The El Nino event causes the changes of the ocean and atmosphere system that induces the rainfall unusual increasing or reduction in some areas and then cause local lives and economical losses. Previous studies have found that the El Nino actually applies impact on the rainfall, however most of the studies focus on the impact of separated stations but little on regional variation. The study on the other hand focus on the variation of the rainfall based on the climatic classification primarily and the physiographic region position auxiliary during the El Nino event and La Nina events. The main method of this research is the correlation analysis, when the correlation coefficient draws close to +1, it mean that the rainfall is positive relative with the parameter of the El Nino, and when the correlation coefficient draws close to -1, it mean that the rainfall is negativity relative with the parameter of the El Nino event. The analysis parameters of the El Nino event index include the sea water temperature and anomaly of every area in Pacific Ocean, sea water surface temperature difference of two areas opposite, Southern Oscillation index and Multivariate ENSO Index. It is found in the study that the best parameter of the El Nino event is the sea water temperature difference of (Nino1¡Ï2- Nino34). The result showed the most climatic classifications have good relation with the parameter of the El Nino, especially winter-dry climatic classifications is the best. Because the result of the research influence on the season variation, it is not to conclude the relation with the El Nino event. It is need to study deeply for calculating the rainfall of the areas where influenced by the El Nino event.

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In this paper, new intra and inter shape coding algorithms are proposed. The new algorithms are based on chain code. Chain code of an object's contour can be divided into several segments by the smooth contour characteristic. By this property, some techniques can be used for the proposed intra and inter shape coding algorithms. In intra mode, each segment is encoded by specific codes, and the decoding result is lossless. Compared with MPEG-4, M4R, DCC and chain code, the compression ratio is improved. Besides, a new coding scheme is proposed for the inter shape coding. It includes finding break points in a series of chain code, correlation between break points and fine scaling with a tolerant threshold between two similar chain code segments. By detecting the segments, break points can be found. The correlation is performed by computing the curvature difference between break points and contour points. The scaling is a technique for extending or shrinking a segment of chain code. Experimental results show that in the condition of high quality or low bit rate our proposed inter shape coding algorithm obtains better performance than MPEG-4 in compression.

Isolation of terrestrially-observed magnetic signals by restoring their diffusive loss due to subsurface electrical conductivity sufficiently correlates these signals with those derived from the Alfven ionospheric electron movement of refraction variation. Temporary magnetic observatories were established on a conductive sedimentary basin (with a sampling interval of 5 s) and on a resistive large igneous intrusion (with a sampling interval of 10 s). Conventional modeling techniques estimate and remove the effects of the magnetometer, geomagnetic diurnal changes, whorls (solar quiet current vortices), and some bays from the acquired signals. Conventional one-dimensional skin depth modeling estimates the diffusive attenuation. The residual magnetic signal and the diffusive filter (as applied to the topography) become quantities in the linear system estimation of the geoelectric subsurface. Angular frequency domain least squares solution of the equations yields an isolated magnetic anomaly spectrum. Interpretive refinement, by selection of the zero or near zero curvature onset of either the spectrum's real or imaginary component, critically prepares the signal solution for correlation to a pseudomagnetic anomaly signal. This is an independently-derived sequence of anomalous values derived from Global Positioning System (GPS) refracted ranges. Detailed application of the Biot-Savart law provides independent anomaly signals to which the magnetic anomalies correlations show great correlation improvement by the isolation. These correlation improvements are from 2% to 83% and 9% to 91% for the sedimentary basin and from 2% to 96% and 24% to 78% for the igneous intrusion.

To detect dependence among variables is an essential task in many scientific investigations. In this study we propose a new measure of association, the coefficient of intrinsic dependence (CID), which takes value in [0,1] and faithfully reflects the full range of dependence for two random variables. The CID is free of distributional and functional assumptions. It can be easily implemented and extended to multivariate situations. Traditionally, the correlation coefficient is the preferred measure of association. However, it's effectiveness is considerably compromised when the random variables are not normally distributed. Besides, the interpretation of the correlation coefficient is difficult when the data are categorical. By contrast, the CID is free of these problems. In our simulation studies, we find that the ability of the CID in differentiating different levels of dependence remains robust across different data types (categorical or continuous) and model features (linear or curvilinear). Also, the CID is particularly effective when the dependence is strong, making it a powerful tool for variable selection. As an illustration, the CID is applied to variable selection in two aspects: classification and prediction. The analysis of actual data from a study of breast cancer gene expression is included. For the classification problem, we identify a pair of genes that best classify a patient's prognosis signature, and for the prediction problem, we identify a pair of genes that best relates to the expression of a specific gene.

Predicting reservoir oil viscosity with numerical correlation equations using field-measured variables is widely used in the petroleum industry. Most published correlation equations, however, have never profoundly realized the genuine relationship between the reservoir oil viscosity and other field-measured parameters. Using the proposed systematic strategy is an effective solution for achieving a high performance correlation equation of reservoir oil viscosity. The proposed strategy begins with creating a large database of pressure-volumetemperature (PVT) reports and screening all possible erroneous data. The relationship between the oil viscosity and other field-measured parameters is intensively analyzed by using theoretical and empirical approaches to determine the influential parameters for correlating reservoir oil viscosity equations. The alternating conditional expectation (ACE) algorithm is applied for correlating saturated and undersaturated oil viscosity equations. The precision of field-measured PVT data is inspected by a data reconciliation technique in order to clarify the correctness of oil viscosity correlations. Finally, the performance of the proposed oil viscosity correlation equations is represented in terms of statistical error analysis functions. The result of this study shows that reservoir oil density turns out to be the most effective parameter for correlating both saturated and undersaturated reservoir oil viscosity equations. Expected errors in laboratory-measured oil viscosity are the main factors that degrade the efficiency of oil viscosity correlation equations. The proposed correlation equations provide a reasonable estimate of reservoir oil viscosity; and their superior performance is more reliable than that of published correlation equations at any reservoir conditions.

Luminescent conjugated polymers are widely used in organic optoelectronics. The device is fabricated by spin coating the polymer solutions into thin films. It is important to understand the chain conformation in the solution phase, which is mainly determined by the solubility properties of the solutes and the solvents. The purpose of this study is focused on the aggregate structures of MDMO-PPV polymer in the solution mixing of toluene, heptanes, and decahydronaphthalene. Compared to the polymer in toluene solution, the absorption and fluorescence spectra in the mixing solutions are red-shifted, which indicates the aggregation between polymer chains. In order to identify the aggregation is inter-chain or intra-chain effect, we perform concentration dependent measurements of the fluorescence spectra down to 10-10 M. Our results suggest that inter-chain aggregation is the major influence in the concentration. Since the intra-chain aggregation is strongly influenced by polymer concentration, we carry out the experiments to identify how the inter-chain effect influences at even lower concentrations. Fluorescence correlation spectroscopy (FCS) is used to determine the particle size at 10-12M concentration, which relates directly to the aggregation size. The results show that particle size in the poor solution is larger than that in the good solution. Therefore, we conclude that the change of the fluorescence spectra is caused by the inter-chain aggregation even at the concentration to 10-12M.

Many researchers had developed polyphase sequences, so called ¡§perfect sequence¡¨ or ¡§ideal sequence¡¨, with ideal periodic auto-correlation function. There are lots of applications of communication system depends on the sequences with good auto-correlation property, i.e., synchronization, channel estimation and multiple access. These sequences cannot maintain the ideal property in implementation, because of the error of quantization in digital signal processing of transmitter. On the contrary, we develop a novel set of perfect sequences, Gaussian Integer Perfect Sequence (GIPS), which only contains Gaussian integers. In this paper, we construct them by linear combination and cyclic shift of the eight base sequences. We present the design and basic properties of the sequences. Furthermore, the design method of sequences with the smallest dynamic range is presented.

Thesis (Ph. D.)--University of Washington, 2002. / Vita. Includes bibliographical references (p. 170-182).

By far the most popular method to account for dependencies in the transportation network analysis literature is the use of the multivariate normal (MVN) distribution. While in certain cases there is some theoretical underpinning for the MVN assumption, in others there is none. This can lead to misleading results: results do not only depend on whether dependence is modeled, but also how dependence is modeled. When assuming the MVN distribution, one is limiting oneself to a specific set of dependency structures, which can substantially limit validity of results. In this report an existing, more flexible, correlation-based approach (where just marginal distributions and their correlations are specified) is proposed, and it is demonstrated that, in simulation studies, such an approach is a generalization of the MVN assumption. The need for such generalization is particularly critical in the transportation network modeling literature, where oftentimes there exists no or insufficient data to estimate probability distributions, so that sensitivity analyses assuming different dependence structures could be extremely valuable. However, the proposed method has its own drawbacks. For example, it is again not able to exhaust all possible dependence forms and it relies on some not-so-known properties of the correlation coefficient. / text