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.

Tuberculosis (TB) is called a disease of poverty and is the main cause of death from infectious diseases among adults. In 1993 the World Health Organisation (WHO) declared TB to be a global emergency; however there were still approximately 1.4 million deaths due to TB in 2011. This thesis contains a detailed study of the existing literature regarding the global risk factors of TB. The risk factors identified from the literature review search which were also available from the NFHS-3 survey were then analysed to determine how well we could identify respondents who are at high risk of TB. We looked at the stigma and misconceptions people have regarding TB and include detailed reports from the existing literature of how a persons wealth, health, education, nutrition, and HIV status affect how likely the person is to have TB. The difference in the risk factor distribution for the TB and non-TB populations were examined and classification trees, nearest neighbours, and logistic regression models were trialled to determine if it was possible for respondents who were at high risk of TB to be identified. Finally gender-specific statistically likely directed acyclic graphs were created to visualise the most likely associations between the variables.

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.

Spatial analysis methods used for detecting, interpolating or predicting local patterns require a delineation of a neighbourhood defining the extent of spatial interaction in geographic data. The most common neighbourhood delineation techniques include fixed distance bands, k-nearest neighbours, or spatial adjacency (contiguity) matrices optimized to represent spatial dependency in data. However, these standard approaches do not take into consideration the geographic or environmental constraints such as impassable mountain ranges, road networks or coastline barriers. Specifically, complex marine landscapes and coastlines present common problematic neighbourhood definitions for standard neighbourhood matrices used in the spatial analysis of marine environments. Therefore, the goal of our research is to present a new approach to constraining spatial neighbourhoods when conducting geographical analysis in marine environments. To meet this goal, we developed methods and software (COnstraining SpatIal NEighbourhoods - COSINE) for modifying spatial neighbourhoods, and demonstrate their utility in two case studies. Our method enables delineation of neighbourhoods that are constrained by coastlines and the direction of marine currents. Our software calculates and evaluates whether neighbouring features are separated by land, or are within a user defined angle that excludes interaction based on directional processes. Using decision rules a modified spatial weight matrix is created, either in binary or row-standardized format. Within open source software (R), a graphical user interface enables users to modify the standard spatial neighbourhood definition distance, inverse distance and k-nearest neighbour. Two case studies are presented to demonstrate the usefulness of the new approach for detecting spatial patterns: the first case study observes marine mammals’ abundance and the second, oil spill observation. Our results indicate that constraining spatial neighbourhoods in marine environments is particularly important at larger spatial scales. The COSINE tool has many applications for modelling both environmental and human processes. / Graduate / 0463 / 0366 / suarezc@uvic.ca

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.

abstract: K-Nearest-Neighbors (KNN) search is a fundamental problem in many application domains such as database and data mining, information retrieval, machine learning, pattern recognition and plagiarism detection. Locality sensitive hash (LSH) is so far the most practical approximate KNN search algorithm for high dimensional data. Algorithms such as Multi-Probe LSH and LSH-Forest improve upon the basic LSH algorithm by varying hash bucket size dynamically at query time, so these two algorithms can answer different KNN queries adaptively. However, these two algorithms need a data access post-processing step after candidates' collection in order to get the final answer to the KNN query. In this thesis, Multi-Probe LSH with data access post-processing (Multi-Probe LSH with DAPP) algorithm and LSH-Forest with data access post-processing (LSH-Forest with DAPP) algorithm are improved by replacing the costly data access post-processing (DAPP) step with a much faster histogram-based post-processing (HBPP). Two HBPP algorithms: LSH-Forest with HBPP and Multi- Probe LSH with HBPP are presented in this thesis, both of them achieve the three goals for KNN search in large scale high dimensional data set: high search quality, high time efficiency, high space efficiency. None of the previous KNN algorithms can achieve all three goals. More specifically, it is shown that HBPP algorithms can always achieve high search quality (as good as LSH-Forest with DAPP and Multi-Probe LSH with DAPP) with much less time cost (one to several orders of magnitude speedup) and same memory usage. It is also shown that with almost same time cost and memory usage, HBPP algorithms can always achieve better search quality than LSH-Forest with random pick (LSH-Forest with RP) and Multi-Probe LSH with random pick (Multi-Probe LSH with RP). Moreover, to achieve a very high search quality, Multi-Probe with HBPP is always a better choice than LSH-Forest with HBPP, regardless of the distribution, size and dimension number of the data set. / Dissertation/Thesis / M.S. Computer Science 2011