A linear periodically time-varying (LPTV) system is a linear time-varying system with the coefficients changing periodically, which is widely used in control, communications, signal processing, and even circuit modeling. This thesis concentrates on identification of LPTV systems. To this end, the representations of LPTV systems are thoroughly reviewed. Identification methods are developed accordingly. The usefulness of the proposed identification methods is verified by the simulation results.<p> A periodic input signal is applied to a finite impulse response (FIR)-LPTV system and measure the noise-contaminated output. Using such periodic inputs, we show that we can formulate the problem of identification of LPTV systems in the frequency domain. With the help of the discrete Fourier transform (DFT), the identification method reduces to finding the least-squares (LS) solution of a set of linear equations. A sufficient condition for the identifiability of LPTV systems is given, which can be used to find appropriate inputs for the purpose of identification.<p> In the frequency domain, we show that the input and the output can be related by using the discrete Fourier transform (DFT) and a least-squares method can be used to identify the alias components. A lower bound on the mean square error (MSE) of the estimated alias components is given for FIR-LPTV systems. The optimal training signal achieving this lower MSE bound is designed subsequently. The algorithm is extended to the identification of infinite impulse response (IIR)-LPTV systems as well. Simulation results show the accuracy of the estimation and the efficiency of the optimal training signal design.

This study examines whether different corporate real estate (CRE) strategies affect the stock outperformance and systemic risk of various companies. The sample of 443 listed companies of 17 industries in Taiwan during 2000 to 2010 was divided into four groups for the different corporate real estate strategies. The pairwise abnormal return and systemic risk of composite and business (without the affect from real estate market) series were empirically examined and compared using a partial adjustment model. This study also conducts the two-stage least squares procedure to determine whether four CRE strategies were considered diversifiable factors when evaluating the firm¡¦s value The results do not indicate an increasingly abnormal return performance associated with the company implementing a certain CRE strategy, but companies with a stable profession and consistent adjustment strategies are considered a good diversifier by stock investors. Aggressive adjustment strategies do not diversify the systematic risk to overall industry, otherwise the scale of total assets would be considered a diversification in companies with aggressive strategies. The companies using an aggressive profession strategy to increase leverage are regarded as risky phenomen for stock investors, and companies with stable profession strategies face higher systemic risk if their book value is greater than their market value. Therefore, this study determines that CRE strategies affect companies¡¦ systematical risk.

Singular value decomposition (SVD) is a popular decomposition method for solving least-squares estimation problems. However, for large datasets, SVD is very time consuming and memory demanding in obtaining least squares solutions. In this paper, we propose a least squares estimator based on an iterative divide-and-merge scheme for large-scale estimation problems. The estimator consists of several levels. At each level, the input matrices are subdivided into submatrices. The submatrices are decomposed by SVD respectively and the results are merged into smaller matrices which become the input of the next level. The process is iterated until the resulting matrices are small enough which can then be solved directly and efficiently by the SVD algorithm. However, the iterative divide-and-merge algorithms executed on a single machine is still time demanding on large scale datasets. We propose two distributed algorithms to overcome this shortcoming by permitting several machines to perform the decomposition and merging of the submatrices in each level in parallel. The first one is implemented in MapReduce on the Hadoop distributed platform which can run the tasks in parallel on a collection of computers. The second one is implemented on CUDA which can run the tasks in parallel using the Nvidia GPUs. Experimental results demonstrate that the proposed distributed algorithms can greatly reduce the time required to solve large-squares problems.

In economy, banking sector has been considered as the main issue for development. Using panel data analyzing to test the relationship between banks performance and economic freedom indexes for 9 emerging market countries in Asia, the paper¡¦s result shows that there is the positive effect between monetary freedom, business freedom, financial freedom and banks performance while investment freedom has a negative impact. It suggests that emerging market countries¡¦ government and the policy maker need to focus deeply on the operation, the efficiency of regulation and improving the economic freedom.

While prior studies on ERP implementation have largely focused on the importance of best practices, this paper examines the impact of knowledge transfer climate and the competence of the players (i.e., the implementing firm and the consultant team). The model divides factors that influence the result of knowledge transfer during ERP implementation into three categories: those implemented by the firm, those implemented by the consultant, and those related to the impact of the knowledge transfer climate. Competence factors from the first two aspects facilitate the building of a better knowledge transfer climate. Survey results from 101 respondents were subjected to multivariate analysis. The significance of player competence factors is verified, and an understanding of the role that the knowledge transfer climate plays in the knowledge transfer process and the impact on the transfer process is developed. This paper provides a broader, richer model of knowledge transfer networks to promote insight into successful ERP implementation. In practice, the key to effective knowledge transfer is the establishment a positive knowledge transfer climate. To achieve a successful ERP implementation, practitioners should focus on possessing robust competences with ERP implementation partners. Additional research may help ERP implementation project teams more effectively promote knowledge transfer in a wider range of conditions with greater confidence and precision.

To prove that a polynomial is nonnegative on Rn, one can try to show that it is a sum of squares of polynomials (SOS). The latter problem is now known to be reducible to a semi-definite programming (SDP) computation that is much faster than classical algebraic methods, thus enabling new speed-ups in algebraic optimization. However, exactly how often nonnegative polynomials are in fact sums of squares of polynomials remains an open problem. Blekherman was recently able to show that for degree k polynomials in n variables with k = 4 fixed those that are SOS occupy a vanishingly small fraction of those that are nonnegative on Rn, as n -> 1. With an eye toward the case of small n, we refine Blekherman'[s bounds by incorporating the underlying Newton polytope, simultaneously sharpening some of his older bounds along the way. Our refined asymptotics show that certain Newton polytopes may lead to families of polynomials where efficient SDP can still be used for most inputs.

In this research, least-squares based finite element formulations and their applications in fluid mechanics are presented. Least-squares formulations offer several computational and theoretical advantages for Newtonian as well as non-Newtonian fluid flows. Most notably, these formulations circumvent the inf-sup condition of Ladyzhenskaya-Babuska- Brezzi (LBB) such that the choice of approximating space is not subject to any compatibility condition. Also, the resulting coefficient matrix is symmetric and positive-definite. It has been observed that pressure and velocities are not strongly coupled in traditional leastsquares based finite element formulations. Penalty based least-squares formulations that fix the pressure-velocity coupling problem are proposed, implemented in a computational scheme, and evaluated in this study. The continuity equation is treated as a constraint on the velocity field and the constraint is enforced using the penalty method. These penalty based formulations produce accurate results for even low penalty parameters (in the range of 10-50 penalty parameter). A stress based least-squares formulation is also being proposed to couple pressure and velocities. Stress components are introduced as independent variables to make the system first order. The continuity equation is eliminated from the system with suitable modifications. Least-squares formulations are also developed for viscoelastic flows and moving boundary flows. All the formulations developed in this study are tested using several benchmark problems. All of the finite element models developed in this study performed well in all cases. A method to exploit orthogonality of modal bases to avoid numerical integration and have a fast computation is also developed during this study. The entries of the coefficient matrix are calculated analytically. The properties of Jacobi polynomials are used and most of the entries of the coefficient matrix are recast so that they can be evaluated analytically.

The conventional finite element models (FEM) of problems in structural mechanics are based on the principles of virtual work and the total potential energy. In these models, the secondary variables, such as the bending moment and shear force, are post-computed and do not yield good accuracy. In addition, in the case of the Timoshenko beam theory, the element with lower-order equal interpolation of the variables suffers from shear locking. In both Euler-Bernoulli and Timoshenko beam theories, the elements based on weak form Galerkin formulation also suffer from membrane locking when applied to geometrically nonlinear problems. In order to alleviate these types of locking, often reduced integration techniques are employed. However, this technique has other disadvantages, such as hour-glass modes or spurious rigid body modes. Hence, it is desirable to develop alternative finite element models that overcome the locking problems. Least-squares finite element models are considered to be better alternatives to the weak form Galerkin finite element models and, therefore, are in this study for investigation. The basic idea behind the least-squares finite element model is to compute the residuals due to the approximation of the variables of each equation being modeled, construct integral statement of the sum of the squares of the residuals (called least-squares functional), and minimize the integral with respect to the unknown parameters (i.e., nodal values) of the approximations. The least-squares formulation helps to retain the generalized displacements and forces (or stress resultants) as independent variables, and also allows the use of equal order interpolation functions for all variables. In this thesis comparison is made between the solution accuracy of finite element models of the Euler-Bernoulli and Timoshenko beam theories based on two different least-square models with the conventional weak form Galerkin finite element models. The developed models were applied to beam problems with different boundary conditions. The solutions obtained by the least-squares finite element models found to be very accurate for generalized displacements and forces when compared with the exact solutions, and they are more accurate in predicting the forces when compared to the conventional finite element models.

The rates of childhood obesity remain high in spite of the enormous efforts dedicated to tackling the disease. This dissertation investigates the effect of two of its causes, including parental time and children's obesity risk behaviors. Trends in these causes have changed over time and might explain changes in obesity. The two factors are analyzed separately given the differences in impact process and concentration of literature. The data for the investigation is drawn from the Parental Time, Role Strains, Coping, and Children's Diet and Nutrition project. In examining parental time, the attention is directed towards the mother's actual time spent with the child which has been associated with reduction in child weight status. The major aim is to test and correct for the problem of endogeneity stemming from unobserved health factors that can distort any meaningful causal impact of maternal time on child weight status. Using the household production theory, parental time allocation decisions towards child health are modeled and analyzed using instrument variable (IV) methods. Results indicate that the effect of mother's time allocation reduces child weight status. Her decision to allocate time to the child is affected by unseen factors. Father's work to family spillover was found to be a valid instrument for mother's time with the child. Results were robust across different estimators. In analyzing the relationship between childhood obesity risk behaviors and weight status, this study focuses on three child practices including breakfast intake, fast food consumption and sleep patterns. The main aim was to score their joint impact, while at the same time accounting for contextual factors. This work adopted the ecological systems framework which accommodates multiple factors. Based on this theory, a simultaneous system of equations considering child weight status, risk behavior and contextual factors was set up and analyzed using 3SLS. Findings indicated that dietary behaviors remain a major factor in affecting weight status. In addition, feedback mechanism from child weight status will influence the diet pattern adopted by the child. Sleep sufficiency had no effect on child weight status.

Orthogonal frequency division multiplexing (OFDM) technique has been extensively used in digital wireless communications, such as Digital Broadcasting and wireless local area network (WLAN). It is considered to be one of the most promising techniques for transmission on the downlinks of broadband wireless access systems to combat multipath and multiple access interference (MAI). Spatial processing that exploits the diversity provided by smart antenna (SA) or intelligent antenna (IA) arrays, in which the adaptive beamformer is employed, is another alternatives to increase the efficiency of wireless system capacity and performance without allocating additional frequency spectrum. It allows the system to make full use of spatial diversity due to multiple antennas [5][6]. To further improve the performance for suppressing various interference sources; including narrowband and wideband interference, flat and frequency selective fading, for different channel environmentin. In this thesis, a smart antenna with wideband beamspace approach array beamformer associated with the slideing window (SW) linearly constrained RLS (SW-LC-RLS) algorithm, and the OFDM systems with smart antenna array are emhasized. Computer simulation results confirmed that our proposed scheme could achieve desired performance compared with the conventional approach, in terms of MAI and other interference suppression.