In Particle Image Velocimetry (PIV), the number of particle images per interrogation region, or particle image density, impacts the strength of the correlation and, as a result, the number of valid vectors and the measurement uncertainty. Therefore, any a-priori estimate of the accuracy and uncertainty of PIV requires knowledge of the particle image density. An autocorrelation-based method for estimating the local, instantaneous, particle image density is presented. Synthetic images were used to develop an empirical relationship based on how the autocorrelation peak magnitude varies with particle image density, particle image diameter, illumination intensity, interrogation region size, and background noise. This relationship was then tested using images from two experimental setups with different seeding densities and flow media. The experimental results were compared to image densities obtained through using a local maximum method as well as manual particle counts and are found to be robust. The effect of varying particle image intensities was also investigated and is found to affect the particle image density.

Robert Cohen. / "June 2, 1948." Issued also as a thesis, M.I.T. Dept. of Electrical Engineering, May, 1948. / Bibliography: p. 100. / Army Signal Corps Contract No. W-36-039 sc-32037.

Various environmental standards have been established for the sake of public health and ecosystem diversity since environmental awareness was awakened in the late 1960s. However, the results were often unsatisfactory. Either environmental goals achieved were far from desired, or regional development was hampered due to some unpractical high environmental standards. The failure of these environmental standards resulted in innovations of environmental policy instruments to find practical environmental goals and methods approaching them scientifically. Another class of environmental policy instruments, so called economic incentive policies, is established based on environmental economics theory. A neo-classical economics framework is founded for setting appropriate environmental goals and assessing efficiency of environmental policies in reaching these goals. This thesis summarizes rationales and factors affecting the performance for environmental policy instruments under the neo-classical economic framework. Since the acid rain program, the first large-scale implementation of the emissions trading policy, has achieved great success in reducing SO₂ emissions from the electricity generators in the United States, the emission trading policy attracted many interests in this kind of environmental policy instrument. Many countries, such as China, plan to adopt the emissions trading policy to address various environmental problems. Hence, factors leading to the success of this program should be identified. Potential risks and problems must be addressed as well lest the emissions trading policy causes some problem during implementation. Feasibility of implementing an emissions trading policy will be discussed based on these results. Three kinds of geographic analyses, change detection, network analysis, and hot spots identification, are conducted in this thesis to study the effectiveness and efficiency of the acid rain program. It is found that the acid rain program is successful in improving the sustainability of the economic development in the United States. But the effectiveness is not as great as the high emissions cutting rate achieved in this program. In addition, the acid rain program lowers the compliance costs of achieving the environmental goal since the radius of the high quality coal service area doubles. Lastly, hot spots are found around the Ohio River valley and Los Angeles. Suggestions on integrating geographic factors into the economic framework are presented in order to eliminate the risk of causing severe environmental problems. Finally, the feasibility of migrating the emissions trading policy to China is discussed. Further work can be conducted in this direction to realize sustainable development quicker with lower costs.

This paper empirically investigates the exchange rate effects of Swedish krona against euro (SEK/Euro) on stock prices in Sweden. The sample period for the study has been taken from March, 2001 to March, 2011 using monthly nominal exchange rate of SEK/Euro and monthly closing values of OMX Stockholm All Share (OMXPI) Index. The developed unit root test and cointegration technique have been applied for the research. It was found that both data series were nonstationary and integrated of order 1. The test result also showed there was no cointegrating relationship between stock prices and exchange rates. Further investigation into their contemporaneous relationship highlighted a statistically significant negative linear relationship between the said variables, suggesting that an appreciation of the Swedish krona against euro leads to a contemporaneous increase in the value of the Swedish stock market.

Various environmental standards have been established for the sake of public health and ecosystem diversity since environmental awareness was awakened in the late 1960s. However, the results were often unsatisfactory. Either environmental goals achieved were far from desired, or regional development was hampered due to some unpractical high environmental standards. The failure of these environmental standards resulted in innovations of environmental policy instruments to find practical environmental goals and methods approaching them scientifically. Another class of environmental policy instruments, so called economic incentive policies, is established based on environmental economics theory. A neo-classical economics framework is founded for setting appropriate environmental goals and assessing efficiency of environmental policies in reaching these goals. This thesis summarizes rationales and factors affecting the performance for environmental policy instruments under the neo-classical economic framework. Since the acid rain program, the first large-scale implementation of the emissions trading policy, has achieved great success in reducing SO₂ emissions from the electricity generators in the United States, the emission trading policy attracted many interests in this kind of environmental policy instrument. Many countries, such as China, plan to adopt the emissions trading policy to address various environmental problems. Hence, factors leading to the success of this program should be identified. Potential risks and problems must be addressed as well lest the emissions trading policy causes some problem during implementation. Feasibility of implementing an emissions trading policy will be discussed based on these results. Three kinds of geographic analyses, change detection, network analysis, and hot spots identification, are conducted in this thesis to study the effectiveness and efficiency of the acid rain program. It is found that the acid rain program is successful in improving the sustainability of the economic development in the United States. But the effectiveness is not as great as the high emissions cutting rate achieved in this program. In addition, the acid rain program lowers the compliance costs of achieving the environmental goal since the radius of the high quality coal service area doubles. Lastly, hot spots are found around the Ohio River valley and Los Angeles. Suggestions on integrating geographic factors into the economic framework are presented in order to eliminate the risk of causing severe environmental problems. Finally, the feasibility of migrating the emissions trading policy to China is discussed. Further work can be conducted in this direction to realize sustainable development quicker with lower costs.

The field of statistical research in weather allows for the application of old and new methods, some of which may describe relationships between certain variables better such as temperatures and pressure. The objective of this study was to apply a variety of traditional and novel statistical methods to analyze data from the National Data Buoy Center, which records among other variables barometric pressure, atmospheric temperature, water temperature and dew point temperature. The analysis included attempts to better describe and model the data as well as to make estimations for certain variables. The following statistical methods were utilized: linear regression, non-response analysis, residual analysis, descriptive statistics, parametric analysis, Kolmogorov-Smirnov test, autocorrelation, normal approximation for the binomial, and chi-squared test of independence. Of the more significant results, one was establishing the Johnson SB as the best fitting parametric distribution for a group of pressures and another was finding that there was high autocorrelation in atmospheric temperature and pressure for small lags. This topic remains conducive to future research, and such endeavors may strengthen the field of applied statistics and improve our understanding of various weather entities.

Many water resources throughout the world are demonstrating changes in historic water levels. Potential reasons for these changes include climate shifts, anthropogenic alterations or basin urbanization. The focus of this research was threefold: 1) to determine the extent of spatio-temporal changes in regional precipitation patterns 2) to determine the statistical changes that occur in lakes with urbanizing watersheds and 3) to develop accurate prediction of trends and lake level return frequencies. To investigate rainfall patterns regionally, appropriate distributions, either gamma or generalized extreme value (GEV), were fitted to variables at a number of rainfall gages utilizing maximum likelihood estimation. The spatial distribution of rainfall variables was found to be quite homogenous within the region in terms of an average annual expectation. Furthermore, the temporal distribution of rainfall variables was found to be stationary with only one gage evidencing a significant trend. In order to study statistical changes of lake water surface levels in urbanizing watersheds, serial changes in time series parameters, autocorrelation and variance were evaluated and a regression model to estimate weekly lake level fluctuations was developed. The following general conclusions about lakes in urbanizing watersheds were reached: 1) The statistical structure of lake level time series is systematically altered and is related to the extent of urbanization 2) in the absence of other forcing mechanisms, autocorrelation and baseflow appear to decrease and 3) the presence of wetlands adjacent to lakes can offset the reduction in baseflow. In regards to the third objective, the direction and magnitude of trends in flood and drought stages were estimated and both long-term and short-term flood and drought stage return frequencies were predicted utilizing the generalized extreme value (GEV) distribution with time and starting stage covariates. All of the lakes researched evidenced either no trend or very small trends unlikely to significantly alter prediction of future flood or drought return levels. However, for all of the lakes, significant improvement in the prediction of extremes was obtained with the inclusion of starting lake stage as a covariate.

The profilogramms are interpreted as realization of casual static function with normal distribution,so the parameters of profilogramms are treated as characteristic of normal process. In this work we assessed the autocorrelation function of profilogramms, and then using the analytic form of these functions we modeled the static process.

Content Based Image Retrieval (CBIR) systems represent images in the database by color, texture, and shape information. In this thesis, we concentrate on tex- ture features and introduce a new generic texture descriptor, namely, Statistical Analysis of Structural Information (SASI). Moreover, in order to increase the re- trieval rates of a CBIR system, we propose a new method that can also adapt an image retrieval system into a con&macr / gurable one without changing the underlying feature extraction mechanism and the similarity function. SASI is based on statistics of clique autocorrelation coe&plusmn / cients, calculated over structuring windows. SASI de&macr / nes a set of clique windows to extract and measure various structural properties of texture by using a spatial multi- resolution method. Experimental results, performed on various image databases, indicate that SASI is more successful then the Gabor Filter descriptors in cap- turing small granularities and discontinuities such as sharp corners and abrupt changes. Due to the &deg / exibility in designing the clique windows, SASI reaches higher average retrieval rates compared to Gabor Filter descriptors. However, the price of this performance is increased computational complexity. Since, retrieving of similar images of a given query image is a subjective task, it is desirable that retrieval mechanism should be con&macr / gurable by the user. In the proposed method, basically, original feature space of a content-based retrieval system is nonlinearly transformed into a new space, where the distance between the feature vectors is adjusted by learning. The transformation is realized by Arti&macr / cial Neural Network architecture. A cost function is de&macr / ned for learning and optimized by simulated annealing method. Experiments are done on the texture image retrieval system, which use SASI and Gabor Filter features. The results indicate that con&macr / gured image retrieval system is signi&macr / cantly better than the original system.

Thesis (M.S.)--West Virginia University, 2008. / Title from document title page. Document formatted into pages; contains x, 111 p. : ill. Includes abstract. Includes bibliographical references (p. 102-106).