# 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, EricShow more Statistically 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.Show more Item Distributed control of multi-agent networks(2020-12-10) Abdulghafoor, Alaa Zaki Abdulrahman; Bakolas, Efstathios; Zanetti, RenatoShow more Motivated by the challenges that arise in controlling mobile agents operating in areas with nonuniform time-varying densities, in this paper we propose a distributed steering control framework for a network of autonomous mobile multi-agents whose members have to deploy and allocate themselves in critical positions over a given region in accordance with a time-varying coverage density function. Our method is based on a two-level description of the multi-agent network. The second level reflects the macroscopic description of the network which corresponds to the probability distribution of the agents' locations over a given region in which the network of multi-agents is treated as one unit. The second level reflects the microscopic description of the network which is described in terms of the collection of all individual positions of all of its agents. Thus, the goal of the multi-agent network is to attain a spatial distribution that matches the reference coverage density function (macroscopic high-level control problem) through the local interactions of the agents at the individual level (microscopic low-level control problem). The high-level control problem is addressed by associating it with a desired reference Gaussian probability density. Moreover, the low control problem is addressed by utilizing the Lloyd's algorithm with a time-varying coverage density function. Therefore, the control laws provided would allow the agents to achieve a desired macroscopic behavior of the network, using only distributed algorithms and local information. Each agent will control its own velocity, based only on knowledge of a few neighboring agents, but in such a way that a desired probability distribution is obtained. Finally, a set of simulation results are provided to show the convergence of the mobile agents to their critical locations and to show the effectiveness of the proposed approach.Show more Item Practicality of algorithmic number theory(2013-08) Taylor, Ariel Jolishia; Luecke, John EdwinShow more This 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.Show more 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.Show more 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.Show more