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Image processing method is capable of contrast enhancement, noise filtering, and photometric distortion removal in near real time. System uses digital image integration and digital video recorder as image buffer. Each frame of data is entered into memory; registers provide readout of stored TV frame.
The accurate identification of biosignatures (indications of life) from data taken from remote or in situ planetary exploration is one of the most important challenges in astrobiology, the interdisciplinary field examining habitability and the potential for extraterrestrial life. This study employs machine learning algorithms to optimize the identification of biosignatures, with an emphasis on those which are agnostic to a specific biochemical basis. We exploit the wealth of terrestrial data available from biogenic and abiogenic systems to enhance efficient feature prioritization. Our dataset, pulled from public databases and laboratory recorded measurements, includes elemental abundance, isotopic fractionation, and VNIR/Raman spectra The data curation process included standardization for detection limits and ranges. Subsequent feature extraction yielded detailed inputs for machine learning, including combinations of elemental content, isotopic ratios, and parameters of spectral peaks and troughs. Feature significance was evaluated across diverse machine learning methodologies, such as k-nearest neighbors, logistic regression, Random Forest, support vector machines, and Gaussian Naïve Bayes, along with a combined voting classifier. We utilized Receiver Operating Characteristic Area Under the Curve (ROC AUC) across 2,000 50% test-train splits as a robust metric of model performance. Results revealed a promising ROC AUC of 0.853 for the combined voting classifier. Removing elemental abundance data notably reduced model accuracy (13% decrease in AUC), highlighting its critical role in biosignature detection. Several other individual data features exhibited significance within their respective data types, offering additional granularity. This research fortifies the relevance of machine learning to astrobiology, potentially enhancing life detection missions by allowing algorithmic prioritization of high-interest samples for further investigation. Future work will refine data standardization, expand the dataset to include more terrestrial systems, and incorporate convolutional neural networks for spectral feature extraction. The potential for public data sharing is also under exploration, reinforcing our commitment to collective scientific advancement.
It is found from model simulations of trace gas and meteorological data from aircraft campaigns that deep convection may enhance the potential for photochemical ozone production in the middle and upper troposphere by up to a factor of 60. Examination of half a dozen individual convective episodes show that the degree of enhancement is highly variable. Factors affecting enhancement include boundary layer NO(x) mixing ratios, differences in the strength and structure of convective cells, as well as variation in the amount of background pollution already in the free troposphere.
The Gravity Recovery and Climate Experiment (GRACE) twin satellites observe time variations in Earth's gravity field which yield valuable information about changes in terrestrial water storage (TWS). GRACE is characterized by low spatial (greater than 150,000 square kilometers) and temporal (greater than 10 day) resolution but has the unique ability to sense water stored at all levels (including groundwater) systematically and continuously. The GRACE Data Assimilation System (GRACE-DAS), based on the Catchment Land Surface Model (CLSM) enhances the value of the GRACE water storage data by enabling spatial and temporal downscaling and vertical decomposition into moisture 39 components (i.e. groundwater, soil moisture, snow), which individually are more useful for scientific applications. In this study, GRACE-DAS was applied to North America and GRACE-based drought indicators were developed as part of a larger effort that investigates the possibility of more comprehensive and objective identification of drought conditions by integrating spatially, temporally and vertically disaggregated GRACE data into the U.S. and North American Drought Monitors. Previously, the Drought Monitors lacked objective information on deep soil moisture and groundwater conditions, which are useful indicators of drought. Extensive datasets of groundwater storage from USGS monitoring wells and soil moisture from the Soil Climate Analysis Network (SCAN) were used to assess improvements in the hydrological modeling skill resulting from the assimilation of GRACE TWS data. The results point toward modest, but statistically significant, improvements in the hydrological modeling skill across major parts of the United States, highlighting the potential value of GRACE assimilated water storage field for improving drought detection.
The electrostatic probe experiment aboard the ISIS-1 satellite detects a quasi-stable dayside plasma trough in the protonosphere with a characteristic time of at least 2 weeks. This behavior contrasts with measurements at 1000 km from Explorer-22, which indicate a trough of a characteristic time less than a satellite orbit period. Associated with the protonospheric trough is an electron temperature maximum approximately 6000 K that is sharp at midnight and broad at noon. In spring and summer, a second noontime temperature maximum often appears poleward of 70 invariant latitude, accompanied by an enhancement in the ionization. The ISIS-1 data indicates that local processes such as dayside F-region photoionization and ionization by energetic cusp particles produce enhancements in plasma density and electron temperature. These enhancements modify the plasmaspheric boundary to varying degrees along a magnetic field tube, thus leading to a plasmas pheric boundary with distinctive features at various positions along a field tube.
The next Landsat satellite, which is scheduled for launch in early 2013, will carry two instruments: the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). Significant design changes over previous Landsat instruments have been made to these sensors to potentially enhance the quality of Landsat image data. TIRS, which is the focus of this study, is a dual-band instrument that uses a push-broom style architecture to collect data. To help understand the impact of design trades during instrument build, an effort was initiated to model TIRS imagery. The Digital Imaging and Remote Sensing Image Generation (DIRSIG) tool was used to produce synthetic "on-orbit" TIRS data with detailed radiometric, geometric, and digital image characteristics. This work presents several studies that used DIRSIG simulated TIRS data to test the impact of engineering performance data on image quality in an effort to determine if the image data meet specifications or, in the event that they do not, to determine if the resulting image data are still acceptable.
Flying in poor visibility conditions, such as rain, snow, fog or haze, is inherently dangerous. However these conditions can occur at nearly any location, so inevitably pilots must successfully navigate through them. At NASA Langley Research Center (LaRC), under support of the Aviation Safety and Security Program Office and the Systems Engineering Directorate, we are developing an Enhanced Vision System (EVS) that combines image enhancement and synthetic vision elements to assist pilots flying through adverse weather conditions. This system uses a combination of forward-looking infrared and visible sensors for data acquisition. A core function of the system is to enhance and fuse the sensor data in order to increase the information content and quality of the captured imagery. These operations must be performed in real-time for the pilot to use while flying. For image enhancement, we are using the LaRC patented Retinex algorithm since it performs exceptionally well for improving low-contrast range imagery typically seen during poor visibility conditions. In general, real-time operation of the Retinex requires specialized hardware. To date, we have successfully implemented a single-sensor real-time version of the Retinex on several different Digital Signal Processor (DSP) platforms. In this paper we give an overview of the EVS and its performance requirements for real-time enhancement and fusion and we discuss our current real-time Retinex implementations on DSPs.
In a recent paper, Boslough and Cygan reported the observation of shock-enhanced chemical weathering kinetics of three silicate minerals. Based on the experimental data and on those of Tyburczy and Ahrens for enhanced dehydration kinetics of shocked serpentine, a mechnaism is proposed by which shock-activated minerals are selectively weathered on the surface of Mars. The purpose of the present abstract is to argue on the basis of relative volumes of shocked materials that, as a direct consequence of selective weathering, the composition of the weathered surface units on Mars should be enriched in meteoritic material.
We propose to develop, test, demonstrate, and deliver to NASA a powerful and versatile data acquisition, manipulation, analysis and visualization system which will enhance scientific capabilities in the display and interpretation of diverse and distributed data within an integrated user-friendly environment. Our approach exploits existing technologies and combines three major elements into an easy-to-use interactive package: (1) innovative visualization software; (2) advanced database techniques; and (3) a rich set of mathematical and image processing tools. Visualization capabilities will include one-, two-, and three-dimensional displays, along with animation, compression, warping and slicing functions. Analysis tools will include generic mathematical and statistical techniques along with the ability to use large scale models for interactive interpretation of large volume data sets. Our system will be implemented on Sun and DEC UNIX workstations and on the Stardent Graphics Super computer. Our final deliverable will include complete documentation and a NASA/NSF-CDAW/SUNDIAL campaign demonstration.
We propose to develop, test, demonstrate, and deliver to NASA a powerful and versatile data acquisition, manipulation, analysis and visualization system which will enhance scientific capabilities in the display and interpretation of diverse and distributed data within an integrated user-friendly environment. Our approach exploits existing technologies and combines three major elements into an easy-to-use interactive package: (1) innovative visualization software; (2) advanced database techniques; and (3) a rich set of mathematical and image processing tools. Visualization capabilities will include one-, two-, and three-dimensinal displays, along with animation, compression, warping and slicing functions. Analysis tools will include generic mathematical and statistical techniques along with the ability to use large scale models for interactive interpretation of large volume data sets. Our system will be implemented on Sun and EC UNIX workstations and on the Stardent Graphics Supercomputer. Our final deliverable will include complete documentation and a NASA/NSF-CDAW/SUNDIAL campaign demonstration.
A prototype system has been developed to aid the experimental space scientist in the display and analysis of spaceborne data acquired from direct measurement sensors in orbit. We explored the implementation of a rule-based environment for semi-automatic generation of visualizations that assist the domain scientist in exploring one's data. The goal has been to enable rapid generation of visualizations which enhance the scientist's ability to thoroughly mine his data. Transferring the task of visualization generation from the human programmer to the computer produced a rapid prototyping environment for visualizations. The visualization and analysis environment has been tested against a set of data obtained from the Hot Plasma Composition Experiment on the AMPTE/CCE satellite creating new visualizations which provided new insight into the data.
The charge composition of solar cosmic rays were measured during two flares occurring in April and September 1971. The results were derived from a solid state dE/dx vs E telescope which was part of the cosmic ray experiment on the IMP 6 spacecraft. The data suggest that the helium to medium ratio may be varying from one flare to the next. The abundance ratios (normalized to oxygen) are compared with measurements of other investigators and significant disagreements are found. In particular, the data do not exhibit any systematic enhancement of heavy nuclei with respect to the spectroscopic abundances such as previously reported. Finally, the results are compared with the spectroscopically determined coronal and photospheric values, and again several important differences between the two sets of data are found.
The evolution and enhancement of the International Space Station (ISS) is currently being planned in conjunction with the on-orbit construction of the baseline configuration. Three principal areas have been identified that will contribute to the evolution of ISS: Pre-Planned Program Improvement (P3I), Utilization & Commercialization, and Human Exploration and Development of Space (HEDS) missions. The ISS Evolution Strategy, under development by the Spacecraft and Sensors Branch of NASA Langley Research Center, seeks to coordinate the P3I technology development with Commercialization/Utilization activities and HEDS advanced mission accommodation to provide synergistic technology developments for all three areas. The focal point of this proposed strategy is the ISS Evolution Data Book (EDB), a tool for aiding the evolution and enhancement of ISS beyond Assembly Complete. This paper will discuss the strategy and provide an overview of the EDB, describing the contents of each section. It will also discuss potential applications of the EDB and present an example Design Reference Mission (DRM). The latest status of the EDB and the plans for completing and enhancing the book will also be summarized.
The tremendous backlog of unanalyzed satellite data necessitates the development of improved methods for data cataloging and analysis. Ford Aerospace has developed an image analysis system, SIANN (Satellite Image Analysis using Neural Networks) that integrates the technologies necessary to satisfy NASA's science data analysis requirements for the next generation of satellites. SIANN will enable scientists to train a neural network to recognize image data containing scenes of interest and then rapidly search data archives for all such images. The approach combines conventional image processing technology with recent advances in neural networks to provide improved classification capabilities. SIANN allows users to proceed through a four step process of image classification: filtering and enhancement, creation of neural network training data via application of feature extraction algorithms, configuring and training a neural network model, and classification of images by application of the trained neural network. A prototype experimentation testbed was completed and applied to climatological data.
Timely data on low altitude energetic protons is very important for predicting the effect on humans and satellites. Because of the high bioeffectivity of protons in tissue, determination of the geographical location of the south Atlantic anomally (SAA) would be especially useful for reducing the risk associated with extravehicular activities. Because the AP8 model was developed with an epoch several decades old, the location of the south Atlantic anomally is incorrectly positioned in the model. The lack of extensive present-epoch energetic proton data at low altitude has prevented the update of the AP8 proton model. While there are studies in progress which are attempting to update the model at low altitude by using the path-integrated column density of the residual atmosphere as an organizing parameter, success in those studies will be defined by consistency in organization of old data. New data will still be required to provide a good present epoch model. As an alternative to using instrumental measurements of proton fluxes, background effects, proxy data sets such as SEUs, anomalies in instruments, enhanced background in shielded sensors believed to be induced by high energy protons, and dosimeter data in which key parameters (energy, efficiency, geometric factor, shielding, etc.) are poorly known can also be used. This in situ data is not as precise as direct measurement of the proton flux as geometric factor and shielding etc. are not calibrated, but these factors do not affect determination of the location of the SAA.
A computational framework for the simulation of dynamic stall suppression with combustion-powered actuation (COMPACT) is validated against wind tunnel experimental results on a VR-12 airfoil. COMPACT slots are located at 10% chord from the leading edge of the airfoil and directed tangentially along the suction-side surface. Helicopter rotor-relevant flow conditions are used in the study. A computationally efficient two-dimensional approach, based on unsteady Reynolds-averaged Navier-Stokes (RANS), is compared in detail against the baseline and the modified airfoil with COMPACT, using aerodynamic forces, pressure profiles, and flow-field data. The two-dimensional RANS approach predicts baseline static and dynamic stall very well. Most of the differences between the computational and experimental results are within two standard deviations of the experimental data. The current framework demonstrates an ability to predict COMPACT efficacy across the experimental dataset. Enhanced aerodynamic lift on the downstroke of the pitching cycle due to COMPACT is well predicted, and the cycleaveraged lift enhancement computed is within 3% of the test data. Differences with experimental data are discussed with a focus on three-dimensional features not included in the simulations and the limited computational model for COMPACT.
NASA's Earth Science Data and Information System (ESDIS) Project collects Earth science data usage metrics on a daily basis through the ESDIS Metrics System (EMS). This includes metrics on distribution of data products, users, data volumes, and number of files, which are key parameters in evaluating system-level performance of any of the Distributed Active Archive Centers (DAACs) encompassed by NASA's Earth Observing System (EOS) Data and Information System (EOSDIS). EOSDIS data usage metrics illustrate the benefits of making NASA data openly available to the public and show a rapid growth in data distribution to a worldwide user community. In fact, each year since 2014 the EOSDIS has distributed over one billion data files of products from EOS satellite, airborne, and in situ observations. An assessment of the long-term trends of data usage metrics and user characterization provides insights into data usability.This study will focus on describing the EMS as a metrics collection tool and will provide a comprehensive analysis of EOSDIS data usage metrics over the last 10 years. This study will also characterize the product distribution metrics by various tools and services, such as Giovanni, the Open-source Project for a Network Data Access Protocol (OPeNDAP), and subsets, to address how these tools/services have extended the usage of data in the EOSDIS collection. Data usage patterns based on discipline and study area will further assist in understanding how EOSDIS data user needs have evolved over time. Results from this study will provide useful information for the DAACs that can help them improve the functionality of their tools and services as well as more efficiently allocate the resources necessary for enhanced access and availability of their data products. Knowledge of these metrics may also benefit user discovery of data in the EOSDIS collection, promote research collaboration, and stimulate new ideas from work and research conducted using specific datasets and data collections.
CALIPSO aerosol backscatter enhancement in the transition zone between clouds and clear sky areas is revisited with particular attention to effects of data selection based on the confidence level of cloud-aerosol discrimination (CAD). The results show that backscatter behavior in the transition zone strongly depends on the CAD confidence level. Higher confidence level data has a flatter backscatter far away from clouds and a much sharper increase near clouds (within 4 km), thus a smaller transition zone. For high confidence level data it is shown that the overall backscatter enhancement is more pronounced for small clear-air segments and horizontally larger clouds. The results suggest that data selection based on CAD reduces the possible effects of cloud contamination when studying aerosol properties in the vicinity of clouds.