A distributed network architecture for video-on-demand

The objective of this thesis is to design a distributed network architecture that provides video - on - demand services to public subscribers. This architecture is proposed as an alternative to a centralized video service system. The latter system is currently being developed by Oracle Corporation and NCube Corporation. A simulator is developed to compare the performance of both the distributed and centralized video server architectures. Moreover, an estimate of the cost of both systems is derived using current price data. It is shown that the distributed video server architecture offers a better cost / performance trade-off than the centralized system. In addition, the distributed system can be scaled up in an incremental fashion to increase the system capacity and throughput. Finally, the distributed system is a more robust system: in the presence of component failure, it can be configured to isolate or bypass failed components. Thus, it allows for graceful performance degradation, which is difficult to achieve in a centralized system

Determination of the Sign of g factors for Conduction Electrons Using Time-resolved Kerr Rotation

The knowledge of electron g factor is essential for spin manipulation in the field of spintronics and quantum computing. While there exist technical difficulties in determining the sign of g factor in semiconductors by the established magneto-optical spectroscopic methods. We develop a time resolved Kerr rotation technique to precisely measure the sign and the amplitude of electron g factor in semiconductors

Robust Adaptive Control of a Class of Nonlinear Strict-feedback Discrete-time Systems with Exact Output Tracking

10.1016/j.automatica.2009.07.025Automatica45112537-2545ATCA

Clinical Use of Aspirin in Treatment and Prevention of Cardiovascular Disease

Cardiovascular disease (CVD), principally heart disease and stroke, is the leading cause of death for both males and females in developed countries. Aspirin is the most widely used and tested antiplatelet drug in CVD, and it is proven to be the cornerstone of antiplatelet therapy in treatment and prevention of CVD in clinical trials in various populations. In acute coronary syndrome, thrombotic stroke, and Kawasaki's disease, acute use of aspirin can decrease mortality and recurrence of cardiovascular events. As secondary prevention, aspirin is believed to be effective in acute coronary syndrome, stable angina, revascularization, stroke, TIA, and atrial fibrillation. Aspirin may also be used for patients with a high risk of future CVD for primary prevention, but the balance between benefits and the possibility of side effects must be considered

The Criteria of Passive and Low Energy in Building Design for Tropical Climate in Thailand

Due to high level of energy consumption and increasing environmental concerns, energy efficiency has become a critical issue today. Buildings alone account for around 30 percent of the world’s total energy consumption. The way buildings are designed and constructed today will not only have an impact on their operating costs, but it will also affect the world’s energy consumption patterns and environmental conditions for many years to come. For much of the building industry in Thailand, the designed-in approach to energy-efficient design does not reflect current market practice. In reality, without passive design, numerous opportunitiesfor designing better performance buildings can be wasted. The integration of passive design is thus a key to energy conscious buildings. The integration of passive design approach optimizes the interactions between the natural environment, building envelope and systems as an integrated system. This research examines which components work best altogether to save energy and reduce environmental impactson buildings in the tropical region. The outcomes of this research aim to set up the criteria of passive and low energy in building design for the tropical climate in Thailand. These fundamental differences will lead to a very different architectural and constructional design. It is imperative that the decision be made at an early stage in the design and there are tremendous opportunitiesto use smart, energy efficient designs to reduce the energy footprint of the built environment for decades to come

Fourth-order compact schemes for solving multidimensional heat problems with Neumann boundary conditions

In this article, two sets of fourth-order compact finite difference schemes are constructed for solving heat-conducting problems of two or three dimensions, respectively. Both problems are with Neumann boundary conditions. These works are extensions of our earlier work (Zhao et al., Fourth order compact schemes of a heat conduction problem with Neumann boundary conditions, Numerical Methods Partial Differential Equations, to appear) for the one-dimensional case. The local one-dimensional method is employed to construct these two sets of schemes, which are proved to be globally solvable, unconditionally stable, and convergent. Numerical examples are also provided. © 2007 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq, 2007Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/57369/1/20255_ftp.pd

Domain Adaptation and Image Classification via Deep Conditional Adaptation Network

Unsupervised domain adaptation aims to generalize the supervised model trained on a source domain to an unlabeled target domain. Marginal distribution alignment of feature spaces is widely used to reduce the domain discrepancy between the source and target domains. However, it assumes that the source and target domains share the same label distribution, which limits their application scope. In this paper, we consider a more general application scenario where the label distributions of the source and target domains are not the same. In this scenario, marginal distribution alignment-based methods will be vulnerable to negative transfer. To address this issue, we propose a novel unsupervised domain adaptation method, Deep Conditional Adaptation Network (DCAN), based on conditional distribution alignment of feature spaces. To be specific, we reduce the domain discrepancy by minimizing the Conditional Maximum Mean Discrepancy between the conditional distributions of deep features on the source and target domains, and extract the discriminant information from target domain by maximizing the mutual information between samples and the prediction labels. In addition, DCAN can be used to address a special scenario, Partial unsupervised domain adaptation, where the target domain category is a subset of the source domain category. Experiments on both unsupervised domain adaptation and Partial unsupervised domain adaptation show that DCAN achieves superior classification performance over state-of-the-art methods. In particular, DCAN achieves great improvement in the tasks with large difference in label distributions (6.1\% on SVHN to MNIST, 5.4\% in UDA tasks on Office-Home and 4.5\% in Partial UDA tasks on Office-Home)