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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS1973

Investigation of the detection and monitoring of forest insect infestation in the Sierra Nevada Mountains of California

The author has identified the following significant results. Results of analysis of ERTS-1 color composites made by NASA from MSS bands 4, 5, and 7, frame #1055-18055 at a scale of 1:1,000,000 indicate that forests damaged by insects can be delineated and mapped from areas with no damage; and at this same scale other details detected include timbered and nontimbered areas, pasture and agricultural land, deserts, lakes, mountain meadows, riparian vegetation, rock domes, old burned areas, and major stream courses. Enlargements from the above to a scale of 1:80,000 have improved detectibility to the point that three degrees of timber mortality can be identified and mapped.

Hall, R. C.↗
DOE OSTI2016

A multi-level anomaly detection algorithm for time-varying graph data with interactive visualization

This work presents a novel modeling and analysis framework for graph sequences which addresses the challenge of detecting and contextualizing anomalies in labelled, streaming graph data. We introduce a generalization of the BTER model of Seshadhri et al. by adding flexibility to community structure, and use this model to perform multi-scale graph anomaly detection. Specifically, probability models describing coarse subgraphs are built by aggregating node probabilities, and these related hierarchical models simultaneously detect deviations from expectation. This technique provides insight into a graph's structure and internal context that may shed light on a detected event. Additionally, this multi-scale analysis facilitates intuitive visualizations by allowing users to narrow focus from an anomalous graph to particular subgraphs or nodes causing the anomaly. For evaluation, two hierarchical anomaly detectors are tested against a baseline Gaussian method on a series of sampled graphs. We demonstrate that our graph statistics-based approach outperforms both a distribution-based detector and the baseline in a labeled setting with community structure, and it accurately detects anomalies in synthetic and real-world datasets at the node, subgraph, and graph levels. Furthermore, to illustrate the accessibility of information made possible via this technique, the anomaly detector and an associated interactive visualization tool are tested on NCAA football data, where teams and conferences that moved within the league are identified with perfect recall, and precision greater than 0.786.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Multi-Level Anomaly Detection on Time-Varying Graph Data

This work presents a novel modeling and analysis framework for graph sequences which addresses the challenge of detecting and contextualizing anomalies in labelled, streaming graph data. We introduce a generalization of the BTER model of Seshadhri et al. by adding flexibility to community structure, and use this model to perform multi-scale graph anomaly detection. Specifically, probability models describing coarse subgraphs are built by aggregating probabilities at finer levels, and these closely related hierarchical models simultaneously detect deviations from expectation. This technique provides insight into a graph's structure and internal context that may shed light on a detected event. Additionally, this multi-scale analysis facilitates intuitive visualizations by allowing users to narrow focus from an anomalous graph to particular subgraphs or nodes causing the anomaly. For evaluation, two hierarchical anomaly detectors are tested against a baseline Gaussian method on a series of sampled graphs. We demonstrate that our graph statistics-based approach outperforms both a distribution-based detector and the baseline in a labeled setting with community structure, and it accurately detects anomalies in synthetic and real-world datasets at the node, subgraph, and graph levels. To illustrate the accessibility of information made possible via this technique, the anomaly detector and an associated interactive visualization tool are tested on NCAA football data, where teams and conferences that moved within the league are identified with perfect recall, and precision greater than 0.786.

anomaly detection↗
NASA NTRS1978

X-ray observations of a flare in NGC 4151 from OSO 8

The 2-60-keV flux from NGC 4151 has been observed to change by a factor of 2 on a time scale of 1.5 days. No fluctuations in excess of a factor of 3 are detected on time scales less than 4 hours. During a total observation of approximately 11 days there were no statistically significant changes in spectral shape. The spectrum can be fitted by a power law with photon index of about 1.42 + or - 0.06 and a hydrogen column density of approximately 7.5 + or - 0.5 x 10 to the 22nd power atoms/sq cm. A 2-sigma residual to this fit implies fluorescent Fe line emission with an equivalent width of about 240 eV. Both synchrotron self-Compton and thermal Compton models are consistent with the X-ray data.

Mushotzky, R. F.↗
DOE OSTI2025

When can we detect lianas from space? Toward a mechanistic understanding of liana‐infested forest optics

Abstract Lianas, woody vines acting as structural parasites of trees, have profound effects on the composition and structure of tropical forests, impacting tree growth, mortality, and forest succession. Remote sensing could offer a powerful tool for quantifying the scale of liana infestation, provided the availability of robust detection methods. We analyze the consistency and global geographic specificity of spectral signals—reflectance across wavelengths—from liana‐infested tree crowns and forest stands, examining the underlying mechanisms of these signals. We compiled a uniquely comprehensive database, including leaf reflectance spectra from 5424 leaves, fine‐scale airborne reflectance data from 999 liana‐infested canopies, and coarse‐scale satellite reflectance data covering 775 ha of liana‐infested forest stands. To unravel the mechanisms of the liana spectral signal, we applied mechanistic radiative transfer models across scales, establishing a synthesis of the relative importance of different mechanisms, which we corroborate with field data on liana leaf chemistry and canopy structure. We find a consistent liana spectral signal at canopy and stand scales across globally distributed sites. This signature mainly arises at the canopy level due to direct effects of more horizontal leaf angles, resulting in a larger projected leaf area, and indirect effects from increased light scattering in the near and short‐wave infrared regions, linked to lianas' less costly leaf construction compared with trees on average. The existence of a consistent global spectral signal for lianas suggests that large‐scale quantification of liana infestation is feasible. However, because the traits responsible for the liana canopy‐reflectance signal are not exclusive to lianas, accurate large‐scale detection requires rigorously validated remote sensing methods. Our models highlight challenges in automated detection, such as potential misidentification due to leaf phenology, tree life history, topography, and climate, especially where the scale of liana infestation is less than a single remote sensing pixel. The observed cross‐site patterns also prompt ecological questions about lianas' adaptive similarities in optical traits across environments, indicating possible convergent evolution due to shared constraints on leaf biochemical and structural traits.

Environmental Sciences & Ecology↗
DOE OSTI2026

A Global Methane Observation System to Reduce Uncertainty for Anthropogenic and Natural Sources and Sinks for Detecting and Attributing Climate Feedbacks

Atmospheric methane (CH4) concentrations are accelerating global warming as net emissions increase. Observing systems that quantify sources remain too sparse and fragmented to detect trends—especially in remote regions where climate‐driven natural emissions may be rising. We provide a framework for quantifying uncertainty reductions through the implementation of a global ecosystem‐methane observing system designed to: (i) substantially lower uncertainty in sectoral and regional emissions, (ii) separate co‐occurring anthropogenic and natural fluxes, and (iii) trend detection at regional scales to verify mitigation progress and provide early warning of natural feedbacks. Using bottom‐up inventories and process‐model ensembles for 2014–2023, we show that anthropogenic emissions remain uncertain by ∼32% globally, while natural sources—tropical and boreal‐arctic wetlands, fires, and inland waters—carry far larger uncertainties (+ 70%) and trend uncertainties reaching ∼200%. Additional observations must match spatial emission structure to increase observability of emissions: high‐resolution satellite constellations for point sources combined with expanded flux networks and wetland mapping for diffuse sources, and denser ground‐based atmospheric column measurements to restore observability in under‐sampled tropics and high latitudes. Notional analyses indicate that targeted additions of flux towers and ∼20 in situ atmospheric column concentration instruments per key tropical region could reduce continental‐scale uncertainties at modest cost. Conceptual illustration of a Global Ecosystem Methane Observing System (GEM‐OS) integrating satellites, aircraft, atmospheric networks, and ecosystem measurements to quantify methane emissions from anthropogenic and natural sources. The multi‐scale observing framework improves source attribution, reduces uncertainty in regional methane budgets, and enables early detection of climate‐driven feedbacks from wetlands, fires, permafrost, agriculture, and fossil‐fuel emissions.

Ciais, P↗
DOE OSTI2015

Effects of thermal inflation on small scale density perturbations

In cosmological scenarios with thermal inflation, extra eras of moduli matter domination, thermal inflation and flaton matter domination exist between primordial inflation and the radiation domination of Big Bang nucleosynthesis. During these eras, cosmological perturbations on small scales can enter and re-exit the horizon, modifying the power spectrum on those scales. The largest modified scale, k{sub b}, touches the horizon size when the expansion changes from deflation to inflation at the transition from moduli domination to thermal inflation. We analytically calculate the evolution of perturbations from moduli domination through thermal inflation and evaluate the curvature perturbation on the constant radiation density hypersurface at the end of thermal inflation to determine the late time curvature perturbation. Our resulting transfer function suppresses the power spectrum by a factor 0∼ 5 at k >> k{sub b}, with k{sub b} corresponding to anywhere from megaparsec to subparsec scales depending on the parameters of thermal inflation. Thus, thermal inflation might be constrained or detected by small scale observations such as CMB distortions or 21cm hydrogen line observations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Coherent photon scattering background in sub- GeV / c 2 direct dark matter searches

Here, proposed dark matter detectors with eV-scale sensitivities will detect a large background of atomic (nuclear) recoils from coherent photon scattering of MeV-scale photons. This background climbs steeply below ~10 eV, far exceeding the declining rate of low-energy Compton recoils. The upcoming generation of dark matter detectors will not be limited by this background, but further development of eV-scale and sub-eV detectors will require strategies, including the use of low nuclear mass target materials, to maximize dark matter sensitivity while minimizing the coherent photon scattering background.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Vortex dynamics induced by scanning SQUID susceptometry

In this work, we measured the local magnetic response of a niobium thin film by applying a millitesla-scale AC magnetic field using a micron-scale field coil and detecting the response with a micron-scale pickup loop in a scanning superconducting quantum interference device (SQUID) susceptometry measurement. Near the film's critical temperature, we observed a steplike nonlinear and dissipative magnetic response due to the dynamics of a small number of vortex-antivortex pairs induced in the film by the local applied AC field. We modeled the dynamics of the measurement using a combined two-dimensional London-Maxwell and time-dependent Ginzburg-Landau approach, allowing us to construct a detailed real-space picture of the vortex motion causing the observed dissipative response. This work pushes scanning SQUID susceptometry of two-dimensional superconductors beyond the regime of linear response and lays the foundation for microscopic studies of vortex dynamics and pinning in superconducting devices and more exotic materials systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2014

Detecting differential protein expression in large-scale population proteomics

Mass spectrometry-based high-throughput quantitative proteomics shows great potential in clinical biomarker studies, identifying and quantifying thousands of proteins in biological samples. However, methods are needed to appropriately handle issues/challenges unique to mass spectrometry data in order to detect as many biomarker proteins as possible. One issue is that different mass spectrometry experiments generate quite different total numbers of quantified peptides, which can result in more missing peptide abundances in an experiment with a smaller total number of quantified peptides. Another issue is that the quantification of peptides is sometimes absent, especially for less abundant peptides and such missing values contain the information about the peptide abundance. Here, we propose a Significance Analysis for Large-scale Proteomics Studies (SALPS) that handles missing peptide intensity values caused by the two mechanisms mentioned above. Our model has a robust performance in both simulated data and proteomics data from a large clinical study. Because varying patients’ sample qualities and deviating instrument performances are not avoidable for clinical studies performed over the course of several years, we believe that our approach will be useful to analyze large-scale clinical proteomics data.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2019

Degradation of Components After Exposure in a Biomass Pyrolysis System

X-ray diffraction, electron probe microanalysis, and scanning transmission electron microscopy with focused ion beam lift-out sample preparation techniques were used to study corrosion products in a 304L (UNS S30403) stainless steel fluidized-bed reactor segment from Iowa State University’s Pyrolysis Process Development Unit Facility. This reactor segment is particularly valuable because a detailed history of operation time, temperature, and biomass feedstock was available. As previously reported for a range of stainless steel pyrolysis-related equipment, external scaling and internal attack along alloy grain boundaries were observed. Here, the scaling was primarily associated with O, although S, Ca, K, Si, Mg, and P were also detected in the outer scale regions. However, unlike those other recent advanced characterization analyses, in this instance the internal alloy grain boundary attack was not directly related to S. Rather, only internal oxidation and localized nanoporosity were observed along the alloy grain boundaries, with associated local nanoscale Cr depletion and Ni enrichment. Mechanistic implications of this finding are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Analyzing the gamma-ray sky with wavelets

We analyze the gamma-ray sky at energies of 0.5 to 50 GeV using the undecimated wavelet transform on the sphere. Focusing on the inner 60°×60° of the sky, we identify and characterize four separate residuals beyond the expected Milky Way diffuse emission. We detect the Fermi bubbles, finding compelling evidence that they are diffuse in nature and contain very little small-scale structure. We detect the “cocoon” inside the southern bubble, and we also identify its northern counterpart above 2 GeV. The northern cocoon lies along the same axis but is ~30% dimmer than the southern one. We characterize the Galactic center excess, which we find extends up to 20° in |b|. At latitudes |b|≤5° we find evidence for power in small angular scales that could be the result of point-source contributions, but for |b|≥5° the Galactic center excess is dominantly diffuse in its nature. Our findings show either that the Galactic center excess and Fermi bubbles connect smoothly or that the bubbles brighten significantly below 15° in latitude. We find that the Galactic center excess appears off-center toward negative ℓ. Additionally, we find and characterize two emissions along the Galactic disk centered at ℓ≃+25° and -20°. Furthermore, these emissions are significantly more elongated along the Galactic disk than the Galactic center excess.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRSDate not supplied

Monocular Ranging for Small Unmanned Aerial Systems in the Far-Field

Recent proliferation of small Unmanned Aerial Systems (sUAS) applications requires onboard collision avoidance systems to mitigate the risk of collision with non-cooperative aircraft and manned aircraft, which may not see sUAS in time to perform an avoidance maneuver. An attractive avenue for onboard collision avoidance is the utilization of machine vision cameras due to their low size, weight and power (SWaP) requirements. In this paper, we characterize the range performance of a machine vision system developed in-house and mounted onto an sUAS. The technique was designed to estimate the performance of a sense-and-avoid system to ensure that the sensing components meet the well-clear requirements for the chosen platform and avoidance strategy. Experimental flight-test data was acquired from test-flights flown along multiple collision geometries for two intruders: a general Aviation (GA) aircraft and a fixed-wing sUAS. The ownship and both intruders were instrumented with inertial navigation systems (INS) recording position and attitude information. The range at first detection, 𝑹𝟎, was extracted from in-flight imagery of head-on collision course geometry synchronized with INS data from both aircraft and ground-truth values extracted from the raw imagery. This initial detection distance, 𝑹𝟎, scales with atmospheric attenuation. Therefore, under clear sky conditions, the derived 𝑹𝟎 value represents the upper bound on the detection range achievable by the test configuration of the detector. Results indicate that the maximum initial detection distance for a 4k resolution action camera fitted with a 41º Field of View (FOV) lens is 2.763 ± 0.037 km for a GA aircraft and 0.881 ± 0.061 km for a fixed-wing sUAS, respectively. The in results this study suggest that a vision-based detect and track system may be analyzed using the sensor characterization and contextualized within aircraft well-clear volumes.

Chester V. Dolph↗
DOE OSTI2021

Energy-efficient Mott activation neuron for full-hardware implementation of neural networks

To circumvent the von Neumann bottleneck, substantial progress has been made towards in-memory computing with synaptic devices. However, compact nanodevices implementing non-linear activation functions are required for efficient full-hardware implementation of deep neural networks. Here, in this work, we present an energy-efficient and compact Mott activation neuron based on vanadium dioxide and its successful integration with a conductive bridge random access memory (CBRAM) crossbar array in hardware. The Mott activation neuron implements the rectified linear unit function in the analogue domain. The neuron devices consume substantially less energy and occupy two orders of magnitude smaller area than those of analogue complementary metal–oxide semiconductor implementations. The LeNet-5 network with Mott activation neurons achieves 98.38% accuracy on the MNIST dataset, close to the ideal software accuracy. We perform large-scale image edge detection using the Mott activation neurons integrated with a CBRAM crossbar array. Our findings provide a solution towards large-scale, highly parallel and energy-efficient in-memory computing systems for neural networks.

electrical and electronic engineering↗
DOE OSTI2014

WE-E-18A-09: Application of a Channelized Hotelling Observer Model to Evaluate Angiographic Imaging Equipment

Purpose: To develop and apply an observer model to objectively evaluate and compare the performance of different angiographic imaging equipment and acquisition variables. Methods: Image Acquisition— Iodine-based phantoms were created with target diameters: 0.5, 1, 2, and 4 mm. The phantoms were imaged using both planes of a bi-plane angiography system with detector pixel dimensions 0.1542 mm{sup 2} and 0.1842 mm{sup 2}, respectively. All four phantoms were imaged with magnification factors 1.5, 1.25 and 1 and with the large and small focal spots. Phantom position and the dose per frame (0.12– 0.24 μ Gy/frame) were varied for a single phantom size, magnification and focal spot. Observer Model— For each experimental condition, 1200 signal-present and signal-absent images were acquired and a detectability index (d') was calculated with a Gabor-channelized Hotelling observer (CHO) model. Detectability indices were evaluated as a function of dose, phantom size, and magnification. The model was then applied to compare d' of the two imaging planes and focal spots. Uncertainty in d' was estimated by bootstrapping the data and by examining the shift-variance of systems. Results: Detectability indices varied linearly with magnification and the square root of dose. For the 2 and 4 mm phantoms, d' varied linearly with diameter. For the 0.5 and 1 mm phantoms, d' expectedly deviated from this linear relationship due to substantial detector and focal spot blurring of the phantoms. The small focal spot yielded up to 50% greater d' values than the large focal spot. For the two detectors, differences in d' did not exceed the estimated ∼7% error. Conclusions: The detectability indices predictably scaled with dose, diameter, magnification, and focal spot size and serve to validate the model. Results demonstrate statistically similar target detectability for both investigated detectors, despite differences in pixel dimensions. This CHO model provides a framework to evaluate angiographic system performance.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Convectively Driven Sinks and Magnetic Fields in the Quiet-Sun

We study the relation between mesogranular flows, convectively driven sinks and magnetic fields using high spatial resolution spectropolarimetric data acquired with the Imaging Magnetograph eXperiment on board Sunrise. We obtain the horizontal velocity flow fields of two quiet-Sun regions (31.2 × 31.2 Mm{sup 2}) via local correlation tracking. Mesogranular lanes and the central position of sinks are identified using Lagrange tracers. We find 6.7×10{sup −2} sinks per Mm{sup 2} in the two observed regions. The sinks are located at the mesogranular vertices and turn out to be associated with (1) horizontal velocity flows converging to a central point and (2) long-lived downdrafts. The spatial distribution of magnetic fields in the quiet-Sun is also examined. The strongest magnetic fields are preferentially located at sinks. We find that 40% of the pixels with longitudinal components of the magnetic field stronger than 500 G are located in the close neighborhood of sinks. In contrast, the small-scale magnetic loops detected by Martínez González et al. in the same two observed areas do not show any preferential distribution at mesogranular scales. The study of individual examples reveals that sinks can play an important role in the evolution of quiet-Sun magnetic features.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

A multi-scale convolutional neural network for autonomous anomaly detection and classification in a laser powder bed fusion additive manufacturing process

In-situ detection of processing defects is a critical challenge for Laser Powder Bed Fusion Additive Manufacturing. Many of these defects are related to interactions between the recoater blade, which spreads the powder, and the powder bed. Here this work leverages Deep Learning, specifically a Convolutional Neural Network (CNN), for autonomous detection and classification of many of these spreading anomalies. Importantly, the input layer of the CNN is modified to enable the algorithm to learn both the appearance of the powder bed anomalies as well as key contextual information at multiple size scales. These modifications to the CNN architecture are shown to improve the flexibility and overall classification accuracy of the algorithm while mitigating many human biases. A case study is used to demonstrate the utility of the presented methodology and the overall performance is shown to be superior to that of methodologies previously reported by the authors.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Change Detection Applications in the Earth Sciences Using UAS-Based Sensing: A Review and Future Opportunities

Over the past decade, advancements in collection platforms such as unoccupied aerial systems (UAS), survey-grade GNSS, sensor packages, processing software, and spatial analytical tools have facilitated change detection analyses at an unprecedented resolution over broader spatial and temporal extents and in environments where such investigations present challenges. These technological improvements, coupled with the accessibility and versatility of UAS technology, have pushed the boundaries of spatial and temporal scales in geomorphic change detection. As a result, the cm-scale analysis of topographic signatures can detect and quantify surface anomalies during geomorphic evolution. This review focuses on the use of UAS photogrammetry for fine spatial (cm) and temporal (hours to days) scale geomorphic analyses, and it highlights analytical approaches to detect and quantify surface processes that were previously elusive. The review provides insight into topographic change characterization with precise spatial validations applied to landscape processes in various fields, such as the cryosphere and geosphere, as well as anthropogenic earth processes and national security applications. This work sheds light on previously unexplored aspects of both natural and human-engineered environments, demonstrating the potential of UAS observations in change detection. Our discussion examines the emerging horizons of UAS-based change detection, including machine learning and LIDAR systems. In addition, our meta-analysis of spatial and temporal UAS-based observations highlights the new fine-scale niche of UAS-photogrammetry. This scale advancement sets a new frontier in change detection, offering exciting possibilities for the future of land surface analysis and environmental monitoring in the field of Earth Science.

Andresen, Christian G.↗