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Raman spectroscopy is typically envisioned as an in situ analysis technique. Raman spectra measured remotely (10s of meters) from a planetary lander can be calibrated to spectral radiance and the Raman scattering efficiency can be determined.
Mineralogy is the key to determining the compositional history of the asteroids and to determining the genetic relationships between the asteroids and meteorites. The most sophisticated remote mineralogical characterizations involve the quantitative extraction of specific diagnostic parameters from reflectance spectra and the use of quantitative interpretive calibrations to determine the presence, abundance and/or composition of mineral phases in a surface material. Although this approach is potentially subject to systematic errors, it provides the only consistent set of asteroid surface material characterizations.
There is significant dispute concerning the interpretation and meteoritic affinities of S-type asteroids. Some of this arises from the use of inappropriate analysis methods and the derivation of conclusions which cannot be supported by those interpretive methodologies [1]. The most frequently applied inappropriate technique is curve matching. Whether matching spectra from a spectral library or mixing end-member spectra to match the asteroid spectrum, curve matching for S-type spectra suffers from a suite of weaknesses that are virtually impossible to overcome. Chief among these is the lack of a comprehensive comparison set. Lacking a complete library that includes both the mineralogical variations and the spectrally significant physical variations (e.g., particle size, petrographic relationships, etc.), curve matches are plagued with potential unresolved ambiguities. The other major weakness of virtually all curve matching efforts is that equal weight is given to matching all portions of the spectrum. In actuality, some portions of the spectrum (e.g., centers of absorption features) must be matched very accurately while other portions of the spectrum (e.g., continuum regions and overall slopes) do not require good matches since they are strongly effected by parameters unrelated to the mineralogy of the sample.
The passage of graviational waves changes the arm lengths between corners of the LISA spacecraft formation. These changes are detected interferometrically and recorded as phase measurements. The time series are reduced to reveal several signal types.
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Laser techniques are widely used for the diagnostics of aerodynamic flow and particle fields. The storage capability of holograms has made this technique an even more powerful. Over 60 researchers in the field of holography, particle sizing and image processing convened to discuss these topics. The research program of ten government laboratories, several universities, industry and foreign countries were presented. A number of papers on holographic interferometry with applications to fluid mechanics were given. Several papers on combustion and particle sizing, speckle velocimetry and speckle interferometry were given. A session on image processing and automated fringe data reduction techniques and the type of facilities for fringe reduction was held.
A program was developed in which asteroids and two planets, namely, Saturn and Uranus, were investigated. This included: (1) asteroid spectrophotometry; (2) the nature of the Trojan asteroids; (3) an investigation to determine asteroid masses; (4) the photometry, structure, and dynamics of the rings surrounding the planet Saturn; and (5) aerosol distribution in the atmosphere of Uranus. Plans were finalized to obtain observations of the nucleus of the dying comet P/Arend-Rigaux. Further work was accomplished in asteroid data reduction. Data were entered into the TRIAD data file and a program generated classifications for over 560 different asteroids. A photoelectric area scanner was used to obtain UBV scans of the disk of the planet Saturn on several winter and spring nights in 1977. Intensity profiles show pronounced limb brightening in U, moderate limb brightening in B, and limb darkening in V. Narrow band photoelectric area-scanning photometry of the Uranus disk is also reported. Results are given.
Aircraft measurement techniques are reviewed. Review of past and present applications of instrument aircraft to atmospheric observations is presented. Questions to be answered relative to measuring mean wind profiles as contrasted to turbulence measurements are then addressed. Requirements of instrumentation and accuracy, data reduction, data acquisition, and theoretical and certainty analysis are considered.
Due to the complexity of future space missions and the large amount of data involved, greater autonomy in data processing is demanded for mission operations, training, and vehicle health management. In this paper, we develop a fuzzy logic intelligent diagnostic system to perform data reduction, data analysis, and fault diagnosis for spacecraft vehicle health management applications. The diagnostic system contains a data filter and an inference engine. The data filter is designed to intelligently select only the necessary data for analysis, while the inference engine is designed for failure detection, warning, and decision on corrective actions using fuzzy logic synthesis. Due to its adaptive nature and on-line learning ability, the diagnostic system is capable of dealing with environmental noise, uncertainties, conflict information, and sensor faults.
Artificial neural networks comprising spiking neurons of a novel type have been conceived as improved pattern-analysis and pattern-recognition computational systems. These neurons are represented by a mathematical model denoted the state-variable model (SVM), which among other things, exploits a computational parallelism inherent in spiking-neuron geometry. Networks of SVM neurons offer advantages of speed and computational efficiency, relative to traditional artificial neural networks. The SVM also overcomes some of the limitations of prior spiking-neuron models. There are numerous potential pattern-recognition, tracking, and data-reduction (data preprocessing) applications for these SVM neural networks on Earth and in exploration of remote planets. Spiking neurons imitate biological neurons more closely than do the neurons of traditional artificial neural networks. A spiking neuron includes a central cell body (soma) surrounded by a tree-like interconnection network (dendrites). Spiking neurons are so named because they generate trains of output pulses (spikes) in response to inputs received from sensors or from other neurons. They gain their speed advantage over traditional neural networks by using the timing of individual spikes for computation, whereas traditional artificial neurons use averages of activity levels over time. Moreover, spiking neurons use the delays inherent in dendritic processing in order to efficiently encode the information content of incoming signals. Because traditional artificial neurons fail to capture this encoding, they have less processing capability, and so it is necessary to use more gates when implementing traditional artificial neurons in electronic circuitry. Such higher-order functions as dynamic tasking are effected by use of pools (collections) of spiking neurons interconnected by spike-transmitting fibers. The SVM includes adaptive thresholds and submodels of transport of ions (in imitation of such transport in biological neurons). These features enable the neurons to adapt their responses to high-rate inputs from sensors, and to adapt their firing thresholds to mitigate noise or effects of potential sensor failure. The mathematical derivation of the SVM starts from a prior model, known in the art as the point soma model, which captures all of the salient properties of neuronal response while keeping the computational cost low. The point-soma latency time is modified to be an exponentially decaying function of the strength of the applied potential. Choosing computational efficiency over biological fidelity, the dendrites surrounding a neuron are represented by simplified compartmental submodels and there are no dendritic spines. Updates to the dendritic potential, calcium-ion concentrations and conductances, and potassium-ion conductances are done by use of equations similar to those of the point soma. Diffusion processes in dendrites are modeled by averaging among nearest-neighbor compartments. Inputs to each of the dendritic compartments come from sensors. Alternatively or in addition, when an affected neuron is part of a pool, inputs can come from other spiking neurons. At present, SVM neural networks are implemented by computational simulation, using algorithms that encode the SVM and its submodels. However, it should be possible to implement these neural networks in hardware: The differential equations for the dendritic and cellular processes in the SVM model of spiking neurons map to equivalent circuits that can be implemented directly in analog very-large-scale integrated (VLSI) circuits.
A newly developed technique for enhanced data reduction provides an improved procedure that allows least squares minimization to become possible between data sets with an unequal number of data points. This technique was applied in the Crew and Equipment Translation Aid (CETA) experiment on the STS-37 Shuttle flight in April 1991 to obtain the velocity profile from the acceleration data. The new technique uses a least-squares method to estimate the initial conditions and calibration constants. These initial conditions are estimated by least-squares fitting the displacements indicated by the Hall-effect sensor data to the corresponding displacements obtained from integrating the acceleration data. The velocity and displacement profiles can then be recalculated from the corresponding acceleration data using the estimated parameters. This technique, which enables instantaneous velocities to be obtained from the test data instead of only average velocities at varying discrete times, offers more detailed velocity information, particularly during periods of large acceleration or deceleration.
Gas chromatograph, mass spectrometer system for chemical and biochemical determinations on Mars, and data reduction program
An experiment in electromyography utilizing surface electrode techniques was developed for the Apollo-Soyuz test project. This report describes the computer program, EMGAN, which was written to provide first order data reduction for the experiment. EMG signals are produced by the membrane depolarization of muscle fibers during a muscle contraction. Surface electrodes detect a spatially summated signal from a large number of muscle fibers commonly called an interference pattern. An interference pattern is usually so complex that analysis through signal morphology is extremely difficult if not impossible. It has become common to process EMG interference patterns in the frequency domain. Muscle fatigue and certain myopathic conditions are recognized through changes in muscle frequency spectra.
Traditional remote sensing instruments are multispectral, where observations are collected at a few different spectral bands. Recently, many hyperspectral instruments, that can collect observations at hundreds of bands, have been operation. Furthermore, there have been ongoing research efforts on ultraspectral instruments that can produce observations at thousands of spectral bands. While these remote sensing technology developments hold a great promise for new findings in the area of Earth and space science, they present many challenges. These include the need for faster processing of such increased data volumes, and methods for data reduction. Dimension Reduction is a spectral transformation, which is used widely in remote sensing, is the Principal Components Analysis (PCA). In light of the growing number of spectral channels of modern instruments, the paper reports on the development of a parallel PCA and its implementation on two Beowulf cluster configurations, on with fast Ethernet switch and the other is with a Myrinet interconnection.
Two data reduction algorithms developed using the scattering and phase matrix approaches are described. In the scattering matrix approach, the scattering matrices of four consecutive along-track pixels are averaged and in the phase matrix approach, the phase matrices of four consecutive along-track pixels are averaged. The basic procedures necessary to generate a synthetic polarization image from original data sets are discussed. The two algorithms are evaluated in terms of data volume reduction and the number of errors introduced in the synthesized images. It is observed that the reduced data set produced by the scattering matrix algorithm is smaller than that generated by the phase matrix algorithm; however, greater errors are introduced into the data set by the scattering matrix algorithm than the phase algorithm. Flowcharts for the scattering and phase matrix approaches and for synthesis of uncompressible data are presented.