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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
NASA NTRS1996

Visualizing Vector Fields Using Line Integral Convolution and Dye Advection

We present local and global techniques to visualize three-dimensional vector field data. Using the Line Integral Convolution (LIC) method to image the global vector field, our new algorithm allows the user to introduce colored 'dye' into the vector field to highlight local flow features. A fast algorithm is proposed that quickly recomputes the dyed LIC images. In addition, we introduce volume rendering methods that can map the LIC texture on any contour surface and/or translucent region defined by additional scalar quantities, and can follow the advection of colored dye throughout the volume.

Shen, Han-Wei↗
NASA NTRS2008

Onboard Image Registration from Invariant Features

This paper describes a feature-based image registration technique that is potentially well-suited for onboard deployment. The overall goal is to provide a fast, robust method for dynamically combining observations from multiple platforms into sensors webs that respond quickly to short-lived events and provide rich observations of objects that evolve in space and time. The approach, which has enjoyed considerable success in mainstream computer vision applications, uses invariant SIFT descriptors extracted at image interest points together with the RANSAC algorithm to robustly estimate transformation parameters that relate one image to another. Experimental results for two satellite image registration tasks are presented: (1) automatic registration of images from the MODIS instrument on Terra to the MODIS instrument on Aqua and (2) automatic stabilization of a multi-day sequence of GOES-West images collected during the October 2007 Southern California wildfires.

descriptors↗
NASA NTRS1981

Digital SAR processing using a fast polynomial transform

A new digital processing algorithm based on the fast polynomial transform is developed for producing images from Synthetic Aperture Radar data. This algorithm enables the computation of the two dimensional cyclic correlation of the raw echo data with the impulse response of a point target, thereby reducing distortions inherent in one dimensional transforms. This SAR processing technique was evaluated on a general-purpose computer and an actual Seasat SAR image was produced. However, regular production runs will require a dedicated facility. It is expected that such a new SAR processing algorithm could provide the basis for a real-time SAR correlator implementation in the Deep Space Network.

Butman, S.↗
NASA NTRS1984

Digital SAR processing using a fast polynomial transform

A new digital processing algorithm based on the fast polynomial transform is developed for producing images from Synthetic Aperture Radar data. This algorithm enables the computation of the two dimensional cyclic correlation of the raw echo data with the impulse response of a point target, thereby reducing distortions inherent in one dimensional transforms. This SAR processing technique was evaluated on a general-purpose computer and an actual Seasat SAR image was produced. However, regular production runs will require a dedicated facility. It is expected that such a new SAR processing algorithm could provide the basis for a real-time SAR correlator implementation in the Deep Space Network. Previously announced in STAR as N82-11295

Truong, T. K.↗
NASA NTRS2001

On Kalman filter solution of space-time interpolation

The approximate Kalman filtering algorithm presented in [1] for image sequence processing can introduce unacceptable negative eigenvalues in the information matrix and can have degraded performance in some applications. The improved algorithm presented in this note guarantees a positive definite information matrix, leading to more stable filter performance.

data↗
DOE OSTI2025

Analog In-Memory Computing for the Synthetic Aperture Radar Polar Format Algorithm

As the utility of synthetic aperture radar (SAR) systems increases in autonomous vehicles, satellites, and other power- and space-constrained edge applications, there is a growing need for processors that can form SAR images at low power. In recent years, analog in-memory compute (AIMC) has shown immense promise for accelerating neural networks and other matrix-vector multiplication (MVM) heavy workloads at the edge. Here, in this work, we examine how the polar format algorithm (PFA), a popular SAR image formation algorithm, can be mapped to these AIMC systems. The PFA maps readily onto analog MVMs because it primarily consists of two linear operations: interpolation of frequency-domain data to a Cartesian grid, followed by a 2-D Fourier transform. This work presents two approaches to map the interpolation operation onto MVMs in analog hardware: a chirp transform and a modified form of sinc interpolation. These mappings introduce algorithmic errors, and their effect on the quality of SAR image formation is examined, both quantitatively and qualitatively. In addition, the impact of errors introduced by the analog hardware is explored to determine which approach is optimal under varying assumptions about the underlying analog memory devices and circuits.

Analog computing↗
DOE OSTI2025

Comparative study between experimental measurements and model predictions of bubble rise velocity in molten LiCl-KCl

This research investigated the shape and rise velocities of gas bubbles (helium, nitrogen, argon, and krypton) in molten LiCl-KCl at 500 °C using a specialized optical visualization apparatus. The apparatus features a custom quartz rectangular prism cell housed in a furnace, enabling the application of the shadowgraph measurement technique. Bubbles with diameters between 0.45 and 3.2 mm were produced via a capillary, and their equivalent diameters and rise velocities were analyzed using high-resolution imaging and centroid tracking algorithms. Important non-dimensional parameters were calculated to characterize behavior. Bubbles with 0.03 < Eo < 1.32, 17 < Re < 718, and Mo order of 10 -11 were generated in the molten LiCl-KCl, revealing that bubbles with diameters less than 1.46 mm remain spherical, whereas those with larger diameters are ellipsoidal. The transition and oscillation onset for bubble shape occurred at larger diameters than those previously observed in air–water systems. Comparative analysis of the gas-molten salt data against existing air–water bubble rise velocity correlations highlighted inaccuracies in predictions, particularly across the different bubble regimes. An existing correlation was calibrated to the molten salt data to improve the bubble velocity predictions. In conclusion, this study contributes essential experimental data for the design and safety evaluation of molten salt reactors and offers insights for the optimization of sparge systems for fission product removal.

Bubble rise velocity↗
DOE OSTI2026

Integrated fluorescence light microscopy-guided cryo-focused ion beam-milling for in situ montage cryo-ET

Cryogenic-electron tomography (cryo-ET) permits the in situ visualization of biological macromolecules at the molecular level. Owing to the variable thickness of cells, tissues and organisms, frozen specimens may need to be thinned by cryo-focused ion beam (FIB) milling to produce thin (<500 nm) cryo-lamellae suitable for cryo-ET. Locating regions of interest remains a challenge because untargeted milling can lead to inadvertent ablation and removal of regions of interest. Correlative light and electron microscopy, combined with cryo-FIB milling, can guide the identification of labeled targets in the cellular milieu. Multiple transfers between cryo-imaging instruments, cumbersome correlation algorithms, limited accuracy and low throughput have hindered the routine adoption of cryo-FIB milling within a multimodal correlative workflow for in situ structural biology. Here, in this study, we present a workflow for 3D correlative cryo-fluorescence light microscopy-FIB-ET that streamlines fluorescence light microscopy-guided FIB milling, improving throughput while preserving both structural and contextual information. The complete integration of hardware and software described here minimizes sample contamination from cross-platform exchanges and greatly enhances the efficiency of 3D targeting in cryo-milling. We then describe procedures for implementing montage parallel array cryo-ET (MPACT), which can be easily adapted to any modern life-science transmission electron microscope. MPACT supports high-throughput cryo-ET acquisitions (10 tilt series in 1.5 h) for structure determination and comprehensive contextual understanding of macromolecules within their native surroundings. A complete session from sample preparation to MPACT data processing takes 5−7 d for an individual experienced in both cryo-EM and cryo-FIB milling.

Yang, Jie E. [Univ. of Wisconsin, Madison, WI (Uni↗
DOE OSTI2025

Scalable Hybrid Learning Techniques for Scientific Data Compression

Data compression is becoming critical for storing scientific data because many scientific applications need to store large amounts of data and post process this data for scientific discovery. Unlike image and video compression algorithms that limit errors to primary data (PD), scientists require compression techniques that accurately preserve derived quantities of interest (QoIs). Here, this article presents a physics-informed compression technique implemented as an end-to-end, scalable, GPU-based pipeline for data compression that addresses this requirement. Our hybrid compression technique combines machine learning techniques and standard compression methods. Specifically, we combine an autoencoder, an error-bounded lossy compressor to provide guarantees on raw data error, and a constraint satisfaction post-processing step to preserve the QoIs within a minimal error (generally less than floating point error). The effectiveness of the data compression pipeline is demonstrated by compressing nuclear fusion simulation data generated by a large-scale fusion code, XGC, which produces hundreds of terabytes of data in a single day. Our approach works within the ADIOS framework and results in compression by a factor of more than 150 while requiring only a few percent of the computational resources necessary for generating the data, making the overall approach highly effective for practical scenarios.

ITER↗
DOE OSTI2025

PDF Entity Annotation Tool (PEAT)

While different text mining approaches – including the use of Artificial Intelligence (AI) and other machine based methods - continue to expand at a rapid pace, the tools used by researchers to create the labeled datasets required for training, modeling, and evaluation remain rudimentary. Labeled datasets contain the target attributes the machine is going to learn; for example, training an algorithm to delineate between images of a car or truck would generally require a set of images with a quantitative description of the underlying features of each vehicle type. Development of labeled textual data that can be used to build natural language machine learning models for scientific literature is not currently integrated into existing manual workflows used by domain experts. Published literature is rich with important information, such as different types of embedded text, plots, and tables that can all be used as inputs to train ML/natural language processing (NLP) models, when extracted and prepared in machine readable formats. Currently, both normalized data extraction of use to domain experts and extraction to support development of ML/NLP models are labor intensive and cumbersome manual processes. Automatic extraction of data and information from formats such as PDFs that are optimized for layout and human readability, not machine readability. The PDF (Portable Document Format) Entity Annotation Tool (PEAT) was developed with the goal of allowing users to annotate publications within their current print format, while also allowing those annotations to be captured in a machine-readable format. One of the main issues with traditional annotation tools is that they require transforming the PDF into plain text to facilitate the annotation process. While doing so lessens the technical challenges of annotating data, the user loses all structure and provenance that was inherent in the underlying PDF. Also, textual data extraction from PDFs can be an error prone process. Challenges include identifying sequential blocks of text and a multitude of document formats (multiple columns, font encodings, etc.). As a result of these challenges, using existing tools for development of NLP/ML models directly from PDFs is difficult because the generated outputs are not interoperable. We created a system that allows annotations to be completed on the original PDF document structure, with no plain text extraction. The result is an application that allows for easier and more accurate annotations. In addition, by including a feature that grants the user the ability to easily create a schema, we have developed a system that can be used to annotate text for different domain-centric schemas of relevance to subject matter experts. Different knowledge domains require distinct schemas and annotation tags to support machine learning.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1972

Digital Handling and Processing of Remote Sensing Data

Details of plans for developing a digital image processing facility to handle remote sensing data are reported. Also given are programs for data acquisition and handling, systems for use in programming various digital image processing tasks, and algorithms for feature enhancement.

Algazi, R.↗
NASA NTRS1976

RM2: rms error comparisons

The root-mean-square error performance measure is used to compare the relative performance of several widely known source coding algorithms with the RM2 image data compression system. The results demonstrate that RM2 has a uniformly significant performance advantage.

Rice, R. F.↗
NASA NTRS1981

Evaluating computer-drawn ground-cover maps

Computer-generated character maps from LANDSAT data are compared to aerial photos for test sites in Florida. Report Describes extraction of ground features by two analytical techniques: unsupervised clustering algorithm, called LANDSAT Signature Development Program (LSDP), and interactive algorithm based on multispectral image analyzer. Study concluded that computer classification of digital LANDSAT multispectral data, supplemented with certain ground-cover information, is valuable tool for analysis of renewable resources.

Arvanitis, L. G.↗
NASA NTRS1980

Methods of using closure phases in radio aperture synthesis

A simple iterative algorithm for reconstructing the images of compact radio sources from very-long-baseline interferometry (VLBI) measurements of visibility amplitudes and 'closure' phases is described. The method makes efficient use of redundant sampling of spatial frequencies by making a global solution of phase 'closure' equations for a complete set of observations.

Rogers, A. E. E.↗
NASA NTRS1982

1982 International Geoscience and Remote Sensing Symposium, Munich, West Germany, June 1-4, 1982, Digest. Volumes 1 & 2

Theoretical and experimental data which have defined and/or extended the effectiveness of remote sensing operations are explored, with consideration given to both scientific and commercial activities. The remote sensing of soil moisture, the sea surface, and oil slicks is discussed, as are programs using satellites for studying geodynamics and geodesy, currents and waves, and coastal zones. NASA, Canadian, and Japanese radar and microwave passive and active systems are described, together with algorithms and techniques for image processing and classification. The SAR-580 project is outlined, and attention is devoted to satellite applications in investigations of the structure of the atmosphere, agriculture and land use, and geology. Design and performance features of various optical scanner, radar, and multispectral data processing systems and procedures are detailed.

Source record↗
NASA NTRS1987

The power of neural nets

Implementation of the Hopfield net which is used in the image processing type of applications where only partial information about the image may be available is discussed. The image classification type of algorithm of Hopfield and other learning algorithms, such as the Boltzmann machine and the back-propagation training algorithm, have many vital applications in space.

Ryan, J. P.↗
NASA NTRS1988

Spaceborne meteorological radar studies

Various radar designs and methods are studied for the estimation of rainfall parameters from space. An immediate goal is to support the development of the spaceborne radar that has been proposed for the Tropical Rain Measuring Mission (TRMM). The effort is divided into two activities: a cooperative airborne rain measuring experiment with the Radio Research Laboratory of Japan (RRL), and the modelling of spaceborne weather radars. An airborne rain measuring experiment was conducted at Wallops Flight Facility in 1985 to 1986 using the dual-wavelength radar/radiometer developed by RRL. The data are presently being used to test a number of methods that are relevant to spaceborne weather radars. An example is shown of path-averaged rain rates as estimated from three methods: the standard reflectivity rain rate method (Z-R), a dual-wavelength method, and a surface reference method. The results from the experiment shows for the first time the feasibility of using attenuation methods from space. The purposes of the modelling are twofold: to understand in a quantitative manner the relationships between a particular radar design and its capability for estimating precipitation parameters and to help devise and test new methods. The models are being used to study the impact of various TRMM radar designs on the accuracy of rain rate estimation as well as to test the performance of range-profiling algorithms, the mirror-image method, and some recently devised graphical methods for the estimation of the drop size distribution.

Meneghini, R.↗