Search NASA⌕ Search

SEARCH · Search NASA

Results for “Low Resolution”

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 OSTI2024

Deep learning-driven super-resolution in Raman hyperspectral imaging: Efficient high-resolution reconstruction from low-resolution data

Deep learning (DL) has become an indispensable tool in hyperspectral data analysis, automatically extracting valuable features from complex, high-dimensional datasets. Super-resolution reconstruction, an essential aspect of hyperspectral data, involves enhancing spatial resolution, particularly relevant to low-resolution hyperspectral data. Yet, the pursuit of super-resolution in hyperspectral analysis is fraught with challenges, including acquiring ground truth high-resolution data for training, generalization, and scalability. The pressing issue of extended spectral acquisition times, notably for high-resolution scans, is a significant roadblock in hyperspectral imaging. Super-resolution methods offer a promising solution by providing higher spatial resolution data to expedite data collection and yield more efficient outcomes. This paper delves into a practical application of these concepts using Raman imaging, where spectral acquisition times can be prohibitively long. In this context, DL-based super-resolution models demonstrate their efficacy by predicting and reconstructing high-resolution Raman data from low-resolution input, eliminating the need for resource-intensive high-resolution scans. While previous work often relied on substantial high-resolution datasets, this study showcases the ability to achieve similar outcomes even with limited data, presenting a more practical and cost-effective approach. In conclusion, the results offer a glimpse into the transformative potential of this technology to streamline hyperspectral imaging applications by saving valuable time and resources through the successful generation of high-resolution data from low-resolution inputs.

42 ENGINEERING↗
DOE OSTI2021

EAM-HLR: Enhancing the low-resolution E3SM Atmosphere Model with an ML model of high-low-resolution residual in convective processes

Focal Area(s). (2) Augment the low-resolution (LR) E3SM Atmosphere Model (EAM) with a machine learning (ML) column model of the residual in convective processes with respect to high-resolution (HR) and ultra-HR (UHR) simulation and observational data. Science Challenge. Substantially improve LR EAM climatology to increase multidecadal water cycle (WC) predictability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Data-Driven Modeling of High-Resolution Residential Load Profiles Using Low-Resolution Smart Meter Measurements

Accurate and high-resolution residential load profiles are essential for power system modeling, demand response planning, and effective grid operation. As the energy sector moves towards a more actively managed distribution system, the ability to understand residential energy consumption at a minute-by-minute scale becomes increasingly critical. High-resolution load profiles provide key insights into demand patterns and user behavior, enabling grid operators to design more effective energy solutions; however, residential load measurements in the field are typically recorded at low resolutions, such as 15-60 minutes, which makes it hard to study the characteristics of different residential customers. This paper addresses these challenges by introducing a data-driven approach to generate realistic, high-resolution residential load profiles based on lowre-solution measurements and weather information. The proposed method retains the key features of the actual residential load measurements while offering appliance-level energy consumption details for each residential building. The results demonstrate the effectiveness of the proposed load profile generator, proving its capability to support utilities in optimizing residential energy management and ensuring a more reliable and resilient grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

New Subgrouping of Multiple Stellar Populations in NGC 2808 Based on Low-resolution Spectroscopy

We performed low-resolution spectroscopy for the red giant branch stars in an intriguing globular cluster (GC) NGC 2808, which hosts subpopulations with extreme helium and light-element abundances. In order to trace N, C, and Ca abundance differences among subpopulations, we measured CN, CH, and Ca ii H&K spectral indices, respectively. We identified four subpopulations (G1, G2, G3, and G4) from CN and CH strength, with CN-weak/CH-strong G1, CN-intermediate/CH-strong G2, CN-strong/CH-intermediate G3, and CN-strong/CH-weak G4. Compared to [Na/O] from high-resolution spectroscopy, we show that CN index can more clearly separate G1 and G2. Since CN traces N abundance in a GC, it implies that G1 and G2 would show a larger difference in [N/Fe] compared to [Na/Fe], as predicted by chemical evolution models. Later generation stars G3 and G4, however, are better separated with high-resolution spectroscopy. We also found that G4 shows a stronger Ca ii H&K line strength compared to that of G1, but we suspect this to be a result of unusually strong He enhancement and/or Mg depletion in G4 of this GC. This work illustrates that combining low- and high-resolution spectroscopic studies can improve the separation of subpopulations in GCs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

The Stars of the HETDEX Survey. I. Radial Velocities and Metal-poor Stars from Low-resolution Stellar Spectra

The Hobby–Eberly Telescope Dark Energy Experiment (HETDEX) is an unbiased, massively multiplexed spectroscopic survey, designed to measure the expansion history of the universe through low-resolution ( R ~ 750) spectra of Ly α emitters. In its search for these galaxies, HETDEX will also observe a few times 10 5 stars. In this paper, we present the first stellar value-added catalog within the internal second data release of the HETDEX Survey (HDR2). The new catalog contains 120,571 low-resolution spectra for 98,736 unique stars between 10 < G < 22 spread across the HETDEX footprint at relatively high ( b ~ 60°) Galactic latitudes. With these spectra, we measure radial velocities (RVs) for ~42,000 unique FGK-type stars in the catalog and show that the HETDEX spectra are sufficient to constrain these RVs with a 1 σ precision of 28.0 km s -1 and bias of 3.5 km s -1 with respect to the Large Sky Area Multi-Object Fibre Spectroscopic Telescope surveys and 1 σ precision of 27.5 km s -1 and bias of 14.0 km s -1 compared to the Sloan Extension for Galactic Understanding and Exploration survey. Since these RVs are for faint ( G ≥ 16) stars, they will be complementary to Gaia. Using t-Distributed Stochastic Neighbor Embedding (tSNE), we also demonstrate that the HETDEX spectra can be used to determine a star’s T eff , and log g and its [Fe/H]. With the tSNE projection of the FGK-type stars with HETDEX spectra we also identify 416 new candidate metal-poor ([Fe/H] < -1 dex) stars for future study. These encouraging results illustrate the utility of future low-resolution stellar spectroscopic surveys.

Astronomy & Astrophysics↗
DOE OSTI2022

Determination of Sodium Abundance Ratio from Low-resolution Stellar Spectra and Its Applications

We present a method to determine sodium abundance ratios ([Na/Fe]) using the Na i D doublet lines in low-resolution (R ~ 2000) stellar spectra. As stellar Na i D lines are blended with those produced by the interstellar medium, we developed a technique for removing the interstellar Na I D lines using the relationship between extinction, which is proportional to E(B – V), and the equivalent width of the interstellar Na i D absorption lines. When measuring [Na/Fe], we also considered corrections for nonlocal thermodynamic equilibrium (NLTE) effects. Comparisons with data from high-resolution spectroscopic surveys suggest that the expected precision of [Na/Fe] from low-resolution spectra is better than 0.3 dex for stars with [Fe/H] > –3.0. We also present a simple application employing the estimated [Na/Fe] values for a large number of stellar spectra from the Sloan Digital Sky Survey (SDSS). After classifying the SDSS stars into Na-normal, Na-high, and Na-extreme, we explore their relation to stars in Galactic globular clusters (GCs). We find that while the Na-high SDSS stars exhibit a similar metallicity distribution function (MDF) to that of the GCs, indicating that the majority of such stars may have originated from GC debris, the MDF of the Na-normal SDSS stars follows that of typical disk and halo stars. As there is a high fraction of carbon-enhanced metal-poor stars among the Na-extreme stars, they may have a non-GC origin, perhaps due to mass-transfer events from evolved binary companions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Jamming Detection for Low-Resolution SC-FDE Systems: A Machine Learning Approach

Jammers interfere with communication between base stations (BSs) and legitimate users, leading to degradation of wireless system performance. Our study focuses on jamming detection for wideband single-carrier frequency domain equalization (SC-FDE) systems with low-resolution analog-to digital converters (ADCs). In such systems, jamming detection is challenging because traditional analytical approaches cannot be directly applied due to the delay dispersion in wideband channels and the non-linearity induced by low-resolution ADCs. We propose a machine learning (ML)-based jamming detection method that directly uses the quantized receive signals. Significantly, our ML-based detector can be integrated into existing standard frameworks, such as unique word (UW)-based SC-FDE systems, as it uses existing pilots without requiring additional pilots for jamming detection. Through numerical simulations, we show that two or more bits provide satisfactory performance compared to unquantized scenarios. Additionally, we demonstrate that using more and well-separated pilot symbols improves performance.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Selecting Post-Processing Schemes for Accurate Detection of Small Objects in Low-Resolution Wide-Area Aerial Imagery

In low-resolution wide-area aerial imagery, object detection algorithms are categorized as feature extraction and machine learning approaches, where the former often requires a post-processing scheme to reduce false detections and the latter demands multi-stage learning followed by post-processing. In this paper, we present an approach on how to select post-processing schemes for aerial object detection. We evaluated combinations of each of ten vehicle detection algorithms with any of seven post-processing schemes, where the best three schemes for each algorithm were determined using average F-score metric. The performance improvement is quantified using basic information retrieval metrics as well as the classification of events, activities and relationships (CLEAR) metrics. We also implemented a two-stage learning algorithm using a hundred-layer densely connected convolutional neural network for small object detection and evaluated its degree of improvement when combined with the various post-processing schemes. The highest average F-scores after post-processing are 0.902, 0.704 and 0.891 for the Tucson, Phoenix and online VEDAI datasets, respectively. The combined results prove that our enhanced three-stage post-processing scheme achieves a mean average precision (mAP) of 63.9% for feature extraction methods and 82.8% for the machine learning approach.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Analytical Assessment of Impurities Found in Process Gases using the InfiTOF, a High Resolution, Low Mass, Variable Time-of-Flight Mass Spectrometer

The JEOL InfiToF is a high-resolution, low mass (HRLM) mass spectrometer (MS) with the application potential to meet analytical needs for tritium handling systems, such as those at the Savannah River Tritium Facilities (SRTE) and those used in fusion research. The Service Level Agreement (SLA) #144 line 27 “InfiTOF R&D” has concluded its first year of efforts under Tritium Recycle and Renewal. The project goals for FY22 were: (1) Production of a finalized modification list for tritium hardened components in the InfiTOF, (2) perform an assessment on impurity gases commonly found in process gases from Tritium Facilities and (3) begin developing a set of standardized work instructions that will be used as a basis for operations on the InfiTOF.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Global Sensitivity Analysis Using the Ultra‐Low Resolution Energy Exascale Earth System Model

Abstract For decades, Arctic temperatures have increased twice as fast as average global temperatures. As a first step toward quantifying parametric uncertainty in Arctic climate, we performed a variance‐based global sensitivity analysis (GSA) using a fully coupled, ultra‐low resolution (ULR) configuration of version 1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SMv1). Specifically, we quantified the sensitivity of six quantities of interests (QOIs), which characterize changes in Arctic climate over a 75 year period, to uncertainties in nine model parameters spanning the sea ice, atmosphere, and ocean components of E3SMv1. Sensitivity indices for each QOI were computed with a Gaussian process emulator using 139 random realizations of the random parameters and fixed preindustrial forcing. Uncertainties in the atmospheric parameters in the Cloud Layers Unified by Binormals (CLUBB) scheme were found to have the most impact on sea ice status and the larger Arctic climate. Our results demonstrate the importance of conducting sensitivity analyses with fully coupled climate models. The ULR configuration makes such studies computationally feasible today due to its low computational cost. When advances in computational power and modeling algorithms enable the tractable use of higher‐resolution models, our results will provide a baseline that can quantify the impact of model resolution on the accuracy of sensitivity indices. Moreover, the confidence intervals provided by our study, which we used to quantify the impact of the number of model evaluations on the accuracy of sensitivity estimates, have the potential to inform the computational resources needed for future sensitivity studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Reconstructing Ly$α$ Fields from Low-resolution Hydrodynamical Simulations with Deep Learning

Hydrodynamical cosmological simulations are a powerful tool for accurately predicting the properties of the intergalactic medium (IGM) and for producing mock skies that can be compared against observational data. However, the need to resolve density fluctuation in the IGM puts a stringent requirement on the resolution of such simulations, which in turn limits the volumes that can be modeled, even on the most powerful supercomputers. In this work, we present a novel modeling method that combines physics-driven simulations with data-driven generative neural networks to produce outputs that are qualitatively and statistically close to the outputs of hydrodynamical simulations employing eight times higher resolution. We show that the Ly$α$ flux field, as well as the underlying hydrodynamic fields, have greatly improved statistical fidelity over a low-resolution simulation. Importantly, the design of our neural network allows for sampling multiple realizations from a given input, enabling us to quantify the model uncertainty. Using test data, we demonstrate that this model uncertainty correlates well with the true error of the Ly$α$ flux prediction. Ultimately, our approach allows for training on small simulation volumes and applying it to much larger ones, opening the door to producing accurate Ly$α$ mock skies in volumes of Hubble size, as will be probed with DESI and future spectroscopic sky surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Ensemble Kalman filter for data assimilation coupled with low-resolution computations techniques applied in fluid dynamics

This paper presents an innovative Reduced-order model (ROM) for merging experimental and simulation data using data assimilation (DA) to estimate the "True" state of a fluid dynamics system, leading to more accurate predictions. Our methodology introduces a novel approach by implementing the ensemble Kalman filter (EnKF) within a reduced-dimensional framework, grounded in a robust theoretical foundation and applied to fluid dynamics. To address the substantial computational demands of DA, the proposed ROM employs low-resolution (LR) techniques to drastically reduce computational costs. This innovative approach involves downsampling datasets for DA computations, followed by an advanced reconstruction technique based on low-cost singular value decomposition (lcSVD). The lcSVD method, a key innovation in this paper, has never been applied to DA before and offers a highly efficient way to enhance resolution with minimal computational resources. Our results demonstrate significant reductions in both computation time and RAM usage through these LR techniques without compromising the accuracy of the estimations. For instance, in a turbulent test case, for a data compression rate of 15.9, the LR approach can achieve a speed-up of 13.7 and a RAM compression of 90.9% while maintaining a low relative root mean square error (RRMSE) of 2.6%, compared to 0.8% in the high-resolution (HR) reference. Furthermore, we highlight the effectiveness of the EnKF in estimating and predicting the state of fluid flow systems based on limited observations and given low-fidelity numerical data. This paper highlights the potential of the proposed DA method in fluid dynamics applications, particularly for improving computational efficiency in CFD and related fields. Its ability to balance accuracy with low computational and memory costs makes it especially suitable for large-scale and real-time applications, such as environmental monitoring or engineering design. This method will be incorporated into ModelFLOWs-app.

Data Assimilation↗
DOE OSTI2021

A zone-level occupancy counting system for commercial office spaces using low-resolution time-of-flight sensors

Understanding the locations of occupants in a commercial built environment is critical for realizing energy savings by delivering lighting, heating, and cooling only where it is needed. In this paper, we present an indoor occupancy counting system using a sparse array of inexpensive, low-resolution, and privacy-preserving time-of-flight sensors. We develop and validate an algorithm for zonal occupancy counting that can deal with multiple people walking underneath the sensors in arbitrary directions, and evaluate the system both in realistic simulations of office spaces and in a real-world installation. Finally, we found that our system has an error rate of around 0.4%, resulting in highly accurate person localization and zone counting using only a few sensors per space.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗