The technique of mobile location has become a popular research topic since the number of related applications for the location information is growing rapidly. The decision to make the location of mobile phones under the U.S. Federal Communications Commission (FCC) in 1996 is one of the driving forces to research and provide solutions to it. But, in wireless communication systems, non line of sight (NLOS) propagation is a key and difficult issue to improve mobile location estimation. We propose an efficient location algorithm which can mitigate the influence of NLOS error. First, based on the geometric relationship between known positions of the base stations, the theorem of ¡§Fermat Point¡¨ is utilized to collect the candidate positions (CPs) of the mobile station. Then, a set of weighting parameters are computed using a density-based clustering method. Finally, the location of mobile station is estimated by solving the optimal solution of the weighted objective function. Different distributions of NLOS error models are used to evaluate the performance of this method. Simulation results show that the performance of the least range measure (LRM) algorithm is slightly better than density-based clustering algorithm (DCA), and superior to the range based linear lines of position algorithm (LLOP) and range scaling algorithm (RSA) on location accuracy under different NLOS environments. The simulation results also satisfy the location accuracy demand of Enhanced 911 (E-911).

The modeling of electromagnetic phenomena described by the Maxwell's equations is of critical importance in many practical applications. The numerical simulation of these equations is challenging and much more involved than initially believed. Consequently, many discretization techniques, most of them quite complicated, have been proposed. In this dissertation, we present and analyze a new methodology for approximation of the time-harmonic Maxwell's equations. It is an extension of the negative-norm least-squares finite element approach which has been applied successfully to a variety of other problems. The main advantages of our method are that it uses simple, piecewise polynomial, finite element spaces, while giving quasi-optimal approximation, even for solutions with low regularity (such as the ones found in practical applications). The numerical solution can be efficiently computed using standard and well-known tools, such as iterative methods and eigensolvers for symmetric and positive definite systems (e.g. PCG and LOBPCG) and reconditioners for second-order problems (e.g. Multigrid). Additionally, approximation of varying polynomial degrees is allowed and spurious eigenmodes are provably avoided. We consider the following problems related to the Maxwell's equations in the frequency domain: the magnetostatic problem, the electrostatic problem, the eigenvalue problem and the full time-harmonic system. For each of these problems, we present a natural (very) weak variational formulation assuming minimal regularity of the solution. In each case, we prove error estimates for the approximation with two different discrete least-squares methods. We also show how to deal with problems posed on domains that are multiply connected or have multiple boundary components. Besides the theoretical analysis of the methods, the dissertation provides various numerical results in two and three dimensions that illustrate and support the theory.

Bus Signal Priority (BSP), which has been deployed in many cities around the world, is a traffic signal enhancement strategy that facilitates efficient movement of buses through signalized intersections. Most BSP systems do not work well in transit networks with nearside bus stop because of the uncertainty in dwell time. Unfortunately, most bus stops on arterial roadways are of this type in the U.S. This dissertation showed that dwell time at nearside bus stops could be modeled using weighted least squares regression. More importantly, the prediction intervals associated with the estimate dwell time were calculated. These prediction intervals were subsequently used in the improved BSP algorithm that attempted to reduce the negative effects of nearside bus stops on BSP operations. The improved BSP algorithm was tested on urban arterial section of Bellaire Boulevard in Houston, Texas. VISSIM, a micro simulation model was used to evaluate the performance of the BSP operations. Prior to evaluating the algorithm, the parameters of the micro simulation model were calibrated using an automated Genetic Algorithm based methodology in order to make the model accurately represent the traffic conditions observed in the field. It was shown that the improved BSP algorithm significantly improved the bus operations in terms of bus delay. In addition, it was found that the delay to other vehicles on the network was not statistically different from other BSP algorithms currently being deployed. It is hypothesized that the new approach would be particularly useful in North America where there are many transit systems that utilize nearside bus stops in their networks.

Research is performed to assess the viability of applying the least squares model to one-dimensional heat transfer and Euler-Bernoulli Beam Theory problems. Least squares models were developed for both the full and mixed forms of the governing one-dimensional heat transfer equation along weak form Galerkin models. Both least squares and weak form Galerkin models were developed for the first order and second order versions of the Euler-Bernoulli beams. Several numerical examples were presented for the heat transfer and Euler- Bernoulli beam theory. The examples for heat transfer included: a differential equation having the same form as the governing equation, heat transfer in a fin, heat transfer in a bar and axisymmetric heat transfer in a long cylinder. These problems were solved using both least squares models, and the full form weak form Galerkin model. With all four examples the weak form Galerkin model and the full form least squares model produced accurate results for the primary variables. To obtain accurate results with the mixed form least squares model it is necessary to use at least a quadratic polynominal. The least squares models with the appropriate approximation functions yielde more accurate results for the secondary variables than the weak form Galerkin. The examples presented for the beam problem include: a cantilever beam with linearly varying distributed load along the beam and a point load at the end, a simply supported beam with a point load in the middle, and a beam fixed on both ends with a distributed load varying cubically. The first two examples were solved using the least squares model based on the second order equation and a weak form Galerkin model based on the full form of the equation. The third problem was solved with the least squares model based on the second order equation. Both the least squares model and the Galerkin model calculated accurate results for the primary variables, while the least squares model was more accurate on the secondary variables. In general, the least-squares finite element models yield more acurate results for gradients of the solution than the traditional weak form Galkerkin finite element models. Extension of the present assessment to multi-dimensional problems and nonlinear provelms is awaiting attention.

Thesis (M.S. in Mechanical Engineering)--Naval Postgraduate School, December 2009. / Thesis Advisor(s): Romano, Marcello ; Bevilacqua, Riccardo. "December 2009." Description based on title screen as viewed on January 27, 2010. Author(s) subject terms: spacecraft, cubesat, nanosat, TINYSCOPE, simulator, test bed, control, system identification, least squares, adaptive mass balancing, mass balancing, three axis simulator, NACL, TAS, CubeTAS, ADCS. Includes bibliographical references (p. 77-82). Also available in print.

Thesis (Ph. D.)--University of Washington, 2001. / Vita. Includes bibliographical references (p. 139-145).

When scientists collect data to be analyzed, regardless of what quantities are being measured, there are inevitably errors in the measurements. In cases where two independent variables are measured with errors, many existing techniques can produce an estimated least-squares linear fit to the data, taking into consideration the size of the errors in both variables. Yet some experiments yield data that do not only contain errors in both variables, but also a non-zero covariance between the errors. In such situations, the experiment results in measurements with error ellipses with tilts specified by the covariance terms. Following an approach suggested by Dr. Edward Robinson, Professor of Astronomy at the University of Texas at Austin, this report describes a methodology that finds the estimates of linear regression parameters, as well as an estimated covariance matrix, for a dataset with tilted error ellipses. Contained in an appendix is the R code for a program that produces these estimates according to the methodology. This report describes the results of the program run on a dataset of measurements of the surface brightness and Sérsic index of galaxies in the Virgo cluster. / text

The second-order least squares (SLS) method in regression model proposed by Wang (2003, 2004) is based on the first two conditional moments of the response variable given the observed predictor variables. Wang and Leblanc (2008) show that the SLS estimator (SLSE) is asymptotically more efficient than the ordinary least squares estimator (OLSE) if the third moment of the random error is nonzero. We apply the SLS method to variable selection problems and propose the adaptively weighted L1 regularized SLSE (L1-SLSE). The L1-SLSE is robust against the shape of error distributions in variable selection problems. Finite sample simulation studies show that the L1-SLSE is more efficient than L1-OLSE in the case of asymmetric error distributions. A real data application with L1-SLSE is presented to demonstrate the usage of this method. / October 2015

When the Fair Housing Act of 1968 was passed 47 years ago, the United States was in the midst of the civil rights movement and fair housing was identified as a pillar of equality. While, progress has been made, there is much work that needs to be done in order to achieve integration. As a country, the United States is a highly segregated country. It is important to understand the factors that contribute to this and it is important to understand the relationships that exists between them in order to attempt to solve the problem. While the legal barriers to integration have been lifted choices continue to be limited to families of color that lack the resources to live in desirable neighborhoods. The ultimate goal of this study is to examine the relationship between the impact of individual indicators and housing patterns in the greater Tucson/Pima county region. An affordability index, the DEYA index, was created to determine where affordability is at its highest. The index includes different weights for foreclosure, Pima County spending on affordable housing, the existence of Pima County general obligations bond affordable housing projects, land value and inclusion in the community land trust. Once this was determined a regression analysis was used to determine the relationship between affordability and individual factors that may be affecting integration. The indicators used were broken down into 3 categories: the categories were education, housing and neighborhoods and employment and economic health.

L'expressiódels gens obtinguts de l'anàliside microarrays s'utilitza en molts casos, per classificar les cèllules. En aquestatesi, unaversióprobabilística del mètodeDiscriminant Partial Least Squares (p-DPLS)s'utilitza per classificar les mostres de les expressions delsseus gens. p-DPLS esbasa en la regla de Bayes de la probabilitat a posteriori. Aquestsclassificadorssónforaçats a classficarsempre.Per superaraquestalimitaciós'haimplementatl'opció de rebuig.Aquestaopciópermetrebutjarlesmostresamb alt riscd'errors de classificació (és a dir, mostresambigüesi outliers).Aquestaopció de rebuigcombinacriterisbasats en els residuals x, el leverage ielsvalorspredits. A més,esdesenvolupa un mètode de selecció de variables per triarels gens mésrellevants, jaque la majoriadels gens analitzatsamb un microarraysónirrellevants per al propòsit particular de classificacióI podenconfondre el classificador. Finalment, el DPLSs'estenen a la classificació multi-classemitjançant la combinació de PLS ambl'anàlisidiscriminant lineal.