In this dissertation we present four papers as chapters. In Chapter 2, we extended the techniques used for the Klein-Gordon Chain by Iooss, Kirchgässner, James, and Sire, to chains with non-nearest neighbor interactions. We look for travelling waves by reducing the Klein-Gordon chain with second nearest neighbor interaction to an advance-delay equation. Then we reduce the equation to a finite dimensional center manifold for some parameter regimes. By using the normal form expansion on the center manifold we were able to prove the existence of three different types of travelling solutions for the Klein Gordon Chain: periodic, quasi-periodic and homoclinic to periodic orbits with exponentially small amplitude. In Chapter 3 we include numerical methods for computing quasi-periodic solutions. We developed very efficient algorithms to compute smooth quasiperiodic equilibrium states of models in 1-D statistical mechanics models allowing non-nearest neighbor interactions. If we discretize a hull function using N Fourier coefficients, the algorithms require O(N) storage and a Newton step for the equilibrium equation requires only O(N log(N)) arithmetic operations. This numerical methods give rise to a criterion for the breakdown of quasi-periodic solutions. This criterion is presented in Chapter 4. In Chapter 5, we justify rigorously the criterion in Chapter 4. The justification of the criterion uses both Numerical KAM algorithms and rigorous results. The hypotheses of the theorem concern bounds on the Sobolev norms of a hull function and can be verified rigorously by the computer. The argument works with small modifications in all cases where there is an a posteriori KAM theorem. / text

In this research work, we propose a facial expression classification method using metric learning-based k-nearest neighbor voting. To achieve accurate classification of a facial expression from frontal face images, we first learn a distance metric structure from training data that characterizes the feature space pattern, then use this metric to retrieve the nearest neighbors from the training dataset, and finally output the classification decision accordingly. An expression is represented as a fusion of face shape and texture. This representation is based on registering a face image with a landmarking shape model and extracting Gabor features from local patches around landmarks. This type of representation achieves robustness and effectiveness by using an ensemble of local patch feature detectors at a global shape level. A naive implementation of the metric learning-based k-nearest neighbor would incur a time complexity proportional to the size of the training dataset, which precludes this method being used with enormous datasets. To scale to potential larger databases, a similar approach to that in [24] is used to achieve an approximate yet efficient ML-based kNN voting based on Locality Sensitive Hashing (LSH). A query example is directly hashed to the bucket of a pre-computed hash table where candidate nearest neighbors can be found, and there is no need to search the entire database for nearest neighbors. Experimental results on the Cohn-Kanade database and the Moving Faces and People database show that both ML-based kNN voting and its LSH approximation outperform the state-of-the-art, demonstrating the superiority and scalability of our method. / text

The aim of the research undertaken in this dissertation was to use medium-range to seasonal precipitation forecasts for hydrologic applications for catchments in the core North American Monsoon (NAM) region. To this end, it was necessary to develop a better understanding of the physical and statistical relationships between runoff processes and the temporal statistics of rainfall. To achieve this goal, development of statistically downscaled estimates of warm season precipitation over the core region of the North American Monsoon Experiment (NAME) were developed. Currently, NAM precipitation is poorly predicted on local and regional scales by Global Circulation Models (GCMs). The downscaling technique used here, the K-Nearest Neighbor (KNN) model, combines information from retrospective GCM forecasts with simultaneous historical observations to infer statistical relationships between the low-resolution GCM fields and the locally-observed precipitation records. The stochastic nature of monsoon rainfall presents significant challenges for downscaling efforts and, therefore, necessitate a regionalization and an ensemble or probabilistic-based approach to quantitative precipitation forecasting. It was found that regionalization of the precipitation climatology prior to downscaling using KNN offered significant advantages in terms of improved skill scores.Selected output variables from retrospective ensemble runs of the National Centers for Environmental Predictions medium-range forecast (MRF) model were fed into the KNN downscaling model. The quality of the downscaled precipitation forecasts was evaluated in terms of a standard suite of ensemble verification metrics. This study represents the first time the KNN model has been successfully applied within a warm season convective climate regime and shown to produce skillful and reliable ensemble forecasts of daily precipitation out to a lead time of four to six days, depending on the forecast month.Knowledge of the behavior of the regional hydrologic systems in NAM was transferred into a modeling framework aimed at improving intra-seasonal hydrologic predictions. To this end, a robust lumped-parameter computational model of intermediate conceptual complexity was calibrated and applied to generate streamflow in three unregulated test basins in the core region of the NAM. The modeled response to different time-accumulated KNN-generated precipitation forcing was investigated. Although the model had some difficulty in accurately simulating hydrologic fluxes on the basis of Hortonian runoff principles only, the preliminary results achieved from this study are encouraging. The primary and most novel finding from this study is an improved predictability of the NAM system using state-of-the-art ensemble forecasting systems. Additionally, this research significantly enhanced the utility of the MRF ensemble forecasts and made them reliable for regional hydrologic applications. Finally, monthly streamflow simulations (from an ensemble-based approach) have been demonstrated. Estimated ensemble forecasts provide quantitative estimates of uncertainty associated with our model forecasts.

The Iterative Closest Point (ICP) algorithm is one of the most commonly used range image processing methods. However, slow operational speeds and high input band-widths limit the use of ICP in high-speed real-time applications. This thesis presents and examines a novel hardware implementation of a high-speed ICP object tracking system that uses stereo vision disparities as input. Although software ICP trackers already exist, this innovative hardware tracker utilizes the efficiencies of custom hardware processing, thus enabling faster high-speed real-time tracking. A custom hardware design has been implemented in an FPGA to handle the inherent bottlenecks that result from the large input and processing band-widths of the range data. The hardware ICP design consists of four stages: Pre-filter, Transform, Nearest Neighbor, and Transform Recovery. This custom hardware has been implemented and tested on various objects, using both software simulation and hardware tests. Results indicate that the tracker is able to successfully track free-form objects at over 200 frames-per-second along arbitrary paths. Tracking errors are low, in spite of substantial noisy stereo input. The tracker is able to track stationary paths within 0.42mm and 1.42degs, linear paths within 1.57mm and 2.80degs, and rotational paths within 0.39degs axis error. With further degraded data by occlusion, the tracker is able to handle 60% occlusion before a slow decline in performance. The high-speed hardware implementation (that uses 16 parallel nearest neighbor circuits), is more then five times faster than the software K-D tree implementation. This tracker has been designed as the hardware component of ‘FastTrack’, a high frame rate, stereo vision tracking system, that will provide a known object’s pose in real-time at 200 frames per second. This hardware ICP tracker is compact, lightweight, has low power requirements, and is integratable with the stereo sensor and stereo extraction components of the FastTrack’ system on a single FPGA platform. High-speed object tracking is useful for many innovative applications, including advanced spaced-based robotics. Because of this project’s success, the ‘FastTrack’ system will be able to aid in performing in-orbit, automated, remote satellite recovery for maintenance. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2008-07-15 22:50:30.369

No description available.

Through the years, there has been a great deal of archaeological research focused on the earliest farming cultures of Europe (i.e. Early Neolithic). However, little effort has been expended to uncover the type and nature of daily activities performed within Early Neolithic dwellings, particularly in the Balkans. This thesis conducts a spatial analysis of the Early Neolithic pit house levels of the Foeni-Salaş site in southeast Romania, in the northern half of the Balkans, to determine the kinds and locations of activities that occurred in these pit houses. Characteristic Early Neolithic dwellings in the northern Balkans are pit houses. The data are analyzed using Geographic Information Systems (GIS) technology in an attempt to identify non-random patterns that will indicate how the pit house inhabitants used their space. Both visual and statistical (Nearest Neighbor) techniques are used to identify spatial patterns. Spreadsheet data are incorporated into the map database in order to compare and contrast the results from the two techniques of analysis. Map data provides precise artefact locations, while spreadsheet data yield more generalized quad centroid information. Unlike the mapped data, the spreadsheet data also included artefacts recovered in sieves. Utilizing both data types gave a more complexand fuller understanding of how space was used at Foeni-Salaş. The results show that different types of activity areas are present within each of the pit houses. Comparison of interior to exterior artifact distributions demonstrates that most activities take place within pit house. Some of the activities present include weaving, food preparation, butchering, hide processing, pottery making, ritual, and other activities related to the running of households. It was found that these activities are placed in specific locations relative to features within the pit house and the physical structure of the pit house itself. This research adds to the growing body of archaeological research that implements GIS to answer questions and solve problems related to the spatial dimension of human behaviour.

Through the years, there has been a great deal of archaeological research focused on the earliest farming cultures of Europe (i.e. Early Neolithic). However, little effort has been expended to uncover the type and nature of daily activities performed within Early Neolithic dwellings, particularly in the Balkans. This thesis conducts a spatial analysis of the Early Neolithic pit house levels of the Foeni-Salaş site in southeast Romania, in the northern half of the Balkans, to determine the kinds and locations of activities that occurred in these pit houses. Characteristic Early Neolithic dwellings in the northern Balkans are pit houses. The data are analyzed using Geographic Information Systems (GIS) technology in an attempt to identify non-random patterns that will indicate how the pit house inhabitants used their space. Both visual and statistical (Nearest Neighbor) techniques are used to identify spatial patterns. Spreadsheet data are incorporated into the map database in order to compare and contrast the results from the two techniques of analysis. Map data provides precise artefact locations, while spreadsheet data yield more generalized quad centroid information. Unlike the mapped data, the spreadsheet data also included artefacts recovered in sieves. Utilizing both data types gave a more complexand fuller understanding of how space was used at Foeni-Salaş. The results show that different types of activity areas are present within each of the pit houses. Comparison of interior to exterior artifact distributions demonstrates that most activities take place within pit house. Some of the activities present include weaving, food preparation, butchering, hide processing, pottery making, ritual, and other activities related to the running of households. It was found that these activities are placed in specific locations relative to features within the pit house and the physical structure of the pit house itself. This research adds to the growing body of archaeological research that implements GIS to answer questions and solve problems related to the spatial dimension of human behaviour.

The work described in this thesis contributes to the development of a system to evaluate sailing performance. This work was motivated by the lack of tools available to evaluate sailing performance. The goal of the work presented is to detect and classify the turns of a sailing yacht. Data was collected using a BlackBerry PlayBook affixed to a J/24 sailing yacht. This data was manually annotated with three types of turn: tack, gybe, and mark rounding. This manually annotated data was used to train classification methods. Classification methods tested were multi-layer perceptrons (MLPs) of two sizes in various committees and nearest- neighbour search. Pre-processing algorithms tested were Kalman filtering, categorization using quantiles, and residual normalization. The best solution was found to be an averaged answer committee of small MLPs, with Kalman filtering and residual normalization performed on the input as pre-processing.

Thesis (Ph. D.)--University of Texas at Austin, 2006. / Vita. Includes bibliographical references.

Ένα πράγμα που θέτει τους στατιστικολόγους πέρα από άλλους επιστήμονες είναι σχετική άγνοια του κοινού γενικά σχετικά με το τι είναι στην πραγματικότητα το πεδίο της στατιστικής. Ο κόσμος έχει μια μικρή γενική ιδέα του τι είναι η χημεία ή η βιολογία — αλλά τι είναι αυτό ακριβώς που κάνουν οι στατιστικολόγοι; Μία απάντηση στο ερώτημα αυτό έχει ως εξής: στατιστική είναι η επιστήμη που ασχολείται με τη συλλογή, περιληπτική παρουσίαση της πληροφορίας, παρουσίαση και ερμηνεία των δεδομένων. Τα δεδομένα είναι το κλειδί, φυσικά — τα πράγματα από τα οποία εμείς αποκτούμε γνώσεις και βγάζουμε αποφάσεις. Ένας πίνακας δεδομένων παρουσιάζει μια συλλογή έγκυρων δεδομένων, αλλά είναι σαφές ότι είναι εντελώς ανεπαρκής για την σύνοψη ή την ερμηνεία τους.Το πρόβλημα είναι ότι δεν έγιναν παραδοχές σχετικά με τη διαδικασία που δημιούργησε αυτά τα δεδομένα (πιο απλά, η ανάλυση είναι καθαρά μη παραμετρική, υπό την έννοια ότι δεν επιβάλλεται καμία τυπική δομή για τα δεδομένα). Επομένως, καμία πραγματική περίληψη ή σύνοψη δεν είναι δυνατή. Η κλασική προσέγγιση σε αυτή τη δυσκολία είναι να υποθέσουμε ένα παραμετρικό μοντέλο για την υποκείμενη διαδικασία, καθορίζοντας μια συγκεκριμένη φόρμα για την υποκείμενη πυκνότητα. Στη συνέχεια, μπορούν να υπολογιστούν διάφορα στατιστικά στοιχεία και μπορούν να παρουσιαστούν μέσω μιας προσαρμοσμένης πυκνότητας.Δυστυχώς, η ισχύς της παραμετρικής μοντελοποίησης είναι επίσης η αδυναμία της. Συνδέοντας ένα συγκεκριμένο μοντέλο, μπορούμε να έχουμε μεγάλα οφέλη, αλλά μόνο εάν το πρότυπο θεωρείται ότι ισχύει (τουλάχιστον κατά προσέγγιση). Εάν το υποτιθέμενο μοντέλο δεν είναι σωστό, οι αποφάσεις που θα αντλήσουμε από αυτό μπορεί να είναι χειρότερες από άχρηστες, οδηγώντας μας σε παραπλανητικές ερμηνείες των δεδομένων. / A thing that places the statisticians beyond other scientists is relative ignorance of public as generally speaking with regard to what it is in reality the field of statistics. The world does have a small general idea what is chemistry or biology - but what is precisely that statisticians do? An answer in this question has as follows: statistics is the science that deals with the collection, general presentation of information, presentation and interpretation of data. The data are the key, from which we acquire knowledge and make decisions. A table of data presents a collection of valid data, but it is obvious that it is completely insufficient for their synopsis or their interpretation. The problem is that no assumptions have been made about the process that created these data (more simply, the analysis is no parametric, under the significance that is no formal structure is imposed on the data). Consequently, no real summary or synopsis is possible. The classical approach in this difficulty is to assume a parametric model for the underlying process, determining a concrete form for the underlying density. Afterwards, can be calculated various statistical elements and a fitted density can manifest itself. The power of parametric modelling is also its weakness. By linking inference to a specific model, we can have big profits, but only if the model is true. If the assumed model is not correct, the decisions that we will draw from this can be worse than useless, leading us to misleading interpretations of data.