To realize efficient parallel processing, which is one of effective methods that deal with computing intensive applications, the technology of solving the problems of task matching and scheduling becomes extremely important. In this thesis, an Ant Colony Optimization (ACO) approach is employed for allocating task graphs onto a heterogeneous computing system. The approach uses a new state transition rule to reduce the time needed for finding a satisfactory solution. And a local search procedure is designed to improve the obtained solution. Furthermore, by applying the Taguchi Method in the technology of Quality Engineering, and further utilizing the Orthogonal Array (OA) to reduce the number of experiments and find the optimal combination of parameters, which allows the Ant Colony Algorithm to find solutions more efficient. The proposed algorithm is compared with the genetic-algorithm-based approach and the dynamic priority scheduling (DPS) heuristic. Experimental results show that the ACO approach outperforms two computing approaches in solving the task matching and scheduling problem.

Abstract Optical fiber communications have been become one of the most popular researches since 1970s. In this field, there are many studies on the coupling between semiconductor lasers and fibers and many conclusions are demonstrated. In this thesis we build a 2-D numerical model to simulate energy coupling between laser diodes (LDs) and tapered optical fibers. Our model is based on the spectral domain integral equation (SDIE) formulation which is derived from Maxwell equations and the principle of mode matching. Through this numerical model we will be able to show the field distribution in LD waveguide junctions. We may also use this tool to study the coupling parameters such as the separation distance and tapered fiber geometry.

Because of several advantages, e.g. compact structure, high air-gap flux density, and high torque capability, the PM synchronous motor plays an important role in recent years. The basic principle of controlling a PMSM is based on vector control. The control performance is influenced by factors as the plant parameter variations, the external load disturbances, and the unmodeled or nonlinear dynamics. In the thesis, we apply a recently proposed robust 2DOF configuration to designing controllers for PMSM to achieve the robust asymptotical tracking under perturbations in both the motor and the controllers. Two design methods are adopted to implement the desired controllers, i.e. the linear algebraic method and the design method. The effect of the well-known internal model principle is addressed in the former design method. The merit of the latter design method is that both time and frequency domain design specifications can be easily included in the design procedure. Computer simulation results are displayed to illustrate the advantages of our designs.

Thin-film theorems are well developed and so are the fabrication processes. Yet under some special conditions, traditional methods (such as the ABCD matrix and the transmission matrix methods) will lead to a serious numerical error. In this thesis, we propose a new method called Couple E/H field formulation, which will overcome this numerical problem in simulating characteristics of complex multi-layered structures. We have verified both the algorithm and its results with the traditional techniques. By extending the impedance matching principle, we came out with a multi-layer anti-reflection coating design optimized for a time-harmonic plane wave incidence with any incident angle. Such a design allows for more plane waves with adjacent angles to pass through the coating layers with minimal reflection. Furthermore, we apply this AR coating design to facets of semiconductor lasers. Our calculation shows that multi-layer coating does a better job than a single layer coating. The reflectivity of a laser diode from single layer coating 0.085% to 5 layer coating 0.056%, which is a 33% improvement.

Abstract¡G With the rapid development of electronic marketplace, buyers and sellers can trade more easily without the limitations of time and distance. Internet trading is benefited to both sides in many ways. However, the matching mechanism between buyers and sellers in the electronic marketplace is not explored deeply. Consequently, the purpose of this research is to study the screening and negotiation in the process of matching. In this research, four screening models and related flow charts have been proposed. A negotiation model is also proposed to deal with the bargaining process. Finally, a prototype based on development method is built to demonstrate how the screening models and negotiation models work.

We present a serially concatenated coding scheme for partial response channels. The encoder consists of an outer irregular LDPC code and an inner matched spectrum trellis code. These codes are shown to offer considerable improvement over the i.i.d. capacity (> 1 dB) of the channel for low rates (approximately 0.1 bits per channel use). We also present a qualitative argument on the optimality of these codes for low rates. We also formulate a performance index for such codes to predict their performance for low rates. The results have been verified via simulations for the (1-D)/sqrt(2) and the (1-D+0.8D^2)/sqrt(2.64) channels. The structure of the encoding/decoding scheme is considerably simpler than the existing scheme to maximize the information rate of encoders over partial response channels.

This dissertation presents the development of a method for quantitative integration of seismic (elastic) anisotropy attributes with reservoir performance data as an aid in characterization of systems of natural fractures in hydrocarbon reservoirs. This new method incorporates stochastic Discrete Feature Network (DFN) fracture modeling techniques, DFN model based fracture system hydraulic property and elastic anisotropy modeling, and non-linear inversion techniques, to achieve numerical integration of production data and seismic attributes for iterative refinement of initial trend and fracture intensity estimates. Although DFN modeling, flow simulation, and elastic anisotropy modeling are in themselves not new technologies, this dissertation represents the first known attempt to integrate advanced models for production performance and elastic anisotropy in fractured reservoirs using a rigorous mathematical inversion. The following new developments are presented: . • Forward modeling and sensitivity analysis of the upscaled hydraulic properties of realistic DFN fracture models through use of effective permeability modeling techniques. . • Forward modeling and sensitivity analysis of azimuthally variant seismic attributes based on the same DFN models. . • Development of a combined production and seismic data objective function and computation of sensitivity coefficients. . • Iterative model-based non-linear inversion of DFN fracture model trend and intensity through minimization of the combined objective function. This new technique is demonstrated on synthetic models with single and multiple fracture sets as well as differing background (host) reservoir hydraulic and elastic properties. Results on these synthetic control models show that, given a well conditioned initial DFN model and good quality field production and seismic observations, the integration procedure results in convergence of both fracture trend and intensity in models with both single and multiple fracture sets. Tests show that for a single fracture set convergence is accelerated when the combined objective function is used as compared to a similar technique using only production data in the objective function. Tests performed on multiple fracture sets show that, without the addition of seismic anisotropy, the model fails to converge. These tests validate the importance of the new process for use in more realistic reservoir models.

Since the plus fraction of reservoir fluids has some uncertainty in its molecular weight and critical properties, equation-of-state, EOS, are generally not predictive without tuning its parameters to match experimental data. Tuning of the EOS is found to be the best method for improving the predictions of compositional reservoir simulators. The proposed strategy for tuning EOS consists of seven steps: (1) split the laboratory plus fraction to single carbon number groups, SCN, usually up to SCN 44; the last component will be C45+, (2) use set of correlations to calculate the critical properties and acentric factor for each SCN group, (3) match the saturation pressure at reservoir temperature by altering the measured value of the molecular weight of the plus fraction using the extended composition, (4) group SCN groups to multiple carbon number groups, MCN, (5) assign critical properties and acentric factor for each MCN group, (6) rematch the saturation pressure at reservoir temperature using the grouped composition, and (7) match the volumetric data by regressing on volume shift parameters of all components in grouped composition. This research shows an accurate method to split the plus fraction to SCN groups. The most accurate set of correlations to calculate the critical properties and acentric factor for each SCN group that will result in a small adjustment for the molecular weight of the plus fraction when saturation pressure is matched using the extended composition. The proposed strategy groups the extended composition to eight pseudocomponents. The binary interaction coefficients between hydrocarbons and between hydrocarbons and non-hydrocarbons are set to zero which dramatically reduces the simulation time. The strategy proposed in this research for tuning EOS to match experimental data has been tested for a wide range of C7+ mole% (4 – 25) which covers gas condensate and volatile oil samples. Also, using this strategy to tune EOS at reservoir temperature will accurately predict the fluid properties at separator conditions and saturation pressures at different temperatures. The scope of this research is to come up with an accurate and systematic technique for tuning an EOS for use in compositional simulation.

Allocation problems have been central to the development of the theory of algorithms and also find applications in several realms of computer science and economics. In this thesis we initiate a systematic study of these problems in situations with limited information. Towards this end we explore several modes by which data may be obfuscated from the algorithm. We begin by investigating temporal constraints where data is revealed to the algorithm over time. Concretely, we consider the online bipartite matching problem in the unknown distribution model and present the first algorithm that breaches the 1-1/e barrier for this problem. Next we study issues arising from data acquisition costs that are prevalent in ad-systems and kidney exchanges. Motivated by these constraints we introduce the query-commit model and present constant factor algorithms for the maximum matching and the adwords problem in this model. Finally we assess the approximability of several classical allocation problems with multiple agents having complex non-linear cost functions. This presents an additional obstacle since the support for the cost functions may be extremely large entailing oracle access. We show tight information theoretic lower bounds for the general class of submodular functions and also extend these results to get lower bounds for a subclass of succinctly representable non-linear cost functions.

During the operation of a geological carbon storage project, verifying that the CO₂ plume remains within the permitted zone is of particular interest both to regulators and to operators. However, the cost of many monitoring technologies, such as time-lapse seismic, limits their application. For adequate predictions of plume migration, proper representation of heterogeneous permeability fields is imperative. Previous work has shown that injection data (pressures, rates) from wells might provide a means of characterizing complex permeability fields in saline aquifers. Thus, given that injection data are readily available and inexpensive, they might provide an inexpensive alternative for monitoring; combined with a flow model like the one developed in this work, these data could even be used for predicting plume migration. These predictions of plume migration pathways can then be compared to field observations like time-lapse seismic or satellite measurements of surface-deformation, to ensure the containment of the injected CO₂ within the storage area. In this work, two novel methods for creating heterogeneous permeability fields constrained by injection data are demonstrated. The first method is an implementation of a probabilistic history matching algorithm to create models of the aquifer for predicting the movement of the CO₂ plume. The geologic property of interest, for example hydraulic conductivity, is updated conditioned to geological information and injection pressures. The resultant aquifer model which is geologically consistent can be used to reliably predict the movement of the CO₂ plume in the subsurface. The second method is a model selection algorithm that refines an initial suite of subsurface models representing the prior uncertainty to create a posterior set of subsurface models that reflect injection performance consistent with that observed. Such posterior models can be used to represent uncertainty in the future migration of the CO₂ plume. The applicability of both methods is demonstrated using a field data set from central Algeria. / text