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NASA NTRS1990

Networks for image acquisition, processing and display

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

Ahumada, Albert J., Jr.↗

NASA NTRS2013

Self Diagnostic Accelerometer Testing on the C-17 Aircraft

Tokars, Roger P.↗

NASA NTRS2013

Arctic Tundra Vegetation Functional Types Based on Photosynthetic Physiology and Optical Properties

Huemmrich, Karl F.↗

NASA NTRSDate not supplied

Nearest-Neighbor Machine Learning Feature Selection for Interpretation of Microbial Molecular Signatures from Isotope Ratio Mass Spectrometry Data

Mass spectrometry (MS) promises to be a powerful tool for potential biosignature detection during astrobiological missions on ocean worlds in our solar system. Accurate and generalizable machine learning methods could enhance science return on investment by predicting seawater chemistry and classifying isotopic biosignatures, either as a signature consistent with microbial life (biotic) or as a novelty (unclassified/unique). However, machine learning models are likely to be complex and involve interactions between MS features, making biosignatures difficult to interpret. Feature selection methods provide biological and chemical context that help interpret the mechanisms of machine learning models, but these methods also need the ability to detect complex interactions. Previously, we developed a machine learning feature selection algorithm called nearest-neighbor projected distance regression (NPDR) that has the ability to identify important model features that involve complex interactions and automatically reduce correlation and the dimensionality in a high-dimensional variable space. The standard distance metrics used in NPDR – Manhattan and Euclidean – assume the multivariate data are isotropic, which is often violated in real data due to differences in the covariance between variables. Thus, we extend NPDR to include a random forest distance, and other anisotropic distance metrics, for computing nearest neighbors. We also augment the isotope-ratio MS data with time-series features from the raw MS signal to improve biotic classification. We test NPDR on our novel experimental ocean world seawater analog MS data. We measure isotope fractionations of volatile CO 2 that could be measured in exospheres or plumes. Samples include baseline abiotic conditions using a range of possible seawater chemistry consistent with Europa and Enceladus, and biotic samples that include microbes in these seawaters. We use penalized NPDR with random forest proximity to identify interpretable microbial molecular signatures. We compare features with random forest importance, and we train a classifier that discriminates between biotic and abiotic samples with high accuracy. These ML-trained ocean-world analog MS data could be used to assist in identifying biosignatures during future missions.

geochemistry↗

NASA NTRS1983

What does the eye see best?

The quantum efficiency of the human eye is measured for detecting a wide variety of spatiotemporal patterns using foveal vision in bright light. The performance of an ideal visual detector whose weighting function is matched to the signal is analyzed in the abstract, and the conditions of a two-interval forced-choice experiment are described. The best stimulus found is a small, briefly exposed circular patch of sinusoidal grating having a spatial frequency of about 7 c/deg, drifting at about 4 Hz. It is proposed that this is the weighting function of the most efficient human contrast detector. This function resembles the receptive field sensitivity profiles of many cortical neurons. The detectors resemble those derived from other recent experiments on both detection and discrimination and may account for a broad range of previous psychophysical results.

Watson, A. B.↗

NASA NTRS2013

Self Diagnostic Accelerometer Ground Testing on a C-17 Aircraft Engine

The self diagnostic accelerometer (SDA) developed by the NASA Glenn Research Center was tested for the first time in an aircraft engine environment as part of the Vehicle Integrated Propulsion Research (VIPR) program. The VIPR program includes testing multiple critical flight sensor technologies. One such sensor, the accelerometer, measures vibrations to detect faults in the engine. In order to rely upon the accelerometer, the health of the accelerometer must be ensured. Sensor system malfunction is a significant contributor to propulsion in flight shutdowns (IFSD) which can lead to aircraft accidents when the issue is compounded with an inappropriate crew response. The development of the SDA is important for both reducing the IFSD rate, and hence reducing the rate at which this component failure type can put an aircraft in jeopardy, and also as a critical enabling technology for future automated malfunction diagnostic systems. The SDA is a sensor system designed to actively determine the accelerometer structural health and attachment condition, in addition to making vibration measurements. The SDA uses a signal conditioning unit that sends an electrical chirp to the accelerometer and recognizes changes in the response due to changes in the accelerometer health and attachment condition. In an effort toward demonstrating the SDAs flight worthiness and robustness, multiple SDAs were mounted and tested on a C-17 aircraft engine. The engine test conditions varied from engine off, to idle, to maximum power. The two SDA attachment conditions used were fully tight and loose. The newly developed SDA health algorithm described herein uses cross correlation pattern recognition to discriminate a healthy from a faulty SDA. The VIPR test results demonstrate for the first time the robustness of the SDA in an engine environment characterized by high vibration levels.

Tokars, Roger P.↗

NASA NTRSDate not supplied

Machine Learning Algorithms for Aerosol and Cloud Detection Using CATS on the ISS

Clouds and aerosols are one of the largest uncertainties in understanding and forecasting the Earth’s changing climate system. The type and height of aerosols are important factors in determining the top-of-atmosphere (TOA) radiation budget, either direct reflection of solar radiation back to space and/or absorption of solar radiation. In addition to their impact on the Earth’s climate system, aerosols near the surface from wildfires, man-made pollution events, and dust storms are hazardous to human health. The phase and height of clouds also play a critical role in determining the role of clouds in the Earth’s climate system. Cirrus clouds in the upper troposphere can induce a significant daytime TOA warming effect, while liquid water clouds near the surface cause a large corresponding cooling effect. Lidar measurements provide accurate vertically resolved information about clouds and aerosols, including complex multi-layer scenes where passive sensors are challenged and at night, when passive sensors are unable to measure cloud and aerosol properties. The Cloud-Aerosol Transport System (CATS) is a lidar instrument that operated for 33 months on the International Space Station (ISS) at the 1064 nm wavelength to measure attenuated total backscatter and depolarization ratio. These fundamental measurements are used to derive “vertical feature mask” cloud and aerosol products, including layer top/base heights, layer geometrical thickness, aerosol type, and cloud phase. While space-based lidar systems like CATS provide cloud and aerosol vertical distributions that improve our understanding of the climate system, averaging of the daytime data from these sensors is required, at the expense of spatial resolution, to improve the daytime signal-to noise (SNR) and thus atmospheric layer detection. This presentation shows results from machine learning (ML) techniques that, when applied to CATS data: 1. improve the 1064 nm SNR 2. enable detection of atmospheric features during daytime with a horizontal resolution of 350 m or 5 km (compared to the 60 km required for standard CATS data products) 3. increase the number of atmospheric layers detected in the CATS data. A Convolutional Neural Network (CNN) trained using CATS standard data products also demonstrated the potential for improved cloud-aerosol discrimination, cloud phase, and aerosol typing compared to the operational CATS algorithms for cloud edges and complex near-surface scenes during daytime. The ML tools described in this paper can facilitate the development of smaller, low-cost lidar systems in the future and enable real-time accessibility of lidar data products from future lidar systems for monitoring and forecasting of hazardous events.

John Yorks↗

NASA NTRS1990

Status of the Delco Systems Operations forward looking windshear detection program

Delco Systems Operations, a division of General Motors Hughes Electronics Corporation, is developing a Forward Looking Windshear Detection System based on the integration of infrared remote sensing and accelerometer reactive sensing technologies. The infrared sensor is a multi-spectral, scanning radiometer operating in the 8 to 14 micron region. A 2 x 5 detector array with parallel-serial scanning produces 60 degrees horizontal and 10 degrees vertical-fields of view. Using multiple wavelength signals, azimuth temperature gradients are analyzed for characteristic signatures of thermally induced windshear phenomena. Elevation temperature gradients are processed through an atmosphere model to continuously compute a stability index for arming microburst detection criteria. The atmosphere model and proprietary computer processing algorithms combine to generate coarse estimates of disturbance ranges based on multiple wavelength radiance data with different extinction coefficients. Computer outputs of atmospheric stability, disturbance intensity, and azimuth and range information provide a situation display capability. A ground operated, experimental radiometer has been developed and is being used to verify the detection and discrimination concepts at an atmospheric and simulated rain test facility in Milwaukee. A prototype airborne radiometer is being developed for flight test evaluation during the summer of 1989.

Gallagher, Brian J.↗

NASA NTRS1994

Two-sample discrimination of Poisson means

This paper presents a statistical test for detecting significant differences between two random count accumulations. The null hypothesis is that the two samples share a common random arrival process with a mean count proportional to each sample's exposure. The model represents the partition of N total events into two counts, A and B, as a sequence of N independent Bernoulli trials whose partition fraction, f, is determined by the ratio of the exposures of A and B. The detection of a significant difference is claimed when the background (null) hypothesis is rejected, which occurs when the observed sample falls in a critical region of (A, B) space. The critical region depends on f and the desired significance level, alpha. The model correctly takes into account the fluctuations in both the signals and the background data, including the important case of small numbers of counts in the signal, the background, or both. The significance can be exactly determined from the cumulative binomial distribution, which in turn can be inverted to determine the critical A(B) or B(A) contour. This paper gives efficient implementations of these tests, based on lookup tables. Applications include the detection of clustering of astronomical objects, the detection of faint emission or absorption lines in photon-limited spectroscopy, the detection of faint emitters or absorbers in photon-limited imaging, and dosimetry.

Lampton, M.↗

NASA NTRS1991

Pi/4-QPSK modems for satellite sound/data broadcast systems

The use of pi/4-quadrature phase-shift keying (QPSK) modems for satellite sound broadcast systems (SSBS) broadcasting to mobile or portable receivers is proposed. Three different differential detectors (including the FM-discriminator followed by integrate-sample-and-dump filter) and a novel coherent detector are discussed. The degradation caused by the frequency offset between the local oscillator (LO) and the unmodulated carrier (CR) in the baseband differential detector is studied. The performance of both coherently and differentially detected pi/4-QPSK in a Gaussian channel is also studied. It is shown that with a frequency offset of more than 3 percent of the symbol rate, the performance degradation is more than 1 dB at 0.0001. The out-of-band power of the nonlinearly amplified bandlimited pi/4-QPSK signals is reduced from -13 dB to -37 dB if a 2-dB output back-off amplifier is used instead of a hardlimiter. The performance of the pi/4-QPSK is equivalent to that of QPSK, although the pi/4-QPSK has the advantage of less spectrum restoration after nonlinear amplification. The coherent demodulator and differential decoder avoid the three-level detection and achieve the same bit-error-rate performance as DEQPSK with a simple circuit.

Liu, Chia-Liang↗

NASA NTRS1993

Satellite observation of lake ice as a climate indicator - Initial results from statewide monitoring in Wisconsin

The research reported herein focused on the general hypothesis that satellite remote sensing of large-area, long-term trends in lake ice phenology (formation and breakup) is a robust, integrated measure of regional and global climate change. To validate this hypothesis, we explored the use of data from the Advanced Very High Resolution Radiometer (AVHRR) to discriminate the presence and extent of lake ice during the winter of 1990-1991 on the 45 lakes and reservoirs in Wisconsin with a surface area greater than 1,000 hectares. Our results suggest both the feasibility of using the AVHRR to determine the date of lake ice breakup as well as the strong correlation (R= -0.87) of the date so derived with local surface-based temperature measurements. These results suggest the potential of using current and archival satellite data to monitor changes in the date of lake ice breakup as a means of detecting regional 'signals' of greenhouse warming.

Wynne, Randolph H.↗

NASA NTRS2013

Arctic Tundra Vegetation Functional Types Based on Photosynthetic Physiology and Optical Properties

Non-vascular plants (lichens and mosses) are significant components of tundra landscapes and may respond to climate change differently from vascular plants affecting ecosystem carbon balance. Remote sensing provides critical tools for monitoring plant cover types, as optical signals provide a way to scale from plot measurements to regional estimates of biophysical properties, for which spatial-temporal patterns may be analyzed. Gas exchange measurements were collected for pure patches of key vegetation functional types (lichens, mosses, and vascular plants) in sedge tundra at Barrow, AK. These functional types were found to have three significantly different values of light use efficiency (LUE) with values of 0.013 plus or minus 0.0002, 0.0018 plus or minus 0.0002, and 0.0012 plus or minus 0.0001 mol C mol (exp -1) absorbed quanta for vascular plants, mosses and lichens, respectively. Discriminant analysis of the spectra reflectance of these patches identified five spectral bands that separated each of these vegetation functional types as well as nongreen material (bare soil, standing water, and dead leaves). These results were tested along a 100 m transect where midsummer spectral reflectance and vegetation coverage were measured at one meter intervals. Along the transect, area-averaged canopy LUE estimated from coverage fractions of the three functional types varied widely, even over short distances. The patch-level statistical discriminant functions applied to in situ hyperspectral reflectance data collected along the transect successfully unmixed cover fractions of the vegetation functional types. The unmixing functions, developed from the transect data, were applied to 30 m spatial resolution Earth Observing-1 Hyperion imaging spectrometer data to examine variability in distribution of the vegetation functional types for an area near Barrow, AK. Spatial variability of LUE was derived from the observed functional type distributions. Across this landscape, a fivefold variation in tundra LUE was observed. LUE calculated from the functional type cover fractions was also correlated to a spectral vegetation index developed to detect vegetation chlorophyll content. The concurrence of these alternate methods suggest that hyperspectral remote sensing can distinguish functionally distinct vegetation types and can be used to develop regional estimates of photosynthetic LUE in tundra landscapes.

VEGETATION↗

NASA NTRS1993

Preliminary Studies Leading Toward the Development of a LIDAR Bathymetry Mapping Instrument

The National Aeronautics and Space Administration (NASA) at Goddard Space Flight Center (GSFC) has developed a laser ranging device (LIDAR) which provides accurate and timely data of earth features. NASA/GSFC recently modified the sensor to include a scanning capability to produce LIDAR swaths. They have also integrated a Global Positioning System (GPS) and an Inertial Navigation System (INS) to accurately determine the absolute aircraft location and aircraft attitude (pitch, yaw, and roll), respectively. The sensor has been flown in research mode by NASA for many years. The LIDAR has been used in different configurations or modes to acquire such data as altimetry (topography), bathymetry (water depth), laser-induced fluorosensing (tracer dye movements, oil spills and oil thickness, chlorophyll and plant stress identification), forestry, and wetland discrimination studies. NASA and HARC are developing a commercial version of the instrument for topographic mapping applications. The next phase of the commercialization project will be to investigate other applications such as wetlands mapping and coastal bathymetry. In this paper we report on preliminary laboratory measurements to determine the feasibility of making accurate depth measurements in relatively shallow water (approximately 2 to 6 feet deep) using a LIDAR system. The LIDAR bathymetry measurements are relatively simple in theory. The water depth is determined by measuring the time interval between the water surface reflection and the bottom surface reflection signals. Depth is then calculated by dividing by the index of refraction of water. However, the measurements are somewhat complicated due to the convolution of the water surface return signal with the bottom surface return signal. Therefore in addition to the laboratory experiments, computer simulations of the data were made to show these convolution effects in the return pulse waveform due to: (1) water depth, and (2) changes in bottom surface reflectivity.

Hill, John M.↗

NASA NTRS1996

Relationship of the time course of venous gas bubbles to altitude decompression illness

The correlation is low between the occurrence of gas bubbles in the pulmonary artery, called venous gas emboli (VGE), and subsequent decompression illness (DCI). The correlation improves when a "grade" of VGE is considered; a zero to four categorical classification based on the intensity and duration of the VGE signal from a Doppler bubble detector. Additional insight about DCI might come from an analysis of the time course of the occurrence of VGE. Using the NASA Hypobaric Decompression Sickness Databank, we compared the time course of the VGE outcome between 322 subjects who exercised and 133 Doppler technicians who did not exercise to evaluate the role of physical activity on the VGE outcome and incidence of DCI. We also compared 61 subjects with VGE and DCI with 110 subjects with VGE but without DCI to identify unique characteristics about the time course of the VGE outcome to try to discriminate between DCI and no-DCI cases. The VGE outcome as a function of time showed a characteristic short lag, rapid response, and gradual recovery phase that was related to physical activity at altitude and the presence or absence of DCI. The average time for DCI symptoms in a limb occurred just before the time of the highest fraction of VGE in the pulmonary artery. It is likely, but not certain, that an individual will report a DCI symptom if VGE are detected early in the altitude exposure, the intensity or grade of VGE rapidly increases from a limb region, and the intensity or grade of VGE remains high.

NASA Discipline Environmental Health↗

NASA NTRS1982

Recent advances in remote sensing; Proceedings of the First International Geoscience and Remote Sensing Symposium, Washington, DC, June 8-10, 1981

The state of the art in remote sensing of the earth and the planets was discussed in terms of sensor performance, signal processing, and data interpretation. Particular attention was given to lidar for characterizing atmospheric particulates, the modulation of short waves by long ocean gravity waves, and runoff modeling for snow-covered areas. The use of NOAA-6 spacecraft AVHRR data to explore hydrologic land surface features, the effects of soil moisture and vegetation canopies on microwave and thermal microwave emissions, and regional scale evapotranspiration rate determination through satellite IR data are examined. A Shuttle experiment to demonstrate high accuracy global time and frequency transfer is described, along with features of the proposed Gravsat, radar image processing for rock-type discrimination, and passive microwave sensing of temperature and salinity in coastal zones.

Mcintosh, R.↗

NASA NTRS1958

Acquisition and Tracking Behavior of Phase-Locked Loops

Phase-locked or APC loops have found increasing applications in recent years as tracking filters, synchronizing devices, and narrowband FM discriminators. Considerable work has been performed to determine the noise-squelching properties of the loop when it is operating in or near phase lock and is functioning as a linear coherent detector. However, insufficient consideration has been devoted to the non-linear behavior of the loop when it is out of lock and in the process of pulling in. Experimental evidence has indicated that there is a strong tendency for phase-locked loops to achieve lock under most circumstances. However, the analysis which has appeared in the literature iis limited to the acquisition of a constant frequency reference signal with only one phase-locked loop filter configuration. This work represents an investigation of frequency acquisition properties of phase-locked loops for a variety of reference-signal behavior and loop configurations

Viterbi, A. J.↗

NASA NTRS1997

Uncertain responses by humans and rhesus monkeys (Macaca mulatta) in a psychophysical same-different task

The authors asked whether animals, like humans, use an uncertain response adaptively to escape indeterminate stimulus relations. Humans and monkeys were placed in a same-different task, known to be challenging for animals. Its difficulty was increased further by reducing the size of the stimulus differences, thereby making many same and different trials difficult to tell apart. Monkeys do escape selectively from these threshold trials, even while coping with 7 absolute stimulus levels concurrently. Monkeys even adjust their response strategies on short time scales according to the local task conditions. Signal-detection and optimality analyses confirm the similarity of humans' and animals' performances. Whereas associative interpretations account poorly for these results, an intuitive uncertainty construct does so easily. The authors discuss the cognitive processes that allow uncertainty's adaptive use and recommend further comparative studies of metacognition.

NASA Discipline Space Human Factors↗

NASA NTRS1987

High-resolution CCD imagers using area-array CCD's for sensing spectral components of an optical line image

CCD imagers with a novel replicated-line-imager architecture are abutted to form an extended line sensor. The sensor is preceded by optics having a slit aperture and having an optical beam splitter or astigmatic lens for projecting multiple line images through an optical color-discriminating stripe filter to the CCD imagers. A very high resolution camera suitable for use in a satellite, for example, is thus provided. The replicated-line architecture of the imager comprises an area-array CCD, successive rows of which are illuminated by replications of the same line segment, as transmitted by respective color filter stripes. The charge packets formed by accumulation of photoresponsive charge in the area-array CCD are read out row by row. Each successive row of charge packets is then converted from parallel to serial format in a CCD line register and its amplitude sensed to generate a line of output signal.

Elabd, Hammam↗