Tracking of an object in six degrees-of-freedom (DOF) produces a position and orientation estimate of the object as it moves in 3D space. This thesis investigates the design and implementation of a prototype optical 6 DOF tracking system. Although optical scanners potentially have issues regarding occlusion they have advantages over electromagnetic scanners in that they can be used without distortion near ferromagnetic materials and can have large working volumes. This thesis focuses on the design of a small camera module named the ‘Black Spot’ that forms part of the overall tracking system. This module is capable of tracking the locations of up to 27 LED markers at 60 frames/s as the module moves in space. These markers provide fixed reference points that are utilised by the tracking system. A number of these modules will, in future revisions of the system, be clustered closely together forming a tracking hub. In this research this hub has been partially implemented in software on a PC. This software implements a ‘pose estimation’ algorithm that iteratively refines the location and position of the camera modules. Results from testing three Black Spot modules indicates that the locations of the LED markers can be determined very precisely using a centroid calculation. Standard deviations of better than 0.01 pixels have been recorded using these modules. The pose estimation algorithm has been tested revealing the need for a better minimisation algorithm. It is recommended that a bundle adjustment algorithm is used in the future to refine the world model used by the hub. The calibration of the system is a task for future research.

No description available.

The quantum simulation of large molecular systems is a formidable task. We explore the use of effective potentials based on the Feynman path centroid variable in order to simulate large quantum clusters at a reduced computational cost. This centroid can be viewed as the “most” classical variable of a quantum system. Earlier work has shown that one can use a pairwise centroid pseudo-potential to simulate the quantum dynamics of hydrogen in the bulk phase at 25 K and 14 K [Chem. Phys. Lett. 249, 231, (1996)]. Bulk hydrogen, however, freezes below 14 K, so we focus on hydrogen clusters and nanodroplets in the very low temperature regime in order to study their structural behaviours. The calculation of the effective centroid potential is addressed along with its use in the context of molecular dynamics simulations. The effective pseudo-potential of a cluster is temperature dependent and shares similar behaviour as that in the bulk phase. Centroid structural properties in three dimensional space are presented and compared to the results of reference path-integral Monte Carlo simulations. The centroid pseudo-potential approach yields a great reduction in computation cost. With large cluster sizes, the approximate pseudo-potential results are in agreement with the exact reference calculations. An approach to deconvolute centroid structural properties in order to obtain real space results for hydrogen clusters of a wide range of sizes is also presented. The extension of the approach to the treatment of confined hydrogen is discussed, and concluding remarks are presented.

The quantum simulation of large molecular systems is a formidable task. We explore the use of effective potentials based on the Feynman path centroid variable in order to simulate large quantum clusters at a reduced computational cost. This centroid can be viewed as the “most” classical variable of a quantum system. Earlier work has shown that one can use a pairwise centroid pseudo-potential to simulate the quantum dynamics of hydrogen in the bulk phase at 25 K and 14 K [Chem. Phys. Lett. 249, 231, (1996)]. Bulk hydrogen, however, freezes below 14 K, so we focus on hydrogen clusters and nanodroplets in the very low temperature regime in order to study their structural behaviours. The calculation of the effective centroid potential is addressed along with its use in the context of molecular dynamics simulations. The effective pseudo-potential of a cluster is temperature dependent and shares similar behaviour as that in the bulk phase. Centroid structural properties in three dimensional space are presented and compared to the results of reference path-integral Monte Carlo simulations. The centroid pseudo-potential approach yields a great reduction in computation cost. With large cluster sizes, the approximate pseudo-potential results are in agreement with the exact reference calculations. An approach to deconvolute centroid structural properties in order to obtain real space results for hydrogen clusters of a wide range of sizes is also presented. The extension of the approach to the treatment of confined hydrogen is discussed, and concluding remarks are presented.

No description available.

Every day we recognize a numerous objects and human brain can recognize objects under many conditions. The way in which humans are able to identify an object is remarkably fast even in different size, colours or other factors. Computers or robots need computational tools to identify objects. Shape descriptors are one of the tools commonly used in image processing applications. Shape descriptors are regarded as mathematical functions employed for investigating image shape information. Various shape descriptors have been studied in the literature. The aim of this thesis is to develop new shape descriptors which provides a reasonable alternative to the existing methods or modified to improve them. Generally speaking shape descriptors can be categorized into various taxonomies based on the information they use to compute their measures. However, some descriptors may use a combination of boundary and interior points to compute their measures. A new shape descriptor, which uses both region and contour information, called centeredness measure has been defined. A new alternative ellipticity measure and sensitive family ellipticity measures are introduced. Lastly familiy of ellipticity measures, which can distinguish between ellipses whose ratio between the length of the major and minor axis differs, have been presented. These measures can be combined and applied in different image processing applications such as image retrieval and classification. This simple basis is demonstrated through several examples.

Q- sort methodology was used to detect underlying structures in 45 statements that reflect women’s attempts to make themselves feel better after incidents of abuse. Eight dichotomous categories were created as plausible descriptors of the 45 statements within the measure. Graduate and advance undergraduate students used the categories to sort the 45 statements. The individual sorts were input with PQMethod software. The Centroid method was used for data analysis. Three of the eight proposed categories were supported: perspective (i.e. the woman’s thought and perceptions regarding the abusive relationship), health behavior, and social relationship. Data analysis displayed that the 45 “feel better” items are able to be grouped into meaningful categories.

Q- sort methodology was used to detect underlying structures in 45 statements that reflect women’s attempts to make themselves feel better after incidents of abuse. Eight dichotomous categories were created as plausible descriptors of the 45 statements within the measure. Graduate and advance undergraduate students used the categories to sort the 45 statements. The individual sorts were input with PQMethod software. The Centroid method was used for data analysis. Three of the eight proposed categories were supported: perspective (i.e. the woman’s thought and perceptions regarding the abusive relationship), health behavior, and social relationship. Data analysis displayed that the 45 “feel better” items are able to be grouped into meaningful categories.

Master of Science / Department of Statistics / Haiyan Wang / Cancer type classification with high throughput molecular data has received much attention. Many methods have been published in this area. One of them is called PAM (nearest centroid shrunken algorithm), which is simple and efficient. It can give very good prediction accuracy. A problem with PAM is that this method selects too many genes, some of which may have no influence on cancer type. A reason for this phenomenon is that PAM assumes that all genes have identical distribution and give a common threshold parameter for genes selection. This may not hold in reality since expressions from different genes could have very different distributions due to complicated biological process. We propose a new method aimed to improve the ability of PAM to select informative genes. Keeping informative genes while reducing false positive variables can lead to more accurate classification result and help to pinpoint target genes for further studies. To achieve this goal, we introduce variable specific test based on Edgeworth expansion to select informative genes. We apply this test on each gene and select some genes based on the result of the test so that a large number of genes will be excluded. Afterward, soft thresholding with cross-validation can be further applied to decide a common threshold value. Simulation and real application show that our method can reduce the irrelevant information and select the informative genes more precisely. The simulation results give us more insight about where the newly proposed procedure could improve the accuracy, especially when the data set is skewed or unbalanced. The method can be applied to broad molecular data, including, for example, lipidomic data from mass spectrum, copy number data from genomics, eQLT analysis with GWAS data, etc. We expect the proposed method will help life scientists to accelerate discoveries with highthroughput data.

No description available.