Nonparametric estimation of mean and variance when a few sample values possibly outliers
Nonparametric estimation of mean and variance in random sampling with observations of varying values
Walsh, J. E.↗
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Exact intervals and tests for median when one sample value possibly an outliner
Available are independent observations (continuous data) that are believed to be a random sample. Desired are distribution-free confidence intervals and significance tests for the population median. However, there is the possibility that either the smallest or the largest observation is an outlier. Then, use of a procedure for rejection of an outlying observation might seem appropriate. Such a procedure would consider that two alternative situations are possible and would select one of them. Either (1) the n observations are truly a random sample, or (2) an outlier exists and its removal leaves a random sample of size n-1. For either situation, confidence intervals and tests are desired for the median of the population yielding the random sample. Unfortunately, satisfactory rejection procedures of a distribution-free nature do not seem to be available. Moreover, all rejection procedures impose undesirable conditional effects on the observations, and also, can select the wrong one of the two above situations. It is found that two-sided intervals and tests based on two symmetrically located order statistics (not the largest and smallest) of the n observations have this property.
Keller, G. J.↗
Exact intervals and tests for median when one sample value possibly an outlier
Confidence intervals and significance tests for mean of symmetrical population with outlier observation not largest or smallest
Walsh, J. E.↗
Exact intervals and test for mean of symmetrical population when one sample value possibly an outlier
Confidence intervals and significance tests for mean of symmetrical population with smallest or largest outlier observation
Walsh, J.↗
Low-Complexity Lossless and Near-Lossless Data Compression Technique for Multispectral Imagery
This work extends the lossless data compression technique described in Fast Lossless Compression of Multispectral- Image Data, (NPO-42517) NASA Tech Briefs, Vol. 30, No. 8 (August 2006), page 26. The original technique was extended to include a near-lossless compression option, allowing substantially smaller compressed file sizes when a small amount of distortion can be tolerated. Near-lossless compression is obtained by including a quantization step prior to encoding of prediction residuals. The original technique uses lossless predictive compression and is designed for use on multispectral imagery. A lossless predictive data compression algorithm compresses a digitized signal one sample at a time as follows: First, a sample value is predicted from previously encoded samples. The difference between the actual sample value and the prediction is called the prediction residual. The prediction residual is encoded into the compressed file. The decompressor can form the same predicted sample and can decode the prediction residual from the compressed file, and so can reconstruct the original sample. A lossless predictive compression algorithm can generally be converted to a near-lossless compression algorithm by quantizing the prediction residuals prior to encoding them. In this case, since the reconstructed sample values will not be identical to the original sample values, the encoder must determine the values that will be reconstructed and use these values for predicting later sample values. The technique described here uses this method, starting with the original technique, to allow near-lossless compression. The extension to allow near-lossless compression adds the ability to achieve much more compression when small amounts of distortion are tolerable, while retaining the low complexity and good overall compression effectiveness of the original algorithm.
Xie, Hua↗
Lossless Compression of Data into Fixed-Length Packets
A computer program effects lossless compression of data samples from a one-dimensional source into fixed-length data packets. The software makes use of adaptive prediction: it exploits the data structure in such a way as to increase the efficiency of compression beyond that otherwise achievable. Adaptive linear filtering is used to predict each sample value based on past sample values. The difference between predicted and actual sample values is encoded using a Golomb code.
Kiely, Aaron B.↗
Apparatus and Method for Measuring Strain in Bragg Gratings
An apparatus and method for measuring strain of gratings written into an optical fiber is disclosed. Optical radiation is transmitted over a plurality of contiguous predetermined wavelength ranges into a reference optical fiber network and an optical fiber network under test to produce a plurality of reference interference fringes and measurement interference fringes. respectively. The reference and measurement fringes are detected and sampled such that each sampled value of the reference and measurement fringes is associated with a corresponding sample number. The wavelength change of the reference optical fiber, for each sample number, due to the wavelength of the optical radiation is determined. Each determined wavelength change is matched with a corresponding sampled value of each measurement fringe. Each sampled measurement fringe of each wavelength sweep is transformed into a spatial domain waveform. The spatial domain waveforms are summed to form a summation spatial domain waveform that is used to determine location of each grating with respect to a reference reflector. A portion of each spatial domain waveform that corresponds to a particular grating is determined and transformed into a corresponding frequency spectrum representation. The strain on the grating at each wavelength of optical radiation is determined by determining the difference between the current wavelength and an earlier, zero-strain wavelength measurement.
Froggatt, Mark E.↗
Filtering device
An electrical filter for removing noise from a voice communications signal is reported; seven sample values of the signal are obtained continuously, updated and subjected to filtering. Filtering is accomplished by adding balanced, with respect to a mid-point sample, spaced pairs of the sampled values, and then multiplying each pair by a selected filter constant. The signal products thus obtained are summed to provide a filtered version of the original signal.
Edwards, T. R.↗
Extraction of Thermal Performance Values from Samples in the Lunar Dust Adhesion Bell Jar
A simulation chamber has been developed to test the performance of thermal control surfaces under dusty lunar conditions. The lunar dust adhesion bell jar (LDAB) is a diffusion pumped vacuum chamber (10(exp -8) Torr) built to test material samples less than about 7 cm in diameter. The LDAB has the following lunar dust simulant processing capabilities: heating and cooling while stirring in order to degas and remove adsorbed water; RF air-plasma for activating the dust and for organic contaminant removal; RF H/He-plasma to simulate solar wind; dust sieving system for controlling particle sizes; and a controlled means of introducing the activated dust to the samples under study. The LDAB is also fitted with an in situ Xe arc lamp solar simulator, and a cold box that can reach 30 K. Samples of thermal control surfaces (2.5 cm diameter) are introduced into the chamber for calorimetric evaluation using thermocouple instrumentation. The object of this paper is to present a thermal model of the samples under test conditions and to outline the procedure to extract the absorptance, emittance, and thermal efficiency from the pristine and sub-monolayer dust covered samples.
Gaier, James R.↗
Extraction of Thermal Performance Values from Samples in the Lunar Dust Adhesion Bell Jar
A simulation chamber has been developed to test the performance of thermal control surfaces under dusty lunar conditions. The lunar dust adhesion bell jar (LDAB) is a diffusion pumped vacuum chamber (10-8 Torr) built to test material samples less than about 7 cm in diameter. The LDAB has the following lunar dust stimulant processing capabilities: heating and cooling while stirring in order to degas and remove absorbed water; RF air-plasma for activating the dust and for organic contaminant removal; RF H/He-plasma to simulate solar wind; dust sieving system for controlling particle sizes; and a controlled means of introducing the activated dust to the samples under study. The LDAB is also fitted with an in situ Xe arc lamp solar simulator, and a cold box that can reach 30 K. Samples of thermal control surfaces (2.5 cm diameter) are introduced into the chamber for calorimetric evaluation using thermocouple instrumentation. The object of this paper is to present a thermal model of the samples under test conditions, and to outline the procedure to extract the absorptance, emittance, and thermal efficiency from the pristine and sub-monolayer dust covered samples
Gaier, James R.↗
Extraction of Thermal Performance Values from Samples in the Lunar Dust Adhesion Bell Jar
A simulation chamber has been developed to test the performance of thermal control surfaces under dusty lunar conditions. The lunar dust adhesion bell jar (LDAB) is a diffusion pumped vacuum chamber (10(exp -8) Torr) built to test material samples less than about 7 cm in diameter. The LDAB has the following lunar dust simulant processing capabilities: heating and cooling while stirring in order to degas and remove adsorbed water; RF air-plasma for activating the dust and for organic contaminant removal; RF H/He-plasma to simulate solar wind; dust sieving system for controlling particle sizes; and a controlled means of introducing the activated dust to the samples under study. The LDAB is also fitted with an in situ Xe arc lamp solar simulator, and a cold box that can reach 30 K. Samples of thermal control surfaces (2.5 cm diameter) are introduced into the chamber for calorimetric evaluation using thermocouple instrumentation. The object of this paper is to present a thermal model of the samples under test conditions and to outline the procedure to extract the absorptance, emittance, and thermal efficiency from the pristine and sub-monolayer dust covered samples.
Gaier, James R.↗
Value of Sample Return and High Precision Analyses: Need for A Resource of Compelling Stories, Metaphors and Examples for Public Speakers
There is widespread agreement among planetary scientists that much of what we know about the workings of the solar system comes from accurate, high precision measurements on returned samples. Precision is a function of the number of atoms the instrumentation is able to count. Accuracy depends on the calibration or standardization technique. For Genesis, the solar wind sample return mission, acquiring enough atoms to ensure precise SW measurements and then accurately quantifying those measurements were steps known to be non‐trivial pre‐flight. The difficulty of precise and accurate measurements on returned samples, and why they cannot be made remotely, is not communicated well to the public. In part, this is be-cause "high precision" is abstract and error bars are not very exciting topics. This paper explores ideas for collecting and compiling compelling metaphors and colorful examples as a resource for planetary science public speakers.
Allton, J. H.↗
Global Hydrogen Abundances on the Lunar Surface
A global map of bulk hydrogen abundances on the Moon is presented. This map was generated using data from the Lunar Prospector Neutron Spectrometer. This map required corrections for variations due to rare-earth elements, and was calibrated to Apollo sample hydrogen abundances. Since neutron-derived measurements sample hydrogen content to a depth of tens of cm, these results provide complementary insights to those provided by studies using spectral reflectance data, which sample depths of order μm. Comparison of these abundances to Apollo sample values suggest that the samples reflect actual hydrogen content on the lunar surface, not dominantly from non-lunar contamination. The average lunar hydrogen abundance is 47 ppm with a systematic uncertainty of ∼10 ppm. This is consistent with bulk hydrogen from solar wind emplacement. A bulk hydrogen enhancement (50–68 ppm) has been identified at the Moon's largest pyroclastic deposit (Aristarchus Plateau), which corroborates prior observations that hydrogen and/or water plays a role in lunar magmatic events. Global data show a correlation between hydrogen and evolved materials rich in incompatible trace elements (i.e., KREEP type rocks), with a hydrogen excess of 14–36 ppm in these materials. Based on this hydrogen enhancement, we estimate a lower-limit water abundance within urKREEP materials (i.e., the final ∼2% of the lunar magma ocean) of 320–820 ppm H2O. This observation implies that water played a role in the original magma-ocean formation and solidification with a lower-limit water content in the original lunar magma ocean of 7–16 ppm or higher.
David J. Lawrence↗
Less-Complex Method of Classifying MPSK
An alternative to an optimal method of automated classification of signals modulated with M-ary phase-shift-keying (M-ary PSK or MPSK) has been derived. The alternative method is approximate, but it offers nearly optimal performance and entails much less complexity, which translates to much less computation time. Modulation classification is becoming increasingly important in radio-communication systems that utilize multiple data modulation schemes and include software-defined or software-controlled receivers. Such a receiver may "know" little a priori about an incoming signal but may be required to correctly classify its data rate, modulation type, and forward error-correction code before properly configuring itself to acquire and track the symbol timing, carrier frequency, and phase, and ultimately produce decoded bits. Modulation classification has long been an important component of military interception of initially unknown radio signals transmitted by adversaries. Modulation classification may also be useful for enabling cellular telephones to automatically recognize different signal types and configure themselves accordingly. The concept of modulation classification as outlined in the preceding paragraph is quite general. However, at the present early stage of development, and for the purpose of describing the present alternative method, the term "modulation classification" or simply "classification" signifies, more specifically, a distinction between M-ary and M'-ary PSK, where M and M' represent two different integer multiples of 2. Both the prior optimal method and the present alternative method require the acquisition of magnitude and phase values of a number (N) of consecutive baseband samples of the incoming signal + noise. The prior optimal method is based on a maximum- likelihood (ML) classification rule that requires a calculation of likelihood functions for the M and M' hypotheses: Each likelihood function is an integral, over a full cycle of carrier phase, of a complicated sum of functions of the baseband sample values, the carrier phase, the carrier-signal and noise magnitudes, and M or M'. Then the likelihood ratio, defined as the ratio between the likelihood functions, is computed, leading to the choice of whichever hypothesis - M or M'- is more likely. In the alternative method, the integral in each likelihood function is approximated by a sum over values of the integrand sampled at a number, 1, of equally spaced values of carrier phase. Used in this way, 1 is a parameter that can be adjusted to trade computational complexity against the probability of misclassification. In the limit as 1 approaches infinity, one obtains the integral form of the likelihood function and thus recovers the ML classification. The present approximate method has been tested in comparison with the ML method by means of computational simulations. The results of the simulations have shown that the performance (as quantified by probability of misclassification) of the approximate method is nearly indistinguishable from that of the ML method (see figure).
Hamkins, Jon↗
An atlas of monthly mean distributions of GEOSAT sea surface height, SSMI surface wind speed, AVHRR/2 sea surface temperature, and ECMWF surface wind components during 1988
Monthly mean global distributions for 1988 are presented with a common color scale and geographical map. Distributions are included for sea surface height variation estimated from GEOSAT; surface wind speed estimated from the Special Sensor Microwave Imager on the Defense Meteorological Satellite Program spacecraft; sea surface temperature estimated from the Advanced Very High Resolution Radiometer on NOAA spacecrafts; and the Cartesian components of the 10m height wind vector computed by the European Center for Medium Range Weather Forecasting. Charts of monthly mean value, sampling distribution, and standard deviation value are displayed. Annual mean distributions are displayed.
Halpern, D.↗
An atlas of monthly mean distributions of SSMI surface wind speed, ARGOS buoy drift, AVHRR/2 sea surface temperature, and ECMWF surface wind components during 1990
The following monthly mean global distributions for 1990 are proposed with a common color scale and geographical map: 10-m height wind speed estimated from the Special Sensor Microwave Imager (SSMI) on a United States (US) Air Force Defense Meteorological Satellite Program (DMSP) spacecraft; sea surface temperature estimated from the advanced very high resolution radiometer (AVHRR/2) on a U.S. National Oceanic and Atmospheric Administration (NOAA) spacecraft; Cartesian components of free drifting buoys which are tracked by the ARGOS navigation system on NOAA satellites; and Cartesian components on the 10-m height wind vector computed by the European Center for Medium-Range Weather Forecasting (ECMWF). Charts of monthly mean value, sampling distribution, and standard deviation values are displayed. Annual mean distributions are displayed.
Halpern, D.↗
An atlas of monthly mean distributions of SSMI surface wind speed, ARGOS buoy drift, AVHRR/2 sea surface temperature, and ECMWF surface wind components during 1991
The following monthly mean global distributions for 1991 are presented with a common color scale and geographical map: 10-m height wind speed estimated from the Special Sensor Microwave Imager (SSMI) on a United States Air Force Defense Meteorological Satellite Program (DMSP) spacecraft; sea surface temperature estimated from the advanced very high resolution radiometer (AVHRR/2) on a U.S. National Oceanic and Atmospheric Administration (NOAA) spacecraft; Cartesian components of free-drifting buoys which are tracked by the ARGOS navigation system on NOAA satellites; and Cartesian components of the 10-m height wind vector computed by the European Center for Medium-Range Weather Forecasting (ECMWF). Charts of monthly mean value, sampling distribution, and standard deviation value are displayed. Annual mean distributions are displayed.
Halpern, D.↗
High-Grading Lunar Samples
Astronauts on long-duration lunar missions will need the capability to high-grade their samples to select the highest value samples for transport to Earth and to leave others on the Moon. We are supporting studies to define the necessary and sufficient measurements and techniques for high-grading samples at a lunar outpost. A glovebox, dedicated to testing instruments and techniques for high-grading samples, is in operation at the JSC Lunar Experiment Laboratory. A reference suite of lunar rocks and soils, spanning the full compositional range found in the Apollo collection, is available for testing in this laboratory. Thin sections of these samples are available for direct comparison. The Lunar Sample Compendium, on-line at http://www-curator.jsc.nasa.gov/lunar/compendium.cfm, summarizes previous analyses of these samples. The laboratory, sample suite, and Compendium are available to the lunar research and exploration community. In the first test of possible instruments for lunar sample high-grading, we imaged 18 lunar rocks and four soils from the reference suite using the Multispectral Microscopic Imager (MMI) developed by Arizona State University and JPL (see Farmer et. al. abstract). The MMI is a fixed-focus digital imaging system with a resolution of 62.5 microns/pixel, a field size of 40 x 32 mm, and a depth-of-field of approximately 5 mm. Samples are illuminated sequentially by 21 light emitting diodes in discrete wavelengths spanning the visible to shortwave infrared. Measurements of reflectance standards and background allow calibration to absolute reflectance. ENVI-based software is used to produce spectra for specific minerals as well as multi-spectral images of rock textures.
Allen, Carlton↗