The goal of this thesis was to explore the effects of dose resolution, respiratory variation and dose calculation method on dose accuracy. To achieve this, two models of lung were created. The first model, called TISSUE, approximated the connective alveolar tissues of the lung. The second model, called BRANCH, approximated the lungs bronchial, arterial and venous branching networks. Both models were varied to represent the full inhalation, full exhalation and midbreath phases of the respiration cycle.<p> To explore the effects of dose resolution and respiratory variation on dose accuracy, each model was converted into a CT dataset and imported into a Monte Carlo simulation. The resulting dose distributions were compared and contrasted against dose distributions from Monte Carlo simulations which included the explicit model geometries. It was concluded that, regardless of respiratory phase, the exclusion of the connective tissue structures in the CT representation did not significantly effect the accuracy of dose calculations. However, the exclusion of the BRANCH structures resulted in dose underestimations as high as 14\% local to the branching structures. As lung density decreased, the overall dose accuracy marginally decreased.<p> To explore the effects of dose calculation method on dose accuracy, CT representations of the lung models were imported into the Pinnacle$^3$ treatment planning system. Dose distributions were calculated using the collapsed cone convolution method and compared to those derived using the Monte Carlo method. For both lung models, it was concluded that the accuracy of the collapsed cone algorithm decreased with decreasing density. At full inhalation lung density, the collapsed cone algorithm underestimated dose by as much as 15\%. Also, the accuracy of the CCC method decreased with decreasing field size.<p> Further work is needed to determine the source of the discrepancy.

Algorithmica Research AB develops software application for the financial markets. One of their products is Quantlab that is a tool for quantitative analyses. An effective method to value several financial instruments is Monte Carlo simulation. Since it is a common method Algorithmica is interesting in investigating if it is possible to create a Monte Carlo framework. A requirement from Algorithmica is that the framework is general and this is the main problem to solve. It is difficult to generate a generalized framework because financial derivatives have very different appearances. To simplify the framework the thesis will be delimitated to European style derivatives where the underlying asset is following a Geometric Brownian Motion. The definition of the problem and delimitation were defined gradually, in parallel with the review of literature, this to be able to decide what purpose, and delimitations that is reasonable to treat. Standard Monte Carlo requires a large number of trials and is therefore slow. To speed up the process there exist different variance reduction techniques and also Quasi Monte Carlo simulation, where deterministic numbers (low discrepancy sequences) is used instead of random. The thesis investigated the variance reduction techniques; control variate technique, antithetic variate technique, and the low discrepancy sequences; Sobol, Faure and Halton. Three test instruments were chosen to test the framework, an Asian option and a Barrier option where the purpose is to conclude which Monte Carle method that performs best, and also a structured product; Smart Start, that is more complex and the purpose is to test that the framework can handle it. To increase the understanding of the theory the Halton, Faure and Sobol sequence were implemented in Quantlab in parallel with the review of literature. The Halton and Faure sequences also seemed to perform worse than Sobol so they were not further analyzed. The developing of the framework was an iterative process. The chosen solution is to design a general framework by using five function pointers; the path generator, the payoff function, the stop criterion function and the volatility and interest rates. The user specifies these functions by him/her given some obligatory input and output values. It is not a problem-free solution to use function pointers and several conflicts and issues are defined, therefore it is not recommended to implement the framework as it is designed today. In parallel with the developing of the framework several experiments on the Asian and Barrier options were performed with varying result and it is not possible to draw a conclusion on which method that is best. Often Sobol seems to converge better and fluctuates less than standard Monte Carlo. The literature indicates that it is important that the user has an understanding of the instrument that should be valued, the stochastic process it follows and the advantages and disadvantages of different Monte Carlo methods. It is recommended to evaluate the different method with experiments, before deciding which method to use when valuing a new derivative.

In this thesis we examine methods for measuring entanglement entropy in spin-1/2 Heisenberg systems using quantum Monte Carlo in the valence bond basis. We begin by presenting the quantum Monte Carlo techniques used in this research. We then use these techniques to directly compare the recently proposed valence bond entanglement entropy to the standard definition of entanglement entropy: the von Neumann entanglement entropy. We find that the valence bond entanglement entropy does not give a bound on the von Neumann entanglement entropy, and that it exhibits a multiplicative logarithmic correction to the area law that is not present in the scaling of the von Neumann entanglement entropy. We then present a method to measure higher orders of the generalized Renyi entanglement entropies using valence bond quantum Monte Carlo, and show results for the second Renyi entropy. We find the results converge to the exact results for one dimensional Heisenberg spin-1/2 chains, and see that the scaling of the second Renyi entropy follows an area law in the two dimensional Heisenberg ground state.

The goal of this thesis was to explore the effects of dose resolution, respiratory variation and dose calculation method on dose accuracy. To achieve this, two models of lung were created. The first model, called TISSUE, approximated the connective alveolar tissues of the lung. The second model, called BRANCH, approximated the lungs bronchial, arterial and venous branching networks. Both models were varied to represent the full inhalation, full exhalation and midbreath phases of the respiration cycle.<p> To explore the effects of dose resolution and respiratory variation on dose accuracy, each model was converted into a CT dataset and imported into a Monte Carlo simulation. The resulting dose distributions were compared and contrasted against dose distributions from Monte Carlo simulations which included the explicit model geometries. It was concluded that, regardless of respiratory phase, the exclusion of the connective tissue structures in the CT representation did not significantly effect the accuracy of dose calculations. However, the exclusion of the BRANCH structures resulted in dose underestimations as high as 14\% local to the branching structures. As lung density decreased, the overall dose accuracy marginally decreased.<p> To explore the effects of dose calculation method on dose accuracy, CT representations of the lung models were imported into the Pinnacle$^3$ treatment planning system. Dose distributions were calculated using the collapsed cone convolution method and compared to those derived using the Monte Carlo method. For both lung models, it was concluded that the accuracy of the collapsed cone algorithm decreased with decreasing density. At full inhalation lung density, the collapsed cone algorithm underestimated dose by as much as 15\%. Also, the accuracy of the CCC method decreased with decreasing field size.<p> Further work is needed to determine the source of the discrepancy.

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We use Particle-in-Cell Method¡]PIC¡^and Monte-Carlo-Collision Method¡]MCC¡^to model a plasma colliding system .By this way¡Awe can realize the behaviors and effects in the motion of plasma collision . Our mainly discuss is that the system will produce a thin layer¡]Sheath¡^between plasma and a workpiece ¡Aand the effects of colliding phenomenon in this thin layer¡]Sheath¡^ .

Computer simulation is now increasingly being used for design and performance evaluation of communication systems. When simulating a mobile wireless channel for communication systems, it is usually assumed that the fading process is a random variate with Rayleigh distribution. The random variates of the fading process should also have other properties, like autocorrelation, spectrum, etc. At present, there are a number of methods to generate the Rayleigh fading process, some of them quite recently proposed. Due to the use of different Rayleigh fading generators, different simulations of the same communication system yield different results. Three methods, viz., the Jakes method, the IDFT method and the filtering WGN method, have been studied, simulated and compared based on the Rayleigh fading process' properties. Various communication systems have been simulated using the Rayleigh fading generators and the difference in the results, if any, have been analyzed. The research studies the different Rayleigh fading generators and compares them using the properties of the Rayleigh fading channel. It is found that the IDFT method and the filtering WGN method generate processes that have properties very close to the ideal Rayleigh fading process.

Thesis (Ph. D.)--University of Washington, 2000. / Vita. Includes bibliographical references (leaves 111-119).

Akademisk avhandling--Göteborgs universitet, 2005. / Bilbiogr. p. 77-79.

Doktorafhandling--Københavns universitet, 2005. / Bibliogr. p. 47-51. Résumé en danois.