Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Activity detection”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

High-Sensitivity rf Detection Using an Optically Pumped Comagnetometer Based on Natural-Abundance Rubidium with Active Ambient-Field Cancellation

To detect a specific radio-frequency (rf) magnetic field, rf optically pumped magnetometers (OPMs) require a static magnetic field to set the Larmor frequency of the atoms equal to the frequency of interest. However, unshielded and variable magnetic field environments (e.g., an rf OPM on a moving platform) pose a problem for rf OPM operation. Here, we demonstrate the use of a natural-abundance rubidium vapor to make a comagnetometer to address this challenge. Our implementation builds upon the simultaneous application of several OPM techniques within the same vapor cell. First, we use a modified implementation of an OPM variometer based on 87 Rb to detect and actively cancel unwanted external fields at frequencies ≲60 Hz using active feedback to a set of field control coils. In this experiment, we exploit this stabilized field environment to implement a high-sensitivity rf magnetometer using 85 Rb. Using this approach, we demonstrate the ability to measure rf fields with a sensitivity of approximately 9 fT Hz -1/2 inside a magnetic shield in the presence of an applied field of approximately 20 μT along three mutually orthogonal directions. This demonstration opens up a path toward completely unshielded operation of a high-sensitivity rf OPM.

47 OTHER INSTRUMENTATION↗
NASA NTRS2013

Detection Thresholds of Falling Snow From Satellite-Borne Active and Passive Sensors

There is an increased interest in detecting and estimating the amount of falling snow reaching the Earths surface in order to fully capture the global atmospheric water cycle. An initial step toward global spaceborne falling snow algorithms for current and future missions includes determining the thresholds of detection for various active and passive sensor channel configurations and falling snow events over land surfaces and lakes. In this paper, cloud resolving model simulations of lake effect and synoptic snow events were used to determine the minimum amount of snow (threshold) that could be detected by the following instruments: the W-band radar of CloudSat, Global Precipitation Measurement (GPM) Dual-Frequency Precipitation Radar (DPR)Ku- and Ka-bands, and the GPM Microwave Imager. Eleven different nonspherical snowflake shapes were used in the analysis. Notable results include the following: 1) The W-band radar has detection thresholds more than an order of magnitude lower than the future GPM radars; 2) the cloud structure macrophysics influences the thresholds of detection for passive channels (e.g., snow events with larger ice water paths and thicker clouds are easier to detect); 3) the snowflake microphysics (mainly shape and density)plays a large role in the detection threshold for active and passive instruments; 4) with reasonable assumptions, the passive 166-GHz channel has detection threshold values comparable to those of the GPM DPR Ku- and Ka-band radars with approximately 0.05 g *m(exp -3) detected at the surface, or an approximately 0.5-1.0-mm * h(exp -1) melted snow rate. This paper provides information on the light snowfall events missed by the sensors and not captured in global estimates.

remote sensing↗
DOE OSTI2023

A comparison of machine learning methods to classify radioactive elements using prompt-gamma-ray neutron activation data

The detection of illicit radiological materials is critical to establishing a robust second line of defence in nuclear security. Neutron-capture prompt-gamma activation analysis (PGAA) can be used to detect multiple radioactive materials across the entire Periodic Table. However, long detection times and a high rate of false positives pose a significant hindrance in the deployment of PGAA-based systems to identify the presence of illicit substances in nuclear forensics. In the present work, six different machine-learning algorithms were developed to classify radioactive elements based on the PGAA energy spectra. The model performance was evaluated using standard classification metrics and trend curves with an emphasis on comparing the effectiveness of algorithms that are best suited for classifying imbalanced datasets. We analyse the classification performance based on Precision, Recall, F1-score, Specificity, Confusion matrix, ROC-AUC curves, and Geometric Mean Score (GMS) measures. The tree-based algorithms (Decision Trees, Random Forest and AdaBoost) have consistently outperformed Support Vector Machine and K-Nearest Neighbours. Based on the results presented, AdaBoost is the preferred classifier to analyse data containing PGAA spectral information due to the high recall and minimal false negatives reported in the minority class.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2012

Detection of Spectral Evolution in the Bursts Emitted During the 2008-2009 Active Episode of SGR J1550 - 5418

In early October 2008, the Soft Gamma Repeater SGRJ1550 - 5418 (1E1547.0 - 5408, AXJ155052 - 5418, PSR J1550 - 5418) became active, emitting a series of bursts which triggered the Fermi Gamma-ray Burst Monitor (GBM) after which a second especially intense activity period commenced in 2009 January and a third, less active period was detected in 2009 March-April. Here we analyze the GBM data of all the bursts from the first and last active episodes. We performed temporal and spectral analysis for all events and found that their temporal characteristics are very similar to the ones of other SGR bursts, as well the ones reported for the bursts of the main episode (average burst durations 170ms). In addition, we used our sample of bursts to quantify the systematic uncertainties of the GBM location algorithm for soft gamma-ray transients to less than or equal to 8 degrees. Our spectral analysis indicates significant spectral evolution between the first and last set of events. Although the 2008 October events are best fit with a single blackbody function, for the 2009 bursts an Optically Thin Thermal Bremsstrahlung (OTTB) is clearly preferred. We attribute this evolution to changes in the magnetic field topology of the source, possibly due to effects following the very energetic main bursting episode.

von Kienlin, Andreas↗
NASA NTRS1975

Application of the Nimbus 5 ESMR to rainfall detection over land surfaces

The ability of the Nimbus 5 Electrically Scanning Microwave Radiometer (ESMR) to detect rainfall over land surfaces was evaluated. The ESMR brightness temperatures (Tb sub B) were compared with rainfall reports from climatological stations for a limited number of rain events over portions of the U.S. The greatly varying emissivity of land surfaces precludes detection of actively raining areas. Theoretical calculations using a ten-layer atmospheric model showed this to be an expected result. Detection of rain which had fallen was deemed feasible over certain types of land surfaces by comparing the Tb sub B fields before and after the rain fell. This procedure is reliable only over relatively smooth terrain having a substantial fraction of bare soil, such as exists in major agricultural regions during the dormant or early growing seasons. Soil moisture budgets were computed at selected sites to show how the observed emissivity responded to changes in the moisture content of the upper soil zone.

Meneely, J. M.↗
NASA NTRS1999

Rapid direct methods for enumeration of specific, active bacteria in water and biofilms

Conventional methods for detecting indicator and pathogenic bacteria in water may underestimate the actual population due to sublethal environmental injury, inability of the target bacteria to take up nutrients and other physiological factors which reduce bacterial culturability. Rapid and direct methods are needed to more accurately detect and enumerate active bacteria. Such a methodological advance would provide greater sensitivity in assessing the microbiological safety of water and food. The principle goal of this presentation is to describe novel approaches we have formulated for the rapid and simultaneous detection of bacteria plus the determination of their physiological activity in water and other environmental samples. The present version of our method involves the concentration of organisms by membrane filtration or immunomagnetic separation and combines an intracellular fluorochrome (CTC) for assessment of respiratory activity plus fluorescent-labelled antibody detection of specific bacteria. This approach has also been successfully used to demonstrate spatial and temporal heterogeneities of physiological activities in biofilms when coupled with cryosectioning. Candidate physiological stains include those capable of determining respiratory activity, membrane potential, membrane integrity, growth rate and cellular enzymatic activities. Results obtained thus far indicate that immunomagnetic separation can provide a high degree of sensitivity in the recovery of seeded target bacteria (Escherichia coli O157:H7) in water and hamburger. The captured and stained target bacteria are then enumerated by either conventional fluorescence microscopy or ChemScan(R), a new instrument that is very sensitive and rapid. The ChemScan(R) laser scanning instrument (Chemunex, Paris, France) provides the detection of individual fluorescently labelled bacterial cells using three emission channels in less than 5 min. A high degree of correlation has been demonstrated between results obtained with the ChemScan and traditional plate counts of mixed natural bacterial populations in water. The continuing evolution of these methods will be valuable in the rapid and accurate analysis of environmental samples.

Review, Tutorial↗
DOE OSTI2025

Quasiprobabilistic Readout Correction of Midcircuit Measurements for Adaptive Feedback via Measurement Randomized Compiling

Quantum measurements are a fundamental component of quantum computing. However, on present-day quantum computers, measurements can be more error prone than quantum gates and are susceptible to nonunital errors as well as nonlocal correlations due to measurement crosstalk. While readout errors can be mitigated in postprocessing, this is inefficient in the number of qubits due to a combinatorially large number of possible states that need to be characterized. In this work, we show that measurement errors can be tailored into a simple stochastic error model using randomized compiling, enabling the efficient mitigation of readout errors via quasiprobability distributions reconstructed from the measurement of a single preparation state in an exponentially large confusion matrix. We demonstrate the scalability and power of this approach by correcting readout errors without matrix inversion on a large number of different preparation states applied to a register of eight superconducting transmon qubits. Moreover, we show that this method can be extended to midcircuit measurements used for active feedback via quasiprobabilistic error cancellation, and we demonstrate the correction of measurement errors on an ancilla qubit used to detect and actively correct bit-flip errors on an entangled memory qubit. Our approach enables the correction of readout errors on large numbers of qubits and offers a strategy for correcting readout errors in adaptive circuits in which the results of midcircuit measurements are used to perform conditional operations on nonlocal qubits in real time.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2023

Potentially Underestimated Gas Flaring Activities—A New Approach to Detect Combustion Using Machine Learning and NASA’s Black Marble Product Suite

Monitoring changes in greenhouse gas (GHG) emission is critical for assessing climate mitigation efforts towards the Paris Agreement goal. A crucial aspect of science-based GHG monitoring is to provide objective information for quality assurance and uncertainty assessment of the reported emissions. Emission estimates from combustion events (gas flaring and biomass burning) are often calculated based on activity data (AD) from satellite observations, such as those detected from the visible infrared imaging radiometer suite (VIIRS) onboard the Suomi-NPP and NOAA-20 satellites. These estimates are often incorporated into carbon models for calculating emissions and removals. Consequently, errors and uncertainties associated with AD propagate into these models and impact emission estimates. Deriving uncertainty of AD is therefore crucial for transparency of emission estimates but remains a challenge due to the lack of evaluation data or alternate estimates. This work proposes a new approach using machine learning (ML) for combustion detection from NASA's Black Marble product suite and explores the assessment of potential uncertainties through comparison with existing detections. We jointly characterize combustion using thermal and light emission signals, with the latter improving detection of probable weaker combustion with less distinct thermal signatures. Being methodologically independent, the differences in ML-derived estimates with existing approaches can indicate the potential uncertainties in detection. The approach was applied to detect gas flares over the Eagle Ford Shale, Texas. We analyzed the spatio-temporal variations in detections and found that approximately 79.04% and 72.14% of the light emission-based detections are missed by ML-derived detections from VIIRS thermal bands and existing datasets, respectively. This improvement in combustion detection and scope for uncertainty assessment is essential for comprehensive monitoring of resulting emissions and we discuss the steps for extending this globally.

gas flaring↗
DOE OSTI2018

Utilizing Weak Indicators to Detect Anomalous Behaviors in Networks

We consider the use of a novel weak in- dicator alongside more commonly used weak indicators to help detect anomalous behavior in a large computer network. The data of the network which we are studying in this research paper concerns remote log-in information (Virtual Private Network, or VPN sessions) from the internal network of Los Alamos National Laboratory (LANL). The novel indicator we are utilizing is some- thing which, while novel in its application to data science/cyber security research, is a concept borrowed from the business world. The Her ndahl-Hirschman Index (HHI) is a computationally trivial index which provides a useful heuristic for regulatory agencies to ascertain the relative competitiveness of a particular industry. Using this index as a lagging indicator in the monthly format we have studied could help to detect anomalous behavior by a particular or small set of users on the network. Additionally, we study indicators related to the speed of movement of a user based on the physical location of their current and previous logins. This data can be ascertained from the IP addresses of the users, and is likely very similar to the fraud detection schemes regularly utilized by credit card networks to detect anomalous activity. In future work we would look to nd a way to combine these indicators for use as an internal fraud detection system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

SU-D-BRF-02: In Situ Verification of Radiation Therapy Dose Distributions From High-Energy X-Rays Using PET Imaging

Purpose: To study the possibility of in situ verification of radiation therapy dose distributions using PET imaging based on the activity distribution of 11C and 15O produced via photonuclear reactions in patient irradiated by 45MV x-rays. Methods: The method is based on the photonuclear reactions in the most elemental composition {sup 12}C and {sup 16}O in body tissues irradiated by bremsstrahlung photons with energies up to 45 MeV, resulting primarily in {sup 11}C and {sup 15}O, which are positron-emitting nuclei. The induced positron activity distributions were obtained with a PET scanner in the same room of a LA45 accelerator (Top Grade Medical, Beijing, China). The experiments were performed with a brain phantom using realistic treatment plans. The phantom was scanned at 20min and 2-5min after irradiation for {sup 11}C and {sup 15}, respectively. The interval between the two scans was 20 minutes. The activity distributions of {sup 11}C and {sup 15}O within the irradiated volume can be separated from each other because the half-life is 20min and 2min for {sup 11}C and {sup 15}O, respectively. Three x-ray energies were used including 10MV, 25MV and 45MV. The radiation dose ranged from 1.0Gy to 10.0Gy per treatment. Results: It was confirmed that no activity was detected at 10 MV beam energy, which was far below the energy threshold for photonuclear reactions. At 25 MV x-ray activity distribution images were observed on PET, which needed much higher radiation dose in order to obtain good quality. For 45 MV photon beams, good quality activation images were obtained with 2-3Gy radiation dose, which is the typical daily dose for radiation therapy. Conclusion: The activity distribution of {sup 15}O and {sup 11}C could be used to derive the dose distribution of 45MV x-rays at the regular daily dose level. This method can potentially be used to verify in situ dose distributions of patients treated on the LA45 accelerator.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2015

STELLAR ACTIVITY AND ITS IMPLICATIONS FOR EXOPLANET DETECTION ON GJ 176

We present an in-depth analysis of stellar activity and its effects on radial velocity (RV) for the M2 dwarf GJ 176 based on spectra taken over 10 yr from the High Resolution Spectrograph on the Hobby-Eberly Telescope. These data are supplemented with spectra from previous observations with the HIRES and HARPS spectrographs, and V- and R-band photometry taken over six years at the Dyer and Fairborn observatories. Previous studies of GJ 176 revealed a super-Earth exoplanet in an 8.8-day orbit. However, the velocities of this star are also known to be contaminated by activity, particularly at the 39-day stellar rotation period. We have examined the magnetic activity of GJ 176 using the sodium I D lines, which have been shown to be a sensitive activity tracer in cool stars. In addition to rotational modulation, we see evidence of a long-term trend in our Na I D index, which may be part of a long-period activity cycle. The sodium index is well correlated with our RVs, and we show that this activity trend drives a corresponding slope in RV. Interestingly, the rotation signal remains in phase in photometry, but not in the spectral activity indicators. We interpret this phenomenon as the result of one or more large spot complexes or active regions which dominate the photometric variability, while the spectral indices are driven by the overall magnetic activity across the stellar surface. In light of these results, we discuss the potential for correcting activity signals in the RVs of M dwarfs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Functional analysis of (4 S )-limonene synthase mutants reveals determinants of catalytic outcome in a model monoterpene synthase

We used crystal structural data for (4S)-limonene synthase [(4S)-LS] of spearmint (Mentha spicata L.) to infer which amino acid residues are in close proximity to the substrate and carbocation intermediates of the enzymatic reaction. Alanine-scanning mutagenesis of 48 amino acids combined with enzyme fidelity analysis [percentage of (-)-limonene produced] indicated which residues are most likely to constitute the active site. Furthermore, the mutation of residues W324 and H579 caused a significant drop in enzyme activity and formation of products (myrcene, linalool, and terpineol) characteristic of a premature termination of the reaction. A double mutant (W324A/H579A) had no detectable enzyme activity, indicating that either substrate binding or the terminating reaction was impaired. Exchanges to other aromatic residues (W324H, W324F, W324Y, H579F, H579Y, and H579W) resulted in enzyme catalysts with significantly reduced activity. Sequence comparisons across the angiosperm lineage provided evidence that W324 is a conserved residue, whereas the position equivalent to H579 is occupied by aromatic residues (H, F, or Y). Our results are consistent with a critical role of W324 and H579 in the stabilization of carbocation intermediates. Finally, the potential of these residues to serve as the catalytic base facilitating the terminal deprotonation reaction is discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Anomaly Detection in Partially Observed Traffic Networks

This paper addresses the problem of detecting anomalous activity in traffic networks where the network is not directly observed. Given knowledge of what the node-tonode traffic in a network should be, any activity that differs significantly from this baseline would be considered anomalous. We propose a Bayesian hierarchical model for estimating the traffic rates and detecting anomalous changes in the network. The probabilistic nature of the model allows us to perform statistical goodness-of-fit tests to detect significant deviations from a baseline network. We show that due to the more defined structure of the hierarchical Bayesian model, such tests perform well even when the empirical models estimated by the EM algorithm are misspecified. Finally, we apply our model to both simulated and real datasets to demonstrate its superior performance over existing alternatives.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1991

Charged particle activation studies on the surface of LDEF spacecraft

High energy proton induced nuclear reaction products are examined using seven elements, namely: Al, Si, Ni, Co, Zr, Ta, and W. Activities were detected due to Na-22 from Al, Co-56 from Ni, Co-58 from Cu, and Y-88 from Zr targets. No induced activity was observed in Si, Ta, and W, most probably due to the long cooling times. Only Zr sample contained a weak Be-7 peak, although Ta and W were also located at the leading edge of the spacecraft. Gamma rays of individual isotopes were measured using high resolution Ge(Li) solid state detector coupled to 4096 multichannel analyzer. Activities were calculated for Co-56 (846 keV) and Co-57 (122 and 136 keV's) at the time of the entry of the spacecraft and found to be 0.014 + or - c/sec g, 0.018 + or - 0.002 c/sec g, and 0.0024 + or - 0.0007 c/sec g, respectively.

Olmez, Ilhan↗
DOE OSTI2023

A Telomerase-Activated Magnetic Resonance Imaging Probe for Consecutively Monitoring Tumor Growth Kinetics and In Situ Screening Inhibitors

Telomerase has long been considered as a biomarker for cancer diagnosis and a therapeutic target for drug discovery. Detecting telomerase activity in vivo could provide more direct information of tumor progression and response to drug treatment, which, however, is hampered by the lack of an effective probe that can generate an output signal without a tissue penetration depth limit. Here in this study, using the principle of distance-dependent magnetic resonance tuning, we constructed a telomerase-activated magnetic resonance imaging probe (TAMP) by connecting superparamagnetic ferroferric oxide nanoparticles (SPFONs) and paramagnetic Gd-DOTA (Gd(III) 1,4,7,10- tetraazacyclododecane-1,4,7,10-tetraazacyclododecane-1,4,7,10-tet-raacetic acid) complexes via telomerase-responsive DNA motifs. Upon telomerase-catalyzed extension of the primer in TAMP, Gd-DOTA-conjugated oligonucleotides can be liberated from the surface of SPFONs through a DNA strand displacement reaction, restoring the T 1 signal of the Gd-DOTA for a direct readout of the telomerase activity. Here we show that, by tracking telomerase activity, this probe provides consistent monitoring of tumor growth kinetics during progression and in response to drug treatment and enables in situ screening of telomerase inhibitors in whole-animal models. This study provides an alternative toolkit for cancer diagnosis, treatment response assessment, and anticancer drug screening.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

California wildfire spread derived using VIIRS satellite observations and an object-based tracking system

Changing wildfire regimes in the western US and other fire-prone regions pose considerable risks to human health and ecosystem function. However, our understanding of wildfire behavior is still limited by a lack of data products that systematically quantify fire spread, behavior and impacts. Here we develop a novel object-based system for tracking the progression of individual fires using 375 m Visible Infrared Imaging Radiometer Suite active fire detections. At each half-daily time step, fire pixels are clustered according to their spatial proximity, and are either appended to an existing active fire object or are assigned to a new object. This automatic system allows us to update the attributes of each fire event, delineate the fire perimeter, and identify the active fire front shortly after satellite data acquisition. Using this system, we mapped the history of California fires during 2012–2020. Our approach and data stream may be useful for calibration and evaluation of fire spread models, estimation of near-real-time wildfire emissions, and as means for prescribing initial conditions in fire forecast models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Autonomous Human Activity Classification From Wearable Multi-Modal Sensors

There has been significant amount of research work on human activity classification relying either on Inertial Measurement Unit (IMU) data or data from static cameras providing a third-person view. There has been relatively less work using wearable cameras, providing first-person or egocentric view, and even fewer approaches combining egocentric video with IMU data. Utilizing only IMU data limits the variety and complexity of the activities that can be detected. For instance, the sitting activity can be detected by IMU data, but it cannot be determined whether the subject has sat on a chair or a sofa, or where the subject is. To perform fine-grained activity classification, and to distinguish between activities that cannot be differentiated by only IMU data, we present an autonomous and robust method using data from both wearable cameras and IMUs. In contrast to convolutional neural network-based approaches, we propose to employ capsule networks to obtain features from egocentric video data. Moreover, Convolutional Long Short Term Memory framework is employed both on egocentric videos and IMU data to capture the temporal aspect of actions. We also propose a genetic algorithm-based approach to autonomously and systematically set various network parameters, rather than using manual settings. Experiments have been conducted to perform 9- and 26-label activity classification, and the proposed method, using autonomously set network parameters, has provided very promising results, achieving overall accuracies of 86.6% and 77.2%, respectively. The proposed approach, combining both modalities, also provides increased accuracy compared to using only egovision data and only IMU data.

47 OTHER INSTRUMENTATION↗
DOE OSTI2016

Auroral radio emission from late L and T dwarfs: A new constraint on dynamo theory in the substellar regime

We have observed six late L and T dwarfs with the Karl G. Jansky Very Large Array (VLA) to investigate the presence of highly circularly polarized radio emission, associated with large-scale auroral currents. Previous surveys encompassing ∼60 L6 or later targets have yielded only one detection. Our sample includes the previously detected T6.5 dwarf 2MASS 10475385+2124234, as well as five new targets selected for the presence of Hα emission and/or optical infrared photometric variability, which are possible manifestations of auroral activity. We detect 2MASS 10475385+2124234, as well as four of the five targets in our biased sample, including the strong IR-variable source SIMP J01365662+0933473 and bright Hα emitter 2MASS 12373919+6526148, reinforcing the possibility that activity at these disparate wavelengths is related. The radio emission frequency corresponds to a precise determination of the lower-bound magnetic field strength near the surface of each dwarf, and this new sample provides robust constraints on dynamo theory in the low-mass brown dwarf regime. Magnetic fields ≳2.5 kG are confirmed for five of six targets. Our results provide tentative evidence that the dynamo operating in this mass regime may be inconsistent with predicted values from a recently proposed model. Further observations at higher radio frequencies are essential for verifying this assertion.

79 ASTRONOMY AND ASTROPHYSICS↗