No description available.

No description available.

No description available.

No description available.

No description available.

Design review is a process of reviewing construction design documents to ensure that they reflect the owner's design intent, and are accurate in describing the owner's desired building or facility. Information generation becomes more intensive as the design stage progresses. The use of valuable information during design review stage can lead to a more comprehensive and high quality design, and a building or facility that is constructible, and within the intended budget. However, in current design practices, valuable design review information is scattered, ineffectively placed, and is not used efficiently. The design review process will be more efficient if this valuable information is integrated and centralized. The author developed a framework to improve the design review process by incorporating a centralized repository of design review information and 3D CAD model, in an interactive Virtual Environment (VE). To develop the framework, the author used Action Research style where he identified and confirmed the design review problem area, promoted the potential solutions to the problem, and developed a prototype. In gathering and analyzing the data for the research, the author used the synthesis of three methods. They include review of literature, a case study (interviews with industry personnel and content analysis of design review documents), and dissemination of the author's progressive findings in conferences, conference proceedings and journal publications. From his findings, the author developed the framework to improve the design review process by using information filtering based on context-aware concept, coupled with the benefits of a VE. The required design review information in the form of textual, numerical and geometric information is processed (queried, retrieved and stored). The author defined four contexts for information filtering: discipline-centric, task-centric, object-centric, and location-centric. IF-THEN rules are used to trigger the processing of the required design review information and present it to the design reviewer in a VE. A low cost 3D Game Engine is used as the enabling development tool to develop a work-in-progress (WIP) prototype design review application in a VE. / Ph. D.

An advanced model of active control of fan noise for ultra high bypass turbofan engines has been developed. This model is based on a boundary integral equation method and simulates the propagation, radiation and control of the noise generated by an engine fan surrounded by a duct of finite length and cylindrical shape, placed in a uniform flow. Control sources, modeled by point monopoles placed along the wall of the engine inlet or outlet duct, inject anti-noise into the duct to destructively interfere with the sound field generated by the fan. The duct inner wall can be lined or rigid. Unlike current methods, reflection from the duct openings is taken into account, as well as the presence of the evanescent modes. Forward, as well as backward (i.e., from the rear of the engine), external radiation is computed. The development of analytical expressions for the sound field resulting from both the fan loading noise and the control sources is presented. Two fan models are described. The first model uses spinning line sources with radially distributed strength to model the loading force that the fan blades exert on the medium. The second model uses radial arrays of spinning point dipoles to simulate the generation of fan modes of specific modal amplitudes. It is shown that these fan models can provide a reasonable approximation of actual engine fan noise in the instance when the modal amplitude of the propagating modes or the loading force distribution on the fan blades, is known. Sample cases of active noise control are performed to demonstrate the feasibility of the model. The results from these tests indicate that this model 1) is conducive to more realistic studies of active control of fan noise on ultra high bypass turbofan engines because it accounts for the presence of evanescent modes and for interference between inlet and outlet radiation, which were shown to have some impact on the performance of the active control system; 2) is very useful because it allows monitoring of any region of the acoustic field; 3) is computationally fast, and therefore suitable to conduct parametric studies. Finally, the potential that active noise control techniques have for reducing fan noise on an ultra high bypass turbofan engine is investigated. Feedforward control algorithms are simulated. Pure active control techniques, as well as hybrid (active/passive) control techniques, are studied. It is demonstrated that active noise control has the potential to reduce substantially, and over a relatively large far field sector, the fan noise radiated by an ultra high bypass turbofan engine. It is also shown that a hybrid control system can achieve significantly better levels of noise reduction than a pure passive or pure active control system, and that its optimum solution is more robust than the one achieved with a pure active control system. The model has shown to realistically predict engine acoustic behavior and is thus likely to be a very useful tool for designing active noise control systems for ultra high bypass turbofan engines. / Ph. D.

With the emerging hybrid multi-core and many-core compute platforms delivering unprecedented high performance within a single chip, and making rapid strides toward the commodity processor market, they are widely expected to replace the multi-core processors in the existing High-Performance Computing (HPC) infrastructures, such as large scale clusters, grids and supercomputers. On the other hand in the realm of bioinformatics, the size of genomic databases is doubling every 12 months, and hence the need for novel approaches to parallelize sequence search algorithms has become increasingly important. This thesis puts a significant step forward in bridging the gap between software and hardware by presenting an efficient and scalable model to accelerate one of the popular sequence alignment algorithms by exploiting multigrain parallelism that is exposed by the emerging multiprocessor architectures. Specifically, we parallelize a dynamic programming algorithm called Smith-Waterman both within and across multiple Cell Broadband Engines and within an nVIDIA GeForce General Purpose Graphics Processing Unit (GPGPU). Cell Broadband Engine: We parallelize the Smith-Waterman algorithm within a Cell node by performing a blocked data decomposition of the dynamic programming matrix followed by pipelined execution of the blocks across the synergistic processing elements (SPEs) of the Cell. We also introduce novel optimization methods that completely utilize the vector processing power of the SPE. As a result, we achieve near-linear scalability or near-constant efficiency for up to 16 SPEs on the dual-Cell QS20 blades, and our design is highly scalable to more cores, if available. We further extend this design to accelerate the Smith-Waterman algorithm across nodes on both the IBM QS20 and the PlayStation3 Cell cluster platforms and achieve a maximum speedup of 44, when compared to the execution times on a single Cell node. We then introduce an analytical model to accurately estimate the execution times of parallel sequence alignments and wavefront algorithms in general on the Cell cluster platforms. Lastly, we contribute and evaluate TOSS -- a Throughput-Oriented Sequence Scheduler, which leverages the performance prediction model and dynamically partitions the available processing elements to simultaneously align multiple sequences. This scheme succeeds in aligning more sequences per unit time with an improvement of 33.5% over the naive first-come, first-serve (FCFS) scheduler. nVIDIA GPGPU: We parallelize the Smith-Waterman algorithm on the GPGPU by optimizing the code in stages, which include optimal data layout strategies, coalesced memory accesses and blocked data decomposition techniques. Results show that our methods provide a maximum speedup of 3.6 on the nVIDIA GPGPU when compared to the performance of the naive implementation of Smith-Waterman. / Master of Science

This paper proposes a cumulant based classification means to identify waveforms for a blind receiver in the presence of time varying channels, which is built from the work done on cumulants in static channels currently in the literature. Results show the classification accuracy is on the order or better than current methods in use in static channels that do not vary over an observation period. This is accomplished by making use of second through tenth order cumulants in a signature vector that the search engine platform has the means of differentiating. A receiver can then blindly identify waveforms accurately in the presence of multipath Rayleigh fading with AWGN noise. Channel learning occurs prior to classification in order to identify the consistent distortion pattern for a waveform that is observable in the signature vector. Then using a database look-up method, the observed waveform is identified as belonging to a particular cluster based on the observed signature vector. If the distortion patterns are collected from a variety of channel types, the database can then classify both the waveform and the rough channel type that the waveform passed through. If the exact channel model or channel parameters is known and used as a limiter, significant improvement on the waveform classification can be achieved. Greater accuracy comes from using the exact channel model as the limiter. / Master of Science

Plants have developed specific responses to external stimuli such as drought, cold, high salinity in soil, and precipitation in addition to internal developmental stimuli. These stimuli trigger signal transduction pathways in plants, leading to cellular adaptation. A signal transduction pathway is a network of entities that interact with one another in response to given stimulus. Such participating entities control and affect gene expression in response to stimulus . For computational purposes, a signal transduction pathway is represented as a network where nodes are biological molecules. The interaction of two nodes is a directed edge. A plethora of research has been conducted to understand signal transduction pathways. However, there are a limited number of approaches to explore and integrate signal transduction pathways. Therefore, we need a platform to integrate together and to expand the information of each signal transduction pathway. One of the major computational challenges in inferring signal transduction pathways is that the addition of new nodes and edges can affect the information flow between existing ones in an unknown manner. Here, I develop the Beacon inference engine to address these computational challenges. This software engine employs a network inference approach to predict new edges. First, it uses mutual information and context likelihood relatedness to predict edges from gene expression time-series data. Subsequently, it incorporates prior knowledge to limit false-positive predictions. Finally, a naive Bayes classifier is used to predict new edges. The Beacon inference engine predicts new edges with a recall rate 77.6% and precision 81.4%. 24% of the total predicted edges are new i.e., they are not present in the prior knowledge. / Master of Science