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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

Processing and analysis of radiometer measurements for airborne reconnaissance

This paper describes selected results of airborne, radiometric imaging measurements at 90 GHz and 140 GHz relevant for the application in reconnaissance. Using a temperature resolution below 0.5 K and an angular resolution of about 1-degree high-quality images show the capability of discriminating between many brightness temperature classes within our natural environment and man-made objects. Measurement examples are given for cloud and fog penetration at 90 GHz, for the detection of vehicles on roads, and for the detection and classification of airports and airplanes. The application of different contour enhancement methods (Marr-Hildreth and Canny) shows the possibility of extracting lines and shapes precisely in order to improve automatic target recognition. The registration of the passive images with corresponding X-band synthetic aperture images from the same area is carried out and the high degree of correlation is discussed.

Suess, Helmut↗
NASA NTRS2014

Improving Sensorimotor Function Using Stochastic Vestibular Stimulation

Astronauts experience sensorimotor changes during spaceflight, particularly during G-transition phases. Post flight sensorimotor changes may include postural and gait instability, spatial disorientation, and visual performance decrements, all of which can degrade operational capabilities of the astronauts and endanger the crew. Crewmember safety would be improved if these detrimental effects of spaceflight could be mitigated by a sensorimotor countermeasure and even further if adaptation to baseline could be facilitated. The goal of this research is to investigate the potential use of stochastic vestibular stimulation (SVS) as a technology to improve sensorimotor function. We hypothesize that low levels of SVS will improve sensorimotor performance through stochastic resonance (SR). The SR phenomenon occurs when the response of a nonlinear system to a weak input signal is optimized by the application of a particular nonzero level of noise. Two studies have been initiated to investigate the beneficial effects and potential practical usage of SVS. In both studies, electrical vestibular stimulation is applied via electrodes on the mastoid processes using a constant current stimulator. The first study aims to determine the repeatability of the effect of vestibular stimulation on sensorimotor performance and perception in order to better understand the practical use of SVS. The beneficial effect of low levels of SVS on balance performance has been shown in the past. This research uses the same balance task repeated multiple times within a day and across days to study the repeatability of the stimulation effects. The balance test consists of 50 sec trials in which the subject stands with his or her feet together, arms crossed, and eyes closed on compliant foam. Varying levels of SVS, ranging from 0-700 micro A, are applied across different trials. The subject-specific optimal SVS level is that which results in the best balance performance as measured by inertial measurement units placed on the upper and lower torso of the subjects. Additionally, each individual’s threshold for illusory motion perception of suprasensory electrical vestibular stimulation is measured multiple times within and across days to better understand how multiple SVS test methods compare. The second study aims to demonstrate stochastic resonance in the vestibular system using a perception based motion recognition task. This task measures an individual’s velocity threshold of motion recognition using a 6-degree of freedom Stewart platform and a 3-down/1-up staircase procedure. For this study, thresholds are determined using 150 trials in the upright, head-centered roll tilt motion direction at a 0.2 Hz frequency. We aim to demonstrate the characteristic bell shaped curve associated with stochastic resonance with each subject’s motion recognition thresholds at varying SVS levels ranging from 0 to 1500 micro A. The curve includes the individual’s baseline threshold with no SVS, optimal or minimal threshold at some mid-level of SVS, and finally degraded or increased threshold at a high SVS level. An additional aim is to formally retest each subject at his or her individual optimal SVS level on a different day than the original testing for additional validity. The overall purpose of this research is to further quantify the effects of SVS on various sensorimotor tasks and investigate the practical implications of its use in the context of human space flight so that it may be implemented in the future as a component of a comprehensive countermeasure plan for adaptation to G-transitions.

Galvan, R. C.↗
NASA NTRS1996

Development of Collaborative Research Initiatives to Advance the Aerospace Sciences-via the Communications, Electronics, Information Systems Focus Group

The primary goal of the Adaptive Vision Laboratory Research project was to develop advanced computer vision systems for automatic target recognition. The approach used in this effort combined several machine learning paradigms including evolutionary learning algorithms, neural networks, and adaptive clustering techniques to develop the E-MOR.PH system. This system is capable of generating pattern recognition systems to solve a wide variety of complex recognition tasks. A series of simulation experiments were conducted using E-MORPH to solve problems in OCR, military target recognition, industrial inspection, and medical image analysis. The bulk of the funds provided through this grant were used to purchase computer hardware and software to support these computationally intensive simulations. The payoff from this effort is the reduced need for human involvement in the design and implementation of recognition systems. We have shown that the techniques used in E-MORPH are generic and readily transition to other problem domains. Specifically, E-MORPH is multi-phase evolutionary leaming system that evolves cooperative sets of features detectors and combines their response using an adaptive classifier to form a complete pattern recognition system. The system can operate on binary or grayscale images. In our most recent experiments, we used multi-resolution images that are formed by applying a Gabor wavelet transform to a set of grayscale input images. To begin the leaming process, candidate chips are extracted from the multi-resolution images to form a training set and a test set. A population of detector sets is randomly initialized to start the evolutionary process. Using a combination of evolutionary programming and genetic algorithms, the feature detectors are enhanced to solve a recognition problem. The design of E-MORPH and recognition results for a complex problem in medical image analysis are described at the end of this report. The specific task involves the identification of vertebrae in x-ray images of human spinal columns. This problem is extremely challenging because the individual vertebra exhibit variation in shape, scale, orientation, and contrast. E-MORPH generated several accurate recognition systems to solve this task. This dual use of this ATR technology clearly demonstrates the flexibility and power of our approach.

Knasel, T. Michael↗
NASA NTRSDate not supplied

Taming the Untamable: Making An Enceladus-Like Plume by Hypervelocity Oblique Impacts at the NASA Ames Vertical Gun Range (AVGR) Facility

Enceladus’ plume was extensively investigated by the Cassini-Huygens mission [1-4], giving information about the icy moon interior, and the existence of a plausibly inhabited global ocean. The plume is a mixture of gas, sub-micrometer to micrometer ice, and non-ice particles, expanding into space from vents in the Tiger Stripes region [5]. Collecting plume’s ice particles for signs of Life is a fundamental objective of future fly-by missions to Enceladus, and other icy moons, e.g., Europa, where icy plumes could exist. However, understanding particle collection on high-speed flybys through a plume environment is a challenging task. To address the above, in collaboration with the Applied Physics Laboratory (APL), we have been testing the collection of impact-generated ice grains from simulated ice plumes at the NASA Ames Vertical GunRange (AVGR) facility [e.g., 6-7].Of particular interest is the alteration of organic material in the particles during the high-speed collection process. Any such alteration could degrade the ability of instruments to detect biomarkers in the samples collected. An experimental and theoretical understanding of the organic alteration as a function of collection speed could be used to unravel these effects. Methods: Plumes of -170°C ice particles traveling at an initial speed of 2 – 3 km/s were created by shooting 3-mm hollow Aluminum (Al) projectiles (up to 6 km/s) at liquid-nitrogen-cooled ice targets of synthetic seawater (SSW). An Al witness foil is positioned to determine particle size, and ice particle impact speed is determined by high-speed video. The ice particles smash and sublimate on impact leaving salt residue, dents, and pits on the Al witness foil. We characterized Ice grains type, abundance, density, shape, and distribution caliper (Ferret)diameter via image pattern recognition analysis (ImageJ Software) of still camera photographs of the Al target. Results: Our hyper-velocity impact experiments generated an Enceladus plume analog with an ice particle mean diameter within the particle size distribution (i.e., 2 micrometers to 50 micrometers) as determined by Cassini’s multiple instruments [8]. Simulated impact speed ranged0.2 km/s to 1-2 km/s. The continued experimental production of high-velocity, micrometer-sized ice grains is underway at the AVGR. The relevance of ice particle generation is multifold, so are its applications. These include testing for fragmentation of molecular organics to interpret Cassini’s data (e.g., Cassini Data Analysis Program) and studies of forwarding contamination (a planetary protection issue).

Rosalba Bonaccorsi↗
NASA NTRS1989

Shape matching utilizing indexed hypotheses generation and testing

An indexing mechanism is developed as part of an overall scheme called SMITH (shape matching utilizing indexed hypothesis generation and testing) for two-dimensional model-based object recognition. The approach is based on a dynamic programming implementation of attributed string matching, is computationally efficient, and works effectively for both nonoccluded and occluded shapes. Another advantage of this technique is that models may be inserted or deleted with relatively little cost.

Mehrotra, Rajiv↗
NASA NTRS1987

Determine precipitation rates from visible and infrared satellite images of clouds by pattern recognition technique

A more advanced cloud pattern analysis algorithm was subsequently developed to take the shape and brightness of the various clouds into account in a manner that is more consistent with the human analyst's perception of GOES cloud imagery. The results of that classification scheme were compared with precipitation probabilities observed from ships of opportunity off the U.S. east coast to derive empirical regressions between cloud types and precipitation probability. The cloud morphology was then quantitatively and objectively used to map precipitation probabilities during two winter months during which severe cold air outbreaks were observed over the northwest Atlantic. Precipitation probabilities associated with various cloud types are summarized. Maps of precipitation probability derived from the cloud morphology analysis program for two months and the precipitation probability derived from thirty years of ship observation were observed.

Weinman, James A.↗
DOE OSTI2026

Molecular To Mesoscale Targeting of Oxoanions with Multi-Tasking Hosts

Achieving a better understanding of anion interactions both in solution and crystalline state was the overarching goal of this project. Anions are everywhere throughout Nature and play important roles in biological and environmental processes. They can be beneficial or deleterious or both in different situations and concentrations. For either reason it is important to have molecules that can bind anions for key needs that benefit society. However, recognition of specific anions is challenging due to the diffuse nature of their negative charge(s) as well as their various shapes and sizes. Understanding the basic properties of anions and how they interact with other molecules and ions in surrounding environments is key to selective recognition. In this project multi-tasking molecules for selective binding of targeted anions were designed to achieve cooperativity and synergism in one rather than multiple host molecules, including (1) cation:anion pair hosts for anions with charges of -2 or greater; (2) pH and redox activated hosts for on-off binding and release; and (3) multiple anion capture in extended host networks. Our design strategy was to combine the use of simple inexpensive building blocks and high yield synthetic pathways to provide economically feasible scale-up for applications. Oxoanions representing multiple shapes and charges were chosen based on having the potential for significant impact on DOE separations needs. Amide/amine-based macrocycles and urea/amine-based chelates and macrocycles with multiple hydrogen bonding sites provided the basic anion-binding frameworks. Successful multi-tasking outcomes were forthcoming in all three tasks. In Task 1, successful ion pair binding for anions with multiple charges was achieved. Furthermore, the ion pair molecules were capable of extended interactions through supramolecular intertwining, like fishing nets for capturing pools of fish (also fitting with Task 3). In Task 2, molecules were synthesized possessing on-off switches. These included a pH sensitive sensor for on-off binding of anions in general, as well as an electrochemical sensor selective for sulfate capture. Three new classes of extended anion host networks capable of binding multiple ions was a major outcome of Task 3. These systems included: anion sensitive, fluorescent organogels; channel-forming macrocycles for studying anion-water including larger macrocyclic cluster sandwiches; and, the offshoot of Task 1, fishing net ion-pair networks for higher valent anions. These strategies can be expanded in the future to other ions and molecules for a better understanding of intermolecular and interionic interactions.

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

Beyond 4D tracking: using cluster shapes for track seeding

Tracking is one of the most time consuming aspects of event reconstruction at the Large Hadron Collider (LHC) and its high-luminosity upgrade (HL-LHC). Innovative detector technologies extend tracking to four-dimensions by including timing in the pattern recognition and parameter estimation. However, present and future hardware already have additional information that is largely unused by existing track seeding algorithms. The shape of pixel-clusters provides an additional dimension for track seeding that can significantly reduce the combinatorial challenge of track finding. We use neural networks to show that cluster shapes can reduce significantly the rate of fake combinatorical backgrounds while preserving a high efficiency. We demonstrate this using the information in cluster singlets, doublets and triplets. Numerical results are presented with simulations from the TrackML challenge.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1984

Fidelity of Satellite Soundings

Two pattern recognition procedures to provide improvements to first guess fields for satellite temperature retrievals were developed. The first technique where a radiometer measurement is used to select one or more historical radiosonde temperature profiles as analog estimates of ambient thermal structure. Radiosondes are the vertical scales of the analogs. The vertical resolving power of the satellite radiometer is relevant only to a decision process. The analog selection process is much more effective if implemented in an orthogonalized space of measurement information. The second procedure partitions a priori dependent data into shape coherent pattern libraries using structure information inherent in the data itself. It is shown that the pattern recognition techniques are able to reduce first guess profile errors by nearly 50%. The impact of pattern recognition on temperature retrieval error is assessed by regression and physical iterative retrieval algorithms. The influence of improved first guess fields is markedly different on these two types of algorithm. Pattern recognition was a strong positive impact on the physical iterative method.

Thompson, O.↗
NASA NTRS1983

Cloud cover typing from environmental satellite imagery. Discriminating cloud structure with Fast Fourier Transforms (FFT)

The use of two dimensional Fast Fourier Transforms (FFTs) subjected to pattern recognition technology for the identification and classification of low altitude stratus cloud structure from Geostationary Operational Environmental Satellite (GOES) imagery was examined. The development of a scene independent pattern recognition methodology, unconstrained by conventional cloud morphological classifications was emphasized. A technique for extracting cloud shape, direction, and size attributes from GOES visual imagery was developed. These attributes were combined with two statistical attributes (cloud mean brightness, cloud standard deviation), and interrogated using unsupervised clustering amd maximum likelihood classification techniques. Results indicate that: (1) the key cloud discrimination attributes are mean brightness, direction, shape, and minimum size; (2) cloud structure can be differentiated at given pixel scales; (3) cloud type may be identifiable at coarser scales; (4) there are positive indications of scene independence which would permit development of a cloud signature bank; (5) edge enhancement of GOES imagery does not appreciably improve cloud classification over the use of raw data; and (6) the GOES imagery must be apodized before generation of FFTs.

Logan, T. L.↗
NASA NTRS1990

Contour-map encoding of shape for early vision

Contour maps provide a general method for recognizing 2-D shapes. All but blank images give rise to such maps, and people are good at recognizing objects and shapes from them. The maps are encoded easily in long feature vectors that are suitable for recognition by an associative memory. These properties of contour maps suggest a role for them in early visual perception. The prevalence of direction sensitive neurons in the visual cortex of mammals supports this view.

Kanerva, Pentti↗
NASA NTRS1988

Recognition of object domain by color distribution

For the image processing of an object in its natural image, it is necessary to extract in advance the object to be processed from its image. To accomplish this the outer shape of an object is extracted through human instructions, which requires a great deal of time and patience. A method involving the setting of a model of color distribution on the surface of an object is described. This method automatically provides color recognition, a piece of knowledge that represents the properties of an object, from its natural image. A method for recognizing and extracting the object in the image according to the color recognized is also described.

Mugitani, Takako↗
NASA NTRS1986

Pattern recognition of satellite cloud imagery for improved weather prediction

The major accomplishment was the successful development of a method for extracting time derivative information from geostationary meteorological satellite imagery. This research is a proof-of-concept study which demonstrates the feasibility of using pattern recognition techniques and a statistical cloud classification method to estimate time rate of change of large-scale meteorological fields from remote sensing data. The cloud classification methodology is based on typical shape function analysis of parameter sets characterizing the cloud fields. The three specific technical objectives, all of which were successfully achieved, are as follows: develop and test a cloud classification technique based on pattern recognition methods, suitable for the analysis of visible and infrared geostationary satellite VISSR imagery; develop and test a methodology for intercomparing successive images using the cloud classification technique, so as to obtain estimates of the time rate of change of meteorological fields; and implement this technique in a testbed system incorporating an interactive graphics terminal to determine the feasibility of extracting time derivative information suitable for comparison with numerical weather prediction products.

Gautier, Catherine↗
NASA NTRS2001

Spatial/Spectral Identification of Endmembers from AVIRIS Data using Mathematical Morphology

During the last several years, a number of airborne and satellite hyperspectral sensors have been developed or improved for remote sensing applications. Imaging spectrometry allows the detection of materials, objects and regions in a particular scene with a high degree of accuracy. Hyperspectral data typically consist of hundreds of thousands of spectra, so the analysis of this information is a key issue. Mathematical morphology theory is a widely used nonlinear technique for image analysis and pattern recognition. Although it is especially well suited to segment binary or grayscale images with irregular and complex shapes, its application in the classification/segmentation of multispectral or hyperspectral images has been quite rare. In this paper, we discuss a new completely automated methodology to find endmembers in the hyperspectral data cube using mathematical morphology. The extension of classic morphology to the hyperspectral domain allows us to integrate spectral and spatial information in the analysis process. In Section 3, some basic concepts about mathematical morphology and the technical details of our algorithm are provided. In Section 4, the accuracy of the proposed method is tested by its application to real hyperspectral data obtained from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) imaging spectrometer. Some details about these data and reference results, obtained by well-known endmember extraction techniques, are provided in Section 2. Finally, in Section 5 we expose the main conclusions at which we have arrived.

Plaza, Antonio↗
NASA NTRS1986

Computer vision for robots; Proceedings of the Meeting, Cannes, France, December 2-6, 1985

The conference presents papers on segmentation techniques, three-dimensional recognition and representation, processing image sequences, and navigation and mobility. Particular attention is given to determining the pose of an object, adaptive least squares correlation with geometrical constraints, and the reliable formation of feature vectors for two-dimensional shape representation. Other topics include the real-time tracking of a target moving on a natural textured background, computer vision for the guidance of roving robots, and integrating sensory data for object recognition tasks.

Faugeras, O. D.↗
NASA NTRS1977

Effect of vegetation on rock and soil type discrimination

The effect of naturally occurring vegetation on the spectral reflectance of earth materials in the wavelength region of 0.45 to 2.4 microns is determined by computer averaging of in situ acquired spectral data. The amount and type of vegetation and the spectral reflectance of the ground are considered. Low albedo materials may be altered beyond recognition with only ten per cent green vegetation cover. Dead or dry vegetation does not greatly alter the shape of the spectral reflectance curve and only changes the albedo with minimum wavelength dependency. With increasing amounts of vegetation the Landsat MSS band ratios 4/6, 4/7, 5/6, and 5/7 are significantly decreased whereas MSS ratios 4/5 and 6/7 remain entirely constant.

Siegal, B. S.↗
NASA NTRS1984

Pattern recognition for Space Applications Center director's discretionary fund

Results and conclusions are presented on the application of recent developments in pattern recognition to spacecraft star mapping systems. Sensor data for two representative starfields are processed by an adaptive shape-seeking version of the Fc-V algorithm with good results. Cluster validity measures are evaluated, but not found especially useful to this application. Recommendations are given two system configurations worthy of additional study,

Singley, M. E.↗
NASA NTRS1986

A multifunction recognition operator for telerobotic vision

Research on developing an operator capable of performing the various subtasks required of a telerobot's vision sensor is reported. The operator uses a goal-driven matching technique which is an application of a linear programming method that readily adapts to the elastic template matching approach to pattern recognition. Four applications of the operator are discussed: (1) three-space location of an isolated object; (2) shape determination of isolated planar figures; (3) image compression/restoration; and (4) shape decomposition.

Goode, P. W., IV↗