In this thesis we study the key requirements and solutions for the feasibility and application of Ethernet-TCP/IP technology to the networks we termed Highly-Variable Real-Time Networks (HVRN). This particular class of networks poses exceptionally demanding requirements because their physical and logical topologies are both temporally and spatially variable. We devised and introduced specific mechanisms for applying Ethernet-TCP/IP to HVRNs with particular emphasis on effective and reliable modular connectivity. Using a railroad train as a reference, this work analyzes the unique requirements of HVRNs and focuses on the backbone architecture for such a system under Ethernet and TCP/IP.

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In this thesis, we focus on the problem of image classification and investigate how its performance can be systematically improved. Improving the performance of different computer vision methods has been the subject of many studies. While different studies take different approaches to achieve this improvement, in this thesis we address this problem by investigating the relevance of the statistics collected from the image. We propose a framework for gradually improving the quality of an already existing image descriptor. In our studies, we employ a descriptor which is composed the response of a series of discriminative components for summarizing each image. As we will show, this descriptor has an ideal form in which all categories become linearly separable. While, reaching this form is not possible, we will argue how by replacing a small fraction of these components, it is possible to obtain a descriptor which is, on average, closer to this ideal form. To do so, we initially identify which components do not contribute to the quality of the descriptor and replace them with more robust components. As we will show, this replacement has a positive effect on the quality of the descriptor. While there are many ways of obtaining more robust components, we introduce a joint feature selection problem to obtain image features that retains class discriminative properties while simultaneously generalising between within class variations. Our approach is based on the concept of a joint feature where several small features are combined in a spatial structure. The proposed framework automatically learns the structure of the joint constellations in a class dependent manner improving the generalisation and discrimination capabilities of the local descriptor while still retaining a low-dimensional representations. The joint feature selection problem discussed in this thesis belongs to a specific class of latent variable models that assumes each labeled sample is associated with a set of different features, with no prior knowledge of which feature is the most relevant feature to be used. Deformable-Part Models (DPM) can be seen as good examples of such models. These models are usually considered to be expensive to train and very sensitive to the initialization. Here, we focus on the learning of such models by introducing a topological framework and show how it is possible to both reduce the learning complexity and produce more robust decision boundaries. We will also argue how our framework can be used for producing robust decision boundaries without exploiting the dataset bias or relying on accurate annotations. To examine the hypothesis of this thesis, we evaluate different parts of our framework on several challenging datasets and demonstrate how our framework is capable of gradually improving the performance of image classification by collecting more robust statistics from the image and improving the quality of the descriptor. / <p>QC 20140506</p>

This study presented the light variation mechanism along with temperature, distance, and changes in projected surface area of the dwarf nova CN Orionis. Modeling the disk and hot spot as blackbodies produced graphs of flux, temperature, and projected surface area over time, making it possible to deduce the cause for the light variation. This is a valid approximation, since the disk is considered to be opaque in nature. The orbital period of CN Orionis was in phase with the above parameters, which affirm projected surface area of the hot spot is heavily responsible for the flux variations, and not temperature variations. Further determinations of these parameters and more data collection would be quite beneficial for confirmation and further study of accretion disk physics. / Department of Physics and Astronomy

This thesis is concerned with problems of variable selection, influence of sample size and related issues in the applications of various techniques of exploratory multivariate analysis (in particular, correspondence analysis, biplots and canonical correspondence analysis) to archaeology and ecology. Data sets (both published and new) are used to illustrate these methods and to highlight the problems that arise - these practical examples are returned to throughout as the various issues are discussed. Much of the motivation for the development of the methodology has been driven by the needs of the archaeologists providing the data, who were consulted extensively during the study. The first (introductory) chapter includes a detailed description of the data sets examined and the archaeological background to their collection. Chapters Two, Three and Four explain in detail the mathematical theory behind the three techniques. Their uses are illustrated on the various examples of interest, raising data-driven questions which become the focus of the later chapters. The main objectives are to investigate the influence of various design quantities on the inferences made from such multivariate techniques. Quantities such as the sample size (e.g. number of artefacts collected), the number of categories of classification (e.g. of sites, wares, contexts) and the number of variables measured compete for fixed resources in archaeological and ecological applications. Methods of variable selection and the assessment of the stability of the results are further issues of interest and are investigated using bootstrapping and procrustes analysis. Jack-knife methods are used to detect influential sites, wares, contexts, species and artefacts. Some existing methods of investigating issues such as those raised above are applied and extended to correspondence analysis in Chapters Five and Six. Adaptions of them are proposed for biplots in Chapters Seven and Eight and for canonical correspondence analysis in Chapter Nine. Chapter Ten concludes the thesis.

With the introduction of high speed semiconductor power devices and the increased application of adjustable speed drives (ASDs) for efficient speed control of ac motors, there has been a growing number of costly motor-drive related process failures. It has been found that the high dv/dt and high switching frequency have caused premature motor insulation failures due to motor terminal over-voltages (exacerbated by longer cable lengths). It is also acknowledged that high dv/dt and high frequency common-mode voltages generated by pulse-width modulated (PWM) inverters contribute significantly to electromagnetic interference (EMI) and may also cause damaging bearing and leakage currents. In response to these problems, a variety of mitigation techniques have been proposed in the past. However, the known solutions usually address these problems one at a time and some of the mitigation techniques are not highly effective. The major objective of this research is to search for solutions to these ASD application issues with an emphasis on solving all of the problems at the source. Therefore, theoretical analysis of all the above adverse effects are presented and the existing mitigation techniques are evaluated in this dissertation. It is found that common-mode voltage is the major cause of both bearing currents and the conducted EMI, thus the research is focused on new inverter topologies and control strategies in order to eliminate the common-mode voltage. To achieve the goal of common-mode voltage cancellation, a novel dual-bridge inverter (DBI) is proposed and studied. The DBI employs an additional inverter output stage to drive a standard three-phase dual-voltage induction motor and is controlled to generate balanced excitation of the motor resulting in a zero common-mode voltage. It is shown through experimental results that the motor bearing current is eliminated and the conducted EMI is significantly reduced. In addition to the DBI, multilevel inverter topologies have also been studied. It has been found in this research that with proper selections of the switching states, certain multilevel PWM inverters will not generate common-mode voltages. This new control method is verified in simulation by using both sine-triangle intersection PWM (SPWM) and voltage space-vector modulation (SVM). / Graduation date: 1999

Often in statistics it is of interest to investigate whether or not a trend is significant. Methods for testing such a trend depend on the assumptions of the error terms such as whether the distribution is known and also if the error terms are independent. Likelihood ratio tests may be used if the distribution is known but in some instances one may not want to make such assumptions. In a time series, these errors will not always be independent. In this case, the error terms are often modelled by an autoregressive or moving average process. There are resampling techniques for testing the hypothesis of interest when the error terms are dependent, such as, modelbased bootstrapping and the wild bootstrap, but the error terms need to be whitened. In this dissertation, a bootstrap procedure is used to test the hypothesis of no trend for variable stars when the error structure assumes a particular form. In some cases, the bootstrap to be implemented is preferred over large sample tests in terms of the level of the test. The bootstrap procedure is able to correctly identify the underlying distribution which may not be χ2.

Most components or structures experience in service a variety of cyclic stresses. In the case of cyclic constant amplitude loading the fatigue crack growth depends only on the crack, the component geometry and the applied loading. In the case of variable amplitude loading it also depends on the preceding cyclic loading history. Various types of load sequence (overloads, under-loads, or combination of them) may induce different load-interaction effects which can cause either acceleration or reduction of the fatigue crack growth rate. The previously developed UniGrow fatigue crack growth model for constant amplitude loading histories which was based on the analysis of the local stress-strain material behaviour at the crack tip has been improved, modified and extended to such a level of sophistication that it can be used for fatigue crack growth analyses of cracked bodies subjected to arbitrary variable amplitude loading spectra. It was shown that the UniGrow model enables to correctly predict the effect of the applied compressive stress and tensile overloads by accounting for the existence of the internal (residual) stresses induced by the reversed cyclic plasticity around the crack tip. This idea together with additional structural memory effect model has been formalized mathematically and coded into computer program convenient for predicting fatigue crack growth under arbitrary variable amplitude loading spectra. The experimental verification of the proposed model was performed using 7075-T6, 2024-T3, 2324-T7, 7010-T7, 7050-T7 aluminium alloys, Ti-17 titanium alloy, and 350WT steel. The good agreement between theoretical and experimental data proved the ability of the UniGrow model to predict fatigue crack growth and fatigue crack propagation life under a wide variety of real variable amplitude loading spectra.

Most components or structures experience in service a variety of cyclic stresses. In the case of cyclic constant amplitude loading the fatigue crack growth depends only on the crack, the component geometry and the applied loading. In the case of variable amplitude loading it also depends on the preceding cyclic loading history. Various types of load sequence (overloads, under-loads, or combination of them) may induce different load-interaction effects which can cause either acceleration or reduction of the fatigue crack growth rate. The previously developed UniGrow fatigue crack growth model for constant amplitude loading histories which was based on the analysis of the local stress-strain material behaviour at the crack tip has been improved, modified and extended to such a level of sophistication that it can be used for fatigue crack growth analyses of cracked bodies subjected to arbitrary variable amplitude loading spectra. It was shown that the UniGrow model enables to correctly predict the effect of the applied compressive stress and tensile overloads by accounting for the existence of the internal (residual) stresses induced by the reversed cyclic plasticity around the crack tip. This idea together with additional structural memory effect model has been formalized mathematically and coded into computer program convenient for predicting fatigue crack growth under arbitrary variable amplitude loading spectra. The experimental verification of the proposed model was performed using 7075-T6, 2024-T3, 2324-T7, 7010-T7, 7050-T7 aluminium alloys, Ti-17 titanium alloy, and 350WT steel. The good agreement between theoretical and experimental data proved the ability of the UniGrow model to predict fatigue crack growth and fatigue crack propagation life under a wide variety of real variable amplitude loading spectra.