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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1979

Application of abstract harmonic analysis to the high-speed recognition of images

Methods are constructed for rapidly computing correlation functions using the theory of abstract harmonic analysis. The theory developed includes as a particular case the familiar Fourier transform method for a correlation function which makes it possible to find images which are independent of their translation in the plane. Two examples of the application of the general theory described are the search for images, independent of their rotation and scale, and the search for images which are independent of their translations and rotations in the plane.

Usikov, D. A.↗
NASA NTRS1981

Basic research planning in mathematical pattern recognition and image analysis

Fundamental problems encountered while attempting to develop automated techniques for applications of remote sensing are discussed under the following categories: (1) geometric and radiometric preprocessing; (2) spatial, spectral, temporal, syntactic, and ancillary digital image representation; (3) image partitioning, proportion estimation, and error models in object scene interference; (4) parallel processing and image data structures; and (5) continuing studies in polarization; computer architectures and parallel processing; and the applicability of "expert systems" to interactive analysis.

Bryant, J.↗
NASA NTRS1985

Proceedings of the Third Annual Symposium on Mathematical Pattern Recognition and Image Analysis

Topics addressed include: multivariate spline method; normal mixture analysis applied to remote sensing; image data analysis; classifications in spatially correlated environments; probability density functions; graphical nonparametric methods; subpixel registration analysis; hypothesis integration in image understanding systems; rectification of satellite scanner imagery; spatial variation in remotely sensed images; smooth multidimensional interpolation; and optimal frequency domain textural edge detection filters.

Guseman, L. F., Jr.↗
NASA NTRS1987

Structure of unsteady flows at leading- and trailing-edges: Flow visualization and its interpretation

Unsteady two- and three-dimensional flow structure at leading and trailing edges of bodies can be characterized effectively using recently developed techniques for acquisition and interpretation of flow visualization. The techniques addressed here include: flow image/surface pressure correlations; 3-D reconstruction of flow structure from flow images; and interactive interpretation of flow images with theoretical simulations. These techniques can be employed in conjunction with: visual correlation and ensemble-averaging, both within a given image and between images; recognition of patterns from images; and estimates of velocity eigenfunctions from images.

Rockwell, D.↗
NASA NTRS1986

Microcomputer-based programmable optical signal processor

A microcomputer-based real-time programmable optical signal processing system utilizing a magneto-optic spatial light modulator (MOSLM) and a liquid crystal light valve (LCLV) is described. This system can perform a myriad of complicated optical operations, such as image correlation, image subtraction, and matrix multiplication. Its important asserts are its programmability and the capability of real-time addressing. These are important for telerobotic vision in space automation applications. Design specifications and suggestions for practical implementation of the system are discussed. Some preliminary experimental demonstrations are conducted to demonstrate applications of the proposed system to image correlation for optical pattern recognition, image subtraction for lC chip inspection, and matrix multiplication for optical computing.

Yu, F. T. S.↗
DOE OSTI2022

Grassmannian Diffusion Maps--Based Dimension Reduction and Classification for High-Dimensional Data

This work introduces the Grassmannian diffusion maps (GDMaps), a novel nonlinear dimensionality reduction technique that defines the affinity between points through their representation as low-dimensional subspaces corresponding to points on the Grassmann manifold. Here, the method is designed for applications, such as image recognition and data-based classification of constrained high-dimensional data where each data point itself is a high-dimensional object (i.e., a large matrix) that can be compactly represented in a lower-dimensional subspace. The GDMaps is composed of two stages. The first is a pointwise linear dimensionality reduction wherein each high-dimensional object is mapped onto the Grassmann manifold representing the low-dimensional subspace on which it resides. The second stage is a multipoint nonlinear kernel-based dimension reduction using diffusion maps to identify the subspace structure of the points on the Grassmann manifold. To this end, an appropriate Grassmannian kernel is used to construct the transition matrix of a random walk on a graph connecting points on the Grassmann manifold. Spectral analysis of the transition matrix yields low-dimensional Grassmannian diffusion coordinates embedding the data into a low-dimensional reproducing kernel Hilbert space. Further, a novel data classification/recognition technique is developed based on the construction of an overcomplete dictionary of reduced dimension whose atoms are given by the Grassmannian diffusion coordinates. Three examples are considered. First, a "toy" example shows that the GDMaps can identify an appropriate parametrization of structured points on the unit sphere. The second example demonstrates the ability of the GDMaps to revealing the intrinsic subspace structure of high-dimensional random field data. In the last ex- ample, a face recognition problem is solved considering face images subject to varying illumination conditions, changes in face expressions, and occurrence of occlusions. The technique presented high recognition rates (i.e., 95% in the best case) using a fraction of the data required by conventional methods.

42 ENGINEERING↗
DOE OSTI2025

Block segmentation in feature space for realtime object detection in high granularity images

Computer vision has applications in object detection, image recognition and classification, and object tracking. One of the challenges of computer vision is the presence of useful information at multiple distance scales. Filtering techniques may sacrifice details at small scales in order to prioritize the analysis of large-scale features of the image. We present a strategy for coarse-graining multidimensional data while maintaining fine-grained detail for subsequent analysis. The algorithm is based on fixed-size block segmentation in the feature space. We apply this strategy to solve the long-standing challenge of detecting particle trajectories at the Large Hadron Collider in real time.

Computer vision↗
NASA NTRS1989

Making A Circular-Harmonic Filter

Optical image-correlating system made rotationally invariant. Experiment shows circular-harmonic filter generated optically. Hologram made by phase-shifted-double-exposure procedure. Filter used in optical image-recognition system based on correlation between target (image to be recognized) and matched spatial filter. In addition to usual invariance under translation of target, circular harmonic filter invariant under rotation of target; peak intensity of illumination at origin of correlation plane does not vary when target rotated about optical axis.

Cheng, Yeou Yen↗
DOE OSTI2021

Tutorial: Machine Learning and Artificial Intelligence in Batteries

Machine learning (ML) promises to compress the time needed to characterize battery performance, lifetime and safety. By coupling ML with physical models and metrics, that learning can bridge across materials, chemistries and cell designs. This tutorial will discuss the most popular ML techniques and resources and review recent work in the electrochemical literature. Applications include materials discovery, image recognition for quantitative microscopy analysis, fast charge algorithm development and life prediction.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Rethinking skip connections in Spiking Neural Networks with Time-To-First-Spike coding

Time-To-First-Spike (TTFS) coding in Spiking Neural Networks (SNNs) offers significant advantages in terms of energy efficiency, closely mimicking the behavior of biological neurons. In this work, we delve into the role of skip connections, a widely used concept in Artificial Neural Networks (ANNs), within the domain of SNNs with TTFS coding. Our focus is on two distinct types of skip connection architectures: (1) addition-based skip connections, and (2) concatenation-based skip connections. We find that addition-based skip connections introduce an additional delay in terms of spike timing. On the other hand, concatenation-based skip connections circumvent this delay but produce time gaps between after-convolution and skip connection paths, thereby restricting the effective mixing of information from these two paths. To mitigate these issues, we propose a novel approach involving a learnable delay for skip connections in the concatenation-based skip connection architecture. This approach successfully bridges the time gap between the convolutional and skip branches, facilitating improved information mixing. We conduct experiments on public datasets including MNIST and Fashion-MNIST, illustrating the advantage of the skip connection in TTFS coding architectures. Additionally, we demonstrate the applicability of TTFS coding on beyond image recognition tasks and extend it to scientific machine-learning tasks, broadening the potential uses of SNNs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Bundle Networks: Fiber Bundles, Local Trivializations, and a Generative Approach to Exploring Many-to-one Maps

Many-to-one maps are ubiquitous in machine learning, from the image recognition model that assigns a multitude of distinct images to the concept of ``cat'' to the time series forecasting model which assigns a range of distinct time-series to a single scalar regression value. While the primary use of such models is naturally to associate correct output to each input, in many problems it is also useful to be able to explore, understand, and sample from a model's fibers, which are the set of input values x such that f(x) = y, for fixed y in the output space. In this paper we show that popular generative architectures are ill-suited to such tasks. Motivated by this we introduce a novel generative architecture, a Bundle Network, based on the concept of a fiber bundle from (differential) topology. BundleNets exploit the idea of a local trivialization wherein a space can be locally decomposed into a product space that cleanly encodes the many-to-one nature of the map. By enforcing this decomposition in BundleNets and by utilizing state-of-the-art invertible components, investigating a network's fibers becomes natural.

Courts, Nicolas C.↗
NASA NTRS1989

Weighted feature selection criteria for visual servoing of a telerobot

Because of the continually changing environment of a space station, visual feedback is a vital element of a telerobotic system. A real time visual servoing system would allow a telerobot to track and manipulate randomly moving objects. Methodologies for the automatic selection of image features to be used to visually control the relative position between an eye-in-hand telerobot and a known object are devised. A weighted criteria function with both image recognition and control components is used to select the combination of image features which provides the best control. Simulation and experimental results of a PUMA robot arm visually tracking a randomly moving carburetor gasket with a visual update time of 70 milliseconds are discussed.

Feddema, John T.↗
NASA NTRS1990

Real-time image processing II; Proceedings of the Meeting, Orlando, FL, Apr. 16-18, 1990

The present conference discusses topics in the fields of feature extraction and implementation, filter and correlation algorithms, optical correlators, high-level algorithms, and digital image processing for ranging and remote driving. Attention is given to a nonlinear filter derived from topological image features, IR image segmentation through iterative thresholding, orthogonal subspaces for correlation masking, composite filter trees and image recognition via binary search, and features of matrix-coherent optical image processing. Also discussed are multitarget tracking via hybrid joint transform correlator, binary joint Fourier transform correlator considerations, global image processing operations on parallel architectures, real-time implementation of a differential range finder, and real-time binocular stereo range and motion detection.

Juday, Richard D.↗
DOE OSTI2021

X-Ray Toolkit Component Classifier Mobile (Android)

The X-Ray Toolkit Component Classifier is a mobile Android application for identifying components using the RECON image recognition algorithm. The application can identify common components within x-ray images such as antennae, batteries, switches and many more. It can be used in conjunction with X-Ray Toolkit Mobile for Android (SCR 2598.0) or as a stand-alone application. 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. SAND2021-8098 O

Gilbert, ChristinaL.↗
NASA NTRS1979

Computer image processing and recognition

A systematic introduction to the concepts and techniques of computer image processing and recognition is presented. Consideration is given to such topics as image formation and perception; computer representation of images; image enhancement and restoration; reconstruction from projections; digital television, encoding, and data compression; scene understanding; scene matching and recognition; and processing techniques for linear systems.

Hall, E. L.↗
DOE OSTI2023

EXSCLAIM!: Harnessing materials science literature for self-labeled microscopy datasets

This work introduces the EXSCLAIM! toolkit for the automatic extraction, separation, and caption-based natural language annotation of images from scientific literature. EXSCLAIM! is used to show how rule-based natural language processing and image recognition can be leveraged to construct an electron microscopy data set containing thousands of keyword-annotated nanostructure images. Moreover, it is demonstrated how a combination of statistical topic modeling and semantic word similarity comparisons can be used to increase the number and variety of keyword annotations on top of the standard annotations from EXSCLAIM! With large-scale imaging datasets constructed from scientific literature, users are well positioned to train neural networks for classification and recognition tasks specific to microscopy-tasks often otherwise inhibited by a lack of sufficient annotated training data.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Deep learning Hamiltonians from disordered image data in quantum materials

The capabilities of image probe experiments are rapidly expanding, providing new information about quantum materials on unprecedented length- and timescales. Many such materials feature inhomogeneous electronic properties with intricate pattern formation on the observable surface. This rich spatial structure contains information about interactions, dimensionality, and disorder—a spatial encoding of the Hamiltonian driving the pattern formation. Image recognition techniques from machine learning are an excellent tool for interpreting information encoded in the spatial relationships in such images. Here, we develop a deep learning framework for using the rich information available in these spatial correlations in order to discover the underlying Hamiltonian driving the patterns. We first vet the method on a known case, scanning near-field optical microscopy on a thin film of V⁢O 2 . We then apply our trained convolutional neural network architecture to new optical microscope images of a different V⁢O 2 film as it goes through the metal-insulator transition. We find that a two-dimensional Hamiltonian with both interactions and random field disorder is required to explain the intricate, fractal intertwining of metal and insulator domains during the transition. This detailed knowledge about the underlying Hamiltonian paves the way for using the model to control the pattern formation via, e.g., tailored hysteresis protocols. Finally, we also introduce a distribution-based confidence measure on the results of a multilabel classifier, which does not rely on adversarial training. In addition, we propose a machine-learning-based criterion for diagnosing a physical system's proximity to criticality.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS2017

Launch Control System Software Development System Automation Testing

The Spaceport Command and Control System (SCCS) is the National Aeronautics and Space Administration's (NASA) launch control system for the Orion capsule and Space Launch System, the next generation manned rocket currently in development. This system requires high quality testing that will measure and test the capabilities of the system. For the past two years, the Exploration and Operations Division at Kennedy Space Center (KSC) has assigned a group including interns and full-time engineers to develop automated tests to save the project time and money. The team worked on automating the testing process for the SCCS GUI that would use streamed simulated data from the testing servers to produce data, plots, statuses, etc. to the GUI. The software used to develop automated tests included an automated testing framework and an automation library. The automated testing framework has a tabular-style syntax, which means the functionality of a line of code must have the appropriate number of tabs for the line to function as intended. The header section contains either paths to custom resources or the names of libraries being used. The automation library contains functionality to automate anything that appears on a desired screen with the use of image recognition software to detect and control GUI components. The data section contains any data values strictly created for the current testing file. The body section holds the tests that are being run. The function section can include any number of functions that may be used by the current testing file or any other file that resources it. The resources and body section are required for all test files; the data and function sections can be left empty if the data values and functions being used are from a resourced library or another file. To help equip the automation team with better tools, the Project Lead of the Automated Testing Team, Jason Kapusta, assigned the task to install and train an optical character recognition (OCR) tool to Brandon Echols, a fellow intern, and I. The purpose of the OCR tool is to analyze an image and find the coordinates of any group of text. Some issues that arose while installing the OCR tool included the absence of certain libraries needed to train the tool and an outdated software version. We eventually resolved the issues and successfully installed the OCR tool. Training the tool required many images and different fonts and sizes, but in the end the tool learned to accurately decipher the text in the images and their coordinates. The OCR tool produced a file that contained significant metadata for each section of text, but only the text and coordinates of the text was required for our purpose. The team made a script to parse the information we wanted from the OCR file to a different file that would be used by automation functions within the automated framework. Since a majority of development and testing for the automated test cases for the GUI in question has been done using live simulated data on the workstations at the Launch Control Center (LCC), a large amount of progress has been made. As of this writing, about 60% of all of automated testing has been implemented. Additionally, the OCR tool will help make our automated tests more robust due to the tool's text recognition being highly scalable to different text fonts and text sizes. Soon we will have the whole test system automated, allowing for more full-time engineers working on development projects.

Automation↗