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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 1,225 records · Page 68
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Scalable Computation of Topological Abstractions for Scalar Data

Topological data analysis has become an important tool for large scale scalar data analysis and visualization, efficiently extracting the inherent structure and features of interest of the data. However, with growing dataset sizes and complexity, it is increasingly becoming infeasible to compute topological abstractions of interest in serial and on single machines. This paper presents the state of the art in the scalable computation of topological abstractions on scalar data, in shared memory parallel on single machines, and in distributed memory parallel on multiple machines. We highlight results for set‐based, graph‐based and complex‐based abstractions and organize the state of the art based on this taxonomy. The paper identifies parallelization and distribution techniques common in topological algorithms and highlights further areas of interest with underdeveloped efforts.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

WE-E-17A-03: FDG-PET-Based Radiomics to Predict Local Control and Survival Following Radiotherapy

Purpose: An exploding field in cancer research is “radiomics,” based on the hypothesis that there is statistical (hidden) information in medical images that is prognostic or predictive of outcomes. Our group has developed an efficient pipeline to extract and analyze quantitative image features from medical images as related to outcomes or diagnosis. In this work, we summarize our previous studies with positron emission tomography (PET) images and show the potential of the use of radiomics for outcomes research. Methods: We analyzed two cancer datasets, each consisting of pre-radiotherapy-treatment PET scans: 163 T1-2N0M0 non-small cell lung cancer (NSCLC) patients and 174 head and neck (H and N) cancer patients with stage III–IV. The PET scans were converted to Computational Environment for Radiological Research (CERR) format, and CERR was used to generate 24 shape, texture, and intensity-histogram based image features. Data-mining and logistic regression methods were then used to model local failure (LF) and overall survival (OS). Unbiased estimates of performance were generated using leave-one-out cross-validation (LOOCV). Results: For predicting LF, the models with biologically equivalent dose (BED) and TLG (metabolic tumor volume (MTV) x SUVmean) in NSCLC, and skewness and MTV in H and N, achieved the best performance with AUC=0.818 (p<0.0001) and AUC=0.826 (p=0.0002), respectively. For predicting OS, the models with kurtosis and volume in NSCLC and SUVmax and homogeneity in H and N achieved the best performance with AUC=0.706 (p<0.0001) and AUC=0.656 (p=0.0003), respectively. On LOOCV, all these models retained significant predictive power. Interestingly, MTV was highly correlated with LF in both sites. Conclusion: PET-based imaged features are promising tools for improving treatment management decision making. Much more research is needed to identify optimal radiomics metrics and to correlate imaging phenotype with other clinical or genomic information.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

FAIR to WISE (F2W) v1.0.0

FAIR to WISE (F2W) is an iterative, large-language model (LLM) driven pipeline that turns unstructured research PDFs into structured, queryable knowledge graphs (KGs). Core features include schema-driven extraction to a LinkML model; full provenance capture; ontology-grounded enrichment (e.g., chemical validation and ChEBI lookup); graph construction to JSON-LD with stable IDs; and KG-RAG question answering with evidence-aware retrieval. The system is engineered for reproducibility and accessibility (open-source Ollama models, temperature=0, NVTX/Nsight profiling) with robust QA (relation verification, deduplication, and deterministic outputs). Primary uses are literature-to-KG automation, knowledge-grounded Q&A, and experimental steering support. We demonstrate the approach in organic photovoltaics, where the pipeline ingests papers, builds a domain KG, and evaluates answers against expert competency questions to guide experimental planning and interpretation. Compared with off-the-shelf LLMs and ad-hoc NLP tools, F2W addresses ontology gaps and reduces hallucination risk by grounding responses in extracted evidence and enforcing schema constraints; it also offers deterministic, provenance-linked outputs and open, cost-aware deployment. Evidence-aware ranking further improves answer quality over pure vector search.

Abramov, David [Lawrence Berkeley National Laborat↗
DOE OSTI2021

Hardware-Based Emulator with Deep Learning Model for Building Energy Control and Prediction Based on Occupancy Sensors’ Data

Heating, ventilation, and air conditioning (HVAC) is the largest source of residential energy consumption. Occupancy sensors’ data can be used for HVAC control since it indicates the number of people in the building. HVAC and sensors form a typical cyber-physical system (CPS). In this paper, we aim to build a hardware-based emulation platform to study the occupancy data’s features, which can be further extracted by using machine learning models. In particular, we propose two hardware-based emulators to investigate the use of wired/wireless communication interfaces for occupancy sensor-based building CPS control, and the use of deep learning to predict the building energy consumption with the sensor data. We hypothesize is that the building energy consumption may be predicted by using the occupancy data collected by the sensors, and question what type of prediction model should be used to accurately predict the energy load. Another hypothesis is that an in-lab hardware/software platform could be built to emulate the occupancy sensing process. The machine learning algorithms can then be used to analyze the energy load based on the sensing data. To test the emulator, the occupancy data from the sensors is used to predict energy consumption. The synchronization scheme between sensors and the HVAC server will be discussed. We have built two hardware/software emulation platforms to investigate the sensor/HVAC integration strategies, and used an enhanced deep learning model—which has sequence-to-sequence long short-term memory (Seq2Seq LSTM)—with an attention model to predict the building energy consumption with the preservation of the intrinsic patterns. Because the long-range temporal dependencies are captured, the Seq2Seq models may provide a higher accuracy by using LSTM architectures with encoder and decoder. Meanwhile, LSTMs can capture the temporal and spatial patterns of time series data. The attention model can highlight the most relevant input information in the energy prediction by allocating the attention weights. The communication overhead between the sensors and the HVAC control server can also be alleviated via the attention mechanism, which can automatically ignore the irrelevant information and amplify the relevant information during CNN training. Our experiments and performance analysis show that, compared with the traditional LSTM neural network, the performance of the proposed method has a 30% higher prediction accuracy.

Ye, Zhijing↗
DOE OSTI2021

Hierarchical Tactile Sensation Integration from Prosthetic Fingertips Enables Multi-Texture Surface Recognition

Multifunctional flexible tactile sensors could be useful to improve the control of prosthetic hands. To that end, highly stretchable liquid metal tactile sensors (LMS) were designed, manufactured via photolithography, and incorporated into the fingertips of a prosthetic hand. Three novel contributions were made with the LMS. First, individual fingertips were used to distinguish between different speeds of sliding contact with different surfaces. Second, differences in surface textures were reliably detected during sliding contact. Third, the capacity for hierarchical tactile sensor integration was demonstrated by using four LMS signals simultaneously to distinguish between ten complex multi-textured surfaces. Four different machine learning algorithms were compared for their successful classification capabilities: K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and neural network (NN). The time-frequency features of the LMSs were extracted to train and test the machine learning algorithms. The NN generally performed the best at the speed and texture detection with a single finger and had a 99.2 ± 0.8% accuracy to distinguish between ten different multi-textured surfaces using four LMSs from four fingers simultaneously. The capability for hierarchical multi-finger tactile sensation integration could be useful to provide a higher level of intelligence for artificial hands.

Abd, Moaed A. (ORCID:0000000284954244)↗
DOE OSTI2022

A dictionary learning algorithm for compression and reconstruction of streaming data in preset order

There has been an emerging interest in developing and applying dictionary learning (DL) to process massive datasets in the last decade. Many of these efforts, however, focus on employing DL to compress and extract a set of important features from data, while considering restoring the original data from this set a secondary goal. On the other hand, although several methods are able to process streaming data by updating the dictionary incrementally as new snapshots pass by, most of those algorithms are designed for the setting where the snapshots are randomly drawn from a probability distribution. In this paper, we present a new DL approach to compress and denoise massive dataset in real time, in which the data are streamed through in a preset order (instances are videos and temporal experimental data), so at any time, we can only observe a biased sample set of the whole data. Here, our approach incrementally builds up the dictionary in a relatively simple manner: if the new snapshot is adequately explained by the current dictionary, we perform a sparse coding to find its sparse representation; otherwise, we add the new snapshot to the dictionary, with a Gram-Schmidt process to maintain the orthogonality. To compress and denoise noisy datasets, we apply the denoising to the snapshot directly before sparse coding, which deviates from traditional dictionary learning approach that achieves denoising via sparse coding. Compared to full-batch matrix decomposition methods, where the whole data is kept in memory, and other mini-batch approaches, where unbiased sampling is often assumed, our approach has minimal requirement in data sampling and storage: i) each snapshot is only seen once then discarded, and ii) the snapshots are drawn in a preset order, so can be highly biased. Through experiments on climate simulations and scanning transmission electron microscopy (STEM) data, we demonstrate that the proposed approach performs competitively to those methods in data reconstruction and denoising.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1973

Combined spectral and spatial processing of ERTS imagery data

A procedure for extracting a set of textural features for ERTS-1 MSS data is presented. The textural features were combined with a set of spectral features and were used to develop a classification algorithm for identifying the land use categories of blocks of digital MSS data. The classification algorithm was derived from a training set of 314 blocks and tested on a set of 310 blocks. The overall accuracy of the classifier was found to be 83.5% on seven land use categories.

Haralick, R. M.↗
NASA NTRS1989

Robotic Target-Tracking Subsystem

Position and orientation of target measured in six degrees of freedom. Robotic vision subsystem measures relative position and orientation of specially designed target. Uses standard image-processing algorithms implemented directly in circuitry instead of computer programs. This feature makes it possible to extract complete sets of target-tracking data from successive image frames at rate of 30 frames per second. Five bright circles of target positioned in such way that video images of them processed into data on position and orientation of target relative to camera. Subsystem useful in industrial assembly operation requiring automatic joining of parts initially oriented and moving randomly.

Shawaga, Lawrence M.↗
NASA NTRS1988

Image processing in optical astronomy

Successful efforts to enhance optical-astronomy images through digital processing often exploit such 'weaknesses' of the image as the objects' near-symmetry, their preferred directionality, or a differentiation in spatial frequency between the object or objects and superimposed clutter. Attention is presently given to the calibration of a camera prior to astronomical data-acquisition, methods for the enhancement of faint surface brightness features, automated target detection and extraction techniques, the importance of the geometric transformations of digital imagery, the preparation of two-dimensional histograms, and the application of polarization.

Lorre, Jean J.↗
NASA NTRS1989

Assembly of objects with not fully predefined shapes

An assembly problem in a non-deterministic environment, i.e., where parts to be assembled have unknown shape, size and location, is described. The only knowledge used by the robot to perform the assembly operation is given by a connectivity rule and geometrical constraints concerning parts. Once a set of geometrical features of parts has been extracted by a vision system, applying such a rule allows the dtermination of the composition sequence. A suitable sensory apparatus allows the control the whole operation.

Arlotti, M. A.↗
NASA NTRS1990

An expert system for geologic mapping with imaging spectrometers

Techniques have been developed for the extraction and characterization of absorption features from visible and infrared reflectance spectra and an expert system has been designed, implemented, and successfully tested that allows automated identification of minerals based on their spectral characteristics. A suite of laboratory spectra of common minerals was analyzed and the absorption band characteristics tabulated and used to develop a generalized knowledge base for analysis of the reflectance spectra. A tree hierarchy was designed to emulate the decision process followed by an experienced analyst for analysis of laboratory and field reflectance spectra and aircraft imaging spectrometer spectra. Good results were obtained with the expert system for all three types of spectra, with the critical factor in the analysis being the signal-to-noise ratio of the spectral data.

Kruse, F. A.↗
NASA NTRS1991

Evidence of chaotic pattern in solar flux through a reproducible sequence of period-doubling-type bifurcations

Presented here is a preliminary study of the limits to solar flux intensity prediction, and of whether the general lack of predictability in the solar flux arises from the nonlinear chaotic nature of the Sun's physical activity. Statistical analysis of a chaotic signal can extract only its most gross features, and detailed physical models fail, since even the simplest equations of motion for a nonlinear system can exhibit chaotic behavior. A recent theory by Feigenbaum suggests that nonlinear systems that can be led into chaotic behavior through a sequence of period-doubling bifurcations will exhibit a universal behavior. As the control parameter is increased, the bifurcation points occur in such a way that a proper ratio of these will approach the universal Feigenbaum number. Experimental evidence supporting the applicability of the Feigenbaum scenario to solar flux data is sparse. However, given the hypothesis that the Sun's convection zones are similar to a Rayleigh-Bernard mechanism, we can learn a great deal from the remarkable agreement observed between the prediction by theory (period doubling - a universal route to chaos) and the amplitude decrease of the signal's regular subharmonics. The authors show that period-doubling-type bifurcation is a possible route to a chaotic pattern of solar flux that is distinguishable from the logarithm of its power spectral density. This conclusion is the first positive step toward a reformulation of solar flux by a nonlinear chaotic approach. The ultimate goal of this research is to be able to predict an estimate of the upper and lower bounds for solar flux within its predictable zones. Naturally, it is an important task to identify the time horizons beyond which predictability becomes incompatible with computability.

Ashrafi, S.↗
NASA NTRS1991

Evidence of chaotic pattern in solar flux through a reproducible sequence of period-doubling-type bifurcations

A preliminary study of the limits to solar flux intensity prediction, and of whether the general lack of predictability in the solar flux arises from the nonlinear chaotic nature of the Sun's physical activity is presented. Statistical analysis of a chaotic signal can extract only its most gross features, and detailed physical models fail, since even the simplest equations of motion for a nonlinear system can exhibit chaotic behavior. A recent theory by Feigenbaum suggests that nonlinear systems that can be led into chaotic behavior through a sequence of period-doubling bifurcations will exhibit a universal behavior. As the control parameter is increased, the bifurcation points occur in such a way that a proper ratio of these will approach the universal Feigenbaum number. Experimental evidence supporting the applicability of the Feigenbaum scenario to solar flux data is sparse. However, given the hypothesis that the Sun's convection zones are similar to a Rayleigh-Bernard mechanism, we can learn a great deal from the remarkable agreement observed between the prediction by theory (period doubling - a universal route to chaos) and the amplitude decrease of the signal's regular subharmonics. It is shown that period-doubling-type bifurcation is a possible route to a chaotic pattern of solar flux that is distinguishable from the logarithm of its power spectral density. This conclusion is the first positive step toward a reformulation of solar flux by a nonlinear chaotic approach. The ultimate goal of this research is to be able to predict an estimate of the upper and lower bounds for solar flux within its predictable zones. Naturally, it is an important task to identify the time horizons beyond which predictability becomes incompatible with computability.

Ashrafi, S.↗
NASA NTRS1997

Feasibility Investigation for Performing Fireball Temperature Tests

NASA Johnson Space Center White Sands Test Facility (WSTF) was requested by the Launch Abort Subpanel and the Power Systems Subpanel of the Interagency Nuclear Safety Review Panel to investigate the feasibility of using spectroscopic techniques to measure propellant fireball gas temperatures. This report outlines the modeling and experimental approaches and results of this investigation. Descriptions of the theoretical particle temperature and mass effusion models are presented along with the results of the survivability of small plutonium dioxide (less than or equal to 1000 microns diameter) particles entrained in various propellant fireball scenarios. The experimental test systems used to measure the hydroxide radical, water, and particle graybody spectral emissions and absorptions are discussed. Spectral results along with temperatures extracted by analyzing the spectral features are presented for the flames investigated in the laboratory environment. Methods of implementing spectroscopic measurements for future testing using the WSTF Large-scale Hydrogen/Oxygen Explosion Facility are discussed, and the accuracy expected for these measurements is estimated from laboratory measurements.

Tapphorn, Ralph M.↗
NASA NTRS2003

Automated Coronal Loop Identification Using Digital Image Processing Techniques

The results of a master thesis project on a study of computer algorithms for automatic identification of optical-thin, 3-dimensional solar coronal loop centers from extreme ultraviolet and X-ray 2-dimensional images will be presented. These center splines are proxies of associated magnetic field lines. The project is pattern recognition problems in which there are no unique shapes or edges and in which photon and detector noise heavily influence the images. The study explores extraction techniques using: (1) linear feature recognition of local patterns (related to the inertia-tensor concept), (2) parametric space via the Hough transform, and (3) topological adaptive contours (snakes) that constrains curvature and continuity as possible candidates for digital loop detection schemes. We have developed synthesized images for the coronal loops to test the various loop identification algorithms. Since the topology of these solar features is dominated by the magnetic field structure, a first-order magnetic field approximation using multiple dipoles provides a priori information in the identification process. Results from both synthesized and solar images will be presented.

Lee, Jong K.↗
NASA NTRS2004

First Global Maps of Stratospheric and Tropospheric NO2 from OMI

The Ozone Monitoring Instrument (OMI) was launched successfully in July 2004, as one of four instruments on the EOS Aura satellite. OMI makes hyperspectral measurements that are used to retrieve column densities of critical trace gases, including formaldehyde, BrO, SO2 and NO2 . We present the first results from the OM1 operational NO2 algorithm and demonstrate its ability to retrieve the tropospheric and stratospheric components of NO2. The DOAS method is used to determine slant column densities, and initial air mass factors (AMFs) are used. to give initial estimates of the vertical column densities (VCDs). VCDs from up to 15 consecutive orbits are collected, and a spatial filtering technique is applied to extract the synoptic-scale features characteristic of the stratospheric, field. features to be evidence of tropospheric excess NO2 , and apply an AMF appropriate to polluted conditions, to obtain an improved retrieval of the NO2 total VCD. We describe the assumptions underlying the algorithm in detail, and show global maps of NO2 VCDs, based on the first operational data from OMI.

Bucsela, Eric J.↗
NASA NTRS1997

pV3-Gold Visualization Environment for Computer Simulations

A new visualization environment, pV3-Gold, can be used during and after a computer simulation to extract and visualize the physical features in the results. This environment, which is an extension of the pV3 visualization environment developed at the Massachusetts Institute of Technology with guidance and support by researchers at the NASA Lewis Research Center, features many tools that allow users to display data in various ways.

Babrauckas, Theresa L.↗
NASA NTRS2015

Continental Spatio-Temporal Data Analysis with Linear Spectral Mixture Model Using FOSS

This work demonstrates the development and implementation of a Fully Constrained Least Squares (FCLS) unmixing model developed in C++ programming language with OpenCV package and boost C++ libraries in the NASA Earth Exchange (NEX). Visualization of the results is supported by GRASS GIS and statistical analysis is carried in R in a Linux system environment. FCLS was first tested on computer simulated data with Gaussian noise of various signal-to-noise ratio, and Landsat data of an agricultural scenario and an urban environment using a set of global end members of substrate (soils, sediments, rocks, and non-photosynthetic vegetation), vegetation that includes green photosynthetic plants and dark objects which encompasses absorptive substrate materials, clear water, deep shadows, etc. For the agricultural scenario, a spectrally diverse collection of 11 scenes of Level 1 terrain corrected, cloud free Landsat-5 TM data of Fresno, California, USA were unmixed and the results were validated with the corresponding ground data. To study an urbanized landscape, a clear sky Landsat-5 TM data were unmixed and validated with coincident World View-2 abundance maps (of 2 m spatial resolution) for an area of San Francisco, California, USA. The results were evaluated using descriptive statistics, correlation coefficient, RMSE, probability of success, boxplot and bivariate distribution function. Finally, FCLS was used for sub-pixel land cover analysis of the monthly WELD (Wen-enabled Landsat data) repository from 2008 to 2011 of North America. The abundance maps in conjunction with DMSP-OLS nighttime lights data were used to extract the urban land cover features and analyze their spatial-temporal growth.

Landsat Satellites↗