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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.

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At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Target Selection and Imaging Requirements for JWST Fine Phasing

To achieve and maintain the fine alignment of its segmented primary mirror the James Webb Space Telescope (JWST) plans to use focus-diverse wavefront sensing (WFS) techniques with science camera imagery. The optical requirements for JWST are such that the error contribution from the WFS itself must be limited 10nm rms over all the controllable degrees of freedom of the telescope. In this paper, we will explore the requirements on the target selection and imaging requirements necessary to achieve the desired level of WFS accuracy. Using Monte Carlo simulations we explore the WFS error as a function of wavefront aberrations level, defocus-diversity level, optical bandwidth and imaging signal-to-noise ratio to establish the key imaging requirements. By taking into account practical integration time limits along with the distribution of the defocused point-spread functions, we establish the bright and faint star magnitude limits suitable for WFS target selection.

wavefront sensing↗
NASA NTRS1992

The effects of video compression on acceptability of images for monitoring life sciences experiments

Future manned space operations for Space Station Freedom will call for a variety of carefully planned multimedia digital communications, including full-frame-rate color video, to support remote operations of scientific experiments. This paper presents the results of an investigation to determine if video compression is a viable solution to transmission bandwidth constraints. It reports on the impact of different levels of compression and associated calculational parameters on image acceptability to investigators in life-sciences research at ARC. Three nonhuman life-sciences disciplines (plant, rodent, and primate biology) were selected for this study. A total of 33 subjects viewed experimental scenes in their own scientific disciplines. Ten plant scientists viewed still images of wheat stalks at various stages of growth. Each image was compressed to four different compression levels using the Joint Photographic Expert Group (JPEG) standard algorithm, and the images were presented in random order. Twelve and eleven staffmembers viewed 30-sec videotaped segments showing small rodents and a small primate, respectively. Each segment was repeated at four different compression levels in random order using an inverse cosine transform (ICT) algorithm. Each viewer made a series of subjective image-quality ratings. There was a significant difference in image ratings according to the type of scene viewed within disciplines; thus, ratings were scene dependent. Image (still and motion) acceptability does, in fact, vary according to compression level. The JPEG still-image-compression levels, even with the large range of 5:1 to 120:1 in this study, yielded equally high levels of acceptability. In contrast, the ICT algorithm for motion compression yielded a sharp decline in acceptability below 768 kb/sec. Therefore, if video compression is to be used as a solution for overcoming transmission bandwidth constraints, the effective management of the ratio and compression parameters according to scientific discipline and experiment type is critical to the success of remote experiments.

Haines, Richard F.↗
DOE OSTI2024

RhizoNet segments plant roots to assess biomass and growth for enabling self-driving labs

Abstract Flatbed scanners are commonly used for root analysis, but typical manual segmentation methods are time-consuming and prone to errors, especially in large-scale, multi-plant studies. Furthermore, the complex nature of root structures combined with noisy backgrounds in images complicates automated analysis. Addressing these challenges, this article introduces RhizoNet, a deep learning-based workflow to semantically segment plant root scans. Utilizing a sophisticated Residual U-Net architecture, RhizoNet enhances prediction accuracy and employs a convex hull operation for delineation of the primary root component. Its main objective is to accurately segment root biomass and monitor its growth over time. RhizoNet processes color scans of plants grown in a hydroponic system known as EcoFAB, subjected to specific nutritional treatments. The root detection model using RhizoNet demonstrates strong generalization in the validation tests of all experiments despite variable treatments. The main contributions are the standardization of root segmentation and phenotyping, systematic and accelerated analysis of thousands of images, significantly aiding in the precise assessment of root growth dynamics under varying plant conditions, and offering a path toward self-driving labs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

High-energy X-ray imaging with a Germanium MM-PAD hybrid pixel detector

Germanium is a high-Z sensor material with excellent energy resolution and attractive properties for high-energy photon detection. Despite its advantages, the need for cryogenic operation and challenges in the fabrication of pixelated high-purity germanium have hindered the development of segmented detectors. We report on the fabrication and performance of a germanium imaging detector with 150 μm pixel pitch hybridized to the MM-PAD ASIC. The detector was capable of high-resolution radiographic imaging and has a fast temporal response. Additionally, we describe the detector's upper operating temperature limits using both the MM-PAD and MARS ASICs, achieving stable operation up to 130 K and 190 K, respectively.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Deep-learning based artificial intelligence tool for melt pools and defect segmentation

Accelerating fabrication of additively manufactured components with precise microstructures is important for quality and qualification of built parts, as well as for a fundamental understanding of process improvement. Accomplishing this requires fast and robust characterization of melt pool geometries and structural defects in images. This paper proposes a pragmatic approach based on implementation of deep learning models and self-consistent workflow that enable systematic segmentation of defects and melt pools in optical images. Deep learning is based on an image-to-image translation–conditional generative adversarial neural network architecture. An artificial intelligence (AI) tool based on this deep learning model enables fast and incrementally more accurate predictions of the prevalent geometric features, including melt pool boundaries and printing-induced structural defects. We present statistical analysis of geometric features that is enabled by the AI tool, showing strong spatial correlation of defects and the melt pool boundaries. The correlations of widths and heights of melt pools with dataset processing parameters show the highest sensitivity to thermal influences resulting from laser passes in adjacent and subsequent layer passes. The presented models and tools are demonstrated on the aluminum alloy and datasets produced with different sets of processing parameters. However, they have universal quality and could easily be adapted to different material compositions. The method can be easily generalized to microstructural characterizations other than optical microscopy.

additive manufacturing↗
NASA NTRS2003

Genetic Algorithm Phase Retrieval for the Systematic Image-Based Optical Alignment Testbed

Phase retrieval requires calculation of the real-valued phase of the pupil fimction from the image intensity distribution and characteristics of an optical system. Genetic 'algorithms were used to solve two one-dimensional phase retrieval problem. A GA successfully estimated the coefficients of a polynomial expansion of the phase when the number of coefficients was correctly specified. A GA also successfully estimated the multiple p h e s of a segmented optical system analogous to the seven-mirror Systematic Image-Based Optical Alignment (SIBOA) testbed located at NASA s Marshall Space Flight Center. The SIBOA testbed was developed to investigate phase retrieval techniques. Tiphilt and piston motions of the mirrors accomplish phase corrections. A constant phase over each mirror can be achieved by an independent tip/tilt correction: the phase Conection term can then be factored out of the Discrete Fourier Tranform (DFT), greatly reducing computations.

Taylor, Jaime↗
DOE OSTI2025

AAPM Truth‐based CT (TrueCT) reconstruction grand challenge

Background: This Special Report summarizes the 2022, AAPM grand challenge on Truth-based CT image reconstruction. Purpose: To provide an objective framework for evaluating CT reconstruction methods using virtual imaging resources consisting of a library of simulated CT projection images of a population of human models with various diseases. Methods: Two hundred unique anthropomorphic, computational models were created with varied diseases consisting of 67 emphysema, 67 lung lesions, and 66 liver lesions. The organs were modeled based on clinical CT images of real patients. The emphysematous regions were modeled using segmentations from patient CT cases in the COPDGene Phase I dataset. For the lung and liver lesion cases, 1–6 malignant lesions were created and inserted into the human models, with lesion diameters ranging from 5.6 to 21.9 mm for lung lesions and 3.9 to 14.9 mm for liver lesions. The contrast defined between the liver lesions and liver parenchyma was 82 ± 12 HU, ranging from 50 to 110 HU. Similarly, the contrast between the lung lesions and the lung parenchyma was defined as 781 ± 11 HU, ranging from 725 to 805 HU. For the emphysematous regions, the defined HU values were −950 ± 17 HU ranging from −918 to −979 HU. The developed human models were imaged with a validated CT simulator. The resulting CT sinograms were shared with the participants. The participants reconstructed CT images from the sinograms and sent back their reconstructed images. Further, the reconstructed images were then scored by comparing the results against the corresponding ground truth values. The scores included both task-generic (root mean square error [RMSE] and structural similarity matrix [SSIM]), and task-specific (detectability index [d’] and lesion volume accuracy) metrics. For the cases with multiple lesions, the measured metric was averaged across all the lesions. To combine the metrics with each other, each metric was normalized to a range of 0 to 1 per disease type, with “0” and “1” being the worst and best measured values across all cases of the disease type for all received reconstructions. Results: The True-CT challenge attracted 52 participants, out of which 5 successfully completed the challenge and submitted the requested 200 reconstructions. Across all participants and disease types, SSIM absolute values ranged from 0.22 to 0.90, RMSE from 77.6 to 490.5 HU, d’ from 0.1 to 64.6, and volume accuracy ranged from 1.2 to 753.1 mm3. The overall scores demonstrated that participant “A” had the best performance in all categories, except for the metrics of d’ for lung lesions and RMSE for liver lesions. Participant “A” had an average normalized score of 0.41 ± 0.22, 0.48 ± 0.32, and 0.42 ± 0.33 for the emphysema, lung lesion, and liver lesion cases, respectively. Conclusions: The True-CT challenge successfully enabled objective assessment of CT reconstructions with the unique advantage of access to a diverse population of diseased human models with known ground truth. This study highlights the significant potential of virtual imaging trials in objective assessment of medical imaging technologies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Effective Defect Detection Using Instance Segmentation for NDI

Ultrasonic testing is a common Non-Destructive Inspection (NDI) method used in aerospace manufacturing. However, the complexity and size of the ultrasonic scans make it challenging to identify defects through visual inspection or machine learning models. Using computer vision techniques to identify defects from ultrasonic scans is an evolving research area. In this study, we used instance segmentation to identify the presence of defects in the ultrasonic scan images of composite panels that are representative of real components manufactured in aerospace. We used two models based on Mask- RCNN (Detectron 2) and YOLO 11 respectively. Additionally, we implemented a simple statistical pre-processing technique that reduces the burden of requiring custom-tailored pre-processing techniques. Our study demonstrates the feasibility and effectiveness of using instance segmentation in the NDI pipeline by significantly reducing data pre-processing time, inspection time, and overall costs.

computer vision techniques↗
NASA NTRS2011

NASA Tech Briefs, March 2011

Topics covered include: Optimal Tuner Selection for Kalman-Filter-Based Aircraft Engine Performance Estimation; Airborne Radar Interferometric Repeat-Pass Processing; Plug-and-Play Environmental Monitoring Spacecraft Subsystem; Power-Combined GaN Amplifier with 2.28-W Output Power at 87 GHz; Wallops Ship Surveillance System; Source Lines Counter (SLiC) Version 4.0; Guidance, Navigation, and Control Program; Single-Frame Terrain Mapping Software for Robotic Vehicles; Auto Draw from Excel Input Files; Observation Scheduling System; CFDP for Interplanetary Overlay Network; X-Windows Widget for Image Display; Binary-Signal Recovery; Volumetric 3D Display System with Static Screen; MMIC Replacement for Gunn Diode Oscillators; Feature Acquisition with Imbalanced Training Data; Mount Protects Thin-Walled Glass or Ceramic Tubes from Large Thermal and Vibration Loads; Carbon Nanotube-Based Structural Health Monitoring Sensors; Wireless Inductive Power Device Suppresses Blade Vibrations; Safe, Advanced, Adaptable Isolation System Eliminates the Need for Critical Lifts; Anti-Rotation Device Releasable by Insertion of a Tool; A Magnetically Coupled Cryogenic Pump; Single Piezo-Actuator Rotary-Hammering Drill; Fire-Retardant Polymeric Additives; Catalytic Generation of Lift Gases for Balloons; Ionic Liquids to Replace Hydrazine; Variable Emittance Electrochromics Using Ionic Electrolytes and Low Solar Absorptance Coatings; Spacecraft Radiator Freeze Protection Using a Regenerative Heat Exchanger; Multi-Mission Power Analysis Tool; Correction for Self-Heating When Using Thermometers as Heaters in Precision Control Applications; Gravitational Wave Detection with Single-Laser Atom Interferometers; Titanium Alloy Strong Back for IXO Mirror Segments; Improved Ambient Pressure Pyroelectric Ion Source; Multi-Modal Image Registration and Matching for Localization of a Balloon on Titan; Entanglement in Quantum-Classical Hybrid; Algorithm for Autonomous Landing; Quantum-Classical Hybrid for Information Processing; Small-Scale Dissipation in Binary-Species Transitional Mixing Layers; Superpixel-Augmented Endmember Detection for Hyperspectral Images; Coding for Parallel Links to Maximize the Expected Value of Decodable Messages; and Microwave Tissue Soldering for Immediate Wound Closure.

Source record↗
NASA NTRS1991

The Filled Arm Fizeau Telescope (FFT)

Attention is given to the design of a Mills Cross imaging interferometer in which the arms are fully filled with mirror segments of a Ritchey-Chretien primary and which has sensitivity to 27th magnitude per pixel and resolution a factor of 10 greater than Hubble. The optical design, structural configuration, thermal disturbances, and vibration, material, control, and metrology issues, as well as scientific capabilities are discussed, and technology needs are identified. The technologies under consideration are similar to those required for the development of the other imaging interferometers that have been proposed over the past decade. A comparison of the imaging capabilities of a 30-m diameter FFT, an 8-m telescope with a collecting area equal to that of the FFT, and the HST is presented.

Synnott, S. P.↗
NASA NTRS2004

High frequency QRS ECG predicts ischemic defects during myocardial perfusion imaging

Changes in high frequency QRS components of the electrocardiogram (HF QRS ECG) (150-250 Hz) are more sensitive than changes in conventional ST segments for detecting myocardial ischemia. We investigated the accuracy of 12-lead HF QRS ECG in detecting ischemia during adenosine tetrofosmin myocardial perfusion imaging (MPI). 12-lead HF QRS ECG recordings were obtained from 45 patients before and during adenosine technetium-99 tetrofosmin MPI tests. Before the adenosine infusions, recordings of HF QRS were analyzed according to a morphological score that incorporated the number, type and location of reduced amplitude zones (RAZs) present in the 12 leads. During the adenosine infusions, recordings of HF QRS were analyzed according to the maximum percentage changes (in both the positive and negative directions) that occurred in root mean square (RMS) voltage amplitudes within the 12 leads. The best set of prospective HF QRS criteria had a sensitivity of 94% and a specificity of 83% for correctly identifying the MPI result. The sensitivity of simultaneous ST segment changes (18%) was significantly lower than that of any individual HF QRS criterion (P less than 0.00l). Analysis of 12-lead HF QRS ECG is highly sensitive and specific for detecting ischemic perfusion defects during adenosine MPI stress tests and significantly more sensitive than analysis of conventional ST segments.

Source record↗
DOE OSTI2024

Correlative X-ray micro-nanotomography with scanning electron microscopy at the Advanced Light Source

Geological samples are inherently multi-scale. Understanding their bulk physical and chemical properties requires characterization down to the nano-scale. A powerful technique to study the three-dimensional microstructure is X-ray tomography, but it lacks information about the chemistry of samples. To develop a methodology for measuring the multi-scale 3D microstructure of geological samples, correlative X-ray micro- and nanotomography were performed on two rocks followed by scanning electron microscopy with energy-dispersive spectroscopy (SEM-EDS) analysis. The study was performed in five steps: (i) micro X-ray tomography was performed on rock sample cores, (ii) samples for nanotomography were prepared using laser milling, (iii) nanotomography was performed on the milled sub-samples, (iv) samples were mounted and polished for SEM analysis and (v) SEM imaging and compositional mapping was performed on micro and nanotomography samples for complimentary information. Correlative study performed on samples of serpentine and basalt revealed multiscale 3D structures involving both solid mineral phases and pore networks. Significant differences in the volume fraction of pores and mineral phases were also observed dependent on the imaging spatial resolution employed. This highlights the necessity for the application of such a multiscale approach for the characterization of complex aggregates such as rocks. Information acquired from the chemical mapping of different phases was also helpful in segmentation of phases that did not exhibit significant contrast in X-ray imaging. Adoption of the protocol used in this study can be broadly applied to 3D imaging studies being performed at the Advanced Light Source and other user facilities.

58 GEOSCIENCES↗
NASA NTRS2006

High frequency QRS ECG predicts ischemic defects during myocardial perfusion imaging

Background: Changes in high frequency QRS components of the electrocardiogram (HF QRS ECG) (150-250 Hz) are more sensitive than changes in conventional ST segments for detecting myocardial ischemia. We investigated the accuracy of 12-lead HF QRS ECG in detecting ischemia during adenosine tetrofosmin myocardial perfusion imaging (MPI). Methods and Results: 12-lead HF QRS ECG recordings were obtained from 45 patients before and during adenosine technetium-99 tetrofosmin MPI tests. Before the adenosine infusions, recordings of HF QRS were analyzed according to a morphological score that incorporated the number, type and location of reduced amplitude zones (RAZs) present in the 12 leads. During the adenosine infusions, recordings of HF QRS were analyzed according to the maximum percentage changes (in both the positive and negative directions) that occurred in root mean square (RMS) voltage amplitudes within the 12 leads. The best set of prospective HF QRS criteria had a sensitivity of 94% and a specificity of 83% for correctly identifying the MPI result. The sensitivity of simultaneous ST segment changes (18%) was significantly lower than that of any individual HF QRS criterion (P<0.001). Conclusions: Analysis of 12-lead HF QRS ECG is highly sensitive and specific for detecting ischemic perfusion defects during adenosine MPI stress tests and significantly more sensitive than analysis of conventional ST segments.

Rahman, Atiar↗
NASA NTRS1997

Polar Spin Axis Anomaly

The Global Geospace Science (GGS) Polar Plasma Laboratory (POLAR) spacecraft was launched on February 24, 1996, by a Delta 2. The spacecraft, a major axis spinner, appeared to function nominally throughout the early mission phase, which included several deployments, and orbit and attitude maneuvers. Of particular interest is the fact that the spacecraft was launched with a deliberate dynamic imbalance. During a segment of early orbit operations, a pair of Lanyard Deployed Booms (LDB) were extended. These booms were not identical; the intent was that the spacecraft would be nearly dynamically balanced after they were deployed. The spacecraft contained two dynamic balance mechanisms intended to fine tune the balance on orbit. However, subsequent images taken by the science instruments on the Despun Platform during the dynamic balancing segment indicated an offset of the principal spin axis from the geometric axis. This offset produced a sinusoidal blurring of the science images sufficiently large to degrade science data below mission requirement specifications. In the end, the imbalance encountered in flight was significantly outside the correction capability of the balances. The purpose of this paper is to examine the flight data during the various deployment and maneuver stages of the early orbit operations coupled with analytical simulations to discuss some of the potential causes of the resultant imbalance.

Crouse, Patrick L.↗
DOE OSTI2025

Benchmarking image processing techniques for porosity measurement in polymer additive manufacturing: Review and experimental analysis

An image processing workflow is proposed for porosity measurement in polymer additive manufacturing. Various techniques, including global and local thresholding, region growing, and K-means clustering, were applied to microscopic images of carbon fiber reinforced acrylonitrile butadiene styrene (CF-ABS) and benchmarked for their ability to accurately measure porosity. Global methods included Otsu, minimum error, iterative, and entropy-based thresholding, while local methods included Niblack, Bernsen, Sauvola, and Bradley-Roth algorithms. Artificial uneven illumination was introduced to test local adaptive thresholds. Results showed significant differences in porosity values across methods. Otsu, region growing, and K-means clustering excelled under uniform illumination, while Sauvola and Bradley-Roth performed better with uneven illumination. Comparison with X-ray computed tomography (XCT) revealed slightly lower porosity values (2.55 %) than optimized methods (2.73–2.79 %) due to XCT's lower resolution excluding smaller pores. While XCT offers finer pore detection, it limits sample volume and underestimates porosity due to spatial variation. Validation using artificial grayscale images with 5 % porosity confirmed that Otsu, Bradley-Roth, region growing, and Sauvola algorithms produced accurate results. Although tested on a single material system, these methods can be adapted to others with optimization. In conclusion, given XCT's high computational and time costs, this study highlights suitable image processing techniques as cost-effective alternatives for porosity analysis in polymer composites.

Additive manufacturing↗
NASA NTRS1979

Manual interpretation of LANDSAT data

The task of the analyst throughout LACIE phases 1 and 2 consisted of outlining representative areas (fields) for all spectral classes within a segment on the basis of their appearance on the LANDSAT image products, and then labeling the crop type (wheat/nonwheat) within the selected training areas. For LACIE phase 3, a procedure was developed and implemented which incorporated clustering for spectral class definition and training statistics generation. The only analyst's task was crop type identification. The logical processes involved in interpretation are described, rather than a step-by-step description of analyst procedures.

Hay, C. M.↗
NASA NTRS1991

Caldera-structure relationships in Kenya - Observations from satellite data

Maps were constructed showing lineaments displayed on four Landsat MSS images, to study volcanic-tectonic relationships in the Kenya segment of the Gregory or eastern sector of the East African rift system. Many of the mapped lineaments correlated well with fractures shown on geological maps. Selected lineaments, transverse to the rift trend, could be extrapolated to intersect rift faults at locations correlative with the locations of Kenya's seven volcanic calderas. The volcano-genetic difference between these intersections vs. similar intersections having no obvious relationship to any type of volcanic vent is not yet understood.

Blodget, Herbert W.↗