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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Local Ferromagnetic Resonance Measurements of Mesoscopically Patterned Ferromagnets Using Deterministically Placed Nanodiamonds

Nitrogen-vacancy centers in diamond have recently been established as effective sensors of the magnetization dynamics in vicinal ferromagnetic materials. Here, we demonstrate sub-100-nm placement accuracy of nitrogen-vacancy-containing nanodiamonds and use these as local sensors that probe optically detected ferromagnetic resonance in mesoscopically patterned Permalloy islands. These measurements reveal variations in the ferromagnetic resonance signal at different sites on these structures with distinct behavior in the edge and the bulk of patterned features. These test measurements establish an easily implemented approach for spatially targeted measurements of spin dynamics in mesoscale ferromagnets. In principle, the methodology can also be extended to local studies of nanoscale ferromagnets, such as single magnetic nanowires and nanoparticles.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2024

Behavioral Segmentation and Clustering of Geospatial Trajectories

The rapid growth of global positioning system (GPS) devices has led to a corresponding increase in the size of GPS datasets. While these large GPS datasets contain a wealth of information about the behaviors of the moving objects in them, manual classification and anomaly detection are prohibitively time consuming. We utilize unsupervised machine learning techniques to first identify the behaviors for individual moving objects and then cluster those objects by their behavioral sequences. In this way, trajectories behaving unusually as well as common patterns of behavior are both detectable in large datasets without requiring an a priori definition of "unusual" or "common."

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Detecting Masquerade Attacks in Controller Area Networks Using Graph Machine Learning

Modern vehicles rely on a myriad of electronic control units (ECUs) interconnected via controller area networks (CANs) for critical operations. Despite their ubiquitous use and reliability, CANs are susceptible to sophisticated cyberattacks, particularly masquerade attacks, which inject false data that mimic legitimate messages at the expected frequency. These attacks pose severe risks such as unintended acceleration, brake deactivation, and rogue steering. Traditional intrusion detection systems (IDS) often struggle to detect these subtle intrusions due to their seamless integration into normal traffic. This paper introduces a novel framework for detecting masquerade attacks in the CAN bus using graph machine learning (ML). We hypothesize that the integration of shallow graph embeddings with time series features derived from CAN frames enhances the detection of masquerade attacks. We show that by representing CAN bus frames as message sequence graphs (MSGs) and enriching each node with contextual statistical attributes from time series, we can enhance detection capabilities across various attack patterns compared to using graph-based features only. Our method ensures a comprehensive and dynamic analysis of CAN frame interactions, improving robustness and efficiency. Extensive experiments on the ROAD dataset validate the effectiveness of our approach, demonstrating statistically significant improvements in the detection rates of masquerade attacks compared to a baseline that uses graph-based features only as confirmed by Mann-Whitney U and Kolmogorov-Smirnov tests (p < 0.05) .

Marfo, William [Univ. of Texas, El Paso, TX (Unite↗
DOE OSTI2022

Implications of gnomonic distortion on electron backscatter diffraction and transmission Kikuchi diffraction

The effect of gnomonic distortion on orientation indexing of electron backscatter diffraction patterns is explored through simulation of electron diffraction patterns for sample-to-detector geometries associated with transmission Kikuchi diffraction (TKD) and electron backscatter diffraction (EBSD). Simulated data were analysed by computing a similarity index for both Hough transformed data and simulated patterns to determine the sensitivity of each method for detecting subtle differences in the effect of gnomonic distortions on electron diffraction patterns. These results indicate that the increased gnomonic distortions in electron diffraction patterns for a TKD geometry enhance the sensitivity for detecting subtle differences in interband angles. Additionally, the utilisation of a Hough transform-based indexing approach further enhances the sensitivity.

36 MATERIALS SCIENCE↗
NASA NTRS1995

Retinal, attentional, and causal aspects of illusory-motion directionality

Extended objects presented instantaneously appeared to propagate or grow into their final shape. A strong bias was found toward perceiving straight lines to grow away from the observer's point of fixation. If attention and fixation were not directed to the same point of the visual field, both influenced illusory motion directionality, and if another small object was present on the screen at a location that was neither fixated nor attended to, the object that was instantaneously presented seemed to grow away from this point. Thus, three factors are involved in illusory-motion directionality: biases toward perceiving objects spreading away from the fixation point, away from an attended area, and away from preexisting objects. The experiments indicate that a satisfactory explanation of the illusion has to include bottom-up and top-down processes. They also challenge existing theories of motion detection.

Pattern Recognition, Visual/physiology↗
DOE OSTI2019

Device and method for the location and identification of a radiation source

A handheld device for the location and identification of a radiation source, including: a radiation transparent housing; a radiation locator device disposed within the radiation transparent housing operable for determining the location of the radiation source, wherein the radiation locator device includes a plurality of gamma detection crystals arranged in a geometric pattern and separated by a gamma shielding material, a plurality of detectors coupled to the plurality of gamma detection crystals, and a processor module coupled to the plurality of detectors; one or more of a neutron detection crystal and a gamma spectroscopy crystal disposed within the radiation transparent housing adjacent to the radiation locator device; and one or more detectors coupled to the one or more of the neutron detection crystal and the gamma spectroscopy crystal and the processor module; wherein the one or more of the neutron detection crystal and the gamma spectroscopy crystal, the one or more detectors, and the processor module are collectively operable for identifying the radiation source.

Vencelj, Matjaz↗
DOE OSTI2016

Development of a beam ion velocity detector for the heavy ion beam probe

In an axisymmetric plasma, the conservation of canonical angular momentum constrains heavy ion beam probe (HIBP) trajectories such that measurement of the toroidal velocity component of secondary ions provides a localized determination of the poloidal flux at the volume where they originated. We have developed a prototype detector which is designed to determine the beam angle in one dimension through the detection of ion current landing on two parallel planes of detecting elements. A set of apertures creates a pattern of ion current on wires in the first plane and solid metal plates behind them; the relative amounts detected by the wires and plates determine the angle which beam ions enter the detector, which is used to infer the toroidal velocity component. The design evolved from a series of simulations within which we modeled ion beam velocity changes due to equilibrium and fluctuating magnetic fields, along with the ion beam profile and velocity dispersion, and studied how these and characteristics such as the size, cross section, and spacing of the detector elements affect performance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1986

Modes of vibration on sqaure fiberglass epoxy composite thick plate

The frequencies and nodal patterns of a square thick plate of unidirectional fiberglass epoxy composite are measured experimentally. The constituent material is transversely isotropic. The plate is transversely excited at the center of the upper face, its resonant frequencies in the frequency range of 3 kHz to 21.73 kHz are detected and the measured nodal patterns are sketched.

Williams, J. H., Jr.↗
DOE OSTI2021

Single molecule-based fliFISH validates radial and heterogeneous gene expression patterns in pancreatic islet ß cells

Single cell RNA sequencing (scRNA-Seq) technologies have greatly enhanced our understanding of islet cell transcriptomes and have revealed the existence of ß cell heterogeneity. However, comparison of scRNA-Seq datasets from different groups have highlighted inconsistencies in gene expression patterns, primarily due to variable detection of lower abundance transcripts. Furthermore, such analyses are unable to uncover the spatial organization of heterogeneous gene expression. Here we used fluctuation localization imaging-based fluorescence in situ hybridization (fliFISH) to quantify transcripts in single cells in mouse pancreatic islet sections. We compared the expression patterns of Insulin 2 (Ins2) with Mafa and Ucn3 – two genes expressed in ß cells as they mature, as well as Rgs4 – a factor with variably reported expression in the islet. This approach accurately quantified transcripts across a wide range of expression levels - from single copies to over hundred copies per cell in one islet. Importantly, fliFISH allowed evaluation of transcript heterogeneity in the spatial context of an intact islet. These studies confirm the existence of a high degree of heterogeneous gene expression levels within the islet and highlight relative and radial expression patterns that likely reflect distinct ß cell maturation states along the radial axis of the islet.

Li, Fangjia↗
NASA NTRS2017

Detection of Hail Storms in Radar Imagery Using Deep Learning

In 2016, hail was responsible for 3.5 billion and 23 million dollars in damage to property and crops, respectively, making it the second costliest weather phenomenon in the United States. In an effort to improve hail-prediction techniques and reduce the societal impacts associated with hail storms, we propose a deep learning technique that leverages radar imagery for automatic detection of hail storms. The technique is applied to radar imagery from 2011 to 2016 for the contiguous United States and achieved a precision of 0.848. Hail storms are primarily detected through the visual interpretation of radar imagery (Mrozet al., 2017). With radars providing data every two minutes, the detection of hail storms has become a big data task. As a result, scientists have turned to neural networks that employ computer vision to identify hail-bearing storms (Marzbanet al., 2001). In this study, we propose a deep Convolutional Neural Network (ConvNet) to understand the spatial features and patterns of radar echoes for detecting hailstorms.

natural hazard↗
NASA NTRS1999

Laser Light Scattering with Multiple Scattering Suppression Used to Measure Particle Sizes

Laser light scattering is the technique of choice for noninvasively sizing particles in a fluid. The members of the Advanced Technology Development (ATD) project in laser light scattering at the NASA Lewis Research Center have invented, tested, and recently enhanced a simple and elegant way to extend the concentration range of this standard laboratory particle-sizing technique by several orders of magnitude. With this technique, particles from 3 nm to 3 mm can be measured in a solution. Recently, laser light scattering evolved to successfully size particles in both clear solutions and concentrated milky-white solutions. The enhanced technique uses the property of light that causes it to form tall interference patterns at right angles to the scattering plane (perpendicular to the laser beam) when it is scattered from a narrow laser beam. Such multiple-scattered light forms a broad fuzzy halo around the focused beam, which, in turn, forms short interference patterns. By placing two fiber optics on top of each other and perpendicular to the laser beam (see the drawing), and then cross-correlating the signals they produce, only the tall interference patterns formed by singly scattered light are detected. To restate this, unless the two fiber optics see the same interference pattern, the scattered light is not incorporated into the signal. With this technique, only singly scattered light is seen (multiple-scattered light is rejected) because only singly scattered light has an interference pattern tall enough to span both of the fiber-optic pickups. This technique is simple to use, easy to align, and works at any angle. Placing a vertical slit in front of the signal collection fibers enhanced this approach. The slit serves as an optical mask, and it significantly shortens the time needed to collect good data by selectively masking out much of the unwanted light before cross-correlation is applied.

Meyer, William V.↗
NASA NTRS1986

A comparison of global FGGE analyses and energetics (GLA vs ECMWF) for January 1979

The differences and similarities between ECMWF and NASA/Goddard Laboratory for Atmospheres (GLA) analyses and resultant energetics for the South Pacific Convergence Zone and the South Atlantic Convergence Zone are studied, as well as the global circulation patterns. The horizontal wind components, temperature, geopotential height, vertical p-velocity, velocity potential, streamlines, and mean sea level pressure derived from ECMWF and GLA analyses at different pressure levels are compared. It is observed that the mean sea level pressure, temperature, streamlines, and vertical velocity are nearly identical, and the least correlation is detected for the vertical velocity patterns. The energy contents and conversion obtained using the two analyses are examined, and the correlations between the data sets are described.

Huang, Huo-Jin↗
NASA NTRS1988

Digital and optical shape representation and pattern recognition; Proceedings of the Meeting, Orlando, FL, Apr. 4-6, 1988

The present conference discusses topics in pattern-recognition correlator architectures, digital stereo systems, geometric image transformations and their applications, topics in pattern recognition, filter algorithms, object detection and classification, shape representation techniques, and model-based object recognition methods. Attention is given to edge-enhancement preprocessing using liquid crystal TVs, massively-parallel optical data base management, three-dimensional sensing with polar exponential sensor arrays, the optical processing of imaging spectrometer data, hybrid associative memories and metric data models, the representation of shape primitives in neural networks, and the Monte Carlo estimation of moment invariants for pattern recognition.

Juday, Richard D.↗
NASA NTRS1971

Pollution detection by digital correlation of multispectral, stero-image pairs.

Remote detection of air pollution circulation patterns is proposed to eventually predict the accumulation of hazardous surface concentrations in time for preventive emission control operations. Earth observations from space platforms will contain information on the height, mean velocity and lateral mixing scales of inversion layers and pollution plumes. Although this information is often not visible on photographs, it could conceivably be retrieved through a digital cross-correlation of multispectral stereo image pairs. Laboratory and field test results are used to illustrate the detection of non-visual inversion layers, the reduction of dominant signal interference, and the spectroscopic identification of combustion products.

Krause, F. R.↗
DOE OSTI2022

3D Time-Lapse Electrical Resistivity Imaging of Rock Damage Patterns and Gas Flow Paths Resulting from Two Underground Chemical Explosions

Abstract Rock damage from underground nuclear explosions (UNEs) has a strong influence on sub-surface gas movement and on seismic waveform characteristics, both of which are used to detect UNEs. Although advanced numerical simulation capabilities exist to predict rock damage patterns and corresponding detection signals, those predictions are dependent on (generally) unknown properties of the host rock. For example, the effects of in-situ mechanical heterogeneities on the explosively generated damage/fractures that provide gas flow pathways to the surface are not well understood, due largely to the difficulty in accessing and characterizing the near-source region. In this paper we demonstrate the emerging use of electrical resistivity tomography (ERT) for imaging rock damage and gas flow patterns resulting from two relatively small-scale underground chemical explosions. Pre-explosion ERT and crosshole seismic imaging revealed a natural fracture zone within the test bed. Post-explosion imaging revealed that the damage zone was non-symmetric and was focused primarily within the pre-existing fracture zone, located 10 m above the first explosion and 5 m above the second explosion. Time-lapse ERT imaging of heated air injected into the detonation borehole revealed the primary gas flow paths to be within the upper margin of the same primary damage zone. These results point to the utility of ERT imaging for understanding rock damage and gas flow patterns under experimental conditions, and to the importance of understanding the effects of geologic heterogeneity on UNE detection signals, particularly gas surface breakthrough times.

58 GEOSCIENCES↗
NASA NTRS1999

Assessment of Localized Deformations in Sand Using X-Ray Computed Tomography

The internal fabric and localized deformation patterns of triaxial sand specimens were investigated using Computed Tomography (CT). Three displacement-controlled, conventional, drained axisymmetric (triaxial) experiments were conducted on dry Ottawa sand specimens at very low effective confining stresses (0.05, 0.52, and 1.30 kPa) in a microgravity environment aboard the Space Shuttle during the NASA STS-79 mission. CT scanning was p'erformed on these flight specimens, as well as on an uncompressed specimen and a specimen tested in a terrestrial laboratory at 1.30 kPa effective confining stress. CT demonstrated high accuracy in detecting specimen inhomogeneity and localization patterns. Formation of deformation patterns is dependent on the effective confining stress and gravity. Multiple symmetrical radial shear bands were observed in the specimens tested in a microgravity environment. In the axial direction, two major conical surfaces were developed. Nonsymmetrical spatial deformation was observed in the 1-G specimen. Analysis tools were developed to quantify the spatial density change. Void ratio variation within and outside the shear bands was calculated and discussed.

Alshibli, Khalid A↗
NASA NTRSDate not supplied

Capturing Complex Multivariate Time Series Interactions to Detect High-Risk Adverse Events During Flight

The reduction of aviation safety metrics below target thresholds continue to drive down the number of aviation fatalities and accidents. To meet future safety demands, sustained efforts by aviation agencies promoting safety assurance processes and systems have prompted ongoing research on identifying and mitigating in-flight risks. With the projected increase in passenger load factor and rollout of more autonomous systems into the national airspace, the need to detect high-risk events in-time or ahead-of-time is becoming increasingly crucial. New anomaly detection and precursor identification algorithms will need to scale to different airframes, levels of autonomy, and system complexity. While the pervasiveness of deep learning has resulted in the development of performant anomaly detection methods, these sophisticated models currently suffer from low end-user interpretability. Building off our previous work on identifying adverse events in multivariate flight data during descent, we propose a data-driven approach for detecting in-flight adverse events caused by the complex interplay of flight variables. Our approach utilizes ordinal patterns of important aircraft stability variables (e.g., airspeed and descent rate) to capture multivariate flight dynamics that can be used to predict the onset of unstable approaches, a high-risk adverse event that can occur during approach. Through the use of ordinal patterns, we aim to create more interpretable detection models of in-flight adverse events that can be translated to future autonomous systems without difficulty. Our analysis shows the presence of distinct ordinal pattern distributions that can be used to predict unstable approaches 1 minute ahead of time with an accuracy of 0.69 and a recall of 0.73 and 30 seconds ahead with an accuracy of 0.70 and a recall of 0.86.

Risk detection↗