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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Using Eye Tracking to Elucidate the Mechanisms Underlying Stimulation-Enhanced Visual Target Detection

Transcranial direct current stimulation (tDCS) is a noninvasive form of brain stimulation that involves passing a weak electrical current between electrodes on the scalp to modulate underlying neural tissue. TDCS has been shown to modulate cognition in a variety of domains, including memory, attention, and visual processing. Prior work from our laboratory has shown positive effects of tDCS on learning to detect target objects hidden in complex naturalistic visual scenes and learn rules for categorizing images, though the mechanism for these benefits remains unknown. One possibility is that tDCS optimizes visual search by modulating visual attention or via the reduction in search errors. One method of quantifying visual attention is to use eye tracking to record search patterns to determine if and how visual search is adjusted under verum stimulation conditions. Eye tracking data allows classification of errors into error types, including sampling errors (failing to look in the relevant region), recognition errors (looking at the critical portion of a scene, but failing to recognize it as such as evidenced by visual fixation), and decision-making errors (fixating on the relevant portion of a scene, but making the wrong determination). Our results indicate that the benefit tDCS confers on visual search for targets stems from the reduction in decision-making errors when targets are present (Cohen’s d = 0.86). Also reported is a replication of previous findings showing a tDCS-dependent improvement in learning this task, learning score (Cohen’s d = 0.88); d’ (Cohen’s d = 1.00). This provides support for moving tDCS into the application space by pairing it with analysts who are concerned with the type of search error that is corrected via stimulation.

attention↗
DOE OSTI2025

Application of automated iterative target detection for standoff hyperspectral imaging

The utility of hyperspectral imaging (HSI) has been well established for a wide array of applications but has generated a need for automated screening of high volumes of large HSI cubes. We report two important automated algorithms for more efficient standoff processing: atmospheric correction and target detection. The atmospheric correction method is based on a fast asymmetric least squares approach that is applied on a pixel-by-pixel basis. Here, the correction can be applied to entire images without manually identifying regions of interest and utilizes only in-scene information, no ancillary modeling of the atmosphere is required. An iterative target detection approach is also introduced which demonstrates faster speeds relative to moving window approaches. The target detection algorithm classifies each pixel as true target detections, near target detections, clutter, and no-calls. The algorithms were tested on forty images of twenty-two solid mineral targets placed at a 14-meter standoff distance allowing general observations on expected detection performance for a variety of minerals. In addition to identifying anomalous pixels, the inclusion of “no-calls” reduced the number of false detections significantly.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Entanglement-Enhanced Neyman-Pearson Target Detection

Quantum illumination (QI) provides entanglement-enabled target-detection enhancement, despite operating in an entanglement-breaking environment. Existing experimental studies of QI have utilized a Bayesian approach, assuming that the target is equally likely to be present or absent before detection, to demonstrate an advantage over classical target detection. However, such a premise breaks down in practical operational scenarios in which the prior probability is unknown, thereby hindering QI's applicability to real-world target-detection scenarios. In this work, we adopt the Neyman-Pearson criterion in lieu of the error probability for equally likely target absence or presence as our figure of merit for QI. We demonstrate an unconditional quantum advantage over the optimal classical-illumination protocol as benchmarked by the receiver operating characteristic, which examines detection probability versus false-alarm probability without resorting to known prior probabilities. Our work represents a critical advancement in adapting quantum-enhanced sensing to practical operational settings.

Ward, William↗
DOE OSTI2024

Underwater Target Detection Software Demonstration on the RivGen Turbine

This repository contains data and processing scripts necessary to train the object detection models utilized in the underwater target detection software demonstration on the RivGen turbine project and to produce performance metrics (precision, recall, mAP50, mAP50-95). - Contents - Data consist of "images" and "labels". Each image has an associated label, both share the same time string in its file name (e.g., 2024_05_25_09_01_57.98.jpg and 2024_05_25_09_01_57.98.txt). Time strings have the format %yyyy_%mm_%dd_%HH_%MM_%SS.%3f. Images and labels were curated from 2021 and 2024 smolt outmigration periods at the project site in Igiugig, AK. Images are monochrome 8-bit images of objects (smolt, debris, and other) passing through the field of view of the deployed cameras during various operational stages of the RivGen turbine. Labels are text files indicating the class and bounding polygon of each object in an image. The provided labels use the "YOLO" label format. - Requirements - Python3.8+ is required to install and run the train and validation script. The README.md provides instruction for installing the requirements from the requirements.py file. - Instructions - The "example_train.py" file ingests the provided data, trains a model, and produces model performance metrics at completion. NOTE: model performance metrics will vary from run to run as a consequence of the random selection of training and validation data.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

Mapping Geothermal Indicator Minerals Using Fusion of Target Detection Algorithms

Mineral mapping from satellite images provides valuable insights into subsurface mineral alteration for geothermal exploration. In previous studies, eight fundamental algorithms were used for mineral mapping utilizing USGS spectra, a collection of reflectance spectra containing samples of minerals, rocks, and soils created by the USGS. We used an ASD FieldSpec 4 Hi-RES NG portable spectrometer to collect spectra for analyzing ASTER images of the Coso Geothermal Field. Then, we established the ground-truth information and the spectral library by analyzing 97 samples. Samples collected from the field were analyzed using the CSIRO TSG (The Spectral Geologist of the Commonwealth Scientific and Industrial Research Organization). Based on the mineralogy study, multiple high-purity spectra of geothermal alteration minerals were selected from collected data, including alunite, chalcedony, hematite, kaolinite, and opal. Eight mineral spectral target detection algorithms were applied to the preprocessed satellite data with a proposed local spectral library. We measured the highest overall accuracy of 87% for alunite, 95% for opal, 83% for chalcedony, 60% for hematite, and 96% for kaolinite out of these eight algorithms. Three, four, five, and eight algorithms were fused to extract mineral alteration with the obtained target detection results. The results prove that the fusion of algorithms gives better results than using individual ones. In conclusion, this paper discusses the significance of evaluating different mapping algorithms. It proposes a robust fusion approach to extract mineral maps as an indicator for geothermal exploration.

Cavur, Mahmut↗
DOE OSTI2023

Remote Sensing Low Signal-to-Noise-Ratio Target Detection Enhancement

In real-time remote sensing application, frames of data are continuously flowing into the processing system. The capability of detecting objects of interest and tracking them as they move is crucial to many critical surveillance and monitoring missions. Detecting small objects using remote sensors is an ongoing, challenging problem. Since object(s) are located far away from the sensor, the target’s Signal-to-Noise-Ratio (SNR) is low. The Limit of Detection (LOD) for remote sensors is bounded by what is observable on each image frame. In this paper, we present a new method, a “Multi-frame Moving Object Detection System (MMODS)”, to detect small, low SNR objects that are beyond what a human can observe in a single video frame. This is demonstrated by using simulated data where our technology-detected objects are as small as one pixel with a targeted SNR, close to 1:1. We also demonstrate a similar improvement using live data collected with a remote camera. The MMODS technology fills a major technology gap in remote sensing surveillance applications for small target detection. Our method does not require prior knowledge about the environment, pre-labeled targets, or training data to effectively detect and track slow- and fast-moving targets, regardless of the size or the distance.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

FastACE

SAND2024-01893O The Fast Adaptive Cosine Estimator (FastACE) algorithm modifies the well-known Adaptive Cosine Estimator for target detection in hyperspectral imagery. Specifically, FastACE modifies the computation of the background precision matrix (C_b^-1) under a Vecchia approximation. That is, each spectral band, conditioned on a local neighborhood of bands around that band, is independent of the other spectral bands. The FastACE algorithm leverages a parameterizable, auto-regressive neighborhood for the conditional independence assumption. The underlying math used for computing detection scores is equivalent to ACE, but it is implemented within FastACE. The software implements a target detection algorithm and associated utilities for target detection in hyperspectral imagery. Provided with a hyperspectral image and corresponding target signature, it produces relevant background statistics and the corresponding detection scores for the target signature in the image. The software is designed to integrate into other end-user applications or processing. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

VanderLaan, John↗
DOE OSTI2024

Not even 6 dB: Gaussian quantum illumination in thermal background

Abstract In analyses of target detection with Gaussian state transmitters in a thermal background, the thermal occupation is taken to depend on the target reflectivity in a way which simplifies the analysis of the symmetric quantum hypothesis testing problem. However, this assumption precludes comparison of target detection performance between an arbitrary transmitter and a vacuum state transmitter, i.e. ‘detection without illumination’, which is relevant in a bright thermal background because a target can be detected by its optical shadow or some other perturbation of the background. Using a target-agnostic thermal environment leads to the result that the oft-claimed 6 dB possible reduction in the quantum Chernoff exponent for a two-mode squeezed vacuum transmitter over a coherent state transmitter in high-occupation thermal background is an unachievable limiting value, only occurring in a limit in which the target detection problem is ill-posed. Further analyzing quantum illumination in a target-agnostic thermal environment shows that a weak single-mode squeezed transmitter performs worse than ‘no illumination’, which is explained by the noise-increasing property of reflected low-intensity squeezed light.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Automating Anomaly Detection for Target systems at Spallation Neutron Source

The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory, produces the world’s most intense pulse neutrons beams. An accelerated proton beam is directed into a mercury target to generate neutrons via spallation. The target system accounted for over 40% of the overall downtime of the facility in 2022. Thus, early detection in anomalies in the target systems can enable taking corrective actions to avoid failures and reduce downtime. Fault prognostics and anomaly detection in accelerators, both at SNS and outside, has largely focused on the beam side. This paper presents one the first studies exploring leveraging machine learning to automate the detection of anomalies in the target system. The target system consists of over 30 different interconnected subsystems, and the present work focuses on the mercury process system as a use case. Analyzing data from 28 process variables from 2022 and 2023, tree-based and reconstruction-based algorithms are employed to detect anomalies in archived data. The algorithms detected previously unreported anomalies, several of which were deemed alert worthy by human experts, particularly those found by reconstruction-based algorithms. Using data from each production run in the accelerator increased the generalizability of the models in time. Efforts are now underway to implement a workflow for incorporating human feedback to update the models and evaluating performance on unseen data. The models will eventually be integrated into the existing System Tracking and Reliability system with a web interface for automated anomaly detection and reporting along with a pathway for incorporating human feedback for model updates.

Raj, Anant [ORNL] (ORCID:0000000306711244)↗
DOE OSTI2023

BlueHat

"Bluehat" is a collection of python routines to facilitate the analysis of spectral (multispectral and hyperspectral) remote sensing imagery, with an emphasis on various kind of target detection. The package provides collections of utilities that developers can use in producing algorithms for specific target detection scenarios. Although "bluehat" is mostly expected to be used for remote sensing imagery (which often comes from satellite sensors), and although early versions of "bluehat" were used to design algorithms that were then rewritten for onboard processing; "bluehat" itself is not not specially designed for space and/or satellite application.

Theiler, James↗
DOE OSTI2026

Data for Comparison of Genotyping Assays for Detection of Targeted CRISPR/Cas Mutagenesis in Highly Polyploid Sugarcane

Sugarcane ( Saccharum spp.) is an important biofuel feedstock and a leading source of global table sugar. Saccharum hybrid cultivars are highly polyploid (2n = 100–130), containing large numbers of functionally redundant hom(e)ologs in their genomes. Genome editing with sequence-specific nucleases holds tremendous promise for sugarcane breeding. However, identification of plants with the desired level of co-editing within a pool of primary transformants can be difficult. While DNA sequencing provides direct evidence of targeted mutagenesis, it is cost-prohibitive as a primary screening method in sugarcane and most other methods of identifying mutant lines have not been optimized for use in highly polyploid species. In this study, non-sequencing methods of mutant screening, including capillary electrophoresis (CE), Cas9 RNP assay, and high-resolution melt analysis (HRMA), were compared to assess their potential for CRISPR/Cas9-mediated mutant screening in sugarcane. These assays were used to analyze sugarcane lines containing mutations at one or more of six sgRNA target sites. All three methods distinguished edited lines from wild type, with co-mutation frequencies ranging from 2% to 100%. Cas9 RNP assays were able to identify mutant sugarcane lines with as low as 3.2% co-mutation frequency, and samples could be scored based on undigested band intensity. CE was highlighted as the most comprehensive assay, delivering precise information on both mutagenesis frequency and indel size to a 1 bp resolution across all six targets. This represents an economical and comprehensive alternative to sequencing-based genotyping methods which could be applied in other polyploid species.

Genomics↗
DOE OSTI2024

Comparison of genotyping assays for detection of targeted CRISPR/Cas mutagenesis in highly polyploid sugarcane

Sugarcane (Saccharum spp.) is an important biofuel feedstock and a leading source of global table sugar. Saccharum hybrid cultivars are highly polyploid (2n = 100–130), containing large numbers of functionally redundant hom(e)ologs in their genomes. Genome editing with sequence-specific nucleases holds tremendous promise for sugarcane breeding. However, identification of plants with the desired level of co-editing within a pool of primary transformants can be difficult. While DNA sequencing provides direct evidence of targeted mutagenesis, it is cost-prohibitive as a primary screening method in sugarcane and most other methods of identifying mutant lines have not been optimized for use in highly polyploid species. In this study, non-sequencing methods of mutant screening, including capillary electrophoresis (CE), Cas9 RNP assay, and high-resolution melt analysis (HRMA), were compared to assess their potential for CRISPR/Cas9-mediated mutant screening in sugarcane. These assays were used to analyze sugarcane lines containing mutations at one or more of six sgRNA target sites. All three methods distinguished edited lines from wild type, with co-mutation frequencies ranging from 2% to 100%. Cas9 RNP assays were able to identify mutant sugarcane lines with as low as 3.2% co-mutation frequency, and samples could be scored based on undigested band intensity. CE was highlighted as the most comprehensive assay, delivering precise information on both mutagenesis frequency and indel size to a 1 bp resolution across all six targets. This represents an economical and comprehensive alternative to sequencing-based genotyping methods which could be applied in other polyploid species.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Advanced Algal Biofoundries for the Production of Polyurethane Precursors [Abstract]

The collaboration between PNNL, LBNL, and UCSD will develop novel algae bioproduction platforms for the generation of polymer precursors. To achieve this, we will design and construct genetic tools for enhanced production of target chemicals. Biosensors will be developed to detect target chemical production. We will expand our knowledge of metabolic and regulatory systems that control or inhibit biosynthesis of the target molecules, and use this knowledge base to optimize production titers. Finally, we will integrate the knowledge gained from each activity to produce a set of the most promising production strains used to demonstrate the economic viability of producing polymer precursors in algal bioproduction platforms at scale under either heterotrophic or photosynthetic growth.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Entanglement-enhanced ac magnetometry in the presence of Markovian noise

Entanglement is a resource to improve the sensitivity of quantum sensors. In an ideal case, using an entangled state as a probe to detect target fields, we can beat the standard quantum limit by which all classical sensors are bounded. However, since entanglement is fragile against decoherence, it is unclear whether entanglement-enhanced metrology is useful in a noisy environment. Its benefit is indeed limited when estimating the amplitude of dc magnetic fields under the effect of parallel Markovian decoherence, where the noise operator is parallel to the target field. In this paper, on the contrary, we show an advantage to using an entanglement over the classical strategy under the effect of parallel Markovian decoherence when we try to detect ac magnetic fields. We consider a scenario to induce a Rabi oscillation of the qubits with the target ac magnetic fields. Although we can, in principle, estimate the amplitude of the ac magnetic fields from the Rabi oscillation, the signal becomes weak if the qubit frequency is significantly detuned from the frequency of the ac magnetic field. We show that, by using the Greenberger-Horne-Zeilinger (GHZ) states, we can significantly enhance the signal of the detuned Rabi oscillation even under the effect of parallel Markovian decoherence. Further, our method is based on the fact that the interaction time between the GHZ states and ac magnetic fields scales as 1/L to mitigate the decoherence effect, where L is the number of qubits, which contributes to improving the bandwidth of the detectable frequencies of the ac magnetic fields. Our results pave the way for new applications of entanglement-enhanced ac magnetometry.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Optimization of Conditions for Photoluminescence-Based Sensing of Critical Minerals: Strategies and Outlook

Economically critical minerals and metals are vital to America’s security, with application areas in energy, advanced technologies, and defense systems. The growing implementation of renewable energy sources such as electric vehicles is expected to significantly increase demand for these minerals, while the global supply is monopolistic, with most of the production occurring in a few countries outside of the US. To mitigate potential supply chain vulnerabilities, the domestic production of economically critical metals has become a bipartisan priority of the US government, and unconventional sources such as coal and its utilization byproducts such as fly ash and acid mine drainage are being actively explored as an alternative method for meeting domestic demand. Slow and expensive characterization methods for critical metals present a significant pain point for domestic production, which has led to the exploration of alternative low-cost, portable platforms capable of detecting target metals during resource prospecting and downstream process monitoring. The National Energy Technology Laboratory has developed a portable fiber optic-based luminescence spectrometer that utilizes a metal-organic framework (MOF) material to detect and distinguish parts-per-billion levels of the visible-emitting rare earth elements terbium, dysprosium, samarium, and europium, which are all economically critical metals. Although preliminary results with this system are promising, a crucial barrier to practical deployment is the ability to operate in low pH, high ionic strength environments, as acidic conditions and the presence of other metal ions can significantly reduce luminescence signal. Here, current strategies related to sample treatment, material design, and sensor configuration are discussed in the context of improving sensor performance in application-relevant conditions. Additionally, future opportunities, such as the optimization of the sensing binding environment through computational methods and specific metals to be removed for treatment will be discussed, presenting a forward-looking view for innovation in luminescent sensing of critical metals.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Development of Nontargeted Workflow of Occupational Exposure by Infrared Ion Spectroscopy and Silicone Wristbands’ Passive Sampling

Monitoring chemical exposure has well-established protocols and procedures for occupational health and industrial hygiene across various career fields and in a variety of occupational and environmental conditions. Iterative analytical development has led to validated methods to provide employers and employees with safe working conditions in terms of industrial hygiene. These validated methods are commonly supported by commercial laboratories. Here, this workflow is explicitly a targeted approach in which sampling media, sample preparation, chemical separation, quantitative target detection, and post processing are well established. This workflow also relies on commercially available chemical surrogates, isotopically labeled internal standards, and certified/standard reference materials from reputable vendors. An untargeted workflow lacks many key ingredients of validated analytical methods to include the known chemical target.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Searching for high-frequency gravitational waves with phonons

The gravitational wave (GW) spectrum at frequencies above a kHz is a largely unexplored frontier. We show that detectors with sensitivity to single-phonon excitations in crystal targets can search for GWs with frequencies, 1 THz ≲ f ≲ 100 THz , corresponding to the range of optical phonon energies, 1 meV ≲ ω ≲ 100 meV . Such detectors are already being built to search for light dark matter (DM), and therefore sensitivity to high-frequency GWs will be achieved as a byproduct. We begin by deriving the absorption rate of a general GW signal into single phonons. We then focus on carefully defining the detector sensitivity to monochromatic and chirp signals, and compute the detector sensitivity for many proposed light DM detection targets. The detector sensitivity is then compared to the signal strength of candidate high-frequency GW sources, e.g., superradiant annihilation and black hole inspiral, as well as other recent detector proposals in the 1 MHz ≲ f ≲ 100 THz frequency range. With a judicious choice of target materials, a collection of detectors could optimistically achieve sensitivities to monochromatic signals with h 0 ∼ 10 − 23 − 10 − 25 over 1 THz ≲ f ≲ 100 THz . Published by the American Physical Society 2024

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗