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No description available.

No description available.

No description available.

No description available.

Two broad surveys of community- level water consumption and pricing behavior are used to answer questions about water demand in a more flexible and dynamic context than is provided in the literature. Central themes of price representation, aggregation, and dynamic adjustment tie together three econometric demand analyses. The centerpiece of each analysis is an exogenous weighted price representation. A model in first-differences is estimated by ordinary least squares using data from a personally-conducted survey of Texas urban water suppliers. Annual price elasticity is found to vary with weather and income, with a value of -0.127 at the data mean. The dynamic model becomes a periodic error correction model when the residuals of 12 static monthly models are inserted into the difference model. Distinct residential, commercial, and industrial variables and historical climatic conditions are added to the integrated model, using new national data. Quantity demanded is found to be periodically integrated with a common stochastic root. Because of this, the structural monthly models must be cointegrated to be consistent, which they appear to be. The error correction coefficient is estimated at -0.187. Demand is found to be seasonal and slow to adjust to shocks, with little or no adjustment in a single year and 90% adjustment taking a decade or more. Residential and commercial demand parameters are found to be indistinguishable. The sources of price endogeneity and historical fixes are reviewed. Ideal properties of a weighted price index are identified. For schedules containing exactly two rates, weighting is equivalent to a distribution function in consumption. This property is exploited to derive empirical weights from the national data, using values from a nonparametric generalization of the structural demand model and a nonparametric cumulative density function. The result is a generalization of the price difference metric to a weighted level-price index. The validity of a uniform weighting is not rejected. The weighted price index is data intensive, but the payoff is increased depth and precision for the economist and accessibility for the practitioner.

Research into adverse events in home care is at a very early stage worldwide. Adverse event research in other health care sectors has demonstrated that patients can and do suffer harm, much of which is preventable, during the receipt of health care services. A stratified, random sample of patients who had received home care nursing service and were discharged in 2004/05 from three Ontario home care programs was studied to develop basic exploratory and descriptive evidence to advance the understanding of AEs in home care. The outcome is an estimate of the incidence of adverse events among patients, description of adverse event types and factors associated with adverse events, and the development of models predictive of home care patients with higher and lower potential for adverse events, and of the location of patients with adverse events. Positive critical indicators were identified in 66.5% of 430 cases. Sixty-one adverse events were identified in 55 (19.2%) of these 286 cases. When adjusted for sampling methodology, the adverse event rate was 13.2 per 100 patients (95%, CI 10.4% - 16.6%, SE 1.6%). Thirty-three percent of the adverse events were rated as having more than a 50% probability of preventability; 1.4% of all patients experienced an adverse event related death. Eight of the 45 factors significantly associated with adverse events formed a single factor model predictive of adverse events. Six two-factor interactions and the absence of one factor were also predictive of the occurrence of adverse events. Five of the 12 critical indicators significantly related to adverse events, as well as 7 critical indicator combinations formed models that reliably located about two-thirds of patients who had, and almost all patients who had not, experienced an adverse event. This study suggests that a significant number of home care patients experience adverse events, two-thirds of which are preventable. Use of adverse event sensitive factors as a screening tool for patients that may benefit from enhanced case management and clinical vigilance, and those unlikely to be placed at increased adverse event risk by maintaining current levels of vigilance, presents an opportunity to improve patient safety. Retrospective critical indicator models identifying home care patients who have experienced an adverse event can be used to estimate adverse event incidence rates and changes in rates over time.

The main contribution of this thesis is the statistical analysis of orthogonal frequency di- vision multiplexing (OFDM) systems operating over wireless channels that are both fre- quency selective and Rayleigh fading. We first describe the instantaneous capacity of such systems using a central limit theorem, as well as the asymptotic capacity of a power lim- ited OFDM system as the number of subcarriers approaches infinity. We then analyse the performance of uncoded OFDM systems by first developing bounds on the block error rate. Next we show that the distribution of the number of symbol errors within each block may be tightly approximated, and derive the distribution of an upper bound on the total variation distance. Finally, the central result of this thesis proposes the use of lattices for encodingOFDMsystems. For this, we detail a particular method of using lattices to encode OFDMsystems, and derive the optimalmaximumlikelihood decodingmetric. Generalised Minimum Distance (GMD) decoding is then introduced as a lower complexity method of decoding lattice encoded OFDM. We derive the optimal reliability metric for GMD decod- ing of OFDM systems operating over frequency selective channels, and develop analytical upper bounds on the error rate of lattice encoded OFDM systems employing GMD decod- ing.

This dissertation presents and analyzes two new algorithms for sparse direct solution methods based on the use of local-global solution (LOGOS) modes. One of the new algorithms is a rigorous error control strategy for LOGOS-based matrix factorizations that utilize overlapped, localizing modes (OL-LOGOS) on a shifted grid. The use of OL-LOGOS modes is critical to obtaining asymptotically efficient factorizations from LOGOS-based methods. Unfortunately, the approach also introduces a non-orthogonal basis function structure. This can cause errors to accumulate across levels of a multilevel implementation, which has previously posed a barrier to rigorous error control for the OL-LOGOS factorization method. This limitation is overcome, and it is shown that it is possible to efficiently decouple the fundamentally non-orthogonal factorization subspaces in a manner that prevents multilevel error propagation. This renders the OL-LOGOS factorization error controllable in a relative RMS sense. The impact of the new, error-controlled OL-LOGOS factorization algorithm on computational resource utilization is discussed and several numerical examples are presented to illustrate the performance of the improved algorithm relative to previously reported results. The second algorithmic development considered is the development of efficient out-of-core (OOC) versions of the OL-LOGOS factorization algorithm that allow associated software tools to take advantage of additional resources for memory management. The proposed OOC algorithm incorporates a memory page definition that is tailored to match the flow of the OL-LOGOS factorization procedure. Efficiency of the function of the part is evaluated using a quantitative approach, because the tested massive storage device performances do not follow analytical results. The performance latency and the memory usage of the resulting OOC tools are compared with in-core performance results. Both the new error control algorithm and the OOC method have been incorporated into previously existing software tools, and the dissertation presents results for real-world simulation problems.

This thesis characterizes the problem of relative camera calibration in the context of three-dimensional volumetric reconstruction. The general effects of camera calibration errors on different parameters of the projection matrix are well understood. In addition, calibration error and Euclidean world errors for a single camera can be related via the inverse perspective projection. However, there has been little analysis of camera calibration for a large number of views and how those errors directly influence the accuracy of recovered three-dimensional models. A specific analysis of how camera calibration error is propagated to reconstruction errors using traditional voxel coloring algorithms is discussed. A review of the Voxel coloring algorithm is included and the general methods applied in the coloring algorithm are related to camera error. In addition, a specific, but common, experimental setup used to acquire real-world objects through voxel coloring is introduced. Methods for relative calibration for this specific setup are discussed as well as a method to measure calibration error. An analysis of effect of these errors on voxel coloring is presented, as well as a discussion concerning the effects of the resulting world-space error.