Search NASA⌕ Search

SEARCH · Search NASA

Results for “local binary patterns”

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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Segmenting water and shadow regions within WorldView imagery using local binary patterns

Many shadow detection algorithms pertaining to remotely sensed imagery exist. Several of these algorithms exploit the spectral characteristics of shadows within imagery to identify shaded regions. However, these algorithms can have problems when water is also present within the imagery because water shares similar spectral characteristics with shadows. Many of these algorithms are applied to small image subsets instead of the image as a whole and frequently are applied to urban environments that require additional use of the normalized difference water index or other features to affect the removal of water from the shadow mask. This diversity of the image scene content coupled with the complexity and wide variety of environmental conditions that satellite imagery can acquire makes reliably separating shadow and water within larger images a complex problem. Thresholding various spectral indices to produce segmentation maps can be challenging when large, complex, and often imbalanced scenes are captured, which may require manual adjustment of algorithmic parameters for different image areas. Here, we present a methodology that makes use of the near-infrared channel using a watershed segmentation algorithm, local binary pattern measurements, and a support vector machine to classify shadow and water within a full WorldView scene. Results are promising with initial accuracies above 97% for both shadow and water.

47 OTHER INSTRUMENTATION↗
NASA NTRS1987

Binary-Symmetry Detection

Transmission errors for zeros and ones tabulated separately. Binary-symmetry detector employs psuedo-random data pattern used as test message coming through channel. Message then modulo-2 added to locally generated and synchronized version of test data pattern in same manner found in manufactured test sets of today. Binary symmetrical channel shows nearly 50-percent ones to 50-percent zeroes correspondence. Degree of asymmetry represents imbalances due to either modulation, transmission, or demodulation processes of system when perturbed by noise.

Lopez, Hiram↗
NASA NTRS1980

Surface relief structures for multiple beam LO generation

Linear and binary holograms for use in heterodyne detection with 10.6 micron imaging arrays are described. The devices match the amplitude and phase of the local oscillator to the received signal and thus maximize the system signal to noise ratio and resolution and minimize heat generation on the focal plane. In both the linear and binary approaches, the holographic surface-relief pattern is coded to generate a set of local oscillator beams when the relief pattern is illuminated by a single planewave. Each beam of this set has the same amplitude shape distribution as, and is collinear with, each single element wavefront illuminating array.

Veldkamp, W. B.↗
NASA NTRS1990

Ground states of partially connected binary neural networks

Neural networks defined by outer products of vectors over (-1, 0, 1) are considered. Patterns over (-1, 0, 1) define by their outer products partially connected neural networks consisting of internally strongly connected, externally weakly connected subnetworks. Subpatterns over (-1, 1) define subnetworks, and their combinations that agree in the common bits define permissible words. It is shown that the permissible words are locally stable states of the network, provided that each of the subnetworks stores mutually orthogonal subwords, or, at most, two subwords. It is also shown that when each of the subnetworks stores two mutually orthogonal binary subwords at most, the permissible words, defined as the combinations of the subwords (one corresponding to each subnetwork), that agree in their common bits are the unique ground states of the associated energy function.

Baram, Yoram↗
NASA NTRS1993

Obstacle detection by recognizing binary expansion patterns

This paper describes a technique for obstacle detection, based on the expansion of the image-plane projection of a textured object, as its distance from the sensor decreases. Information is conveyed by vectors whose components represent first-order temporal and spatial derivatives of the image intensity, which are related to the time to collision through the local divergence. Such vectors may be characterized as patterns corresponding to 'safe' or 'dangerous' situations. We show that essential information is conveyed by single-bit vector components, representing the signs of the relevant derivatives. We use two recently developed, high capacity classifiers, employing neural learning techniques, to recognize the imminence of collision from such patterns.

Baram, Yoram↗
NASA NTRS2017

Vascular Patterning Analysis by VESGEN 2D/3D with Bioinformatics: Updates for Rodent Tissues

Fractally branching vascular systems are a complex physiological requirement shared by humans with all higher terrestrial life forms, including other vertebrates, insects, and higher land plants. Vascular trees, networks, and tree-network composites are therefore mapped and quantified by the VESsel GENeration Analysis (VESGEN) software according to weighted physiological vascular rules that include vessel connectivity, tapering and bifurcational branching. According to fluid dynamics, successful vascular transport depends upon a complex distributed system of highly regulated laminar flow. VESGEN has elucidated changes in vascular patterning resulting from inflammatory, developmental and other signaling pathways within numerous tissues of major model organisms important for Space Biology, especially for rodents. Important early stage regenerative opportunities have been identified by VESGEN vascular analysis for visual impairments in the human retina, and is currently being used for research into astronaut visual and ocular disorders associated with long duration missions. The VESGEN 2D software is a mature, automated, widely published capability for which beta testing and public release by NASA is planned for the upcoming year. Early-stage capabilities for VESGEN 3D analysis are under development for the rodent retina and intestine as prototype tissues. A prototype VESGEN 2D Bioinformatics software capability has also been developed to associate phenotypic changes in molecular expression with vascular structure and function. By new VESGEN bioinformatic innovations, expression patterns of the genetic, transcriptional, protein and other markers for regulatory molecules such as vascular endothelial growth factor (VEGF) and their receptors, often indicators of tissue oxygenation status, are co-localized with alterations in vascular pattern. Biomarkers are therefore mapped and quantified as information dimensions directly correlated with the spatial dimensions of a vascular pattern. Further important technology innovations by NASA include substantial image segmentation advances for more automated binary extraction of the grayscale vascular patterns, together with informative associated image quality assessments. Vascular mapping and quantification capabilities for the rodent retina and intestine are illustrated for VESGEN 2D, along with technology status reports on VESGEN 3D and Bioinformatic capabilities. Research partially supported by Ames Center Innovation Awards.

Parsons-Wingerter, P.↗
NASA NTRS2019

Mutual Orbit Orientations of Transneptunian Binaries

We present Keplerian orbit solutions for the mutual orbits of 17 transneptunian binary systems (TNBs). For ten of them, the orbit had not previously been known: 60458 2000 CM (sub 114), 119979 2002 WC (sub 19), 160091 2000 OL (sub 67), 160256 2002 PD (sub 149), 469514 2003 QA (sub 91), 469705 ǂKagara, 508788 2000 CQ (sub 114), 508869 2002 VT (sub 130), 1999 RT (sub 214), and 2002 XH (sub 91). Seven more are systems where the size, shape, and period of the orbit had been published, but new observations have now eliminated the sky plane mirror ambiguity in its orientation: 90482 Orcus, 120347 Salacia-Actaea, 1998 WW (sub 31), 1999 OJ (sub 4), 2000 QL (sub 251), 2001 XR (sub 254), and 2003 TJ (sub 58). The dynamical masses we obtain from TNB mutual orbits can be combined with estimates of the objects' sizes from thermal observations or stellar occultations to estimate their bulk densities. The ǂKagara system is currently undergoing mutual events in which one component casts its shadow upon the other and/or obstructs the view of the other. Such events provide valuable opportunities for further characterization of the system. Combining our new orbits with previously published orbits yields a sample of 35 binary orbits with known orientations that can provide important clues about the environment in which outer solar system planetesimals formed, as well as their subsequent evolutionary history. Among the relatively tight binaries, with semimajor axes less than about 5 percent of their Hill radii, prograde mutual orbits vastly outnumber retrograde orbits. This imbalance is not attributable to any known observational bias. We suggest that this distribution could be the signature of planetesimal formation through gravitational collapse of local density enhancements such as caused by the streaming instability. Wider binaries, with semimajor axes greater than 5 percent of their Hill radii, are somewhat more evenly distributed between prograde and retrograde orbits, but with mutual orbits that are aligned or anti-aligned with their heliocentric orbits. This pattern could perhaps result from Kozai-Lidov cycles coupled with tidal evolution eliminating high inclination wide binaries.

Grundy, W. M.↗
DOE OSTI2025

Large‐Scale Statistically Meaningful Patterns (LSMPs) Associated With Precipitation Extremes Over Northern California

Abstract We analyze large‐scale statistically meaningful patterns (LSMPs) that precede extreme precipitation (PEx) events over Northern California (NorCal). We find LSMPs by applying k‐means clustering to the two leading principal components of daily 500 hPa geopotential height anomalies two days before the onset, from October to March during 1948–2015. Statistical significance testing based on Monte Carlo simulations suggests a minimum of four statistically distinguished LSMP clusters. The four LSMP clusters are characterized as Northwest continental negative height anomaly, Eastward positive “Pacific‐North American Pattern (PNA),” Westward negative “PNA,” and Prominent Alaskan ridge. These four clusters, shown in multiple variables, evolve very differently and have differing links to the Arctic and tropical Pacific regions. Using binary forecast skill measures and a new copula‐based framework for predicting PEx events, we find LSMP indices that are useful predictors of NorCal PEx events, with moisture‐based variables being the best predictors of PEx events at least 6 days before the onset, and the lower atmospheric variables being better than their upper atmospheric counterparts any day in advance tested. To ensure statistical rigor, the LSMPs analyzed here (with the modified acronym) include local tests of both significance and consistency, which are not always featured in the literature on large‐scale meteorological patterns.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Hierarchical Data-Driven Protection for Microgrid with 100% Renewable Penetration: Preprint

The accurate detection and isolation of faults is critical for the reliable operation of microgrids (MGs). Traditional protection approaches are even more challenged for 100% renewable MGs because inverter-based resources (IBRs) are the only sources for fault current which are usually low and unpredictable/non-uniform. This calls for new protection scheme that can identify IBR fault responses and detect faults in MGs. Data-driven based protection can learn the pattern of IBR fault responses and make the correct decision to identify faults. Therefore, this paper presents a data-driven approach for fault localization in island MGs. The approach builds a training dataset of comprehensive fault scenarios that can be used to learn fault characteristics from processed measurements. The localization task is modeled as a binary classification problem at each relay, which simplifies the learning process. Then, a hierarchical decision mechanism is used to identify the fault location. The proposed approach is assessed using an exemplary MG with several grid-forming (GFM) and grid-following (GFL) inverters, where accurate estimation of fault location is achieved. The data-driven based protection approach developed in this paper provides a generic framework and useful guidance for power system protection engineers to achieve reliable protection for MGs with 100% renewables.

artificial intelligence↗
NASA NTRS1992

Propagating confined states in phase dynamics

Theoretical treatment is given to the possibility of the existence of propagating confined states in the nonlinear phase equation by generalizing stationary confined states. The nonlinear phase equation is set forth for the case of propagating patterns with long wavelengths and low-frequency modulation. A large range of parameter values is shown to exist for propagating confined states which have spatially localized regions which travel on a background with unique wavelengths. The theoretical phenomena are shown to correspond to such physical systems as spirals in Taylor instabilities, traveling waves in convective systems, and slot-convection phenomena for binary fluid mixtures.

Brand, Helmut R.↗
NASA NTRS2005

Digital Equivalent Data System for XRF Labeling of Objects

A digital equivalent data system (DEDS) is a system for identifying objects by means of the x-ray fluorescence (XRF) spectra of labeling elements that are encased in or deposited on the objects. As such, a DEDS is a revolutionary new major subsystem of an XRF system. A DEDS embodies the means for converting the spectral data output of an XRF scanner to an ASCII alphanumeric or barcode label that can be used to identify (or verify the assumed or apparent identity of) an XRF-scanned object. A typical XRF spectrum of interest contains peaks at photon energies associated with specific elements on the Periodic Table (see figure). The height of each spectral peak above the local background spectral intensity is proportional to the relative abundance of the corresponding element. Alphanumeric values are assigned to the relative abundances of the elements. Hence, if an object contained labeling elements in suitably chosen proportions, an alphanumeric representation of the object could be extracted from its XRF spectrum. The mixture of labeling elements and for reading the XRF spectrum would be compatible with one of the labeling conventions now used for bar codes and binary matrix patterns (essentially, two-dimensional bar codes that resemble checkerboards). A further benefit of such compatibility is that it would enable the conversion of the XRF spectral output to a bar or matrix-coded label, if needed. In short, a process previously used only for material composition analysis has been reapplied to the world of identification. This new level of verification is now being used for "authentication."

Schramm, Harry F.↗
DOE OSTI2022

Mapping structural heterogeneity at the nanoscale with scanning nano-structure electron microscopy (SNEM)

Here, in this work, we explore the use of scanning electron diffraction (also known as 4D-STEM) coupled with electron atomic pair distribution function analysis (ePDF) to understand the local order (structure and chemistry) as a function of position in a complex multicomponent system, a hot rolled, Ni-encapsulated, Zr 65 Cu 17.5 Ni 10 Al 7.5 bulk metallic glass (BMG), with a spatial resolution of 3 nm. We show that it is possible to gain insight into the chemistry and chemical clustering/ordering tendency in different regions of the sample, including in the vicinity of nano-scale crystallites that are identified from virtual dark field images and in heavily deformed regions at the edge of the BMG. In addition to simpler analysis, unsupervised machine learning was used to extract partial PDFs from the material, modeled as a quasi-binary alloy, and map them in space. These maps allowed key insights not only into the local average composition, as validated by EELS, but also a unique insight into chemical short-range ordering tendencies in different regions of the sample during formation. The experiments are straightforward and rapid and, unlike spectroscopic measurements, don’t require energy filters on the instrument. We spatially map different quantities of interest (QoI’s), defined as scalars that can be computed directly from positions and widths of ePDF peaks or parameters refined from fits to the patterns. We developed a flexible and rapid data reduction and analysis software framework that allows experimenters to rapidly explore images of the sample on the basis of different QoI’s. The power and flexibility of this approach are explored and described in detail. Because of the fact that we are getting spatially resolved images of the nanoscale structure obtained from ePDFs we call this approach scanning nano-structure electron microscopy (SNEM), and we believe that it will be powerful and useful extension of current 4D-STEM methods.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Uncontrolled Learning: Codesign of Neuromorphic Hardware Topology for Neuromorphic Algorithms

Neuromorphic computing has the potential to revolutionize future technologies and our understanding of intelligence, yet it remains challenging to realize in practice. The learning-from-mistakes algorithm, inspired by the brain's simple learning rules of inhibition and pruning, is one of the few brain-like training methods. This algorithm is implemented in neuromorphic memristive hardware through a codesign process that evaluates essential hardware trade-offs. While the algorithm effectively trains small networks as binary classifiers and perceptrons, performance declines significantly with increasing network size unless the hardware is tailored to the algorithm. This work investigates the trade-offs between depth, controllability, and capacity—the number of learnable patterns—in neuromorphic hardware. This highlights the importance of topology and governing equations, providing theoretical tools to evaluate a device's computational capacity based on its measurements and circuit structure. The findings show that breaking neural network symmetry enhances both controllability and capacity. Additionally, by pruning the circuit, neuromorphic algorithms in all-memristive circuits can utilize stochastic resources to create local contrasts in network weights. Through combined experimental and simulation efforts, the parameters are identified that enable networks to exhibit emergent intelligence from simple rules, advancing the potential of neuromorphic computing.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2001

Cellular Spacing Selection During the Directional Solidification of Binary Alloys. A Numerical Approach

The evolution of cellular solid/liquid interfaces from an initially unstable planar front was studied by means of a two-dimensional computer simulation. The developed numerical model makes use of an interface tracking procedure and has the capability to describe the dynamics of the interface morphology based on local changes of the thermodynamic conditions. The fundamental physics of this formulation was validated against experimental microgravity results and the predictions of the analytical linear stability theory. The performed simulations revealed that in certain conditions, based on a competitive growth mechanism, an interface could become unstable to random perturbations of infinitesimal amplitude even at wavelengths smaller than the neutral wavelength, lambda(sub c), predicted by the linear stability theory. Furthermore, two main stages of spacing selection have been identified. In the first stage, at low perturbations amplitude, the selection mechanism is driven by the maximum growth rate of instabilities while in the second stage the selection is influenced by nonlinear phenomena caused by the interactions between the neighboring cells. Comparison of these predictions with other existing theories of pattern formation and experimental results will be discussed.

Catalina, Adrian V.↗
DOE OSTI2024

From phase decomposition to evaporation: A multi-modal evaluation of thermally degraded model lightweight high-entropy alloy

Lightweight high-entropy alloys (LHEAs) have the potential to replace conventional lightweight materials due to their superior mechanical properties and thermal stability. However, the thermal degradation pattern of LHEAs from phase decomposition to evaporation is not clear. We develop a new Al-based dual phase (FCC + HCP) LHEA—AlTi 0.45 CuZn, and further investigate its thermal degradation behavior for potential high-temperature structural applications. Using multimodal advanced characterization techniques such as differential scanning calorimetry/thermogravimetric analysis, scanning/transmission electron microscopy, and synchrotron X-ray diffraction/pair distribution function (XRD/PDF), a sequence of thermal degradation events beyond the thermal phase stability limit—between 250 and 360 °C—is observed. These include phase decomposition at ~360 °C, Zn evaporation at ~750 °C, and LHEA melting at 880 °C which results in ~25% cumulative weight loss. The formation of Al-Ti phase off the AlTi 0.45 CuZn matrix is due to the largest negative mixing enthalpy for Al-Ti than other binary pairs. Similarly, Zn evaporation from AlTi 0.45 CuZn LHEA is due to its faster evaporation rate than other constituent elements. The high-resolution synchrotron XRD and PDF results support the aforementioned observations; in addition, they reveal local atomic arrangements, local strain, and sluggish grain growth in the LHEA. Among other LHEAs of close density range (5.55 ≤ ρ ≤ 5.85 g/cc), the investigated LHEA exhibits outstanding nano-indentation hardness values due to the coupled grain size effect and HCP phase strengthening of the FCC matrix. As the search for LHEAs for lightweight applications grows, this study shows the potential use of AlTi 0.45 CuZn LHEA for structural applications even at elevated temperatures.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Phase-field model of freeze casting

Directional solidification of water-based solutions has emerged as a versatile technique for templating hierarchical porous materials. However, the underlying mechanisms of pattern formation remain incompletely understood. In this work, we present a detailed derivation and analysis of a quantitative phase-field model for simulating this nonequilibrium process. The phase-field model extends the thin-interface formulation of dilute binary alloy solidification with antitrapping to incorporate the highly anisotropic energetic and kinetic properties of the partially faceted ice-water interface. This interface is faceted in the basal plane normal to the ⟨0001⟩ directions and atomically rough in other directions within the basal plane. On the basal plane, the model reproduces a linear or nonlinear relationship between the interface growth rate and the kinetic undercooling that can be linked to experimental measurements. In both cases, spontaneous parity breaking of the solidification front is observed when the preferred growth direction is aligned with the temperature gradient. This phenomenon leads to the formation of partially faceted ice lamellae that drift laterally in one of the ⟨0001⟩ directions. Here, we demonstrate that the drifting velocity of the ice lamellae is controlled by the kinetics on the basal plane and converges as the thickness of the diffuse solid-liquid interface decreases. Furthermore, we examine the effect of the form of the kinetic anisotropy, which is chosen here such that the inverse of the kinetic coefficient varies linearly from a finite value in the ⟨0001⟩ directions to zero in all other directions within the basal plane, consistent with the assumption that the interface grows in local thermodynamic equilibrium in this plane. Our results indicate that the drifting velocity of ice lamellae is not affected by the slope of this linear relation, and the radius and undercooling at the tip of an ice lamella converge at relatively small slope values. Consequently, the phase-field simulations remain quantitative with computationally tractable choices of both the interface thickness and the slope assumed in the form of the kinetic anisotropy.

Materials science↗
DOE OSTI2025

Geometric control of magnetic domain morphology in electrodeposited Ni rich NiPt mesoscopic wires

In this paper, we study the electrodeposited Ni—Pt magnetic wires, exploring the mechanisms by which changes in geometry and composition influence the local magnetic structure. The samples were prepared by electrodeposition, their elemental composition was analyzed by energy dispersive X-ray spectroscopy, and phase analysis was carried out by an X-ray diffraction measurement. Magnetic force microscopy measurements, and magnetic force microscopy measurements in magnetic field were carried out to study the domain morphology of the prepared samples. Magnetization characteristics were measured via vibrating sample magnetometry mode of a superconducting quantum interference device. Here, the results were analyzed and compared with micromagnetic simulations. It is shown that a strong perpendicular magnetic anisotropy is responsible for the characteristic checkerboard-like surface magnetization patterns, which can be tuned by geometrical constriction and Ni content. Understanding these relationships provides insights into the design of magnetic materials with tailored properties for advanced applications, like magnetic sensors.

Binary alloy↗
DOE OSTI2023

Unveiling the chemical fingerprint of phosphorus-rich stars: I. In the infrared region of APOGEE-2

The origin of phosphorus, one of the essential elements for life on Earth, is currently unknown. Prevalent models of Galactic chemical evolution (GCE) systematically underestimate the amount of P compared to observations, especially at low metallicities. The recently discovered P-rich ([P/Fe] ≳ 1.2 dex) and metal-poor ([Fe/H] ≃ –1.0 dex) giants further challenge the GCE models, calling current theories on stellar nucleosynthesis into question. Since the observed low-mass giants are not expected to produce their high P contents themselves, our primary goal is to find clues on their progenitor or polluter. By increasing the number of known P-rich stars, we aim to narrow down a statistically reliable chemical abundance pattern that defines these peculiar stars. In this way, we place more robust constraints on the nucleosynthetic mechanism that causes the unusually high P abundances. In the long term, identifying the progenitor of the P-rich stars may contribute to the search for the source of P in our Galaxy. We performed a detailed chemical abundance analysis based on the high-resolution near-infrared (H band) spectra from the latest data release (DR17) of the APOGEE-2 survey. Employing the BACCHUS code, we measured the abundances of 13 elements in the inspected sample, which is mainly composed of a recent collection of Si-enhanced giants. We also analyzed the orbital motions and compared the abundance results to possible nucleosynthetic formation scenarios, and also to detailed GCE models. These models were produced with the OMEGA+ chemical evolution code, using four different massive star yield sets to investigate different scenarios for massive star evolution. We enlarged the sample of confirmed P-rich stars from 16 to a group of 78 giants, which represents the largest sample of P-rich stars to date. The sample includes the first detection of a P-rich star in a Galactic globular cluster. Significant enhancements in O, Al, Si, and Ce, as well as systematic correlations among the studied elements, unveil the unique chemical fingerprint of the P-rich stars. In contrast, the high [Mg/Fe] and [(C+N)/Fe] found in some of the P-rich stars with respect to P-normal stars is not confirmed over the full sample because of the current uncertainties. Strikingly, the strong overabundance in the α-element Si is accompanied by normal Ca and S abundances. This is at odds with current stellar nucleosynthesis models of massive stars. Our analysis of the orbital motion showed that the P-rich stars do not belong to a locally specific population in the Galaxy. In addition, we confirm that the majority of the sample stars are not part of binary systems.

79 ASTRONOMY AND ASTROPHYSICS↗