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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

Networks for image acquisition, processing and display

The human visual system comprises layers of networks which sample, process, and code images. Understanding these networks is a valuable means of understanding human vision and of designing autonomous vision systems based on network processing. Ames Research Center has an ongoing program to develop computational models of such networks. The models predict human performance in detection of targets and in discrimination of displayed information. In addition, the models are artificial vision systems sharing properties with biological vision that has been tuned by evolution for high performance. Properties include variable density sampling, noise immunity, multi-resolution coding, and fault-tolerance. The research stresses analysis of noise in visual networks, including sampling, photon, and processing unit noises. Specific accomplishments include: models of sampling array growth with variable density and irregularity comparable to that of the retinal cone mosaic; noise models of networks with signal-dependent and independent noise; models of network connection development for preserving spatial registration and interpolation; multi-resolution encoding models based on hexagonal arrays (HOP transform); and mathematical procedures for simplifying analysis of large networks.

Ahumada, Albert J., Jr.↗
NASA NTRS1992

Automatic rule generation for high-level vision

A new fuzzy set based technique that was developed for decision making is discussed. It is a method to generate fuzzy decision rules automatically for image analysis. This paper proposes a method to generate rule-based approaches to solve problems such as autonomous navigation and image understanding automatically from training data. The proposed method is also capable of filtering out irrelevant features and criteria from the rules.

Rhee, Frank Chung-Hoon↗
NASA NTRS1992

Automatic rule generation for high-level vision

Many high-level vision systems use rule-based approaches to solving problems such as autonomous navigation and image understanding. The rules are usually elaborated by experts. However, this procedure may be rather tedious. In this paper, we propose a method to generate such rules automatically from training data. The proposed method is also capable of filtering out irrelevant features and criteria from the rules.

Rhee, Frank Chung-Hoon↗
NASA NTRS1996

Feature Selection for Classification of Polar Regions Using a Fuzzy Expert System

Labeling, feature selection, and the choice of classifier are critical elements for classification of scenes and for image understanding. This study examines several methods for feature selection in polar regions, including the list, of a fuzzy logic-based expert system for further refinement of a set of selected features. Six Advanced Very High Resolution Radiometer (AVHRR) Local Area Coverage (LAC) arctic scenes are classified into nine classes: water, snow / ice, ice cloud, land, thin stratus, stratus over water, cumulus over water, textured snow over water, and snow-covered mountains. Sixty-seven spectral and textural features are computed and analyzed by the feature selection algorithms. The divergence, histogram analysis, and discriminant analysis approaches are intercompared for their effectiveness in feature selection. The fuzzy expert system method is used not only to determine the effectiveness of each approach in classifying polar scenes, but also to further reduce the features into a more optimal set. For each selection method,features are ranked from best to worst, and the best half of the features are selected. Then, rules using these selected features are defined. The results of running the fuzzy expert system with these rules show that the divergence method produces the best set features, not only does it produce the highest classification accuracy, but also it has the lowest computation requirements. A reduction of the set of features produced by the divergence method using the fuzzy expert system results in an overall classification accuracy of over 95 %. However, this increase of accuracy has a high computation cost.

Penaloza, Mauel A.↗
NASA NTRS2002

Bioinspired engineering of exploration systems for NASA and DoD

A new approach called bioinspired engineering of exploration systems (BEES) and its value for solving pressing NASA and DoD needs are described. Insects (for example honeybees and dragonflies) cope remarkably well with their world, despite possessing a brain containing less than 0.01% as many neurons as the human brain. Although most insects have immobile eyes with fixed focus optics and lack stereo vision, they use a number of ingenious, computationally simple strategies for perceiving their world in three dimensions and navigating successfully within it. We are distilling selected insect-inspired strategies to obtain novel solutions for navigation, hazard avoidance, altitude hold, stable flight, terrain following, and gentle deployment of payload. Such functionality provides potential solutions for future autonomous robotic space and planetary explorers. A BEES approach to developing lightweight low-power autonomous flight systems should be useful for flight control of such biomorphic flyers for both NASA and DoD needs. Recent biological studies of mammalian retinas confirm that representations of multiple features of the visual world are systematically parsed and processed in parallel. Features are mapped to a stack of cellular strata within the retina. Each of these representations can be efficiently modeled in semiconductor cellular nonlinear network (CNN) chips. We describe recent breakthroughs in exploring the feasibility of the unique blending of insect strategies of navigation with mammalian visual search, pattern recognition, and image understanding into hybrid biomorphic flyers for future planetary and terrestrial applications. We describe a few future mission scenarios for Mars exploration, uniquely enabled by these newly developed biomorphic flyers.

Space Flight↗
NASA NTRS1973

ERTS-1 image contributes to understanding of geologic structures related to Managua earthquake, 1972

ERTS-1 imaged the western portion of Nicaragua on December 24, 1972, one day after the central part of the city of Managua was devastated by a major earthquake which measured 5.6 on the Richter scale. ERTS-1 images reveal sets of lineaments (which may reflect fault systems) along any one of which movement could have taken place. One set includes a line of active volcanoes that parallels the coast and constitutes the southwestern edge of the Nicaraguan Depression, a regional graben which cuts obliquely across the Central American isthmus. This trend is offset approximately 10km in a right lateral geometric sense just west of the city of Managua. A parallel lineament, north of Lake Managua, marks the northeast edge of the graben. A second set, extends northward to northwestward from the mouth of the Rio Grande (Viejo) north of Lake Managua and can be projected southward across the lake to Managua. It is this set along which geometric offset of the volcanic lineament appears to have taken place.

Carter, W. D.↗
DOE OSTI2022

Particle classification by image analysis improves understanding of corn stover degradation mechanisms during deconstruction

Biomass feedstock heterogeneity is a principal roadblock to implementation of the biorefinery concept. Even within an identical cultivar of corn stover, different bales contain not only varying abundance moisture, ash, glucan, and other chemical compounds, but also varying abundance of tissue anatomies (e.g., leaf, husk, cob, or stalk). These different anatomical components not only differ in their response to pretreatment and enzymatic hydrolysis to glucose, but also vary in their mechanical and conveyance properties. Although this heterogeneous nature of corn stover feedstock has been identified as a challenge, a fundamental knowledge gap of how these tissues behave during biorefining processing remains. Here, we demonstrate the use of a commercial fiber image analyzer typically used for wood fiber characterization to monitor the particle size and shapes of non-woody feedstock during milling, pretreatment, and hydrolysis. Additionally, we present novel use of Gaussian process classification to distinguish bundle, parenchyma, and fiber particles to an accuracy of 96.4%. Quantitative probability distribution plots for characteristics such as length and roundness allow elucidation of particle morphology as pretreatment and enzymatic hydrolysis progress. In both stalk pith and stalk rind, particles peel into individual cells whose walls are subsequently fragmented during enzymatic hydrolysis.

09 BIOMASS FUELS↗
NASA NTRS2022

Look-up Table Approach for Uncertainty Determination for Operational Vicarious Calibration of Earth Imaging Sensors

Understanding the uncertainty of a vicarious calibration is essential for any application to Earth imaging sensors. The Radiometric Calibration Network provides SI-traceable spectral top-of-atmosphere (TOA) reflectance from a network of ground sites and uses a look-up table (LUT) approach for uncertainty determination. The uncertainty LUT was derived using Monte Carlo techniques applied to the relevant solar geometry, surface, and atmospheric variables. While surface reflectance is typically the dominant uncertainty source, atmospheric contributions do play an important role, depending upon the exact scenario and conditions. This approach allows knowledge of TOA reflectance uncertainty to within 0.5%.

Brian N. Wenny↗
NASA NTRS1983

Use of radar image texture in geologic mapping

Large slope angle radar and small slope angle radar techniques are discussed. The techniques are developed to aid in the geologic interpretation of synthetic aperture radar (SAR) images. The application presented is for heavy vegetation and where very little other data can be obtained directly from remote sensing images. To understand the relationships between image texture, topography, lithology, geomorphology, and climate improves, textural information from SAR images are used for the identification of rock types to discriminate units. An active program is to integrate textural information from radar images directly with backscatter data from the same images, and with compositional information derived from visible near infrared sensors such as LANDSAT is explored. The role of quantitative textural information in this type of multisensor analysis which promises to be significant is outlined.

Farr, T. G.↗
DOE OSTI2023

Progressive and instantaneous nature of lithium nucleation discovered by dynamic and operando imaging

The understanding of lithium (Li) nucleation and growth is important to design better electrodes for high-performance batteries. However, the study of Li nucleation process is still limited because of the lack of imaging tools that can provide information of the entire dynamic process. We developed and used an operando reflection interference microscope (RIM) that enables real-time imaging and tracking the Li nucleation dynamics at a single nanoparticle level. This dynamic and operando imaging platform provides us with critical capabilities to continuously monitor and study the Li nucleation process. We find that the formation of initial Li nuclei is not at the exact same time point, and Li nucleation process shows the properties of both progressive and instantaneous nucleation. In addition, the RIM allows us to track the individual Li nucleus’s growth and extract spatially resolved overpotential map. The nonuniform overpotential map indicates that the localized electrochemical environments substantially influence the Li nucleation.

25 ENERGY STORAGE↗
DOE OSTI2020

Impact of krypton irradiation on a single crystal tungsten: Multi-modal X-ray imaging study

Understanding microstructural and strain evolutions induced by noble gas production in the nuclear fuel matrix or plasma-facing materials is crucial for designing next generation nuclear reactors, as they are responsible for volumetric swelling and catastrophic failure. In this paper we describe a multimodal approach combining synchrotron-based nanoscale X-ray imaging techniques with atomic-scale electron microscopy techniques for mapping chemical composition, morphology and lattice distortion in a single crystal W induced by Kr irradiation. We report that Kr-irradiated single crystal W undergoes surface deformation, forming Kr containing cavities. Furthermore, positive strain fields are observed in Kr-irradiated regions, which lead to compression of underlying W matrix.

36 MATERIALS SCIENCE↗
NASA NTRS2020

Detecting Seasonal Ice Dynamics in Satellite Images

Fully understanding how glaciers respond to environmental change will require new methods to help us identify the onset of ice acceleration events and observe how dynamic signals propagate within glaciers. In particular, observations of ice dynamics on seasonal timescales may offer insights into how a glacier interacts with various forcing mechanisms throughout the year. The task of generating continuous ice velocity time series that resolve seasonal variability is made more difficult by a spotty satellite record that contains no optical observations throughout the dark, polar winters. Furthermore, velocities obtained by feature tracking are marked by high noise when image pairs are separated by short time intervals and contain no direct insights into variability that occurs between images separated by long time intervals. In this paper, we describe a method of analyzing optical or SAR-derived feature-tracked velocities to characterize the magnitude and timing of seasonal ice dynamic variability. Our method is agnostic to data gaps and is able to recover climatological average winter velocities regardless of the availability of direct observations during winter. Using characteristic image acquisition times and error distributions from Antarctic image pairs in the ITS_LIVE dataset, we generate synthetic ice velocity time series, then apply our method to recover imposed magnitudes of seasonal variability within ±1.4 m yr−1. We then validate the techniques by comparing our results to GPS data collected on Russell Glacier in Greenland. The methods presented here may be applied to better understand how ice dynamic signals propagate on seasonal timescales, and what mechanisms control the flow of the world’s ice.

Chad A. Greene↗
DOE OSTI2021

Analyzing the effect of misalignment on single-filament carbon fiber tensile testing via stereoscopic computer vision imaging

An understanding of the constitutive properties of carbon fibers (CF) is critical to the accuracy of high-resolution composite simulations and to the development of CF derived from low-cost alternative precursor materials. Single-fiber tensile testing is a capable tool to measure CF properties and is well suited to research efforts where only a small number of fibers may be available. However, single-fiber tensile tests are challenging to conduct due to the difficulty in handling small diameter fibers (5-15 μm), the brittleness of single fibers, and the required nanoscale/microscale resolution of testing equipment. The accuracy of the measured properties depends on several factors, but a critical factor is fiber misalignment, especially at short gauge lengths. Current standards do not address the effect of tensile specimen misalignment on measured properties. Furthermore, this work presents a robust method of fiber alignment using stereoscopic computer vision that enables users to align fibers vertically for tensile testing to improve the accuracy of resulting mechanical properties. Additionally, an analytical relationship between fiber misalignment angle and measured properties is developed and validated against the experimental results. As a result, new best practices for single-fiber tensile testing of CF are recommended.

47 OTHER INSTRUMENTATION↗
NASA NTRS2003

Detecting Edges in Images by Use of Fuzzy Reasoning

A method of processing digital image data to detect edges includes the use of fuzzy reasoning. The method is completely adaptive and does not require any advance knowledge of an image. During initial processing of image data at a low level of abstraction, the nature of the data is indeterminate. Fuzzy reasoning is used in the present method because it affords an ability to construct useful abstractions from approximate, incomplete, and otherwise imperfect sets of data. Humans are able to make some sense of even unfamiliar objects that have imperfect high-level representations. It appears that to perceive unfamiliar objects or to perceive familiar objects in imperfect images, humans apply heuristic algorithms to understand the images

Dominguez, Jesus A.↗