Thesis (Ph. D.)--University of Missouri-Columbia, 1998. / Typescript. Vita. Includes bibliographical references (leaves 85-87). Also available on the Internet.

Title from PDF of title page (University of Missouri--Columbia, viewed on Feb. 12, 2010). The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Thesis advisor: Dr. Praveen K. Edara. Includes bibliographical references.

Adaptive filters are frequently employed in many applications, such as, system identification, adaptive echo cancellation (AEC), active noise control (ANC), adaptive beamforming, speech signal processing and other related problems, in which the statistic of the underlying signals is either unknown a priori, or slowly-varying. Given the observed signals under study, we shall consider, in this dissertation, the time-varying linear model with Gaussian or contaminated Gaussian (CG) noises. In particular, we focus on recursive local estimation and its applications in linear systems. We base our development on the concept of local likelihood function (LLF) and local posterior probability for parameter estimation, which lead to efficient adaptive filtering algorithms. We also study the convergence performance of these algorithms and their applications by theoretical analyses. As for applications, another important one is to utilize adaptive filters to obtain recursive hypothesis testing and model order selection methods. It is known that the maximum likelihood estimate (MLE) may lead to large variance or ill-conditioning problems when the number of observations is limited. An effective approach to address these problems is to employ various form of regularization in order to reduce the variance at the expense of slightly increased bias. In general, this can be viewed as adopting the Bayesian estimation, where the regularization can be viewed as providing a certain prior density of the parameters to be estimated. By adopting different prior densities in the LLF, we derive the variable regularized QR decomposition-based recursive least squares (VR-QRRLS) and recursive least M-estimate (VR-QRRLM) algorithms. An improved state-regularized variable forgetting factor QRRLS (SR-VFF-QRRLS) algorithm is also proposed. By approximating the covariance matrix in the RLS, new variable regularized and variable step-size transform domain normalized least mean square (VR-TDNLMS and VSS-TDNLMS) algorithms are proposed. Convergence behaviors of these algorithms are studied to characterize their performance and provide useful guidelines for selecting appropriate parameters in practical applications. Based on the local Bayesian estimation framework for linear model parameters developed previously, the resulting estimate can be utilized for recursive nonstationarity detection. This can be cast under the problem of hypothesis testing, as the hypotheses can be viewed as two competitive models between stationary and nonstationary to be selected. In this dissertation, we develop new regularized and recursive generalized likelihood ratio test (GLRT), Rao’s and Wald tests, which can be implemented recursively in a QRRLS-type adaptive filtering algorithm with low computational complexity. Another issue to be addressed in nonstationarity detection is the selection of various models or model orders. In particular, we derive a recursive method for model order selection from the Bayesian Information Criterion (BIC) based on recursive local estimation. In general, the algorithms proposed in this dissertation have addressed some of the important problems in estimation and detection under the local and recursive Bayesian estimation framework. They are intrinsically connected together and can potentially be utilized for various applications. In this dissertation, their applications to adaptive beamforming, ANC system and speech signal processing, e.g. adaptive frequency estimation and nonstationarity detection, have been studied. For adaptive beamforming, the difficulties in determining the regularization or loading factor have been explored by automatically selecting the regularization parameter. For ANC systems, to combat uncertainties in the secondary path estimation, regularization techniques can be employed. Consequently, a new filtered-x VR-QRRLM (Fx-VR-QRRLM) algorithm is proposed and the theoretical analysis helps to address challenging problems in the design of ANC systems. On the other hand, for ANC systems with online secondary-path modeling, the coupling effect of the ANC controller and the secondary path estimator is thoroughly studied by analyzing the Fx-LMS algorithm. For speech signal processing, new approaches for recursive nonstationarity detection with automatic model order selection are proposed, which provides online time-varying autoregressive (TVAR) parameter estimation and the corresponding stationary intervals with low complexity. / published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

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published_or_final_version / Physics / Master / Master of Philosophy

This thesis presents the development of a semi-analytical technique that can be used to estimate the gas-in-place for volumetric gas reservoirs. This new methodology utilizes plotting functions, plots, extrapolations, etc. - where all analyses are based on the following governing identity. The 'governing identity' is derived and validated by others for pi less than 6000 psia. We have reproduced the derivation of this result and we provide validation using numberical simulation for cases where pi greater than 6000 psia. The relevance of this work is straightforward using a simple governing relation, we provide a series of plotting functions which can be used to extrapolate or interpret an estimate of gas-in-place using only production data (qg and Gp). The proposed methodology does not require a prior knowledge of formation and or fluid compressibility data, nor does it require average reservoir pressure. In fact, no formation or fluid properties are directly required for this analysis and interpretation approach. The new methodology is validated demonstrated using results from numerical simulation (i.e., cases where we know the exact answer), as well as for a number of field cases. Perhaps the most valuable component of this work is our development of a "spreadsheet" approach in which we perform multiple analyses interpretations simultaneously using MS Excel. This allows us to visualize all data plots simultaneously - and to "link" the analyses to a common set of parameters. While this "simultaneous" analysis approach may seem rudimentary (or even obvious), it provides the critical (and necessary) "visualization" that makes the technique functional. The base relation (given above) renders different behavior for different plotting functions, and we must have a "linkage" that forces all analyses to "connect" to one another. The proposed multiplot spreadsheet approach provides just such a connection.

The parameter estimation problem or the inverse problem of ordinary differential equations is prevalent in many process models in chemistry, molecular biology, control system design and many other engineering applications. It concerns the re-construction of auxillary parameters by fitting the solution from the system of ordinary differential equations( from a known mathematical model) to that of measured data obtained from observing the solution trajectory. Some of the traditional techniques (for example, initial value technques, multiple shooting, etc.) used to solve this class of problem have been discussed. A new algorithm, motivated by algorithms proposed by Childs and Osborne(1996) and Z.F.Li's dissertation(2000), has been proposed. The new algorithm inherited the advantages exhibited in the above-mentioned algorithms and, most importantly, the parameters can be transformed to a form that are convenient and suitable for computation. A statistical analysis has also been developed and applied to examples. The statistical analysis yields indications of the tolerance of the estimates and consistency of the observations used.

In finishing work of the master the review and the wide analysis by principles of rate effective activity of a factory, an opportunity of an estimation and methodology is made. In connection with the analysis of the scientific literature, the place of the analysis of efficiency in the general financial analysis of firm is marked down and allocated. In work the main opportunities of methods effective of rate which on specificity of an analyzed factory have been adapted at an estimation efficiency activity of joint-stock company "Eserys" are allocated. At the analysis efficiency the enterprises are adapted horizontal and vertical balance analyses of reports, solvency, stuff turnover and other parameters. On the received joint-stock companies "Eserys" to results of the analysis, the forecast main descriptive parameters of effectiveness has been made: the forecast on volume of sale by means of function of the Trend, the forecast of the general and bacon, the opportunity of the bankrupt of the enterprise is marked down.

The estimation of the software cost remains one of the most challenging problems in software engineering; as a preliminary estimate of cost includes many elements of uncertainty. Reliable and early estimates are difficult to obtain because of the lack of the detailed information about the future system at an early stage. However, the early estimates are really important when bidding for a contract or determining whether a project is feasible in terms of cost-benefit analysis. Estimators often rely on their past experiences for the prediction of effort for software projects. The fundamental factors that are contributing towards inaccuracy of the cost estimation process are imprecise and drifting requirements, information not readily available on past projects, and the methods that were developed and trained on specific data. In this thesis, we have developed a software cost estimation tool that helps commercial software-development organizations to effectively and quantitatively measure and analyze the software metrics based upon the functional requirements, operational constraints and organization’s capability to handle a project. This cost estimation tool is a fusion implementation or an essence of certain software measurement and estimation techniques that help a software organization to evaluate and analyze fundamental software metrics such as complexity, time, effort, and cost all of which are essential to improving turnaround time and attaining organizational maturity. The new cost estimation method is proposed for the iterative software development projects. The use case technique is implemented per iteration for the specification of the software requirements. COCOMO II and Function Point were used to compute the effort required for successive iterations. We also computed the magnitude of relative error for successive iterations. We tested the proposed method on student projects in order to illustrate its usefulness.

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