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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Functional Near Infrared Spectroscopy: Watching the Brain in Flight

Functional Near Infrared Spectroscopy (fNIRS) is an emerging neurological sensing technique applicable to optimizing human performance in transportation operations, such as commercial aviation. Cognitive state can be determined via pattern classification of functional activations measured with fNIRS. Operational application calls for further development of algorithms and filters for dynamic artifact removal. The concept of using the frequency domain phase shift signal to tune a Kalman filter is introduced to improve the quality of fNIRS signals in real-time. Hemoglobin concentration and phase shift traces were simulated for four different types of motion artifact to demonstrate the filter. Unwanted signal was reduced by at least 43%, and the contrast of the filtered oxygenated hemoglobin signal was increased by more than 100% overall. This filtering method is a good candidate for qualifying fNIRS signals in real time without auxiliary sensors.

Harrivel, Angela↗
NASA NTRS2019

Probability Index of Low Stratus and Fog at Dawn using Dual Geostationary Satellite Observations from COMS and FY-2D near the Korean Peninsula

We developed a new remote sensing method for detecting low stratus and fog (LSF) at dawn in terms of probability index (PI) of LSF from simultaneous stereo observations of two geostationary-orbit satellites; the Korean Communication, Ocean, and Meteorological Satellite (COMS; 128.2°E); and the Chinese FengYun satellite (FY-2D; 86.5°E). The algorithm was validated near the Korean Peninsula between the months of April and August from April 2012 to June 2015, by using surface observations at 45 meteorological stations in South Korea. The optical features of LSF were estimated by using satellite retrievals and simulated data from the radiative transfer model. The PI was calculated using the combination of three satellite-observed variables: 1) the reflectance at 0.67 μm (R(0.67)) from COMS, and 2) the FY-2D R(0.67) minus the COMS R(0.67) (△R(0.67)) and (3) the FY-2D-COMS difference in the brightness temperature difference between 3.7 and 11.0 μm (ΔBTD(3.7-11)). The three variables, adopted from the top three probability of detection (POD) scores for their fog detection thresholds: △R(0.67) (0.82) > ΔBTD(3.7-11) (0.73) > R(0.67) (0.70) > BTD(3.7-11) (0.51). The LSF PI for this algorithm was significantly better in the two case studies compared to that using COMS only (i.e., R(0.67) or BTD(3.7-11)), so that this improvement was due to △R(0.67) and ΔBTD(3.7-11). Overall, PI in the LSF spatial distribution has the merits of a high detection rate, a specific probability display, and a low rate of seasonality and variability in detection accuracy. Therefore, PI would be useful for monitoring LSF in near-real-time, and to further its forecast ability, using next-generation satellites.

fog↗
NASA NTRS1979

A study of the application of singular perturbation theory

A hierarchical real time algorithm for optimal three dimensional control of aircraft is described. Systematic methods are developed for real time computation of nonlinear feedback controls by means of singular perturbation theory. The results are applied to a six state, three control variable, point mass model of an F-4 aircraft. Nonlinear feedback laws are presented for computing the optimal control of throttle, bank angle, and angle of attack. Real Time capability is assessed on a TI 9900 microcomputer. The breakdown of the singular perturbation approximation near the terminal point is examined Continuation methods are examined to obtain exact optimal trajectories starting from the singular perturbation solutions.

Mehra, R. K.↗
NASA NTRS2017

An Unobtrusive System to Measure, Assess, and Predict Cognitive Workload in Real-World Environments

Across many careers, individuals face alternating periods of high and low attention and cognitive workload, which can result in impaired cognitive functioning and can be detrimental to job performance. For example, some professions (e.g., fire fighters, emergency medical personnel, doctors and nurses working in an emergency room, pilots) require long periods of low workload (boredom), followed by sudden, high-tempo operations during which they may be required to respond to an emergency and perform at peak cognitive levels. Conversely, other professions (e.g., air traffic controllers, market investors in financial industries, analysts) require long periods of high workload and multitasking during which the addition of just one more task results in cognitive overload resulting in mistakes. An unobtrusive system to measure, assess, and predict cognitive workload could warn individuals, their teammates, or their supervisors when steps should be taken to augment cognitive readiness. In this talk I will describe an approach to this problem that we have found to be successful across work domains including: (1) a suite of unobtrusive, field-ready neurophysiological, physiological, and behavioral sensors that are chosen to best suit the target environment; (2) custom algorithms and statistical techniques to process and time-align raw data originating from the sensor suite; (3) probabilistic and statistical models designed to interpret the data into the human state of interest (e.g., cognitive workload, attention, fatigue); (4) and machine-learning techniques to predict upcoming performance based on the current pattern of events, and (5) display of each piece of information depending on the needs of the target user who may or may not want to drill down into the functioning of the system to determine how conclusions about human state and performance are determined. I will then focus in on our experimental results from our custom functional near-infrared spectroscopy sensor, designed to operate in real-world environments to be worn comfortably (e.g., positioned into a baseball cap or a surgeons cap) to measure changes in brain blood oxygenation without adding burden to the individual being assessed.

workload collection↗
NASA NTRS2003

A Generalized Fluid Formulation for Turbomachinery Computations

A generalized formulation of the equations of motion of an arbitrary fluid are developed for the purpose of defining a common iterative algorithm for computational procedures. The method makes use of the equations of motion in conservation form with separate pseudo-time derivatives used for defining the numerical flux for a Riemann solver and the convergence algorithm. The partial differential equations are complemented by an thermodynamic and caloric equations of state of a complexity necessary for describing the fluid. Representative solutions with a new code based on this general equation formulation are provided for three turbomachinery problems. The first uses air as a working fluid while the second uses gaseous oxygen in a regime in which real gas effects are of little importance. These nearly perfect gas computations provide a basis for comparing with existing perfect gas code computations. The third case is for the flow of liquid oxygen through a turbine where real gas effects are significant. Vortex shedding predictions with the LOX formulations reduce the discrepancy between perfect gas computations and experiment by approximately an order of magnitude, thereby verifying the real gas formulation as well as providing an effective case where its capabilities are necessary.

Merkle, Charles L.↗
NASA NTRS2002

Comparisons of Satellite Retrieval of Aerosol Properties from SeaWiFS and TOMS to the AERONET Measurements during ACE-Asia

The primary goal of the ACE (Aerosol Characterization Experiment)-Asia mission is to increase our understanding of how atmospheric aerosol particles over the Asian-Pacific region affect the Earth climate system. In support of the day-to-day flight planning of ACE-Asia, we built a near real-time system to provide satellite data from the polar-orbiting instruments Earth Probe TOMS (Total Ozone Mapping Spectrometer) (in the form of absorbing aerosol index) and SeaWiFS (Sea-Viewing Wide Field-of-View Sensor) (in the form of aerosol optical thickness and Angstrom exponent). The results were available via web access. These satellite data provide a 'big picture' of aerosol distribution in the region, which is complementary to the ground based measurements. In this paper, we will briefly discuss the algorithms used to generate these data. The retrieved aerosol optical thickness and Angstrom exponent from SeaWiFS will be compared with those obtained from various AERONET (Aerosol Robotic Network) sites over the Asian-Pacific region. The TOMS aerosol index will also be compared with AERONET aerosol optical thickness over different aerosol conditions. Finally, we will discuss the climate implication of our studies using the combined satellite and AERONET observations.

Hsu, Christina N.↗
NASA NTRS2008

Parallel-Processing Software for Creating Mosaic Images

A computer program implements parallel processing for nearly real-time creation of panoramic mosaics of images of terrain acquired by video cameras on an exploratory robotic vehicle (e.g., a Mars rover). Because the original images are typically acquired at various camera positions and orientations, it is necessary to warp the images into the reference frame of the mosaic before stitching them together to create the mosaic. [Also see "Parallel-Processing Software for Correlating Stereo Images," Software Supplement to NASA Tech Briefs, Vol. 31, No. 9 (September 2007) page 26.] The warping algorithm in this computer program reflects the considerations that (1) for every pixel in the desired final mosaic, a good corresponding point must be found in one or more of the original images and (2) for this purpose, one needs a good mathematical model of the cameras and a good correlation of individual pixels with respect to their positions in three dimensions. The desired mosaic is divided into slices, each of which is assigned to one of a number of central processing units (CPUs) operating simultaneously. The results from the CPUs are gathered and placed into the final mosaic. The time taken to create the mosaic depends upon the number of CPUs, the speed of each CPU, and whether a local or a remote data-staging mechanism is used.

Klimeck, Gerhard↗
NASA NTRS1985

Energy management of three-dimensional minimum-time intercept

A real-time computer algorithm to control and optimize aircraft flight profiles is described and applied to a three-dimensional minimum-time intercept mission. The proposed scheme has roots in two well known techniques: singular perturbations and neighboring-optimal guidance. Use of singular-perturbation ideas is made in terms of the assumed trajectory-family structure. A heading/energy family of prestored point-mass-model state-Euler solutions is used as the baseline in this scheme. The next step is to generate a near-optimal guidance law that will transfer the aircraft to the vicinity of this reference family. The control commands fed to the autopilot (bank angle and load factor) consist of the reference controls plus correction terms which are linear combinations of the altitude and path-angle deviations from reference values, weighted by a set of precalculated gains. In this respect the proposed scheme resembles neighboring-optimal guidance. However, in contrast to the neighboring-optimal guidance scheme, the reference control and state variables as well as the feedback gains are stored as functions of energy and heading in the present approach. Some numerical results comparing open-loop optimal and approximate feedback solutions are presented.

Kelley, H. J.↗
NASA NTRS1981

The wind-speed measurement capability of spaceborne radar altimeters

This study represents an attempt to quantitatively assess the capability of a spaceborne radar altimeter to infer ocean surface wind speeds from a measurement of the backscattered power at vertical incidence. The study uses data acquired during 184 near overflights of NOAA data buoys with the GEOS-3 satellite radar altimeter and encompasses a wind-speed range from less than 1 to 18 m/s. An algorithm is derived from the data comparison for converting measurements of the normalized scattering cross section of the ocean surface at 13.9 GHz into estimates of the surface wind speed at the standard anemometer height of 10 m. The algorithm is straightforward and potentially useful for on-board processing of raw altimeter data for the purpose of providing real-time estimates of surface wind speed. For winds in the range of 1 to 18 m/s, the mean difference between the altimeter-inferred winds and the buoy measurements is negligible while the standard deviation of the difference is 1.74 m/s.

Brown, G. S.↗
NASA NTRS1988

The Alaska SAR processor

The Alaska SAR processor was designed to process over 200 100 km x 100 km (Seasat like) frames per day from the raw SAR data, at a ground resolution of 30 m x 30 m from ERS-1, J-ERS-1, and Radarsat. The near real time processor is a set of custom hardware modules operating in a pipelined architecture, controlled by a general purpose computer. Input to the processor is provided from a high density digital cassette recording of the raw data stream as received by the ground station. A two pass processing is performed. During the first pass clutter-lock and auto-focus measurements are made. The second pass uses the results to accomplish final image formation which is recorded on a high density digital cassette. The processing algorithm uses fast correlation techniques for range and azimuth compression. Radiometric compensation, interpolation and deskewing is also performed by the processor. The standard product of the ASP is a high resolution four-look image, with a low resolution (100 to 200 m) many look image provided simultaneously.

Carande, R. E.↗
NASA NTRS2010

The Geostationary Lighting Mapper (GLM) for GOES-R: A New Operational Capability to Improve Storm Forecasts and Warnings

The next generation Geostationary Operational Environmental Satellite (GOES-R) series is a follow on to the existing GOES system currently operating over the Western Hemisphere. Superior spacecraft and instrument technology will support expanded detection of environmental phenomena, resulting in more timely and accurate forecasts and warnings. Advancements over current GOES capabilities include a new capability for total lightning detection (cloud and cloud-to-ground flashes) from the Geostationary Lightning Mapper (GLM), and improved spectral (3x), spatial (4x), and temporal (5x) resolution for the Advanced Baseline Imager (ABI). The GLM, an optical transient detector and imager operating in the near-IR at 777.4 nm will map all (in-cloud and cloud-to-ground) lighting flashes continuously day and night with near-uniform spatial resolution of 8 km with a product refresh rate of less than 20 sec over the Americas and adjacent oceanic regions, from the west coast of Africa (GOES-E) to New Zealand (GOES-W) when the constellation is fully operational. This will aid in forecasting severe storms and tornado activity, and convective weather impacts on aviation safety and efficiency. In parallel with the instrument development (a prototype and 4 flight models), a GOES-R Risk Reduction Team and Algorithm Working Group Lightning Applications Team have begun to develop the Level 2 algorithms and applications. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional test beds are being used to develop the pre-launch algorithms and applications, and also improve our knowledge of thunderstorm initiation and evolution. Real time lightning mapping data are being provided in an experimental mode to selected National Weather Service (NWS) national centers and forecast offices via the GOES-R Proving Ground to help improve our understanding of the application of these data in operational settings and facilitate Day-1 user readiness for this new capability.

Goodman, Steven J.↗
NASA NTRS1992

Evaluation of an optimal guidance algorithm for aero-assisted orbit transfer

A detailed evaluation is performed of a guidance algorithm for aeroassisted orbit transfer that was developed earlier based on the method of matched asymptotic expansions. It is shown that, by exploiting the structure of the matched asymptotic expansion solution procedure, the original problem which requires the solution of a set of 20 implicit algebraic equations can be reduced to a problem of six implicit equations in six unknowns. The main contribution here is that it was possible to obtain a solution that is near optimal, requires a minimum of computation, and thus can be implemented in real time and on-board the vehicle. Guidance law implementation entails treating the current state as a new initial state and repetitively solving the matched asymptotic expansion problem to obtain the feedback controls.

Melamed, N.↗
NASA NTRS2017

Evaluating a Priori Ozone Profile Information Used in TEMPO (Tropospheric Emissions: Monitoring of Pollution) Tropospheric Ozone Retrievals

A primary objective for TOLNet is the evaluation and validation of space-based tropospheric O3 retrievals from future systems such as the Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite. This study is designed to evaluate the tropopause-based O3 climatology (TB-Clim) dataset which will be used as the a priori profile information in TEMPO O3 retrievals. This study also evaluates model simulated O3 profiles, which could potentially serve as a priori O3 profile information in TEMPO retrievals, from near-real-time (NRT) data assimilation model products (NASA Global Modeling and Assimilation Office (GMAO) Goddard Earth Observing System (GEOS-5) Forward Processing (FP) and Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA2)) and full chemical transport model (CTM), GEOS-Chem, simulations. The TB-Clim dataset and model products are evaluated with surface (0-2 km) and tropospheric (0-10 km) TOLNet observations to demonstrate the accuracy of the suggested a priori dataset and information which could potentially be used in TEMPO O3 algorithms. This study also presents the impact of individual a priori profile sources on the accuracy of theoretical TEMPO O3 retrievals in the troposphere and at the surface. Preliminary results indicate that while the TB-Clim climatological dataset can replicate seasonally-averaged tropospheric O3 profiles observed by TOLNet, model-simulated profiles from a full CTM (GEOS-Chem is used as a proxy for CTM O3 predictions) resulted in more accurate tropospheric and surface-level O3 retrievals from TEMPO when compared to hourly (diurnal cycle evaluation) and daily-averaged (daily variability evaluation) TOLNet observations. Furthermore, it was determined that when large daily-averaged surface O3 mixing ratios are observed (65 ppb), which are important for air quality purposes, TEMPO retrieval values at the surface display higher correlations and less bias when applying CTM a priori profile information compared to all other data products. The primary reason for this is that CTM predictions better capture the spatio-temporal variability of the vertical profiles of observed tropospheric O3 compared to the TB-Clim dataset and other NRT data assimilation models evaluated during this study.

Pollution↗
NASA NTRS2016

Mapping of Spacecraft Atmosphere Monitor Signal to Major Constituent Abundances

The Spacecraft Atmosphere Monitor (S.A.M.) follows the JPL’s commitment to introduce and develop next-generation instrumentation concepts for sensing the air quality on manned space flights via continuous sampling, measuring, and reporting in 2s intervals on all gaseous pollutants. The S.A.M. will have two modes of operation: the Major Constituent Analysis (MCA) mode and the Trace Gas Analysis (TGA) mode. The MCA mode will report on molecular analytes such as CH4, H2O, N2, O2, Ar, and CO2 while the TGA mode will acquire minute amounts of volatile organic compounds. Both modes assess the composition of the ambient air with twenty full mass spectra per second giving rise to a substantial amount of data to be processed by a set of small footprint software stacks hosted by an on-board computer. Mass spectra will be accumulated as the number of counts recorded in a given mass-to-charge channel and converted into the absolute abundances of detected species using an efficient algorithm. The decomposition algorithm contains four units: peak identification, mass calibration, background and dead time correction, and an abundance analysis unit. The abundance analysis module identifies target species through their characteristic fragmentation patterns in the presence of molecular isobars, such as CO and N2. For example, in order to identify N2 analyte, the code will simultaneously monitor abundance ratios of the 14, 28 and 29 Th signals and will adapt to any instability caused by a decrease in ambient pressure or changes in humidity. This requirement becomes critical for instruments designed to monitor the near real-time quality of cabin air and promptly provide accurate feedbacks.

Nikolic, D.↗
NASA NTRSDate not supplied

A Principal Component and Machine Learning Approach to Spatially Gap Fill Hyperspectral Ocean Color Satellite Retrievals

Retrievals of ocean color properties from space are important for monitoring the health of the ocean ecosystem but such retrievals tend to be limited in spatial coverage due to conditions such as clouds, aerosols, and sun glint. Gap filling of ocean color retrievals is typically performed by combining retrievals from multiple satellites or temporally averaging multiple days of retrievals but despite these techniques large gaps still exist posing challenges for near real time monitoring of events like harmful algae blooms. To address these limitations, we propose a spatial gap filling approach using machine learning to learn how to perform an atmospheric correction under challenging retrieval conditions. In this approach a principal component analysis is used to decompose the hyperspectral measurements into spectral features that describe the scattering and absorption of the atmosphere as well as the underlying surface. The coefficients of the principal components are then used to train a neural network to predict ocean color properties derived from a standard ocean color algorithm such as the MODIS atmospheric correction algorithm. This machine learning approach is independent of a priori information and does not rely on any radiative transfer modeling. We apply the approach to two hyperspectral UV/VIS instruments, the Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) to show that it can be used to estimate ocean color properties such as chlorophyll, remote sensing reflectance, and fluorescence line height. This method could be used as a gap-filling technique for the future Ocean Color Instrument (OCI) which will be onboard NASA's Plankton, Aerosol Cloud, ocean Ecosystem (PACE) ocean color satellite to provide additional information for monitoring the health of our global oceans. Additionally, it could be applied to the geostationary satellite Tropospheric Emissions: Monitoring of Pollution (TEMPO) to better understand diurnal variability in ocean ecology.

MODIS atmospheric correction algorithm↗
NASA NTRS1997

Ground-Support Algorithms for Simulation, Processing, and Calibration of Barnes Static Earth Sensor Measurements: Applications to Tropical Rainfall Measuring Mission Observatory

New algorithms are described covering the simulation, processing, and calibration of penetration angles of the Barnes static Earth sensor assembly (SESA) as implemented in the Goddard Space Flight Center Flight Dynamics Division ground support system for the Tropical Rainfall Measuring Mission (TRMM) Observatory. The new treatment involves a detailed analysis of the measurements by individual quadrants. It is shown that, to a good approximation, individual quadrant misalignments can be treated simply as penetration angle biases. Simple formulas suitable for real-time applications are introduced for computing quadrant-dependent effects. The simulator generates penetration angles by solving a quadratic equation with coefficients uniquely determined by the spacecraft's position and the quadrant's orientation in GeoCentric Inertial (GCI) coordinates. Measurement processing for attitude determination is based on linearized equations obtained by expanding the coefficients of the aforementioned quadratic equation as a Taylor series in both the Earth oblateness coefficient (alpha approx. 1/150) and the angle between the pointing axis and the geodetic nadir vector. A simple formula relating a measured value of the penetration angle to the deviation of the Earth-pointed axis from the geodetic nadir vector is derived. It is shown that even near the very edge of the quadrant's Field Of View (FOV), attitude errors resulting from quadratic effects are a few hundredths of a degree, which is small compared to the attitude determination accuracy requirement (0.18 degree, 3 sigma) of TRMM. Calibration of SESA measurements is complicated by a first-order filtering used in the TRMM onboard algorithm to compute penetration angles from raw voltages. A simple calibration scheme is introduced where these complications are avoided by treating penetration angles as the primary raw measurements, which are adjusted using biases and scale factors. In addition to three misalignment parameters, the calibration state vector contains only two average penetration angle biases (one per each pair of opposite quadrants) since, because of the very narrow sensor FOV (+/- 2.6 degrees), differences between biases of the penetration angles measured by opposite quadrants cannot be distinguished from roll and pitch sensor misalignments. After calibration, the estimated misalignments and average penetration angle biases are converted to the four penetration angle biases and to the yaw misalignment angle. The resultant biases and the estimated scale factors are finally used to update the coefficients necessary for onboard computations of penetration angles from measured voltages.

Natanson, G. A.↗
NASA NTRS2003

The Rondonia Lightning Detection Network: Network Description, Science Objectives, Data Processing Archival/Methodology, and Results

A four station Advanced Lightning Direction Finder (ALDF) network was established in the state of Rondonia in western Brazil in 1999 through a collaboration of U.S. and Brazilian participants from NASA, INPE, INMET, and various universities. The network utilizes ALDF IMPACT (Improved Accuracy from Combined Technology) sensors to provide cloud-to-ground lightning observations (i.e., stroke/flash locations, signal amplitude, and polarity) using both time-of- arrival and magnetic direction finding techniques. The observations are collected, processed and archived at a central site in Brasilia and at the NASA/Marshall Space Flight Center in Huntsville, Alabama. Initial, non-quality assured quick-look results are made available in near real-time over the Internet. The network, which is still operational, was deployed to provide ground truth data for the Lightning Imaging Sensor (LIS) on the Tropical Rainfall Measuring Mission (TRMM) satellite that was launched in November 1997. The measurements are also being used to investigate the relationship between the electrical, microphysical and kinematic properties of tropical convection. In addition, the long-time series observations produced by this network will help establish a regional lightning climatological database, supplementing other databases in Brazil that already exist or may soon be implemented. Analytic inversion algorithms developed at the NASA/Marshall Space Flight Center have been applied to the Rondonian ALDF lightning observations to obtain site error corrections and improved location retrievals. The data will also be corrected for the network detection efficiency. The processing methodology and the results from the analysis of four years of network operations will be presented.

Blakeslee, R. J.↗
NASA NTRS1999

The Rondonia Lightning Detection Network: Network Description, Science Objectives, Data Processing/Archival Methodology, and First Results

A four station Advanced Lightning Direction Finder (ALDF) network was recently established in the state of Rondonia in western Brazil through a collaboration of U.S. and Brazilian participants from NASA, INPE, INMET, and various universities. The network utilizes ALDF IMPACT (Improved Accuracy from Combined Technology) sensors to provide cloud-to-ground lightning observations (i.e., stroke/flash locations, signal amplitude, and polarity) using both time-of-arrival and magnetic direction finding techniques. The observations are collected, processed and archived at a central site in Brasilia and at the NASA/Marshall Space Flight Center (MSFC) in Huntsville, Alabama. Initial, non-quality assured quick-look results are made available in near real-time over the internet. The network will remain deployed for several years to provide ground truth data for the Lightning Imaging Sensor (LIS) on the Tropical Rainfall Measurement Mission (TRMM) satellite which was launched in November 1997. The measurements will also be used to investigate the relationship between the electrical, microphysical and kinematic properties of tropical convection. In addition, the long-term observations from this network will contribute in establishing a regional lightning climatological data base, supplementing other data bases in Brazil that already exist or may soon be implemented. Analytic inversion algorithms developed at NASA/MSFC are now being applied to the Rondonian ALDF lightning observations to obtain site error corrections and improved location retrievals. The processing methodology and the initial results from an analysis of the first 6 months of network operations will be presented.

Blakelee, Richard↗