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At least 55 records · Page 3

The onset of galactic winds in early-type galaxies

We completed the spectral analysis of 31 early-type galaxies to investigate whether their x-ray emission was predominantly due to thermal bremsstrahlung from a hot gaseous corona or emission from discrete, galactic sources such as x-ray binaries. If a corona dominates the x-ray emission, its spectra is expected to be relatively cool (0.5 - 1 keV) compared to the harder emission associated with x-ray binaries in our galaxy, the Magellanic Clouds and M31. While it is generally accepted that the x-ray emission in luminous E and S0 galaxies arises from hot coronae, the status of hot gas in lower luminosity (and hence lower mass) galaxies is less clear. Calculations show that, for a given supernova rate, a critical galaxy luminosity (mass) exists below which the gas cannot be gravitationally confined and a galactic wind is predicted to be effective in expelling gas from the galaxy. Since significant mass (a dark halo) is required to hold a hot, gaseous corona around a galaxy, we expect that the faintest, smallest galaxies will not have a hot corona, but their x-ray emission will be dominated by galactic sources or by an active galactic nuclei. In the sample we tested which spanned the absolute magnitude range from -21.5 to -19.5, we found that except for two galaxies whose x-ray emission was dominated by an active nucleus, that the others were consistent with emission from hot gas. We also found that there is a correlation between gas temperature and galaxy magnitude (mass), such that the brighter, more luminous galaxies have hotter gas temperatures. Thus even at relatively faint magnitudes, the dominant emission from early-type galaxies appears to be hot gas. We also carried out an investigation of the x-ray surface brightness distribution of the x-ray emission for about 100 early type galaxies to determine whether the x-ray emission from galaxies are extended. Extended x-ray emission is expected if the emission is due to a hot gaseous corona. We determined the ratio of the source counts in two annuli (0-80 arc seconds and 80-160 arc seconds) for each galaxy and analyzed these ratios using a maximum likelihood estimator to determine the errors on the ratios. Even for weak sources, this ratio provides a sensitive test for source extent. We then compared these ratios to a sample of quasars (all unresolved sources) and have determined which galaxies are extended and which are consistent with point sources. A first paper including the Einstein x-ray fluxes for 147 early-type galaxies has been published in the Astrophysical Journal Supplement Series (with Roberts, Hogg, Bregman, Forman entitled 'Interstellar Matter in Early-Type Galaxies'). A second paper will describe the spectral and extent analysis carried out for this galaxy sample. These results also have been presented at scientific conferences and in colloquia.

Jones, Christine↗

Comparison of two weighted integration models for the cueing task: linear and likelihood

In a task in which the observer must detect a signal at two locations, presenting a precue that predicts the location of a signal leads to improved performance with a valid cue (signal location matches the cue), compared to an invalid cue (signal location does not match the cue). The cue validity effect has often been explained with a limited capacity attentional mechanism improving the perceptual quality at the cued location. Alternatively, the cueing effect can also be explained by unlimited capacity models that assume a weighted combination of noisy responses across the two locations. We compare two weighted integration models, a linear model and a sum of weighted likelihoods model based on a Bayesian observer. While qualitatively these models are similar, quantitatively they predict different cue validity effects as the signal-to-noise ratios (SNR) increase. To test these models, 3 observers performed in a cued discrimination task of Gaussian targets with an 80% valid precue across a broad range of SNR's. Analysis of a limited capacity attentional switching model was also included and rejected. The sum of weighted likelihoods model best described the psychophysical results, suggesting that human observers approximate a weighted combination of likelihoods, and not a weighted linear combination.

NASA Program Biomedical Research and Countermeasur↗

Mapping soil types from multispectral scanner data.

Multispectral remote sensing and computer-implemented pattern recognition techniques were used for automatic ?mapping' of soil types. This approach involves subjective selection of a set of reference samples from a gray-level display of spectral variations which was generated by a computer. Each resolution element is then classified using a maximum likelihood ratio. Output is a computer printout on which the researcher assigns a different symbol to each class. Four soil test areas in Indiana were experimentally examined using this approach, and partially successful results were obtained.

Kristof, S. J.↗

Geological mapping in northwestern Saudi Arabia using LANDSAT multispectral techniques

Various computer enhancement and data extraction systems using LANDSAT data were assessed and used to complement a continuing geologic mapping program. Interactive digital classification techniques using both the parallel-piped and maximum-likelihood statistical approaches achieve very limited success in areas of highly dissected terrain. Computer enhanced imagery developed by color compositing stretched MSS ratio data was constructed for a test site in northwestern Saudi Arabia. Initial results indicate that several igneous and sedimentary rock types can be discriminated.

Blodget, H. W.↗

Simons Observatory: Constraining inflationary gravitational waves with multitracer B-mode delensing

We introduce and validate a delensing framework for the Simons Observatory (SO), which will be used to improve constraints on inflationary gravitational waves by reducing the lensing noise in measurements of the B modes in CMB polarization. SO will initially observe CMB by using three small aperture telescopes and one large-aperture telescope. While polarization maps from small-aperture telescopes will be used to constrain inflationary gravitational waves, the internal CMB lensing maps used to delens will be reconstructed from data from the large-aperture telescope. Since lensing maps obtained from the SO data will be noise dominated on subdegree scales, the SO lensing framework constructs a template for lensing-induced B modes by combining internal CMB lensing maps with maps of the cosmic infrared background from Planck as well as galaxy density maps from the LSST survey. We construct a likelihood for constraining the tensor-to-scalar ratio r that contains auto and cross spectra between observed B modes and the lensing B-mode template. We test our delensing analysis pipeline on map-based simulations containing survey nonidealities, but that, for this initial exploration, does not include contamination from Galactic and extragalactic foregrounds. We find that the SO survey masking and inhomogeneous and atmospheric noise have very little impact on the delensing performance, and the r constraint becomes σ(r)≈0.0015 which is close to that obtained from the idealized forecasts in the absence of the Galactic foreground and is nearly a factor of 2 tighter than without delensing. We also find that uncertainties in the external large-scale structure tracers used in our multitracer delensing pipeline lead to bias much smaller than the 1σ statistical uncertainties.

Toshiya Namikawa↗

Geological mapping in northwestern Saudi Arabia using LANDSAT multispectral techniques

Various computer enhancement and data extraction systems using LANDSAT data were assessed and used to complement a continuing geologic mapping program. Interactive digital classification techniques using both the maximum-likelihood and thresholding statistical approaches achieve very limited success in areas of highly dissected terrain. Computer enhanced imagery developed by color compositing stretched MSS ratio data was constructed for a test site in northwestern Saudi Arabia. Initial results indicate that several igneous and sedimentary rock-types can be discriminated.

Blodget, H. W.↗

Program for Weibull Analysis of Fatigue Data

A Fortran computer program has been written for performing statistical analyses of fatigue-test data that are assumed to be adequately represented by a two-parameter Weibull distribution. This program calculates the following: (1) Maximum-likelihood estimates of the Weibull distribution; (2) Data for contour plots of relative likelihood for two parameters; (3) Data for contour plots of joint confidence regions; (4) Data for the profile likelihood of the Weibull-distribution parameters; (5) Data for the profile likelihood of any percentile of the distribution; and (6) Likelihood-based confidence intervals for parameters and/or percentiles of the distribution. The program can account for tests that are suspended without failure (the statistical term for such suspension of tests is "censoring"). The analytical approach followed in this program for the software is valid for type-I censoring, which is the removal of unfailed units at pre-specified times. Confidence regions and intervals are calculated by use of the likelihood-ratio method.

Krantz, Timothy L.↗

A dual-mode generalized likelihood ratio approach to self-reorganizing digital flight control system design

Analytic techniques have been developed for detecting and identifying abrupt changes in dynamic systems. The GLR technique monitors the output of the Kalman filter and searches for the time that the failure occured, thus allowing it to be sensitive to new data and consequently increasing the chances for fast system recovery following detection of a failure. All failure detections are based on functional redundancy. Performance tests of the F-8 aircraft flight control system and computerized modelling of the technique are presented.

Source record↗

Evidence-Based Approach to the Analysis of Serious Decompression Sickness with Application to EVA Astronauts

It is important to understand the risk of serious hypobaric decompression sickness (DCS) in order to develop procedures and treatment responses to mitigate the risk. Since it is not ethical to conduct prospective tests about serious DCS with humans, the necessary information was gathered from 73 published reports. We hypothesize that a 4-hr 100% oxygen (O2) prebreathe results in a very low risk of serious DCS, and test this through analysis. We evaluated 258 tests containing information from 79,366 exposures in attitude chambers. Serious DCS was documented in 918 men during the tests. Serious DCS are signs and symptoms broadly classified as Type II DCS. A risk function analysis with maximum likelihood optimization was performed to identify significant explanatory variables, and to create a predictive model for the probability of serious DCS [P(serious DCS)]. Useful variables were Tissue Ratio, the planned time spent at altitude (T(sub alt)), and whether or not repetitive exercise was performed at altitude. Tissue Ratio is P1N2/P2, where P1N2 is calculated nitrogen (N2) pressure in a compartment with a 180-min half-time for N2 pressure just before ascent, and P2 is ambient pressure after ascent. A prebreathe and decompression profile Shuttle astronauts use for extravehicular activity (EVA) includes a 4-hr prebreathe with 100% O2, an ascent to P2 = 4.3 lb per sq. in. absolute, and a T(sub alt) = 6 hr. The P(serious DCS) is: 0.0014 (0.00096 - 0.00196, 95% confidence interval) with exercise and 0.00025 (0.00016 - 0.00035) without exercise. Given 100 Shuttle EVAs to date and no report of serious DCS, the true risk is less than 0.03 with 95% confidence (Binomial Theorem). It is problematic to estimate the risk of serious DCS since it appears infrequently, even if the estimate is based on thousands of altitude chamber exposures. The true risk to astronauts may lie between the extremes of the confidence intervals (0.00016 - 0.00196) since the contribution of other factors, particularly exercise, to the risk of serious DCS during EVA is unknown. A simple model that only accounts for four important variables in retrospective data is still helpful to increase our understanding about the risk of serious DCS.

Conkin, Johnny↗

Inverse sequential procedures for the monitoring of time series

When one or more new values are added to a developing time series, they change its descriptive parameters (mean, variance, trend, coherence). A 'change index (CI)' is developed as a quantitative indicator that the changed parameters remain compatible with the existing 'base' data. CI formulate are derived, in terms of normalized likelihood ratios, for small samples from Poisson, Gaussian, and Chi-Square distributions, and for regression coefficients measuring linear or exponential trends. A substantial parameter change creates a rapid or abrupt CI decrease which persists when the length of the bases is changed. Except for a special Gaussian case, the CI has no simple explicit regions for tests of hypotheses. However, its design ensures that the series sampled need not conform strictly to the distribution form assumed for the parameter estimates. The use of the CI is illustrated with both constructed and observed data samples, processed with a Fortran code 'Sequitor'.

Radok, Uwe↗

Radio Frequency Interference Detection for Passive Remote Sensing Using Eigenvalue Analysis

Radio frequency interference (RFI) can corrupt passive remote sensing measurements taken with microwave radiometers. With the increasingly utilized spectrum and the push for larger bandwidth radiometers, the likelihood of RFI contamination has grown significantly. In this work, an eigenvalue-based algorithm is developed to detect the presence of RFI and provide estimates of RFI-free radiation levels. Simulated tests show that the proposed detector outperforms conventional kurtosis-based RFI detectors in the low-to-medium interference-to-noise-power-ratio (INR) regime under continuous wave (CW) and quadrature phase shift keying (QPSK) RFIs.

kurtosis↗

Radio Frequency Interference Detection for Passive Remote Sensing Using Eigenvalue Analysis

Radio frequency interference (RFI) can corrupt passive remote sensing measurements taken with microwave radiometers. With the increasingly utilized spectrum and the push for larger bandwidth radiometers, the likelihood of RFI contamination has grown significantly. In this work, an eigenvalue-based algorithm is developed to detect the presence of RFI and provide estimates of RFI-free radiation levels. Simulated tests show that the proposed detector outperforms conventional kurtosis-based RFI detectors in the low-to-medium interferece-to-noise-power-ratio (INR) regime under continuous wave (CW) and quadrature phase shift keying (QPSK) RFIs.

Passive remote sensing↗

Application of system identification to analytic rotor modeling from simulated and wind tunnel dynamic test data, part 2

An introduction to aircraft state and parameter identification methods is presented. A simplified form of the maximum likelihood method is selected to extract analytical aeroelastic rotor models from simulated and dynamic wind tunnel test results for accelerated cyclic pitch stirring excitation. The dynamic inflow characteristics for forward flight conditions from the blade flapping responses without direct inflow measurements were examined. The rotor blades are essentially rigid for inplane bending and for torsion within the frequency range of study, but flexible in out-of-plane bending. Reverse flow effects are considered for high rotor advance ratios. Two inflow models are studied; the first is based on an equivalent blade Lock number, the second is based on a time delayed momentum inflow. In addition to the inflow parameters, basic rotor parameters like the blade natural frequency and the actual blade Lock number are identified together with measurement bias values. The effect of the theoretical dynamic inflow on the rotor eigenvalues is evaluated.

Hohenemser, K. H.↗

Probabilistic Modeling of the Renal Stone Formation Module

The Integrated Medical Model (IMM) is a probabilistic tool, used in mission planning decision making and medical systems risk assessments. The IMM project maintains a database of over 80 medical conditions that could occur during a spaceflight, documenting an incidence rate and end case scenarios for each. In some cases, where observational data are insufficient to adequately define the inflight medical risk, the IMM utilizes external probabilistic modules to model and estimate the event likelihoods. One such medical event of interest is an unpassed renal stone. Due to a high salt diet and high concentrations of calcium in the blood (due to bone depletion caused by unloading in the microgravity environment) astronauts are at a considerable elevated risk for developing renal calculi (nephrolithiasis) while in space. Lack of observed incidences of nephrolithiasis has led HRP to initiate the development of the Renal Stone Formation Module (RSFM) to create a probabilistic simulator capable of estimating the likelihood of symptomatic renal stone presentation in astronauts on exploration missions. The model consists of two major parts. The first is the probabilistic component, which utilizes probability distributions to assess the range of urine electrolyte parameters and a multivariate regression to transform estimated crystal density and size distributions to the likelihood of the presentation of nephrolithiasis symptoms. The second is a deterministic physical and chemical model of renal stone growth in the kidney developed by Kassemi et al. The probabilistic component of the renal stone model couples the input probability distributions describing the urine chemistry, astronaut physiology, and system parameters with the physical and chemical outputs and inputs to the deterministic stone growth model. These two parts of the model are necessary to capture the uncertainty in the likelihood estimate. The model will be driven by Monte Carlo simulations, continuously randomly sampling the probability distributions of the electrolyte concentrations and system parameters that are inputs into the deterministic model. The total urine chemistry concentrations are used to determine the urine chemistry activity using the Joint Expert Speciation System (JESS), a biochemistry model. Information used from JESS is then fed into the deterministic growth model. Outputs from JESS and the deterministic model are passed back to the probabilistic model where a multivariate regression is used to assess the likelihood of a stone forming and the likelihood of a stone requiring clinical intervention. The parameters used to determine to quantify these risks include: relative supersaturation (RS) of calcium oxalate, citrate/calcium ratio, crystal number density, total urine volume, pH, magnesium excretion, maximum stone width, and ureteral location. Methods and Validation: The RSFM is designed to perform a Monte Carlo simulation to generate probability distributions of clinically significant renal stones, as well as provide an associated uncertainty in the estimate. Initially, early versions will be used to test integration of the components and assess component validation and verification (V&V), with later versions used to address questions regarding design reference mission scenarios. Once integrated with the deterministic component, the credibility assessment of the integrated model will follow NASA STD 7009 requirements.

kidney stones↗

Use of ERTS-1 data: Summary report of work on ten tasks

The author has identified the following significant results. Depth mapping's for a portion of Lake Michigan and at the Little Bahama Bank test site have been verified by use of navigation charts and on-site visits. A thirteen category recognition map of Yellowstone Park has been prepared. Model calculation of atmospheric effects for various altitudes have been prepared. Radar, SLAR, and ERTS-1 data for flooded areas of Monroe County, Michigan are being studied. Water bodies can be reliably recognized and mapped using maximum likelihood processing of ERTS-1 digital data. Wetland mapping has been accomplished by slicing of single band and/or ratio processing of two bands for a single observation date. Both analog and digital processing have been used to map the Lake Ontario basin using ERTS-1 data. Operating characteristic curves were developed for the proportion estimation algorithm to determine its performance in the measurement of surface water area. The signal in band MSS-5 was related to sediment content of waters by modelling approach and by relating surface measurements of water to processed ERTS data. Radiance anomalies in ERTS-1 data could be associated with the presence of oil on water in San Francisco Bay, but the anomalies were of the same order as those caused by variations in sediment concentration and tidal flushing.

Thomson, F. J.↗

Teal Cycle Ergometer Vibration Isolation and Stabilization System Human-in-the-Loop Ground Evaluation for Long-Duration Spaceflight

Purpose: To counter the deleterious effects of weightlessness on the cardiopulmonary system, astronauts living on the International Space Station exercise on a variety of countermeasure equipment including the Cycle Ergometer Vibration Isolation and Stabilization System (CEVIS). Operational since 2001, the onboard CEVIS will be replaced by a new model, known as Teal CEVIS (TC). As a part of ground evaluation, TC hardware underwent human-in-the-loop (HITL) testing to verify the TC hardware produces workloads that elicit physiologic responses comparable to a laboratory cycle ergometer (LAB). Methods: Seven subjects (5 M/2 F) performed submaximal cycle ergometer testing with indirect calorimetry measures on TC and LAB on separate test days. Testing consisted of graded 30 watt increases in workload until subjects reached 85% of age-predicted max heart rate (HR). Exercise outcomes included rate of oxygen uptake (VO2; liters/min), rate of energy expenditure (REE; kcal/min), and HR (beats/min). Linear mixed models (LMM) were fitted to compare VO2, REE, and HR responses between devices across power outputs with fixed effects for power (P) and device (D) and with random effects for subject. LMM effect coefficients (β), std errors (SE), pseudo-partial R2 of effects (pR2), and model likelihood ratio statistics (χ2, Pr(> chisq); α<.05) are provided. Results: LMM main effects for P and D were observed for VO2 (βp=.0107, SE = .0002, pR2=0.97; βd = -0.075, SE = 0.019, pR2=0.14; χ2(1)= 13.57, p<.001), such that VO2 was higher across stages on TC. Main effects of P and D were observed for REE (βp= .0586, SE = 0.001, pR2= 0.97; βd = -0.33208, SE = 0.099, pR2= 0.056; χ2(1)=10.481, p < .01), such that REE was higher across power outputs on TC. A P x D interaction was observed for HR, along with main effects for P and D (βp x d =-0.048, SE = 0.02, pR2=0.048; βp= 0.42, SE = 0.016, pR2=0.87; χ2(1) = 5.398, p = 0.02), such that higher workloads elicited a greater difference in HR between devices. Conclusions: HITL results show, TC elicits greater physiologic responses across power outputs compared to LAB. However, pR2 for device effects show small differences between devices. Therefore, TC can be expected to provide appropriate physiological stimulus across workloads and be considered a reliable tool to mitigate the effects of weightlessness.

Cycle Ergometer Vibration and Isolation System↗

Detecting change as it occurs

Traditionally climate changes have been detected from long series of observations and long after they have happened. Our 'inverse sequential' procedure, for detecting change as soon as it occurs, describes the existing or most recent data by their frequency distribution. Its parameter(s) are estimated both from the existing set of observations and from the same set augmented by 1,2,....j new observations. Individual-value probability products ('likelihoods') are used to form ratios which yield two probabilities for erroneously accepting the existing parameter(s) as valid for the augmented data set, and vice versa. A genuine parameter change is signaled when these probabilities (or a more stable compound probability) show a progressive decrease. New parameter values can then be estimated from the new observations alone using standard statistical techniques. The inverse sequential procedure will be illustrated for global annual mean temperatures (assumed normally distributed), and for annual numbers of North Atlantic hurricanes (assumed to represent Poisson distributions). The procedure was developed, but not yet tested, for linear or exponential trends, and for chi-squared means or degrees of freedom, a special measure of autocorrelation.

Radok, Uwe↗