We consider combinatorial t-design problems as discrete optimization problems. Our motivation is that only a few studies have been done on the use of exact optimization techniques in designs, and that classical methods in design theory have still left many open existence questions. Roughly defined, t-designs are pairs of discrete sets that are related following some strict properties of size, balance, and replication. These highly structured relationships provide optimal solutions to a variety of problems in computer science like error-correcting codes, secure communications, network interconnection, design of hardware; and are applicable to other areas like statistics, scheduling, games, among others. We give a new approach to combinatorial t-designs that is useful in constructing t-designs by polyhedral methods. The first contribution of our work is a new result of equivalence of t-design problems with a graph theory problem. This equivalence leads to a novel integer programming formulation for t-designs, which we call GDP. We analyze the polyhedral properties of GDP and conclude, among other results, the associated polyhedron dimension. We generate new classes of valid inequalities to aim at approximating this integer program by a linear program that has the same optimal solution. Some new classes of valid inequalities are generated as Chv´atal-Gomory cuts, other classes are generated by graph complements and combinatorial arguments, and others are generated by the use of incidence substructures in a t-design. In particular, we found a class of valid inequalities that we call stable-set class that represents an alternative graph equivalence for the problem of finding a t-design. We analyze and give results on the strength of these new classes of valid inequalities. We propose a separation problem and give its integer programming formulation as a maximum (or minimum) edge-weight biclique subgraph problem. We implement a pure cutting-plane algorithm using one of the stronger classes of valid inequalities derived. Several instances of t-designs were solved efficiently by this algorithm at the root node of the search tree. Also, we implement a branch-and-cut algorithm and solve several instances of 2-designs trying different base formulations. Computational results are included.

Modern superscalar pipelines have tremendous capacity to consume the instruction stream. This has been possible owing to improvements in process technology, technology scaling and microarchitectural design improvements that allow programs to speculate past control and data dependencies in the superscalar architecture. However, the speed of the memory subsystem lags behind due to physical constraints in bringing in huge amounts of data to the processor core. Cache hierarchies have subdued the impact of this speed gap; however, there is much that can be still done in improving microarchitecture. Data prefetching techniques bring in memory content significantly before the instruction stream actually witnesses demand misses. However, a majority of the techniques proposed so far depend upon an initial demand miss that initiates a stream of previously identified prefetches. In this thesis, we propose a novel prefetching algorithm, which leverages branch prediction to facilitate deep memory system speculation. The branch predictor directed lookahead mechanism builds a speculative control flow path for the instruction stream about to be fetched by the main superscalar pipeline. Prefetches are generated along this speculative path from a condensed representation of the memory instructions, leveraging register index based correlation. The technique integrates eloquently with the main pipeline's branch predictor to filter out prefetches along invalid speculative paths. Impact of the prefetching scheme is analyzed using out- of-order model of the Gem5 cycle accurate simulator. Evaluation shows that on a set of 13 memory intensive SPEC CPU2006 benchmarks, our prefetching technique improves performance by an average of 5.6% over the baseline out-of-order processor.

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This study is focused on designing conformal antennas to be deployed with the inflatable wings for unmanned aerial vehicles (UAV). The main emphasis is on utilizing the structure of the wing to develop antennas for various frequency bands, while maintaining the wing’s aerodynamic performance. An antenna modeler and optimizer software called 4NEC2 and a program called WIRECODE were used to design and determine the characteristics of the antennas. The effect of flexibility of the inflatable wing on the antenna characteristics during flight is also evaluated.

This study focused on identification of success factors of strategy implementation in retail branches of a bank. The objective of this study was to identify and investigate the possible factors which influence successful implementation of strategic plans in a retail banking environment. In doing so, establish various factors that inhibit successful strategy implementation and explore approaches or best practices that could be adopted to facilitate effective implementation of strategic decisions. The data was collected through questionnaires distributed to the branch managers of the institution. 153 respondents out of a population of 615 participated in the study. The results showed that understanding one’s local market, knowing your customer, communication, leadership, culture-strategy alignment, resources-strategy alignment, rewards and tactical plan are the top success factors of strategy implementation within branches of this bank. / Thesis (MBA)--North-West University, Potchefstroom Campus, 2013

Populus tremuloides Michx. (trembling aspen) is a tree species native to much of North America, characterized by an excurrent crown with horizontal to ascending branches and a dominant terminal leader. An unusual clone of trembling aspen was discovered in the 1940s near Hafford, Saskatchewan. This clone demonstrates abnormal crown morphology, in which vigorous shoots bend down, ultimately leading to an overall twisted or crooked appearance. The objectives of the present study were to investigate the mechanism of shoot bending by (1) characterizing the process and timing of bending, (2) evaluating structural aspects of developing wild-type and crooked aspen shoots, and (3) comparing anatomical features of bending shoots with wild-type shoots. L-system reconstruction models of 3-D digitized shoot development revealed dramatic bending midway through the growing season. Morphological analyses revealed that crooked aspen shoots had greater taper compared to the wild-type, typically known to create shoots resist deflection and bending. However, preliminary strength analyses indicated that crooked aspen shoots were less rigid, with smaller values of Young’s modulus compared to wild-type shoots. Anatomical investigations revealed differences in several structural tissues between developing wild-type and crooked aspen shoots, and differences within crooked aspen shoots. Primary phloem fibres on the upper side of bending shoots maintained relatively large lumens while those on the lower side were fully lignified, similar to those of mature vertically oriented wild-type leader shoots. These differences may result in differential extension growth early in development, and/or uneven mechanical support later on, ultimately resulting in bending due to self-weight. Gelatinous fibres (G-fibres), characteristic of tension wood (TW), were found throughout older wild-type and vertically oriented crooked aspen shoots; however, G-fibres were only found on the lower side of crooked aspen shoots. These lateral differences could have contributed to shoot bending by actively bending shoots downwards, or lack of TW on the upper side may not have prevented biomechanical bending from self weight. Nevertheless, shoot bending stops at the end of the growing season, suggesting that the mechanisms involved in creating bent shoots are only functional during the first growing season.

In recent years, highly accurate branch predictors have been proposed primarily for high performance processors. Unfortunately such predictors are extremely energy consuming and in some cases not practical as they come with excessive prediction latency. Perceptron and O-GEHL are two examples of such predictors. To achieve high accuracy, these predictors rely on large tables and extensive computations and require high energy and long prediction delay. In this thesis we propose power optimization techniques that aim at reducing both computational complexity and storage size for these predictors. We show that by eliminating unnecessary data from computations, we can reduce both predictor's energy consumption and prediction latency. Moreover, we apply information theory findings to remove noneffective storage used by O-GEHL, without any significant accuracy penalty. We reduce the dynamic and static power dissipated in the computational parts of the predictors. Meantime we improve performance as we make faster prediction possible.

Banks occasionally employ frontier efficiency analyses to objectively identify best practices within their organizations. Amongst the frontier efficiency analyses identified in the literature, Data Envelopment Analysis (DEA) was found to be one of the leading approaches. DEA has been successfully applied in many bank branch performance evaluations using traditional intermediation, profitability and production approaches. However, there has been little focus on assessing the growth potential of individual branches. This research presents six models that examine four perspectives of branch growth. Each model was applied to the branch network of one of Canada’s top five banks to gauge the growth potential of individual branches and to provide tailored improvement recommendations. Using various analysis methodologies, the results of each model were examined and their functionality assessed. Based on these findings, three models were deemed to produce significant results, while the remaining three failed to attain viable results.