine learning (ML) framework, AnisONet, built upon an integration of a convolutional neural network, Swin transformer, and the deep operator network architecture, designed to predict anisotropic permeability and upscale predictions to larger spatial domains. First, AnisONet was evaluated with three classes of two-dimensional (2D) porous media, including synthetic circular and elliptical grains and natural sandstone grains from micro-computed tomography images. A lattice Boltzmann model (LBM) was used to calculate directional permeabilities at every 10° angle, producing 19 data points per image of porous media. AnisONet is then trained to predict permeability as a function of rotation angle. AnisONet showed strong predictive capability of directional permeability. Second, we tested our model for five upscaling cases with a large image size in the finite-element method (FEM) for 2D Darcy flow with various permeability tensor construction methods. Overall, upscaled permeability tensors in FEM simulations produce a reasonably good match with LBM results, highlighting the importance of selecting appropriate tensor formation strategies for accurate permeability upscaling. AnisONet, as a directional permeability estimator, could be further developed for more complex geometries, with the potential to develop a foundational ML model for various applications in porous media.

42 ENGINEERING↗
NASA NTRS2023

The Nature of Insoluble Organic Matter in Sutter’s Mill and Murchison Carbonaceous Chondrites: Testing the Effect of X-ray Computed Tomography (XCT) and Exploring Parent Body Organic Molecular Evolution

This study analyzed samples of the Murchison and Sutter’s Mill carbonaceous chondrite meteorites in support of the future analysis of samples returned from the asteroid (10155) Bennu by the OSIRIS-REx (Origins, Spectral Interpretation, Resource Identification, and Security–Regolith Explorer) mission. Focusing specifically on the insoluble organic matter (IOM), this study establishes that a total of 1.3 g of bulk sample from a single chondritic meteorite is sufficient to obtain a wide range of cosmochemical information, including light element analysis (H, C, and N), isotopic analysis (D/H, 13 C/ 12 C, and 15 N/ 14 N), and X-ray fluorescence spectroscopy for major elemental abundances. IOM isolated from the bulk meteorite samples was analyzed by light element and isotopic analysis as described above, 1 H and 13 C solid-state nuclear magnetic resonance spectroscopy, Raman spectroscopy, and complete noble gas analyses (abundances and isotopes). The samples studied included a pair from Murchison (CM2), one of which had been irradiated with high-energy X-rays in the course of computed tomographic imaging. No differences between the irradiated and non-irradiated Murchison samples were observed in the many different chemical and spectroscopic analyses, indicating that any X-ray–derived sample damage is below levels of detection. Elemental, isotopic, and molecular spectroscopic data derived from IOM isolated from the Sutter’s Mill sample reveals evidence that this meteorite falls into the class of heated CM chondrites.

planetary science↗
DOE OSTI2026

Two-Stage Wildlife Event Classification for Edge Deployment

Camera-based wildlife monitoring is often overwhelmed by non-target triggers and slowed by manual review or cloud-dependent inference, which can prevent timely intervention for high stakes human–wildlife conflicts. Our key contribution is a deployable, fully offline edge vision sensor that achieves near-real-time, highly accurate wildlife event classification by combining detector-based empty-image suppression with a lightweight classifier trained with a staged transfer-learning curriculum. Specifically, Stage 1 uses a pretrained You Only Look Once (YOLO)-family detector for permissive animal localization and empty-trigger suppression, and Stage 2 uses a lightweight EfficientNet-based binary classifier to confirm puma on detector crops and gate downstream actions. Our design is robust to low-quality nighttime monochrome imagery (motion blur, low contrast, illumination artifacts, and partial-body captures) and operates using commercially available components in connectivity-limited settings. In field deployments running since May 2025, end-to-end latency from camera trigger to action command is approximately 4 s. Ablation studies using a dataset of labeled wildlife images (pumas, not pumas) show that the two-stage approach substantially reduces false alarms in identifying pumas relative to a full-image classifier while maintaining high recall. On the held-out test set (N = 1434 events), the proposed two-stage cascade achieves precision 0.983, recall 0.975, F1 0.979, accuracy 0.986, and balanced accuracy 0.983, with only 8 false positives and 12 false negatives. The system can be easily adapted for other species, as demonstrated by rapid retraining of the second stage to classify ringtails. Downstream responses (e.g., notifications and optional audio/light outputs) provide flexible actuation capabilities that can be configured to support intervention.

58 GEOSCIENCES↗
NASA NTRS2004

Software for Analyzing Sequences of Flow-Related Images

Spotlight is a computer program for analysis of sequences of images generated in combustion and fluid physics experiments. Spotlight can perform analysis of a single image in an interactive mode or a sequence of images in an automated fashion. The primary type of analysis is tracking of positions of objects over sequences of frames. Features and objects that are typically tracked include flame fronts, particles, droplets, and fluid interfaces. Spotlight automates the analysis of object parameters, such as centroid position, velocity, acceleration, size, shape, intensity, and color. Images can be processed to enhance them before statistical and measurement operations are performed. An unlimited number of objects can be analyzed simultaneously. Spotlight saves results of analyses in a text file that can be exported to other programs for graphing or further analysis. Spotlight is a graphical-user-interface-based program that at present can be executed on Microsoft Windows and Linux operating systems. A version that runs on Macintosh computers is being considered.

Klimek, Robert↗
DOE OSTI2025

Contaminant Investigation and Pre‐Processing Opportunities for Textile‐To‐Textile Recycling

Millions of metric tons of textiles are landfilled or incinerated each year in the United States, with less than 1% of textiles recycled into new clothing or fabrics. To counter this trend, a growing number of companies and researchers are exploring how a circular economy can be applied to support textile‐to‐textile recycling. A significant barrier they face comes down to quickly and efficiently extracting pure feedstock material from post‐consumer garments that feature a mix of natural and synthetic fibers. Textile recyclers prefer pure feedstocks, as working with mixed sources typically means lower throughput, higher risk of equipment failure, and diminished business margins. To facilitate a circular economy for textiles, methods, and technologies are needed that can efficiently separate out materials and contaminants from end‐of‐life textiles to increase the flow of pure feedstocks to recyclers. This paper summarizes findings from interviews with a cross section of textile recyclers and from a review of literature to define basic feedstock requirements. In addition to our qualitative research, we deconstruct a bale of post‐consumer textiles and analyze them using computer‐vision imaging, Fourier transform infrared spectroscopy (FTIR), and machine learning. The resulting data are used to set system‐level design inputs for an automated contaminant removal system to process post‐consumer clothing into appropriate feedstocks for recycling. To set the system's levels for automated real‐time near‐infrared analysis, we identify the minimum percentage of primary material that any single garment in a load of used clothing must contain for the average of the full output stream to meet the target purity levels of recyclers. Here, the envisioned automated system can also address undesirable trace materials that might contaminate the processed stream by using imaging cameras coupled with artificial intelligence to identify sections of clothing for de‐trimming. Proof‐of‐concept machine learning algorithms are evaluated to locate and identify trims or garment areas with hidden contaminant materials. Integrating these methods into automated textile cutting systems can provide a cost‐effective means for increasing feedstock purity from used clothing, which can advance circularity for textiles by helping recyclers to reach production volumes and quality targets that were not possible solely with manual dismantling operations.

Parsons, Ryan [Rochester Institute of Technology, ↗
DOE OSTI2024

Effect of laminae count and manufacturing methods on mechanical properties of thin‐ply woven composites

Abstract Thin‐ply woven composites exhibit exceptional mechanical performance by delaying the onset of damage, making them suitable for high‐performance structural applications. This study outlines the impact of manufacturing techniques (vacuum bagging and hot pressing), ply count, and fiber waviness on the mechanical properties of aromatic thermosetting copolyester/carbon‐fiber (ATSP/CF) composites. The analysis encompasses several different ply counts, evaluating their effects on the in‐plane elastic modulus (0°–90°) and tensile strength. Results demonstrate that reducing the waviness (or crimp) ratio significantly enhances the in‐plane elastic modulus, with improvements up to 188%, highlighting the importance of fiber alignment. Vacuum bagging produced ply thicknesses from 128 to 58 μm for 1–36 plies, with in‐plane elastic moduli from 30.3 to 87.2 GPa, and 4‐ and 8‐ply samples showed approximately 40% higher stiffness than those made by hot pressing. ATSP resin with high thermal stability (up to 400°C) and recyclability was used. Furthermore, this study highlights the microstructural enhancements achieved through vacuum bagging, as revealed by scanning electron and optical microscopy, and micro‐computed tomography imaging. This paper provides insights into damage mitigation and achieving enhanced mechanical properties by controlling fiber waviness using vacuum bagging and higher ply count in thin‐ply ATSP/CF composites. Highlights Crimp ratio reduction enhances elastic in‐plane elastic modulus up to 188%. Vacuum bagging increases modulus over the hot pressing manufacturing method by 40%. Lowering thickness‐per‐ply increases failure strain from 0.8% to 1.2%. Explored the performance of a novel ATSP matrix with high recyclability and thermal stability.

Kushwaha, Shashank [The Grainger College of Engine↗
DOE OSTI2025

Optimizing inference of segmentation on high-resolution images in MLExchange

MLExchange is a machine learning (ML) operations platform providing web user-interfaces (UIs) for data visualization and analysis pipelines at synchrotron facilities. Among these UIs is the segmentation app which helps synchrotron users utilize ML algorithms to automatically segment high-resolution scientific images with minimal manual annotation effort. In this work, we share code optimizations that significantly speed up the segmentation inference workflow of large data in short time. By optimizing the sequence of CPU-GPU data transfers and introducing CPU parallelization to key operations, we improve the per-device, per-image frame computational efficiency and observe close to 3×$$\times$$ speedup over the original segmentation inference workflow run time when utilizing a single GPU. Further adaptations enabling multi-GPU inference yield more than 40×$$\times$$ speedup with 100 GPUs compared to the optimized single GPU inference workflow. This acceleration of the segmentation inference workflow will provide MLExchange users with easy access to segmentation results with little wait time.

Lu, Shizhao↗
DOE OSTI2025

Insight into the deformation features and capacity loss mechanisms of lithium-ion pouch cells under spherical indentation conditions

Mechanical deformation under extreme conditions is one of the important reasons for the failure of lithium-ion batteries in automotive application. However, the deformation features and component failure of lithium-ion cells to external loading has never been a design consideration. Here, in this study, we conduct spherical indentation tests on a dozen of lithium-ion cells with different capacities under different control mode conditions to investigate their deformation features and capacity loss mechanisms. The experimental results show that, under mechanical deformation conditions, internal faults of cells occur in stages, and energy accumulation and sudden release are two key processes of cell's mechanical failure. The cells' state of charge is the main factor affecting their thermal runaway behaviors. In addition, a finite element model is developed to simulate the deformation features and the failure mechanism of key components of lithium-ion pouch cells; the 3D x-ray computed tomography is employed to demonstrate its internal configuration. With this model, the force-strain response, the deformation features as well as the size of the failure area of lithium-ion cells under spherical indentation conditions are accurately predicted. In 3D x-ray computed tomography images, unique mud cracks in cooper current collector are observed, and the influence mechanisms of the isolated fragments on the cell capacities are revealed. These results may provide useful information for the mechanical structure design of the components of lithium-ion pouch cells.

25 ENERGY STORAGE↗
DOE OSTI2025

Three-Dimensional Pore Networks in Miocene Stevens Sandstone of California: Implications for CO 2 Geologic Storage

The Miocene Stevens Sandstone in the San Joaquin Basin of California is increasingly recognized as a promising candidate for CO 2 geological storage due to the enormous storage capacity, proven sealing, and existing infrastructure. In this study, computed microtomography imaging and pore network modeling were employed to investigate the influence of pore geometry and wettability on the CO 2 injectivity and residual trapping. Image analysis revealed that a significant fraction of the cement and matrix consists of microporous regions. The microporosity can substantially increase the overall pore space, yet its contribution to permeability remains modest, particularly in samples with low permeability. The intrinsic heterogeneity of turbidite reservoirs further complicates the reservoir properties among different layers. Two-phase flow simulations under varying wettability conditions (water-wet, weak water-wet, and neutral-wet) demonstrated that the CO 2 injection is predominantly controlled by macropores. CO 2 invades microporous regions only after these larger pores are filled. The presence of microporosity leads to a decrease in both initial and residual CO 2 saturations, with the magnitude of the reduction being influenced by wettability. Neutral-wet scenarios exhibit higher CO 2 mobility and thus lower residual trapping than water-wet scenarios. The results imply that heterogeneity in pore geometry and cement distribution across different layers can result in stratified CO 2 flow pathways, complicating efforts to predict injection performance. Overall, the Stevens Sandstone shows considerable promise for CO 2 geologic storage, but effective implementation will require detailed characterization of the pore structure as well as the integration of reactive fluid flow to account for potential mineral dissolution and fines migration.

fluids↗
DOE OSTI2024

Unveiling mechanisms and onset threshold of humping in high-speed laser welding

The fabrication of fuel cells relies on a rapid laser welding process. However, challenges arise with the occurrence of humping when the welding speed surpasses a critical threshold, which poses difficulties in achieving a smooth surface finish and a consistent weld strength. This study aims to elucidate the humping mechanisms by analyzing the morphology of molten pool and the characteristics of melt flow at varying welding speeds via in situ synchrotron high-speed X-ray imaging and computational fluid dynamics simulations. Our findings indicate that the short keyhole rear wall, the high backward melt velocity, and the prolonged tail of molten pool are the primary factors contributing to the onset of humping. Furthermore, a dimensionless humping index (π h ) was introduced, which successfully captured the onset threshold of humping across different literatures. This index not only provides a quantitative description of the humping formation tendency but also serves as a valuable tool for optimizing the laser welding process.

42 ENGINEERING↗
DOE OSTI2024

Mechanical forces orchestrate the metabolism of the developing oilseed rape embryo

The initial free expansion of the embryo within a seed is at some point inhibited by its contact with the testa, resulting in its formation of folds and borders. Although less obvious, mechanical forces appear to trigger and accelerate seed maturation. However, the mechanistic basis for this effect remains unclear. Manipulation of the mechanical constraints affecting either the in vivo or in vitro growth of oilseed rape embryos was combined with analytical approaches, including magnetic resonance imaging and computer graphic reconstruction, immunolabelling, flow cytometry, transcriptomic, proteomic, lipidomic and metabolomic profiling. Our data implied that, in vivo, the imposition of mechanical restraints impeded the expansion of testa and endosperm, resulting in the embryo's deformation. An acceleration in embryonic development was implied by the cessation of cell proliferation and the stimulation of lipid and protein storage, characteristic of embryo maturation. The underlying molecular signature included elements of cell cycle control, reactive oxygen species metabolism and transcriptional reprogramming, along with allosteric control of glycolytic flux. Constricting the space allowed for the expansion of in vitro grown embryos induced a similar response. The conclusion is that the imposition of mechanical constraints over the growth of the developing oilseed rape embryo provides an important trigger for its maturation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

High-Throughput Computing: Case Study of Medical Image Processing Applications

HPC is designed for large-scale simulations using monolithic codes of tightly coupled processes highly optimized to deliver decreased time to solution. Medical image processing is not a traditional field of HPC. Similar to AI applications, medical image processing parses large datasets, typically multiple times, to support a variety of studies for classification, diagnosis or monitoring purposes. The convergence of AI, HPC and Big Data encouraged more fields using image processing to transition to HPC. However, not all applications benefit from the same optimizations. In this paper we focus on high throughput medical image processing applications that analyze a huge dataset of small MRI images and that require HPC systems to decrease the time of parsing the entire dataset and not individual MRIs. We show in this research the performance of running SLANT, an image processing application for a whole brain segmentation, on large-scale systems and highlight performance limitations. We present optimizations prioritizing throughput that exhibit a 3.5x speed-up on the Summit Supercomputer that can be used as a baseline for building a high-throughput execution framework for other HPC systems.

Predescu, Maria↗
DOE OSTI2025

Intrinsic Defect-Induced Local Semiconducting-to-Metallic Regions Within Monolayer 1T-TiS2 Displayed by First-Principles Calculations and Scanning Tunneling Microscopy

Using density functional theory (DFT) and scanning tunneling microscopy (STM), the intrinsic point defects, formation energy, and electronic structure of 1T-TiS2 were investigated. Defect systems include single-atom vacancies, interstitial and adatom additions, and direct atomic substitution. Using a collective approach for analyzing realistic systems for point defect investigation, we provide a more straightforward comparison to the experimental measurements, reproducing more realistic environmental conditions related to thin film growth. STM images are compared to computationally simulated electron density images to identify specific geometries that result from favorable point defects. DFT suggests that titanium interstitials are the most energetically favorable intrinsic defect, and sulfur vacancies are more likely to form than titanium vacancies within this realistic analysis, which is in agreement with STM data. A pristine, stoichiometric monolayer system is calculated to have a direct band gap of 0.422 eV, which varies based on local point defects. Local semiconducting-to-metallic electronic transitions are predicted to occur based on the presence of Ti interstitials.

Keeney, P. J.↗
DOE OSTI2024

Electrofracturing of Shale at Elevated Pressure

Electrofracturing deeply buried shale formations could be used to increase reservoir permeability and improve reservoir production without requiring large volumes of freshwater. This paper describes a novel experimental system and initial test results to electrofracture shale under high confining pressures. Core-scale laboratory testing was performed on twelve rock samples recovered from a shale gas reservoir. Each sample was subjected to confining pressures of 20.7 MPa (3000 psi) or 58.6 MPa (8000 psi), representative of overburden pressures at depth. Samples were then subjected to application of high voltage until specimen fracture. The experiments produced deformed samples with multiple fracture types, both parallel and oblique to bedding planes. Electrofracturing increased permeabilities by up to nine orders of magnitude for extended time periods. Rock fracture and throughgoing fractures were demonstrated. Computed tomography images revealed the creation of fractures and tube/tunnel flow channels, which resisted closure under hydrostatic pressures up to 58.6 MPa. The breakdown energy and permeability changes in the sample were independent of applied confining pressure. The cumulative energy input required for fracture depended on applied confining pressure and sample length. The energy required to fracture samples up to 9 cm in length is generally more than 0.5 kJ/cm, but no greater than 1 kJ/cm. Our results show that electrofracture of shales under confining pressure is possible and could be a possible water-free mechanism for reservoir stimulation.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2024

CT584 Is Not a Protective Vaccine Antigen against Respiratory Chlamydial Challenge in Mice

Background: Chlamydia trachomatis is the most prevalent bacterial sexually transmitted pathogen in humans worldwide. Since chlamydial infection is largely asymptomatic with the potential for serious complications, a preventative vaccine is likely the most viable long-term answer to this public health threat. Cell-free protein synthesis (CFPS) utilizes the cellular protein manufacturing machinery decoupled from the requirement for maintaining cellular viability, offering the potential for flexible, rapid, and decentralized production of recombinant protein vaccine antigens. Methods: Here, we use CFPS to produce the full-length putative chlamydial type three secretion system (T3SS) needle-tip protein, CT584, for evaluation as a vaccine antigen in mouse models. High-speed atomic force microscopy (HS-AFM) (RIBM, Tsukuba, Japan) imaging and computer simulations confirm that CFPS-produced CT584 retains a native-like structure prior to immunization. Female mice were primed with CT584 adjuvanted with CpG-1826 intranasally (i.n.) or CpG-1826 + Montanide ISA 720 intramuscularly (i.m.), followed four weeks later by an i.m. boost before respiratory challenge with 10 4 inclusion forming units (IFU) of Chlamydia muridarum. Results: Immunization with CT584 generated robust antibody responses but weak cell-mediated immunity and failed to protect against i.n. challenge as demonstrated by body weight loss, increased lung weights, and the presence of high numbers of IFUs in the lungs. Conclusion: While CT584 was not a protective vaccine candidate, the speed and flexibility with which CFPS can be used to produce other potential chlamydial antigens make it an attractive technique for antigen production.

60 APPLIED LIFE SCIENCES↗