Algorithm-Based Efficient Approaches for Motion Estimation Systems Teahyung Lee 121 pages Directed by Dr. David V. Anderson This research addresses algorithms for efficient motion estimation systems. With the growth of wireless video system market, such as mobile imaging, digital still and video cameras, and video sensor network, low-power consumption is increasingly desirable for embedded video systems. Motion estimation typically needs considerable computations and is the basic block for many video applications. To implement low-power video systems using embedded devices and sensors, a CMOS imager has been developed that allows low-power computations on the focal plane. In this dissertation efficient motion estimation algorithms are presented to complement this platform. In the first part of dissertation we propose two algorithms regarding gradient-based optical flow estimation (OFE) to reduce computational complexity with high performance. The first is a checkerboard-type filtering (CBTF) algorithm for prefiltering and spatiotemporal derivative calculations. Another one is spatially recursive OFE frameworks using recursive LS (RLS) and/or matrix refinement to reduce the computational complexity for solving linear system of derivative values of image intensity in least-squares (LS)-OFE. From simulation results, CBTF and spatially recursive OFE show improved computational efficiency compared to conventional approaches with higher or similar performance. In the second part of dissertation we propose a new algorithm for video coding application to improve motion estimation and compensation performance in the wavelet domain. This new algorithm is for wavelet-based multi-resolution motion estimation (MRME) using temporal aliasing detection (TAD) to enhance rate-distortion (RD) performance under temporal aliasing noise. This technique gives competitive or better performance in terms of RD compared to conventional MRME and MRME with motion vector prediction through median filtering.

In this document, we study the nonnegative least squares primal-dual method for solving linear programming problems. In particular, we investigate connections between this primal-dual method and the classical Hungarian method for the assignment problem. Firstly, we devise a fast procedure for computing the unrestricted least squares solution of a bipartite matching problem by exploiting the special structure of the incidence matrix of a bipartite graph. Moreover, we explain how to extract a solution for the cardinality matching problem from the nonnegative least squares solution. We also give an efficient procedure for solving the cardinality matching problem on general graphs using the nonnegative least squares approach. Next we look into some theoretical results concerning the minimization of p-norms, and separable differentiable convex functions, subject to linear constraints described by node-arc incidence matrices for graphs. Our main result is the reduction of the assignment problem to a single nonnegative least squares problem. This means that the primal-dual approach can be made to converge in one step for the assignment problem. This method does not reduce the primal-dual approach to one step for general linear programming problems, but it appears to give a good starting dual feasible point for the general problem.

System stability plays an important role in electric power systems. With the development of electric power system, the scale of the electric grid is now becoming larger and larger, and many renewable energy resources are integrated in the grid. However, at the same time, the stability and safety issues of electric power system are becoming more complicated. Frequency and phasors are two critical parameters of the system stability. Obtaining these two parameters have been great challenges for decades. Researchers have provided various kinds of algorithms for frequency tracking and phasor estimation. Among them, Least Squares (LS) algorithm is one of the most commonly used algorithm. This thesis studies the LS algorithm and the Total Least Squares (TLS) algorithm working on frequency tracking and phasor estimation. In order to test the performance of the two algorithms, some simulations have been made in the Matlab. The Total Vector Error (TVE) is a commonly used performance criteria, and the TVE results of the two algorithms are compared. The TLS algorithm performs better than LS algorithm when the frequencies of all harmonic components are given.

The healthcare labor market has experienced some significant changes in the last half century, including the establishment of Medicare and Medicaid in 1965, the emergence of managed care in the 1980s, and the worldwide mobility of labor encouraged by globalization. Currently, more than 25% of physicians working in the U.S. are foreign-born. The existing body of literature related to the impact of immigration on local wages has to date found conflicting results. The purpose of this research is to evaluate the impact of immigration of foreign physicians on local physician wages. This study employs physician survey data from the AMA Physician Masterfile for the years 1997 through 2007 combined with wage data published by the Bureau of Labor Statistics and data from other government sources. Several econometric models are employed to analyze the wage impacts of immigration, including ordinary least squares, fixed effects, two-stage least squares and a first-difference approach to control for endogenous location choice. The results of this study provide evidence that in the short-run, the impacts of immigration of physicians on area wages is small but positive. In the long run, however, wages adjust and the impact becomes negative and statistically significant, although the magnitude of the impact of a one percentage point increase in the share of immigrant physicians in an area is less than 0.2%. The negative wage effects of immigration tend to be larger for foreign-born physicians educated in the U.S. compared with foreign-born international medical graduates. The study also finds evidence that the negative effects of immigration tend to be offset by outflows of the lowest paid native physicians. Furthermore, physicians tend to locate in areas where wages are already higher, and foreign-born physicians are more likely than their native counterparts to work in larger cities as opposed to rural areas. The research has important policy implications in the presence of current debate over immigration law and healthcare reform and in an era of increasing mobility of labor due to globalization.

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O presente trabalho propõe um procedimento de valoração de marca a partir de uma modelagem por equações estruturais. Este estudo aponta inicialmente um modelo conceitual de valoração de marca baseado na linha comportamental, ou seja, nas percepções dos consumidores e não no valor monetário da marca. Para aplicação do procedimento proposto, foram escolhidas as marcas das operadoras de telefonia celular Vivo e Claro. A partir da modelagem por equações estruturais, ajustou-se o modelo teórico proposto a cada uma das marcas em questão. Com o modelo ajustado foi possível calcular não apenas o valor de marca para cada um dos consumidores, como todas as variáveis que o compõe. A possibilidade de se calcular o valor de marca para cada consumidor é extremamente inovadora, uma vez que nenhum dos modelos revistos na literatura científica aponta esta funcionalidade. Com a base de dados contendo o valor de marca para cada consumidor entrevistado, foi possível efetuar diversas análises estatísticas, que permitiram compreender de forma profunda quais variáveis mais impactam o valor das marcas, ou ainda quais são seus pontos fortes e fracos. Com isto, foi possível desenhar estratégias de marketing específicas para que cada uma das marcas aumente o seu valor. / The present study brings a procedure that measures brand equity using a structural equation modeling. This thesis proposes a conceptual model that measures brand equity based in a behaviorist approach, i.e., based on the consumers perception instead of monetary brand value. To apply this procedure two cellular phone operator brands were chosen: Vivo and Claro, and then, using structural equation modeling, it was possible to adjust the initial theory model to each brand. From the adjusted model it was possible to measure not only brand equity to each consumer, but all variables that are part of it. The possibility to measure brand equity to each consumer is extremely new, once no other models - reviewed in the scientific literature - pointed this feature. Using the database and the brand value to each interviewed consumer it was possible to make statistic analysis that allowed comprehending deeply which variables impact brand equity, or what are their strong and weak points. Then it was possible to make specific marketing strategies to each brand to increase their equity.

This project was dedicated to the problem of improving a non-invasive blood glucose monitor being developed by the VivaScan Corporation. The company has made some progress in the non-invasive blood glucose device development and approached WPI for a statistical assistance in the improvement of their model in order to predict the glucose level more accurately. The main goal of this project was to improve the ability of the non-invasive blood glucose monitor to predict the glucose values more precisely. The goal was achieved by finding and implementing the best regression model. The methods included ordinary least squared regression, partial least squares regression, robust regression method, weighted least squares regression, local regression, and ridge regression. VivaScan calibration data for seven patients were analyzed in this project. For each of these patients, the individual regression models were built and compared based on the two factors that evaluate the model prediction ability. It was determined that partial least squares and ridge regressions are two best methods among the others that were considered in this work. Using these two methods gave better glucose prediction. The additional problem of data reduction to minimize the data collection time was also considered in this work.

<p><strong>Syfte:</strong> Syftet är att försöka urskilja samband mellan hushålls socioekonomiska faktorer och efterfrågan på specifika bostadsattribut. Vår frågeställning är: Finns det samband mellan vilka specifika attribut som efterfrågas hos bostadsrätter och hushålls socioekonomiska faktorer? Vår förhoppning är att åtminstone få en indikation på vad olika typer av hushåll efterfrågar.<strong>Metod: </strong>Denna studie baseras på en databas bestående av bostadsrättsförsäljningar som skett i Gävle under år 2008 samt socioekonomisk information om de hushåll som förvärvat dessa bostadsrätter. För att estimera hushålls efterfrågan på ett specifikt bostadsrättsattribut har en tvåstegsmetod använts. I ett första steg avslöjas de underförstådda marginalpriserna av bostadsrätters egenskaper med hjälp av den hedoniska metoden. Dessa marginalpriser används i ett andra steg där efterfrågeekvationer för enskilda bostadsrättsattribut estimeras. Beräkningarna utförs i statistikprogrammet EViews.<strong>Resultat & slutsats: </strong>Resultatet visar att vissa bostadsrättsattribut kan sammankopplas med hushålls socioekonomiska faktorer. Vidare visar resultatet tydliga men mycket svaga mönster vad gäller efterfrågan på marknaden, trots att prisstrukturen på samma marknad är oerhört utmärkande. Vi kan konstatera att prissättningen på bostadsrätters attribut är väldigt tydlig medan konsumtionen av samma attribut är otydlig.<strong>Förslag till fortsatt forskning:</strong> Intressant vore att jämföra studier av denna typ med hyresmarknadens hyressättningsmodell och se om den utgår från samma värdering av bostadens attribut som det visat sig att hushåll efterfrågar. Ytterligare ett förslag är att skatta en faktisk boendekostnad för samtliga hushåll i datamaterialet och använda disponibel inkomst istället för taxerad förvärvsinkomst i efterfrågefunktionen.<strong>Uppsatsens bidrag: </strong>Studien har visat hur man kan estimera och finna samband mellan bostadsattribut och hushålls socioekonomiska faktorer. Därutöver har problemet med endogenitet behandlats genom instrumentvariabler och en uppdelning av datamaterialet i fyra delområden.</p> / <p><strong>Aim:</strong> The purpose of this study is to try to distinguish a relationship between household socio-economic factors and the demand for specific housing attributes. Our question is: Is there a connection between the specific attributes requested by households and its socio-economic factors? Our hope is to at least get an indication of what different types of households demand.<strong>Method:</strong> This study is based on information of tenant-owner flats sales made in Gävle in 2008 as well as socio-economic information of those households who bought these flats. In order to estimate demand for a specific housing attribute a two-step method is used. In a first step the implicit marginal prices of housing attributes are revealed by the hedonic method. These marginal rates are used in a second step to reveal the specific household demand for individual housing attributes. The calculations are made in the statistical program EViews.<strong>Result & Conclusions: </strong>The result shows that some housing attributes can be linked with household socio-economic factors. The result shows a clear but very weak pattern of demand in the market, despite that the price structure in the same market is extremely remarkable. We note that the prices of housing attributes are very clear while the consumption of the same attributes is unclear.<strong>Suggestions for future research: </strong>It would be interesting to compare the results of this type of study with rental markets rent-model and see if it is based on the same valuation of the dwelling attributes that household’s demand. Another proposal is to estimate the actual housing costs for all of the households in the data and use disposable income rather than actual income in the demand function.<strong>Contribution of the thesis: </strong>The study has revealed how to estimate and identify household demand for specific housing attributes. In addition, the problem of endogeneity has been treated with instrument variables and a separation of the data set into four submarkets.</p>

Syfte: Syftet är att försöka urskilja samband mellan hushålls socioekonomiska faktorer och efterfrågan på specifika bostadsattribut. Vår frågeställning är: Finns det samband mellan vilka specifika attribut som efterfrågas hos bostadsrätter och hushålls socioekonomiska faktorer? Vår förhoppning är att åtminstone få en indikation på vad olika typer av hushåll efterfrågar.Metod: Denna studie baseras på en databas bestående av bostadsrättsförsäljningar som skett i Gävle under år 2008 samt socioekonomisk information om de hushåll som förvärvat dessa bostadsrätter. För att estimera hushålls efterfrågan på ett specifikt bostadsrättsattribut har en tvåstegsmetod använts. I ett första steg avslöjas de underförstådda marginalpriserna av bostadsrätters egenskaper med hjälp av den hedoniska metoden. Dessa marginalpriser används i ett andra steg där efterfrågeekvationer för enskilda bostadsrättsattribut estimeras. Beräkningarna utförs i statistikprogrammet EViews.Resultat &amp; slutsats: Resultatet visar att vissa bostadsrättsattribut kan sammankopplas med hushålls socioekonomiska faktorer. Vidare visar resultatet tydliga men mycket svaga mönster vad gäller efterfrågan på marknaden, trots att prisstrukturen på samma marknad är oerhört utmärkande. Vi kan konstatera att prissättningen på bostadsrätters attribut är väldigt tydlig medan konsumtionen av samma attribut är otydlig.Förslag till fortsatt forskning: Intressant vore att jämföra studier av denna typ med hyresmarknadens hyressättningsmodell och se om den utgår från samma värdering av bostadens attribut som det visat sig att hushåll efterfrågar. Ytterligare ett förslag är att skatta en faktisk boendekostnad för samtliga hushåll i datamaterialet och använda disponibel inkomst istället för taxerad förvärvsinkomst i efterfrågefunktionen.Uppsatsens bidrag: Studien har visat hur man kan estimera och finna samband mellan bostadsattribut och hushålls socioekonomiska faktorer. Därutöver har problemet med endogenitet behandlats genom instrumentvariabler och en uppdelning av datamaterialet i fyra delområden. / Aim: The purpose of this study is to try to distinguish a relationship between household socio-economic factors and the demand for specific housing attributes. Our question is: Is there a connection between the specific attributes requested by households and its socio-economic factors? Our hope is to at least get an indication of what different types of households demand.Method: This study is based on information of tenant-owner flats sales made in Gävle in 2008 as well as socio-economic information of those households who bought these flats. In order to estimate demand for a specific housing attribute a two-step method is used. In a first step the implicit marginal prices of housing attributes are revealed by the hedonic method. These marginal rates are used in a second step to reveal the specific household demand for individual housing attributes. The calculations are made in the statistical program EViews.Result &amp; Conclusions: The result shows that some housing attributes can be linked with household socio-economic factors. The result shows a clear but very weak pattern of demand in the market, despite that the price structure in the same market is extremely remarkable. We note that the prices of housing attributes are very clear while the consumption of the same attributes is unclear.Suggestions for future research: It would be interesting to compare the results of this type of study with rental markets rent-model and see if it is based on the same valuation of the dwelling attributes that household’s demand. Another proposal is to estimate the actual housing costs for all of the households in the data and use disposable income rather than actual income in the demand function.Contribution of the thesis: The study has revealed how to estimate and identify household demand for specific housing attributes. In addition, the problem of endogeneity has been treated with instrument variables and a separation of the data set into four submarkets.

Due to the inherently hierarchical nature of many natural phenomena, data collected rests in nested entities. As an example, students are nested in schools, school are nested in districts, districts are nested in counties, and counties are nested within states. Multilevel models provide a statistical framework for investigating and drawing conclusions regarding the influence of factors at differing hierarchical levels of analysis. The work in this paper serves as an introduction to multilevel models and their comparison to Ordinary Least Squares (OLS) regression. We overview three basic model structures: variable intercept model, variable slope model, and hierarchical linear model and illustrate each model with an example of student data. Then, we contrast the three multilevel models with the OLS model and present a method for producing confidence intervals for the regression coefficients. / text