Detailed and exact collision detection for large amounts of objects has for a long time been a non real-time affair because of the immense amount of computations necessary. This was however not only because of the complexity of the algorithms but also because discussed of the computations would not have had to be done in the first place. This paper has through literature research and empirical testing examined two different broad phase approaches to object culling in a three dimensional environment. The aim of such a broad phase algorithm is to decrease the amount of computation heavy narrow phase collision detection checks and thus enhancing application performance. Potential weaknesses of these approaches were addressed and possible solutions discussed. Performance comparisons were made to give a better overview of what kind of performance enhancements can be expected and to give a theoretical base for further research.

Surface pasting is an interactive hierarchical modelling technique used to construct surfaces with varying levels of local detail. The concept is similar to that of the physical process of modelling with clay, where features are placed on to a base surface and attached by a smooth join obtained by adjusting the feature. Cylindrical surface pasting extends this modelling paradigm by allowing for two base surfaces to be joined smoothly via a blending cylinder, as in attaching a clay head to the body using a neck. Unfortunately, computer-based pasting involves approximations that can cause cracks to appear in the composite surface. In particular this occurs when the pasted feature boundary does not lie exactly over the user-specified pasting region on the base surface. Determining pasted locations for the feature boundary control points that give a close to exact join is non-trivial, especially in the case of cylinders as their control points can not be defined to lie on their closed curve boundary. I propose and compare six simple methods for positioning a feature cylinder's control points such that the join boundary discontinuities are minimized. The methods considered are all algorithmically simple alternatives having low computational costs. While the results demonstrate an order of magnitude quality improvement for some methods on a convex-only curved base, as the complexity of the base surface increases, all the methods show similar performance. Although unexpected, it turns out that a simple mapping of the control points directly onto the pasting closed curve given on the base surface offers a reasonable cylindrical boundary pasting technique.

This thesis presents two novel coding schemes and applications to both two- and three-dimensional image compression. Image compression can be viewed as methods of functional approximation under a constraint on the amount of information allowable in specifying the approximation. Two methods of approximating functions are discussed: Iterated function systems (IFS) and wavelet-based approximations. IFS methods approximate a function by the fixed point of an iterated operator, using consequences of the Banach contraction mapping principle. Natural images under a wavelet basis have characteristic coefficient magnitude decays which may be used to aid approximation. The relationship between quantization, modelling, and encoding in a compression scheme is examined. Context based adaptive arithmetic coding is described. This encoding method is used in the coding schemes developed. A coder with explicit separation of the modelling and encoding roles is presented: an embedded wavelet bitplane coder based on hierarchical context in the wavelet coefficient trees. Fractal (spatial IFSM) and fractal-wavelet (coefficient tree), or IFSW, coders are discussed. A second coder is proposed, merging the IFSW approaches with the embedded bitplane coder. Performance of the coders, and applications to two- and three-dimensional images are discussed. Applications include two-dimensional still images in greyscale and colour, and three-dimensional streams (video).

<p>This thesis presents spatially and temporally dependent hierarchical Bayesian models for the analysis of social networks and associated textual data. Social network analysis has received significant recent attention and has been applied to fields as varied as analysis of Supreme Court votes, Congressional roll call data, and inferring links between authors of scientific papers. In many traditional social network analysis models, temporal and spatial dependencies are not considered due to computational difficulties, even though significant such dependencies often play a significant role in the underlying generative process of the observed social network data.</p><p>Thus motivated, this thesis presents four new models that consider spatial and/or temporal dependencies and (when available) the associated text. The first is a time-dependent (dynamic) relational topic model that models nodes by their relevant documents and uses probit regression construction to map topic overlap between nodes to a link. The second is a factor model with dynamic random effects that is used to analyze the voting patterns of the United States Supreme Court. hTe last two models present the primary contribution of this thesis two spatially and temporally dependent models that jointly analyze legislative roll call data and the their associated legislative text and introduce a new paradigm for social network factor analysis: being able to predict new columns (or rows) of matrices from the text. The first uses a nonparametric joint clustering approach to link the factor and topic models while the second uses a text regression construction. Finally, two other models on analysis of and tracking in video are also presented and discussed.</p> / Dissertation

Both advertising and research and development (R&D) can be viewed as two factors crucial to long-term corporate growth. The purpose of this study is to investigate the effects of the advertising, R&D and interaction between advertising and R&D on shareholder value concerning economic scale and industry concentration. The empirical results show R&D investments may generate innovative products which enhance shareholder value. Moreover, the interaction between advertising and R&D is significantly and positively related to shareholder value. In practice, advertising plays a role to build brand awareness for innovative products. Additionally, we examine how economic scale and industry concentration influence the effects of advertising and R&D on shareholder value individually. With the respect to economic scale, advertising and R&D strategies may increase shareholder value more significantly for firms with high economic scale (large firms). The synergy between advertising and R&D is only significant and positive for firms with low economic scale (small firms). This implies that small firms should invest in advertising to build brand awareness and promote new products while large firms have already developed brand awareness, so the large firms should specialize in core competences. Firms in competitive industry rely more on successful advertising campaigns to increase sales. Moreover, economic scale and industry concentration significantly moderate the effectiveness of advertising and R&D. Under the limited firm sources, managers should decide the appropriate mix of advertising and R&D to maximize shareholder value significantly according to economic scale and industry concentration.

In this research, hills-like hierarchical structured TiO2 photoanodes for dye-sensitized solar cells (DSSCs) have been prepared. We expected these appropriately aggregated TiO2 clusters in photoanode layer could cause stronger light scattering and higher dye loading that increased efficiency of photovoltaics. For detailed light-harvesting study, different molecular weight of polyvinyl alcohol (PVA) polymers were used as binders for TiO2 nanoparticles (P-25 Degussa) aggregation. After preparing a series of TiO2 films with dissimilar morphology, the reflection of TiO2 films, absorbance of attached dye, amount of dye loading, and performance of fabricated DSSC devices were measured and investigated. An optimized device had higher dye loading and well light harvesting at the same time that induced a 23% increase of short-circuit current Jsc in DSSCs. Moreover, we found that electrolyte could penetrate or diffuse easily in this higher porous structure. We fabricated dye-sensitized solar cells with MPN-based liquid electrolyte and gel polymer electrolyte. From this structure, the short-circuit current Jsc was increased around 16% and 19% respectively compared to conventional layers with liquid electrolyte and gel polymer electrolyte devices. The increase in highly viscous system of gel polymer electrolyte is due to easier penetration by such hills-like hierarchical structure.

Quadtree algorithm and morphological image processing are combined in the proposed method in this paper. A new method is proposed to improve the previous pattern mapping method for faster processing. The previous pattern mapping method is a pattern mapping method by storing the tree pattern by string form, which is a pointless data structure. In the proposed method the tree pattern is saved in a point data structure. Therefore, the pointer tree can be applied to the quadtree immediately without the transforming time, which was required in the previous pattern mapping method. In this paper, the pointless quad tree work is modified to pointer quad tree to reduce the processing time. The modified algorithm is applied to circuit detection, image restoration, image segmentation and cell counting.

The purpose of this study is to discuss the relationship among company characteristics, brand traits and organizational attractiveness. Most of previous studies about organizational attractiveness are mainly focus on job information, industry and organization performance. Therefore, this study is seeking to understand the influence of company characteristics and brand traits to organizational attractiveness during the job seeking period. This study selects 30 Taiwanese local companies with stock release from the research of Cheers Magazine ¡u2011 The most attractive company for the new generation- Top 100 ¡vand 460 MBA students as questionnaires. By adapting the hierarchical linear model to analyze the data and obtain the result. The study found out that company characteristics and brand traits both have positive influence on organizational attractiveness. Besides, there are also influence between the company characteristics and brand traits.

Engineering for Sustainable Development (ESD) is an integrated systems approach, which aims at developing a balance between the requirements of the current stakeholders without compromising the ability of the future generations to meet their needs. This is a multi-criteria decision-making process that involves the identification of the most optimal sustainable process, which satisfies economic, ecological and social criteria as well as safety and health requirements. Certain difficulties are encountered when ESD is applied, such as ill-defined criteria, scarcity of information, lack of process-specific data, metrics, and the need to satisfy multiple decision makers. To overcome these difficulties, ESD can be broken down into three major steps, starting with the Life Cycle Assessment (LCA) of the process, followed by generation of non-dominating alternatives, and finally selecting the most sustainable process by employing an analytic hierarchical selection process. This methodology starts with the prioritization of the sustainability metrics (health and safety, economic, ecological and social components). The alternatives are then subjected to a pair-wise comparison with respect to each Sustainable Development (SD) indicator and prioritized depending on their performance. The SD indicator priority score and each individual alternative’s performance score together are used to determine the most sustainable alternative. The proposed methodology for ESD is applied for bio-diesel production in this thesis. The results obtained for bio-diesel production using the proposed methodology are similar to the alternatives that are considered to be economically and environmentally favorable by both researchers and commercial manufacturers; hence the proposed methodology can be considered to be accurate. The proposed methodology will also find wide range of application as it is flexible and can be used for the sustainable development of a number of systems similar to the bio-diesel production system; it is also user friendly and can be customized with ease. Due to these benefits, the proposed methodology can be considered to be a useful tool for decision making for sustainable development of chemical processes.

Observations of textual document management by individuals and organizations have suggested the popularity of using categories (e.g., folders) to organize, archive and access documents. The document grouping behavior is intentional acts, reflecting a user¡¦s preferential perspective on semantic coherency or relevant groupings between subjects. Although becoming less adequate as new documents are accumulated, the existing category set or hierarchy may preserve to some extent the user¡¦s preferential perspective on document grouping. Thus, when deriving a new category set or hierarchy, the category set or hierarchy previously established by the user (i.e., semantic coherency of the documents embedded in the existing category set or category hierarchy) should be taken into consideration. In this study, we have proposed an evolution-based technique, Category Hierarchy Evolution (CHE), for managing category hierarchy rather than category set. Specifically, in CHE, the overall similarity between two documents is measured not only by their content similarity but also by their location similarity in the existing category hierarchy. Our empirical evaluation results suggest that the proposed CHE technique outperformed the discovery-based technique (i.e., the traditional content-based document-clustering technique).