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This thesis is missing page 355 which is not in any other copy of the thesis. -Digitization Centre / The ontogeny of a specific high altitude (30,000') airform pattern was investigated. The possibilities of using this pattern, and certain related phenomena which appear with it,for sub-surface ice prediction was demonstrated. For the purpose of laying out the general background of the controls of paludification, as they affect indirectly the pattern development, a rather detailed account of the geomorphology, geology and climate of the study areas was given. The summaries of these accounts demonstrate their effect on pattern evolution. These background data as a foundation for a more specific account of the developmental processes of the airform pattern were given as based on abiotic and biotic interplay in the development. Finally in order to demonstrate universal application of aerial interpretation of muskeg a brief comparison of the analogous conditions of paludification and pattern in Finnish and Canadian muskeg was given. / Thesis / Doctor of Philosophy (PhD)

<p>Two important deficiencies have been identified for the integration of CAD and automated process planning. These deficiencies stem from the lack of a uniform representation scheme of pans and products, and an effective communication for CAD and process planning. This thesis presents a new approach and original knowledge regarding the integration and individual aspects of feature-based design, cellular manufacturing planning, inspection planning and assembly sequence planning.</p> <p>A high-level new language called Feature-based Design Description Language (FDDL) has been proposed and designed with a feature representation scheme. Its syntax, semantics and vocabulary have been defined with consideration given to the user, the engineering terminology, and the computer implementation. The FDDL system consists of a number of lexical analyzers, a parser and three code generators. Once the products or parts modeled by the FDDL, or by a feature-based modeler, are processed using the FDDL system, inputs are created for manufacturing tasks planning systems.</p> <p>A feature-based modeling and manufacturing tasks planning system has been designed and implemented, and consists of a prototype of a feature-based modeler, the FDDL system, a feature-based cellular manufacturing planning system, a feature-based automated inspection task planner, and a prototype assembly sequence planner. The prototype feature-based modeler is used to model components using features. All expert tolerancing consultant module has been included in the modeler to assist the user. Cellular manufacturing planning deals with group formation and parts assignment to cells. A clustering-based optimization approach has been proposed and implemented for the formation of machine cells and part families. A feature-based assignment system has been developed to integrate the feature-based design and the formed cells. Automata and pattern recognition techniques, in combination with manufacturing knowledge, are used in the system. The feature-based inspection planner has been developed to integrate the feature-based design and a Coordinate Measuring Machine (CMM). Original inspection strategies and knowledge have been developed for CMM, based on the analysis of CMM characteristics, tolerancing theories, features representation, part structure and geometry. A knowledge-based approach has been presented to integrate CAD with the assembly sequence planning. A prototype of such an assembly sequence planner has been developed for generating the assembly sequence for products from the design directly.</p> / Doctor of Philosophy (PhD)

Best friends, Harry and Dave, both come to face their own versions of mid-life crises. Harry is a former hockey player stuck in his glory days, and Dave is a recently divorced, try-hard dad without any glory days. Harry realizes he’s not in the “prime” he thinks he is but still wants another shot at the Olympics, so he decides to trade hockey in for the most exciting and physical sport in the world – Curling. He drags along Dave to form a team of misfits, including the local shuffleboard champ and hairdresser, Ramona, and a high-school janitor, Gail. Under the training of Harry’s former hockey coach, the team combines their surprising strengths and overcome their many weaknesses in order to pursue the Winter Olympics.

Analyses based upon rank correlation methods, such as Spearman's Rho and Kendall's Tau, can provide quick insights into large biological data sets. Comparing expression levels between different technologies and models is problematic due to the different units of measure. Here again, rank correlation provides an effective means of comparison between the two techniques. Massively Parallel Signature Sequencing (MPSS) transcript abundance levels to microarray signal intensities for Arabidopsis thaliana are compared. Rank correlations can be applied to subsets as well as the entire set. Results of subset comparisons can be used to improve the capabilities of predictive models, such as Predicted Highly Expressed (PHX). This is done for Escherichia coli. Methods are given to combine predictive models based upon feedback from experimental data. The problem of feature selection in supervised learning situations is also considered, where all features are drawn from a common domain and are best interpreted via ordinal comparisons with other features, rather than as numerical values. This is done for synthetic data as well as for microarray experiments examining the life cycle of Drosophila melanogaster and human leukemia cells. Two novel methods are presented based upon Rho and Tau, and their efficacy is tested with synthetic and real world data. The method based upon Spearman's Rho is shown to be more effective. / Ph. D.

Linear models are the dominant means of extracting summaries of events in fMRI for feature vector based machine learning. While they are both useful and robust, they are limited by the assumptions made in modeling. In this work, we examine a number of feature extraction techniques adjacent to linear models that account for or allow wider variation. Primarily, we construct mixed effects models able to account for variation between stimuli of the same class and perform empirical tests on the resulting feature extraction – classifier system. We extend this analysis to spatial temporal models as well as summary models. We find that mixed effects models increase classifier performance at the cost of increased uncertainty in prediction estimates. In addition, these models identify similar regions of interest in separating classes. While they currently require knowledge hidden during testing, we present these results as an optimum to be reached in additional works. / Doctor of Philosophy / Machine learning is a popular tool for extracting useful information from functional MR images. One approach is classification using feature vectors derived from observations. In this work, we examine new strategies for extracting feature vectors time varying data and explore the effect these feature vectors have on the results of machine learning analysis. In a set of simulations and real data, we compare a range of standard methods for feature extraction to new methods developed for this work. We find the most effective approach for successful classification is feature extraction through the use of mixed effects models. We also find that these models preserve the selection of feature sets that are maximally important to classification. We then explore the range of considerations required to use any of the methods examined in this work for a range of cases. We hope this provides solid ground for both future expansion of feature extraction methods and helpful advice for future users of these methods.

This paper describes a complete algorithm to estimate the motion of a fixed-wing aircraft given a series of digitized flight images. The algorithm was designed for fixed-wing aircraft because carefully procured flight images and corresponding navigation data were available to us for testing. After image pre-processing, optic flow data is determined by automatically finding and tracking good features between pairs of images. The image coordinates of matched features are then processed by a rigid-object linear optic flow-motion estimation algorithm. Input factors are weighed to provide good testing techniques. Error analysis is performed with simulation data keeping these factors in mind to determine the effectiveness of the optic flow algorithm. The output of this program is an estimate of rotation and translation of the imaged environment in relation to the camera, and thereby the airplane. Real flight images from NASA test flights are used to confirm the accuracy of the algorithm. Where possible, the estimated motion parameters are compared with recorded flight instrument data to confirm the correctness of the algorithm. Results show that the algorithm is accurate to within a degree provided that enough optic flow feature points are tracked. / Master of Science

High dimensional data have been frequently collected in many fields of scientific research and technological development. The traditional idea of best subset selection methods, which use penalized L_0 regularization, is computationally too expensive for many modern statistical applications. A large number of variable selection approaches via various forms of penalized least squares or likelihood have been developed to select significant variables and estimate their effects simultaneously in high dimensional statistical inference. However, in modern applications in areas such as genomics and proteomics, ultra-high dimensional data are often collected, where the dimension of data may grow exponentially with the sample size. In such problems, the regularization methods can become computationally unstable or even infeasible. To deal with the ultra-high dimensionality, Fan and Lv (2008) proposed a variable screening procedure via correlation learning to reduce dimensionality in sparse ultra-high dimensional models. Since then many authors further developed the procedure and applied to various statistical models. However, they all focused on single type of predictors, that is, the predictors are either all continuous or all discrete. In practice, we often collect mixed type of data, which contains both continuous and discrete predictors. For example, in genetic studies, we can collect information on both gene expression profiles and single nucleotide polymorphism (SNP) genotypes. Furthermore, outliers are often present in the observations due to experimental errors and other reasons. And the true trend underlying the data might not follow the parametric models assumed in many existing screening procedures. Hence a robust screening procedure against outliers and model misspecification is desired. In my dissertation, I shall propose a robust feature screening procedure for mixed type of data. To gain insights on screening for individual types of data, I first studied feature screening procedures for single type of data in Chapter 2 based on marginal quantities. For each type of data, new feature screening procedures are proposed and simulation studies are performed to compare their performances with existing procedures. The aim is to identify a best robust screening procedure for each type of data. In Chapter 3, I combine these best screening procedures to form the robust feature screening procedure for mixed type of data. Its performance will be assessed by simulation studies. I shall further illustrate the proposed procedure by the analysis of a real example. / Ph. D.

3D facial data has a great potential for overcoming the problems of illumination and pose variation in face recognition. In this paper, we present a 3D facial system based on the machine learning. We used landmarks for feature extraction and Cascade Correlation neural network to make the final decision. Experiments are presented using 3D face images from the Face Recognition Grand Challenge database version 2.0. For CCNN using Jack-knife evaluation, an accuracy of 100% has been achieved for 7 faces with different expression, with 100% for both of specificity and sensitivity.