Browsing by Subject "Gaussian"
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Item Detection of burst noise using the chi-squared goodness of fit test(2009-08) Marwaha, Shubra; Hassibi, Arjang; Swanson, EricStatistically more test samples obtained from a single chip would give a better picture of the various noise processes present. Increasing the number of samples while testing one chip would however lead to an increase in the testing time, decreasing the overall throughput. The aim of this report is to investigate the detection of non-Gaussian noise (burst noise) in a random set of data with a small number of samples. In order to determine whether a given set of noise samples has non-Gaussian noise processes present, a Chi-Squared ‘Goodness of Fit’ test on a modeled set of random data is presented. A discussion of test methodologies using a single test measurement pass as well as two passes is presented from the obtained simulation results.Item Optimization of a petroleum producing assets portfolio: development of an advanced computer model(2009-05-15) Aibassov, GizatullaPortfolios of contemporary integrated petroleum companies consist of a few dozen Exploration and Production (E&P) projects that are usually spread all over the world. Therefore, it is important not only to manage individual projects by themselves, but to also take into account different interactions between projects in order to manage whole portfolios. This study is the step-by-step representation of the method of optimizing portfolios of risky petroleum E&P projects, an illustrated method based on Markowitz?s Portfolio Theory. This method uses the covariance matrix between projects? expected return in order to optimize their portfolio. The developed computer model consists of four major modules. The first module generates petroleum price forecasts. In our implementation we used the price forecasting method based on Sequential Gaussian Simulation. The second module, Monte Carlo, simulates distribution of reserves and a set of expected production profiles. The third module calculates expected after tax net cash flows and estimates performance indicators for each realization, thus yielding distribution of return for each project. The fourth module estimates covariance between return distributions of individual projects and compiles them into portfolios. Using results of the fourth module, analysts can make their portfolio selection decisions. Thus, an advanced computer model for optimization of the portfolio of petroleum assets has been developed. The model is implemented in a MATLAB? computational environment and allows optimization of the portfolio using three different return measures (NPV, GRR, PI). The model has been successfully applied to the set of synthesized projects yielding reasonable solutions in all three return planes. Analysis of obtained solutions has shown that the given computer model is robust and flexible in terms of input data and output results. Its modular architecture allows further inclusion of complementary ?blocks? that may solve optimization problems utilizing different measures (than considered) of risk and return as well as different input data formats.Item Practicality of algorithmic number theory(2013-08) Taylor, Ariel Jolishia; Luecke, John EdwinThis report discusses some of the uses of algorithms within number theory. Topics examined include the applications of algorithms in the study of cryptology, the Euclidean Algorithm, prime generating functions, and the connections between algorithmic number theory and high school algebra.Item Three transdimensional factors for the conversion of 2D acoustic rough surface scattering model results for comparison with 3D scattering(2013-12) Tran, Bryant Minh; Wilson, Preston S.; Isakson, Marcia J.Rough surface scattering is a problem of interest in underwater acoustic remote sensing applications. To model this problem, a fully three-dimensional (3D) finite element model has been developed, but it requires an abundance of time and computational resources. Two-dimensional (2D) models that are much easier to compute are often employed though they don’t natively represent the physical environment. Three quantities have been developed that, when applied, allow 2D rough surface scattering models to be used to predict 3D scattering. The first factor, referred to as the spreading factor, adopted from the work of Sumedh Joshi [1], accounts for geometrical differences between equivalent 2D and 3D model environments. A second factor, referred to as the perturbative factor, is developed through the use of small perturbation theory. This factor is well-suited to account for differences in the scattered field between a 2D model and scattering from an isotropically rough 2D surface in 3D. Lastly, a third composite factor, referred to as the combined factor, of the previous two is developed by taking their minimum. This work deals only with scattering within the plane of the incident wave perpendicular to the scatterer. The applicability of these factors are tested by comparing a 2D scattering model with a fully three-dimensional Monte Carlo finite element method model for a variety of von Karman and Gaussian power spectra. The combined factor shows promise towards a robust method to adequately characterize isotropic 3D rough surfaces using 2D numerical simulations.