Thesis (Ph. D.)--University of Missouri-Columbia, 2000. / Typescript. Vita. Includes bibliographical references (leaves 104-107). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 2001. / Typescript. Vita. Includes bibliographical references (leaves 112-115). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 1999. / Typescript. Vita. Includes bibliographical references (leaves 196-200). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 2001. / Typescript. Vita. Includes bibliographical references (leaves 132-137). Also available on the Internet.

Thesis (M. Sc.)--University of Alberta, 2009. / Title from pdf file main screen (viewed on Dec. 11, 2009). "A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of Science in Statistics, Department of Mathematical and Statistical Sciences, University of Alberta." Includes bibliographical references.

Imaging is often used in scientific applications as a measurement tool. The location of a target, brightness of a star, and size of a tumor are all examples of object features that are sought after in various imaging applications. A perfect measurement of these quantities from image data is impossible because of, most notably, detector noise fluctuations, finite resolution, sensitivity of the imaging instrument, and obscuration by undesirable object structures. For these reasons, sophisticated image-processing techniques are designed to treat images as random variables. Quantities calculated from an image are subject to error and fluctuation; implied by calling them estimates of object features.This research focuses on estimator error for tasks common to imaging applications. Computer simulations of imaging systems are employed to compare the estimates to the true values. These computations allow for algorithm performance tests and subsequent development. Estimating the location, size, and strength of a signal embedded in a background structure from noisy image data is the basic task of interest. The estimation task's degree of difficulty is adjusted to discover the simplest data-processing necessary to yield successful estimates.Even when using an idealized imaging model, linear Wiener estimation was found to be insufficient for estimating signal location and shape. These results motivated the investigation of more complex data processing. A new method (named the scanning-linear estimator because it maximizes a linear functional) is successful in cases where linear estimation fails. This method has also demonstrated positive results when tested in realistic simulations of tomographic SPECT imaging systems. A comparison to a model of current clinical estimation practices found that the scanning-linear method offers substantial gains in performance.

Prior to signal demodulation, blind recognition of the modulation scheme of the received signal is an important task for intelligent radios in various commercial and military applications such as spectrum management, surveillance of broadcasting activities and adaptive transmission. Antenna arrays provide spatial diversity and increase channel capacity. This thesis focuses on the algorithms and performance analysis of the blind modulation classification (MC) for a multiple antenna receiver configuration. For a single-input-multiple-output (SIMO) configuration with unknown channel amplitude, phase, and noise variance, we investigate likelihood-based algorithms for linear digital MC. The existing algorithms are presented and extended to SIMO. Using recently proposed blind estimates of the unknown parameters, a new algorithm is developed. In addition, two upper bounds on the classification performance of MC algorithms are provided. We derive the exact Cramer-Rao Lower Bounds (CRLBs) of joint estimates of the unknown parameters for one- and two-dimensional amplitude modulations. The asymptotic behaviors of the CRLBs are obtained for the high signal-to-noise-ratio (SNR) region. Numerical results demonstrate the accuracy of the CRLB expressions and confirm that the expressions in the literature are special cases of our results. The classification performance of the proposed algorithm is compared with the existing algorithm and upper bounds. It is shown that the proposed algorithm outperforms the existing one significantly with reasonable computational complexity. The proposed algorithm in this thesis can be used in modern intelligent radios equipped with multiple antenna receivers and the provided performance analysis, i.e., the CRLB expressions, can be employed to design practical systems involving estimation of the unknown parameters and is not limited to MC. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2012-09-21 00:51:43.938

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This thesis considers estimation of the quantiles of the smallest extreme value distribution, sometimes referred to as the log - Weibull distribution. The estimators considered are linear combinations of two order statistics. A table of the best linear estimates (BLUE's) is presented for sample sizes two through twenty. These estimators are compared to the asymptotic estimators of Kubat and Epstein (1980).

Estimation theory is a branch of statistics and probability that derives information about random variables based on known information. In process engineering, state estimation is used for a variety of purposes, such as: soft sensing, digital filter design, model predictive control and performance monitoring. In literature, there exist numerous estimation algorithms. In this study, we provide guidelines for choosing the appropriate estimator for a system under consideration. Various estimators are compared and their advantages and disadvantages are highlighted. This has been done through case studies which use examples from process engineering. We also address certain robustness issues of application of estimation techniques to chemical processes. Choice of estimator in case of high plant-model mismatch has also been discussed. The study is restricted to unconstrained nonlinear estimators. / Process Control