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Data obtained in the framework of an Optical Plume Anomaly Detection (OPAD) program intended to create a rocket engine health monitor based on spectrometric detections of anomalous atomic and molecular species in the exhaust plume are analyzed. The major results include techniques for handling data noise, methods for registration of spectra to wavelength, and a simple automatic process for estimating the metallic component of a spectrum.
A numerical design methodology for multistage turbomachinery is being developed that minimizes the use of experimental data required in the design process. This approach is resulting in an efficient method of enlarging the design database for turbomachinery blading. This capability has been demonstrated for blade sections, and the goal is to extend it to fully three dimensional, multistage configurations.
Development of quadtree as hierarchical data structuring technique for representing spatial data (like points, regions, surfaces, lines, curves, volumes, etc.) has been motivated to a large extent by storage requirements of images, maps, and other multidimensional (spatially structured) data. For many spatial algorithms, time-efficiency of quadtrees in terms of execution may be as important as their space-efficiency concerning storage conditions. Briefly, the quadtree is a class of hierarchical data structures which is based on the recursive partition of a square region into quadrants and sub-quadrants until a predefined limit. Beyond the wide applicability of quadtrees in image processing, spatial information analysis, and building digital databases (processes becoming ordinary for the astronomical community), there may be numerous further applications in astronomy. Some of these practicable applications based on quadtree representation of astronomical data are presented and suggested for further considerations. Examples are shown for use of point as well as region quadtrees. Statistics of different leaf and non-leaf nodes (homogeneous and heterogeneous sub-quadrants respectively) at different levels may provide useful information on spatial structure of astronomical data in question. By altering the principle guiding the decomposition process, different types of spatial data may be focused on. Finally, a sampling method based on quadtree representation of an image is proposed which may prove to be efficient in the elaboration of sampling strategy in a region where observations were carried out previously either with different resolution or/and in different bands.
This report summarizes achievements during year three of our project to investigate the use of ERS-1 SAR data to study Arctic ice and ice/atmosphere processes. The project was granted a one year extension, and goals for the final year are outlined. The specific objects of the project are to determine how the development and evolution of open water/thin ice areas within the interior ice pack vary under different atmospheric synoptic regimes; compare how open water/thin ice fractions estimated from large-area divergence measurements differ from fractions determined by summing localized openings in the pack; relate these questions of scale and process to methods of observation, modeling, and averaging over time and space; determine whether SAR data might be used to calibrate ice concentration estimates from medium and low-rate bit sensors (AVHRR and DMSP-OLS) and the special sensor microwave imager (SSM/I); and investigate methods to integrate SAR data for turbulent heat flux parametrization at the atmosphere interface with other satellite data.
A system and method for monitoring an apparatus or process asset including partitioning an unpartitioned training data set into a plurality of training data subsets each having an operating mode associated thereto; creating a process model comprised of a plurality of process submodels each trained as a function of at least one of the training data subsets; acquiring a current set of observed signal data values from the asset; determining an operating mode of the asset for the current set of observed signal data values; selecting a process submodel from the process model as a function of the determined operating mode of the asset; calculating a current set of estimated signal data values from the selected process submodel for the determined operating mode; and outputting the calculated current set of estimated signal data values for providing asset surveillance and/or control.
Images and the radial profiles of the temperature, abundance, and brightness for 70 clusters of galaxies observed by XMM-Newton are presented along with a detailed discussion of the data reduction and analysis methods, including background modeling, which were used in the processing. Proper consideration of the various background components is vital to extend the reliable determination of cluster parameters to the largest possible cluster radii. The various components of the background including the quiescent particle background, cosmic diffuse emission, soft proton contamination, and solar wind charge exchange emission are discussed along with suggested means of their identification, filtering, and/or their modeling and subtraction. Every component is spectrally variable, sometimes significantly so, and all components except the cosmic background are temporally variable as well. The distributions of the events over the FOV vary between the components, and some distributions vary with energy. The scientific results from observations of low surface brightness objects and the diffuse background itself can be strongly affected by these background components and therefore great care should be taken in their consideration.
Prognostics of large composite structures is a topic of increasing interest in the field of structural health monitoring for aerospace, civil, and mechanical systems. Along with recent advancements in real-time structural health data acquisition and processing for damage detection and characterization, model-based stochastic methods for life prediction are showing promising results in the literature. Among various model-based approaches, particle-filtering algorithms are particularly capable in coping with uncertainties associated with the process. These include uncertainties about information on the damage extent and the inherent uncertainties of the damage propagation process. Some efforts have shown successful applications of particle filtering-based frameworks for predicting the matrix crack evolution and structural stiffness degradation caused by repetitive fatigue loads. Effects of other damage modes such as delamination, however, are not incorporated in these works. It is well established that delamination and matrix cracks not only co-exist in most laminate structures during the fatigue degradation process but also affect each other's progression. Furthermore, delamination significantly alters the stress-state in the laminates and accelerates the material degradation leading to catastrophic failure. Therefore, the work presented herein proposes a particle filtering-based framework for predicting a structure's remaining useful life with consideration of multiple co-existing damage-mechanisms. The framework uses an energy-based model from the composite modeling literature. The multiple damage-mode model has been shown to suitably estimate the energy release rate of cross-ply laminates as affected by matrix cracks and delamination modes. The model is also able to estimate the reduction in stiffness of the damaged laminate. This information is then used in the algorithms for life prediction capabilities. First, a brief summary of the energy-based damage model is provided. Then, the paper describes how the model is embedded within the prognostic framework and how the prognostics performance is assessed using observations from run-to-failure experiments
Far-infrared continuum studies from the Kuiper Airborne Observatory are described that are designed to fully exploit the small-scale spatial information that this facility can provide. This work gives the clearest picture to data on the structure of galactic and extragalactic star forming regions in the far infrared. Work is presently being done with slit scans taken simultaneously at 50 and 100 microns, yielding one-dimensional data. Scans of sources in different directions have been used to get certain information on two dimensional structure. Planned work with linear arrays will allow us to generalize our techniques to two dimensional image restoration. For faint sources, spatial information at the diffraction limit of the telescope is obtained, while for brighter sources, nonlinear deconvolution techniques have allowed us to improve over the diffraction limit by as much as a factor of four. Information on the details of the color temperature distribution is derived as well. This is made possible by the accuracy with which the instrumental point-source profile (PSP) is determined at both wavelengths. While these two PSPs are different, data at different wavelengths can be compared by proper spatial filtering. Considerable effort has been devoted to implementing deconvolution algorithms. Nonlinear deconvolution methods offer the potential of superresolution -- that is, inference of power at spatial frequencies that exceed D lambda. This potential is made possible by the implicit assumption by the algorithm of positivity of the deconvolved data, a universally justifiable constraint for photon processes. We have tested two nonlinear deconvolution algorithms on our data; the Richardson-Lucy (R-L) method and the Maximum Entropy Method (MEM). The limits of image deconvolution techniques for achieving spatial resolution are addressed.
The Nondestructive Evaluation Sciences Branch at NASA s Langley Research Center has been actively involved in the development of thermographic inspection techniques for more than 15 years. Since the Space Shuttle Columbia accident, NASA has focused on the improvement of advanced NDE techniques for the Reinforced Carbon-Carbon (RCC) panels that comprise the orbiter s wing leading edge. Various nondestructive inspection techniques have been used in the examination of the RCC, but thermography has emerged as an effective inspection alternative to more traditional methods. Thermography is a non-contact inspection method as compared to ultrasonic techniques which typically require the use of a coupling medium between the transducer and material. Like radiographic techniques, thermography can be used to inspect large areas, but has the advantage of minimal safety concerns and the ability for single-sided measurements. Principal Component Analysis (PCA) has been shown effective for reducing thermographic NDE data. A typical implementation of PCA is when the eigenvectors are generated from the data set being analyzed. Although it is a powerful tool for enhancing the visibility of defects in thermal data, PCA can be computationally intense and time consuming when applied to the large data sets typical in thermography. Additionally, PCA can experience problems when very large defects are present (defects that dominate the field-of-view), since the calculation of the eigenvectors is now governed by the presence of the defect, not the good material. To increase the processing speed and to minimize the negative effects of large defects, an alternative method of PCA is being pursued when a fixed set of eigenvectors is used to process the thermal data from the RCC materials. These eigen vectors can be generated either from an analytic model of the thermal response of the material under examination, or from a large cross section of experimental data. This paper will provide the details of the analytic model; an overview of the PCA process; as well as a quantitative signal-to-noise comparison of the results of performing both embodiments of PCA on thermographic data from various RCC specimens. Details of a system that has been developed to allow insitu inspection of a majority of shuttle RCC components will be presented along with the acceptance test results for this system. Additionally, the results of applying this technology to the Space Shuttle Discovery after its return from flight will be presented.
Transmission photoelastic analysis of composite models has been shown in recent years to be a viable stress analysis method for composite structures. However, difficulties in the fabrication of the model materials require the development of efficient mechanical and optical calibration procedures. In this paper, the methods for measuring elastic and photoelastic constants of composites are briefly reviewed. Then a new method, utilizing a relatively small calibration specimen, is described. In this method, electrical resistance strain gauges are mounted at selected points of a half-plane model and photoelastic measurements and strain readings are simultaneously taken. The data are processed by a least-squares procedure to yield the elastic and photoelastic constants. Comparison of results obtained by the proposed method with the results from conventional tests shows good agreement.
The definition of boundaries of discrete structural provinces on the surface of Mars has previously been made by the assumptions of circular or radial symmetry (in the case of the Tharsis olateau), by artificial quadrangle boundaries for regional studies, or simply by regional geographical features. Much of the planetary structural mapping has been presented in a line or spot format in existing consortium data. Methods are being generated to convert the detailed maps of tectonic features to a format that can be used with standard image processing techniques. In this manner such data can be more readily compared to age and other attributes deduced from geological mapping, gravity results, and topography. The 24 photomosaics covering the Eastern Hemisphere of Mars have already been examined for four types of structural features: erosional remnants, ridges, graben, and scarps. When digitized, each feature was flagged according to feature type, azimuth and length. These values were stored as text files on a quad-by-quad basis for each quad studied.
Multi-person teams are sometimes responsible for critical tasks, such as flying an airliner. Here we present a method using gaze tracking data to assess shared visual attention, a term we use to describe the situation where team members are attending to a common set of elements in the environment. Gaze data are quantized with respect to a set of N areas of interest (AOIs); these are then used to construct a time series of N dimensional vectors, with each vector component representing one of the AOIs, all set to 0 except for the component corresponding to the currently fixated AOI, which is set to 1. The resulting sequence of vectors can be averaged in time, with the result that each vector component represents the proportion of time that the corresponding AOI was fixated within the given time interval. We present two methods for comparing sequences of this sort, one based on computing the time-varying correlation of the averaged vectors, and another based on a chi-square test testing the hypothesis that the observed gaze proportions are drawn from identical probability distributions. We have evaluated the method using synthetic data sets, in which the behavior was modeled as a series of "activities," each of which was modeled as a first-order Markov process. By tabulating distributions for pairs of identical and disparate activities, we are able to perform a receiver operating characteristic (ROC) analysis, allowing us to choose appropriate criteria and estimate error rates. We have applied the methods to data from airline crews, collected in a high-fidelity flight simulator (Haslbeck, Gontar & Schubert, 2014). We conclude by considering the problem of automatic (blind) discovery of activities, using methods developed for text analysis.
Four methods of detection of bodies of water are under development as means to enable autonomous robotic ground vehicles to avoid water hazards when traversing off-road terrain. The methods involve processing of digitized outputs of optoelectronic sensors aboard the vehicles. It is planned to implement these methods in hardware and software that would operate in conjunction with the hardware and software for navigation and for avoidance of solid terrain obstacles and hazards. The first method, intended for use during the day, is based on the observation that, under most off-road conditions, reflections of sky from water are easily discriminated from the adjacent terrain by their color and brightness, regardless of the weather and of the state of surface waves on the water. Accordingly, this method involves collection of color imagery by a video camera and processing of the image data by an algorithm that classifies each pixel as soil, water, or vegetation according to its color and brightness values (see figure). Among the issues that arise is the fact that in the presence of reflections of objects on the opposite shore, it is difficult to distinguish water by color and brightness alone. Another issue is that once a body of water has been identified by means of color and brightness, its boundary must be mapped for use in navigation. Techniques for addressing these issues are under investigation. The second method, which is not limited by time of day, is based on the observation that ladar returns from bodies of water are usually too weak to be detected. In this method, ladar scans of the terrain are analyzed for returns and the absence thereof. In appropriate regions, the presence of water can be inferred from the absence of returns. Under some conditions in which reflections from the bottom are detectable, ladar returns could, in principle, be used to determine depth. The third method involves the recognition of bodies of water as dark areas in short-wavelength infrared (SWIR) images. This method is based on the fact, well known among experts in remote sensing, that water bodies of any appreciable depth appear very dark in near-infrared, overhead imagery. Even under a thick layer of marine fog, SWIR illumination is present. Hence, this method may work even in the presence of clouds, though it is unlikely to work at night. Snow and ice also exhibit very strong absorption at wavelengths greater than about 1.4 m. Hence, the wavelength range of about 1.5 to 1.6 m might be useable in this method for recognizing water, snow, and ice. One notable drawback of this method is that useful look-ahead distance could be limited by surface reflections. The fourth method, intended for use at night, involves the contrast between water and terrain in thermal-infrared (medium-wavelength infrared) imagery. This method is based on the fact that at night, water is usually warmer than the adjacent terrain. Look-ahead distance could be limited in this method because, for reasons not yet fully understood, water appears to darken in the thermal infrared with increasing distance.
The search for radio signals from extraterrestrial intelligent beings (SETI) requires the use of large instantaneous bandwidth (500 MHz) and high resolution (20 Hz) spectrometers. Digital systems with a high degree of modularity can be used to provide this capability, and this method has been widely discussed. Another technique for meeting the SETI requirement is to use a crossed Bragg cell spectrometer as described by Psaltis and Casasent. This technique makes use of the Folded Spectrum concept, introduced by Thomas. The Folded Spectrum is a 2-D Fourier Transform of a raster scanned 1-D signal. It is directly related to the long 1-D spectrum of the original signal and is ideally suited for optical signal processing. The folded spectrum technique has received little attention to date, primarily because early systems made use of photographic film which are unsuitable for the real time data analysis and voluminous data requirements of SETI. An analysis of the crossed Bragg cell spectrometer is presented as a method to achieve the spectral processing requirements for SETI. Systematic noise contributions unique to the Bragg cell system will be discussed.
NASA has made science and technology investments to better utilize its large space-borne remote sensing data holdings of the Earth. With the launch of Terra, NASA created a data-rich environment where the challenge is to fully utilize the data collected from EOS however, despite unprecedented amounts of observed data, there is a need for increasing the frequency, resolution, and diversity of observations. Current terrestrial models that use remote sensing data were constructed in a relatively data and compute limited era and do not take full advantage of on-line learning methods and assimilation techniques that can exploit these data. NASA has invested in visualization, data mining and knowledge discovery methods which have facilitated data exploitation, but these methods are insufficient for improving Earth science models that have extensive background knowledge nor do these methods refine understanding of complex processes. Investing in interdisciplinary teams that include computational scientists can lead to new models and systems for online operation and analysis of data that can autonomously improve in prediction skill over time.
Recent fundamental jet noise tests were conducted with the objective of providing data that challenges jet noise prediction methods. Three cases have been extracted to pose as challenges to the jet noise prediction community. Case 1 targets efforts in LES to appropriately mimic unresolveable turbulent boundary layers in nozzles, and to demonstrate where they are, and are not, important for noise. Case 2 targets acoustic analogy methods, providing cardinal data for the question of azimuthal localization and the broader issue of designing nozzles with favorable azimuthal directivity. Case 3 is an example where nozzle systems exhibit global resonances, which cannot be attacked by steady-state calculations. Given that it is very difficult to foresee such unsteady behavior (speaking from experience), it is crucial that low-cost methods be developed to catch these during the design process. Analysis and documentation of the test data is ongoing.
Planetary science missions have the opportunity to enhance science return through deployment of autonomous capabilities designed to dynamically respond to new information. Future outer solar system missions to ocean worlds in particular would benefit from this technology - intelligent science payloads (ISP) - because it would allow for a coordinated, near real-time response to ephemeral ‘events’ such as plumes, tectonism, surface implantation, volatile releases, thermal and magnetic anomalies, or radiation, as well as increasing the cadence and coverage of data collection. Prioritization and decision-making frameworks from ISP could be deployed at various scales - from analysis onboard a spacecraft with multiple instruments – to coordinated analyses among separate spacecraft in an e.g., distributed systems mission (DSM) composed of multiple SmallSats. Goddard’s Intelligent Science Payload team is developing an agile autonomous architecture for an icy ocean worlds DSM concept. Our goals are to coordinate data collection and onboard data analysis, and to make autonomous decisions for new data collection and analysis based on science priorities between multiple spacecraft with variable instrumentation and orbits. We use a range of data analysis tools to coordinate the DSM response, spanning from observations of data over a specified threshold to more computationally intensive machine learning algorithms (ML). ML algorithms here currently focus on determining the composition of an ocean world using mass spectrometry, and specifically methods for understanding ‘novelties’ and potential biosignatures. These algorithms could be used to quickly process and analyze onboard data that would be significantly delayed in downlink due to long communication delays for outer solar system missions in order to make dynamic science observations. Our ocean worlds case study ISP architecture is intended as an ‘agile’ and modular framework that could be used as a whole or as particular modules based on mission needs.
A measurement-based radiance estimation approach for vicarious radiometric calibration of spaceborne multispectral remote sensing systems has been developed. This simplified process eliminates the use of radiative transfer codes and reduces the number of atmospheric assumptions required to perform sensor calibrations. Like prior approaches, the simplified method involves the collection of ground truth data coincident with the overpass of the remote sensing system being calibrated, but this approach differs from the prior techniques in both the nature of the data collected and the manner in which the data are processed. In traditional vicarious radiometric calibration, ground truth data are gathered using ground-viewing spectroradiometers and one or more sun photometer( s), among other instruments, located at a ground target area. The measured data from the ground-based instruments are used in radiative transfer models to estimate the top-of-atmosphere (TOA) target radiances at the time of satellite overpass. These TOA radiances are compared with the satellite sensor readings to radiometrically calibrate the sensor. Traditional vicarious radiometric calibration methods require that an atmospheric model be defined such that the ground-based observations of solar transmission and diffuse-to-global ratios are in close agreement with the radiative transfer code estimation of these parameters. This process is labor-intensive and complex, and can be prone to errors. The errors can be compounded because of approximations in the model and inaccurate assumptions about the radiative coupling between the atmosphere and the terrain. The errors can increase the uncertainty of the TOA radiance estimates used to perform the radiometric calibration. In comparison, the simplified approach does not use atmospheric radiative transfer models and involves fewer assumptions concerning the radiative transfer properties of the atmosphere. This new technique uses two neighboring uniform ground target areas having different reflectance values. The target areas can be natural or artificial and must be large enough to minimize adjacent-pixel contamination effects. The radiative coupling between the atmosphere and the terrain needs to be approximately the same for the two targets. This condition can be met for relatively uniform backgrounds when the distance between the targets is within a few hundred meters. For each target area, the radiance leaving the ground in the direction of the satellite is measured with a radiometrically calibrated spectroradiometer. Using the radiance measurements from the two targets, atmospheric adjacency and atmospheric scattering effects can be subtracted, thereby eliminating many assumptions about the atmosphere and the radiative interaction between the atmosphere and the terrain. In addition, the radiometrically calibrated spectroradiometer can be used with a known reflectance target to estimate atmospheric transmission and diffuse- to-global ratios without the need for ancillary sun photometers. Several comparisons between the simplified method and traditional techniques were found to agree within a few percent. Hence, the simplified method reduces the overall complexity of performing vicarious calibrations and can serve as a method for validating traditional radiative transfer models