No description available.

No description available.

Approximating the facial features for forensic facial approximation is challenging, especially the nose. Numerous methods have been published to position the tip of the nose in profile with variable results. Gerasimov’s two-tangent method is the most commonly used. However, a recent article published by Ullrich and Stephan (2011) states that the method was not properly performed and provides new guidelines. This research used a sample of CT scans from a Denmark population (N=66) to determined which of Gerasimov’s literal translation or Ullrich and Stephan’s (2011) new version of the two-tangent method is the most accurate. A combination of the two methods was also evaluated to determine the effect of each tangent independently, and the effect of intraobserver error. It was determine that the new guidelines result in smaller mean difference but no method can accurately position the tip of the nose due to the lack of experience from the practitioner.

No description available.

Underwater acoustic modelling is an important aspect of Synthetic Aperture Sonar (SAS) system design and algorithm development. Sea-trials are an expensive and time-consuming exercise and simulations provide an efficient and economic alternative. However, there are few simulators (in the open literature) that can efficiently provide realistic SAS data for large, complicated scenes. Conventional side-scan sonar simulators are not suitable for SAS data simulation. These simulators utilise narrow-beam and narrow-band approximations; typical SAS systems are wide-beam and wide-band and these approximations are invalid. Moreover, conventional side-scan sonar is a non-coherent imaging technique and SAS processing relies on the phase. Existing SAS simulators are capable of modelling very simple scenes only. They utilise a decomposition of the scene into point or smooth facet primitives, which is very inefficient. Many primitives are required and this imposes a severe restriction on scene complexity and size. This thesis presents a rigorous mathematical framework for the modelling of SAS imagery. A novel acoustic scattering model is developed and its implementation in a wide-beam and wide-band, multiple-receiver Interferometric SAS (InSAS) simulator is detailed. The scattering model utilises a decomposition of the scene into rough (rather than smooth) facet primitives. The use of rough facet primitives provides a significant increase in computational efficiency since scenes are decomposed into fewer primitives. This facilitates the simulation of larger and more complicated scenes. Each rough facet is characterised by its far-field beampattern. The statistics of the beampattern are related to the facet shape and roughness statistics using the Kirchhoff approximation. The beampattern is realised from its first and second-order statistics. The SAS imagery is obtained using a coherent sum of the facet responses and occlusions and multiple-scattering are resolved by ray-tracing. The simulator is implemented for use on a parallel computing cluster. The simulator is shown to provide realistic SAS data that is qualitatively and quantitatively similar to real data. The simulated results are considered, in many ways, superior to the simulated results in the literature.

Approximating the facial features for forensic facial approximation is challenging, especially the nose. Numerous methods have been published to position the tip of the nose in profile with variable results. Gerasimov’s two-tangent method is the most commonly used. However, a recent article published by Ullrich and Stephan (2011) states that the method was not properly performed and provides new guidelines. This research used a sample of CT scans from a Denmark population (N=66) to determined which of Gerasimov’s literal translation or Ullrich and Stephan’s (2011) new version of the two-tangent method is the most accurate. A combination of the two methods was also evaluated to determine the effect of each tangent independently, and the effect of intraobserver error. It was determine that the new guidelines result in smaller mean difference but no method can accurately position the tip of the nose due to the lack of experience from the practitioner.

In this work, we study the problem of clearing contamination spreading through a large network where we model the problem as a graph searching game. The problem can be summarized as constructing a search strategy that will leave the graph clear of any contamination at the end of the searching process in as few steps as possible. We show that this problem is NP-hard even on directed acyclic graphs and provide an efficient approximation algorithm. We experimentally observe the performance of our approximation algorithm in relation to the lower bound on several large online networks including Slashdot, Epinions and Twitter. The experiments reveal that in most cases our algorithm performs near optimally. / Graduate

No description available.

One central question in theoretical computer science is how to solve problems accurately and quickly. Despite the encouraging development of various algorithmic techniques in the past, we are still at the very beginning of the understanding of these techniques. One particularly interesting paradigm is the greedy algorithm paradigm. Informally, a greedy algorithm builds a solution to a problem incrementally by making locally optimal decisions at each step. Greedy algorithms are important in algorithm design as they are natural, conceptually simple to state and usually efficient. Despite wide applications of greedy algorithms in practice, their behaviour is not well understood. However, we do know that in several specific settings, greedy algorithms can achieve good results. This thesis focuses on examining contexts in which greedy and greedy-like algorithms are successful, and extending them to more general settings. In particular, we investigate structural properties of graphs and set systems, families of special functions, and greedy approximation algorithms for several classic NP-hard problems in those contexts. A natural phenomenon we observe is a trade-off between the approximation ratio and the generality of those contexts.

One central question in theoretical computer science is how to solve problems accurately and quickly. Despite the encouraging development of various algorithmic techniques in the past, we are still at the very beginning of the understanding of these techniques. One particularly interesting paradigm is the greedy algorithm paradigm. Informally, a greedy algorithm builds a solution to a problem incrementally by making locally optimal decisions at each step. Greedy algorithms are important in algorithm design as they are natural, conceptually simple to state and usually efficient. Despite wide applications of greedy algorithms in practice, their behaviour is not well understood. However, we do know that in several specific settings, greedy algorithms can achieve good results. This thesis focuses on examining contexts in which greedy and greedy-like algorithms are successful, and extending them to more general settings. In particular, we investigate structural properties of graphs and set systems, families of special functions, and greedy approximation algorithms for several classic NP-hard problems in those contexts. A natural phenomenon we observe is a trade-off between the approximation ratio and the generality of those contexts.