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With and without bioconvective pattern formation, a theoretical model predicts growth in light-limited cultures of motile algae. At the critical density for pattern formation, the resulting doubly exponential population curves show an inflection. Such growth corresponds quantitatively to experiments in mechanically unstirred cultures. This attaches survival value to synchronized pattern formation.
Hierarchical pattern classifier reduces number of comparisons between input and memory vectors without reducing detail of final classification by dividing classification process into coarse-to-fine hierarchy that comprises first "grouping" step and second classification step. Three-layer neural network reduces computation further by reducing number of vector dimensions in processing. Concept applicable to pattern-classification problems with need to reduce amount of computation necessary to classify, identify, or match patterns to desired degree of resolution.
A number of interesting discoloration patterns are clearly evident on MOOO2-1 which resides on the three faces of the Long Duration Exposure Facility (LDEF). Most interesting is the pattern of blue oxidation on polished single crystal silicon apparently produced by scattered or direct ram oxygen atoms along the earth face. A complete explanation for the patterns has not yet been obtained. All honeycomb outgassing holes have a small discoloration ring around them that varies in color. The shadow cast by a suspended wire on the earth face surface is not easily explained by either solar photons or by ram flux. The shadows and the dark/light regions cannot be explained consistently by the process of solar ultraviolet paint-darkening modulated by ram flux oxygen bleaching of the paint.
Modeling global atmospheric circulations and forecasting the weather would improve greatly if worldwide information on winds aloft were available. Recognition of this led to the inclusion of the LAser Wind Sounder (LAWS) system to measure Doppler shifts from aerosols in the planned for Earth Observation System (EOS). However, gaps will exist in LAWS coverage where heavy clouds are present. The RAdar Wind Sensor (RAWS) is an instrument that could fill these gaps by measuring Doppler shifts from clouds and rain. Previous studies conducted at the University of Kansas show RAWS as a feasible instrument. This thesis pertains to the signal-to-noise ratio (SNR) sensitivity, transmit waveform, and limitations to the antenna scan pattern of the RAWS system. A dop-size distribution model is selected and applied to the radar range equation for the sensitivity analysis. Six frequencies are used in computing the SNR for several cloud types to determine the optimal transmit frequency. the results show the use of two frequencies, one higher (94 GHz) to obtain sensitivity for thinner cloud, and a lower frequency (24 GHz) to obtain sensitivity for thinner cloud, and a lower frequency (24 GHz) for better penetration in rain, provide ample SNR. The waveform design supports covariance estimation processing. This estimator eliminates the Doppler ambiguities compounded by the selection of such high transmit frequencies, while providing an estimate of the mean frequency. the unambiguous range and velocity computation shows them to be within acceptable limits. The design goal for the RAWS system is to limit the wind-speed error to less than 1 ms(exp -1). Due to linear dependence between vectors for a three-vector scan pattern, a reasonable wind-speed error is unattainable. Only the two-vector scan pattern falls within the wind-error limits for azimuth angles between 16 deg to 70 deg. However, this scan only allows two components of the wind to be determined. As a result, a technique is then shown, based on the Z-R-V relationships, that permit the vertical component (i.e., rain) to be computed. Thus the horizontal wind components may be obtained form the covariance estimator and the vertical component from the reflectivity factor. Finally, a new candidate system is introduced which summarizes the parameters taken from previous RAWS studies, or those modified in this thesis.
Conditions simulating low- and high-gravity, reveal changes in macroscopic pattern formation in selected microorganisms, but whether these structures are gravity dependent is not clear. Two theories have been identified in the fluid dynamics community which support macroscopic pattern formation. The first one is gravity dependent (fluid density models) where small concentrated regions of organisms sink unstably, and the second is gravity independent (wave reinforcement theory) where organisms align their movements in concert, such that either their swimming strokes beat in phase or their vortices entrain neighbors to follow parallel paths. Studies have shown that macroscopic pattern formation is consistent with the fluid density models for protozoa and algae and wave reinforcement hypothesis for caprine spermatozoa.
Human beings judge patterns (such as images) by complex mental processes, some of which may not be known, while computing machines extract features. By representing the human judgements with simple measurements and reducing them and the machine extracted features to a common metric space and fitting them by regression, the judgements of human experts rendered on a sample of patterns may be imposed on a pattern population to provide automatic classification.
This invention relates generally to pattern matching systems, and more particularly to a method for dynamically adapting the system to enhance the effectiveness of a pattern match. Apparatus and methods for calculating the similarity between patterns are known. There is considerable interest, however, in the storage and retrieval of data, particularly, when the search is called or initiated by incomplete information. For many search algorithms, a query initiating a data search requires exact information, and the data file is searched for an exact match. Inability to find an exact match thus results in a failure of the system or method.
During this research period, September 16, 1990 to March 15, 1991, a design method for selecting a low-loss impedance material coating for a horn antenna pattern control has been developed. This method and the stepped waveguide technique can be employed to accurately compute the electromagnetic wave phenomenon inside the transition region of the horn antenna, with or without the impedance surfaces, from the feed to the radiating aperture. For moment method solutions of the electric and magnetic current distributions on the radiating aperture and the outer surface of the horn antenna, triangular surface-patch modes are introduced to replace the sinusoidal surface-patch modes as expansion and testing functions to provide a more physical expansion of the current distributions. In the synthesis problem, a numerical optimization process is formulated to minimize the error function between the desired waveguide modes and the modes provided by the horn transition with impedance surfaces. Since the modes generated by the horn transition with impedance surface are computed by analytical techniques, the computational error involved in the synthesis of the antenna pattern is minimum. Therefore, the instability problem can be avoided. A preliminary implementation of the techniques has demonstrated that the developed theory of the horn antenna pattern control using the impedance surfaces is realizable.
A hybrid numerical technique is developed for electrically large pyramidal horn antennas radiating in free space. A stepped-waveguide method is used to analyze the interior surfaces of the horn transition. The Electric Field Integral Equation (EFIE) is employed on the outer surfaces of the pyramidal horn including the radiating aperture. Meanwhile, the Magnetic Field Integral Equation (MFIE) is used on the aperture to relate the aperture fields and those in the horn transition. The resultant hybrid field integral equation (HFIE) is solved numerically by the method of moments. This formulation is both accurate and numerically stable so that high-gain microwave pyramidal horns can be analyzed rigorously. Far-field radiation patterns, both computed and measured, are presented for three electrically-large x-band horn antennas. The comparisons demonstrate that this method is accurate enough to predict the fine pattern structure at wide angles and in the back region. Computed far-field patterns and aperture field distribution of two smaller x-band horns are also presented along with a discussion on the validity of the approximate aperture field distributions routinely used in the analysis and design of pyramidal horns.
A numerical model was developed to calculate the interference pattern at the end of a multimode weakly guiding optical fiber under stress. Whenever an optical fiber is under stress, the modal phase in the interference term of the intensity formula changes. Plots of the simulated output of a stressed fiber are presented. For multimode fibers, very complicated patterns result. Under stress, lobes in the pattern are generated, displaced and power is exchanged among them.
A stress analysis of tectonic patterns near Venusian coronae is reported. Combined local corona stresses and uniform regional stresses are used to predict patterns of surface tectonic features. The patterns are compared to those of coronae on Magellan images to determine the regional stress and elastic lithospheric thickness about the coronae. Regional stresses of 0.1-0.6 kbar and elastic lithospheric thicknesses of 10 +/- 5 km are estimated for three specific coronae.
A dynamic two-dimensional laser-beam-pattern steering technique using photorefractive holograms in conjunction with electrically addressed spatial light modulators is proposed and investigated. The experimental results demonstrate the dynamic steering of random combinations of basis beam patterns. The proposed method has the advantages of random beam-pattern combination, good beam intensity uniformity, and higher diffraction efficiency compared with conventional methods.
The development of an automated technique for the eduction of 3-D spatial patterns in vector or scalar diagnostics was completed. The method is based on an iterative convolution between a trial pattern and the data field. It was applied to the analysis of low Reynolds number turbulent channel flow and homogeneous shear flow. The results yielded new information on the dominant flow structures in these flows, particularly with respect to the spatial relationships between various forms of organized motion. A particular application of the pattern eduction method, which is tentatively referred to as an 'adaptive wavelet transformation', is proposed with the objective of investigating the way turbulence structure changes with scale. Preliminary results using data from homogeneous turbulent shear flow simulations are presented. At the low Reynolds numbers of the simulations, there is no evidence of scale similarity. The small scales appear to be associated with the edges of the larger scale vortical structures.
A method for determining the streamwise flow pattern of a nonviscous incompressible fluid about an oblique airfoil from the corresponding flow pattern about the airfoil in normal position is presented and illustrated in two examples. The method can be extended to account approximately for compressibility effects by applying the Prandtl-Glauert correction factor to the flow pattern that is normal to the leading edge of the airfoil. The method is expected to be useful in determining the shape of a fuselage or nacelle having a minimum of interference with the flow over a swept-back wing.
Improved method of determining radiation pattern of synthetic-aperture radar (SAR) antenna with respect to elevation-angle coordinate (angle of roll about flight path) devised. Essence of method is solving radar equation in such way as to enable extraction of antenna pattern from SAR images of uniform distributed targets. Needed because radiation pattern in flight can differ from that observed in laboratory test of antenna. Computations include subtraction of noise, test for uniformity, and least-squares estimate.
Four test pilots were instrumented for in-flight EEG recordings using a custom portable recording system. Each flew six, two minute tracking tasks in the Calspan NT-33 experimental trainer at Edwards AFB. With the canopy blacked out, pilots used a HUD display to chase a simulated aircraft through a random flight course. Three configurations of flight controls altered the flight characteristics to achieve low, moderate, and high workload, as determined by normative Cooper-Harper ratings. The test protocol was administered by a command pilot in the back seat. Corresponding EEG and tracking data were compared off-line. Tracking performance was measured as deviation from the target aircraft and combined with control difficulty to achieve an estimate of 'cognitive workload'. Trended patterns of parietal EEG activity at 8-12 Hz were sorted according to this classification. In all cases, high workload produced a significantly greater suppression of 8-12 Hz activity than low workload. Further, a clear differentiation of EEG trend patterns was obtained in 80 percent of the cases. High workload produced a sustained suppression of 8-12 Hz activity, while moderate workload resulted in an initial suppression followed by a gradual increment. Low workload was associated with a modulated pattern lacking any periods of marked or sustained suppression. These findings suggest that quantitative analysis of appropriate EEG measures may provide an objective and reliable in-flight index of cognitive effort that could facilitate workload assessment.
The Magellan data set constitutes an example of the large volumes of data that today's instruments can collect, providing more detail of Venus than was previously available from Pioneer Venus, Venera 15/16, or ground-based radar observations put together. However, data analysis technology has not kept pace with data collection and storage technology. Due to the sheer size of the data, complete and comprehensive scientific analysis of such large volumes of image data is no longer feasible without the use of computational aids. Our progress towards developing a pattern recognition system for aiding in the detection and cataloging of small-scale natural features in large collections of images is reported. Combining classical image processing, machine learning, and a graphical user interface, the detection of the 'small-shield' volcanoes (less than 15km in diameter) that constitute the most abundant visible geologic feature in the more that 30,000 synthetic aperture radar (SAR) images of the surface of Venus are initially targeted. Our eventual goal is to provide a general, trainable tool for locating small-scale features where scientists specify what to look for simply by providing examples and attributes of interest to measure. This contrasts with the traditional approach of developing problem specific programs for detecting Specific patterns. The approach and initial results in the specific context of locating small volcanoes is reported. It is estimated, based on extrapolating from previous studies and knowledge of the underlying geologic processes, that there should be on the order of 10(exp 5) to 10(exp 6) of these volcanoes visible in the Magellan data. Identifying and studying these volcanoes is fundamental to a proper understanding of the geologic evolution of Venus. However, locating and parameterizing them in a manual manner is forbiddingly time-consuming. Hence, the development of techniques to partially automate this task were undertaken. The primary constraints for this particular problem are that the method must be reasonably robust and fast. Unlike most geological features, the small volcanoes of Venus can be ascribed to a basic process that produces features with a short list of readily defined characteristics differing significantly from other surface features on Venus. For pattern recognition purposes the relevant criteria include (1) a circular planimetric outline, (2) known diameter frequency distribution from preliminary studies, (3) a limited number of basic morphological shapes, and (4) the common occurrence of a single, circular summit pit at the center of the edifice.
The morphology and global distribution of volcanic centers and their association with other geological characteristics offers significant insight into the global patterns of geology, tectonic style, thermal state, and interior dynamics of Venus. Magellan data permit the detailed geological interpretation necessary to address questions about interior dynamics of Venus particularly as they reflect relatively physical, chemical, and thermal conditions of the interior. This paper focuses on the distribution of anomalous concentrations of volcanic centers on Venus and regional patterns of tectonic deformation as it may relate to the identification of global internal anomalies, including mantle dynamic, petrological, or thermal patterns.