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Nowadays Optical fiber communication is one of the important way to convey information, and there is strong competition in optical long-haul transmission to achieve high channel bit rates and large transmission capacity. Therefore, it is important to study a technology to improve the performance of such system. As we have already known, the dispersion flattened fiber (DFF) and Return-to-zero differential phase shift keying (RZ-DPSK) modulation are attractive, because it can improve the transmission performance of the long-haul system, therefore, it is possible to improve the transmission performance by a combination of the RZ-DPSK and the DFF, and the performance improvement investigation is required, because it will contribute to improve the system design of the long-haul optical fiber communication systems in near future. In this master thesis, a theoretical study focused on the RZ-DPSK transmission system using DFF is conducted. One important technology of current long-haul optical fiber communication system is a dispersion map. In this master thesis, difference of the transmission performance is characterized with regard to number of dispersion blocks within the dispersion map, different repeater output power and different compensation scheme within dispersion map, etc., all of them calculated by a numerical simulator .The goal is to clarify the optimum dispersion map design of the long-haul DPSK based transmission and find the effective method to improve the transmission performance.

Neural network models were developed to model the short-term concentration distribution of aerosols released from point sources. Those models were based on data from a wide range field experiments (November 2002, March, May and August 2003). The study focused on relative dispersion from the puff centroid. The influence of puff/cloud meandering and large-scale gusts were not considered, the modelling was limited to studying the dispersion caused by small-scale turbulence. The data collected were based on short range/time dispersion, usually shorter than 150 s. The ANN (Artificial Neural Network) models considered explicitly a number of meteorological and turbulence parameters, as opposed to the Gaussian models that used a single fitting parameter, the dispersion coefficient. The developed ANN models were compared with predictions generated from COMBIC (Combined Obscuration Model for Battlefield Induced Contaminants), a sophisticated model based on Gaussian distributions, and a traditional Gaussian puff model using Slade’s dispersion coefficients. Neural network predictions have been found to have better agreement with concentration measurements than either of the other two Gaussian puff models. All models underestimate the maximum concentration, but ANN predictions are much closer to observations. Simulations of concentration distributions under different stability conditions were also checked using the developed ANN model, and it showed that, for a short time, Gaussian distributions are a good fit for puff dispersion in the downwind, crosswind and vertical directions. For Gaussian puff models, the key issue is to determine appropriate dispersion coefficients (standard deviations). ANN models for puff dispersion coefficients were trained and their average predictions were compared with the results of measurements. Very good agreement was observed, with a high correlation coefficient (>0.99). The ANN models for dispersion coefficients were used to analyze which input variables were more significant for puff expansions. Dispersion time, particle position relative to the centroid, turbulent kinetic energy and insolation showed the most significant influence on puff dispersion. The Gaussian puff model with dispersion coefficients from the ANN models was compared with COMBIC and a Gaussian puff model using Slade’s dispersion coefficients. Generally speaking, predictions generated by the Gaussian puff model with dispersion coefficients generated by ANN models showed better agreement with concentration measurements than the other two Gaussian puff models, by giving a much higher fraction within a factor of two, and lower normalised mean square errors. / Thesis (Ph.D, Chemical Engineering) -- Queen's University, 2007-10-17 12:13:42.923 / NSERC, DGNS

Call number: LD2668 .T4 1965 W246 / Master of Science

The focus of this research is to gain insight into the role of dispersion for synchronized development and yield of mature embryos on solid and in liquid culture medium. It is hypothesized that dispersion and synchronous development of embryos will result in high yield of plants. The increase in yield would be helpful in practical implementation of somatic embryogenesis for large-scale clonal propagation of plants and agricultural goods. This doctoral research investigates the yield and the synchronized development of mature embryos, if immature embryos in aggregates of proembryogenic masses (PEMs) have access to same nutritional environment. In order to explore this, a dispersion mechanism was automated and used to provide the same nutritional environment to all PEMs. The effectiveness of the dispersion system was investigated by culturing the PEMs of Norway spruce (Picea abies) on solid medium and in a well-functioning liquid culture system, i.e., bioreactor that could be used for large-scale clonal propagation of plants. Distribution of nutrient concentrations at different locations in an aggregate of PEMs during the culture period was studied using a mathematical model. Results have indicated that dispersion of aggregates of PEMs of Norway spruce has a favorable effect on the rate of proliferation of PEMs and subsequent development of mature somatic embryos. Compared to non-dispersed aggregates of PEMs with dispersed aggregates of PEMs, embryo development increased two folds on solid medium and three to five folds in liquid medium in bioreactors in this study. Bioreactor culture has shown significantly higher yield of mature embryos compared to that on solid culture. The effect of dispersion on synchronized development of mature embryos appears to be cell line dependent. Dispersion has improved synchronization of embryo development in one of two cell lines used in liquid medium experiments and two of four cell lines examined on solid medium. Cell line 11:12:02 has shown more synchronized development of embryos in dispersed PEMs in both medium. To further investigate the details to understand the association between development of mature embryos and nutrient uptake by cells and tissues, a nutrient diffusion model was developed using the volume averaging technique. It estimated the concentration distributions of nutrients, e.g. sugars, in a PEMs cluster on solid medium over a period of culture. The Michaelis-Menten enzyme kinetics was used in the model for uptake of nutrients by cells and tissues. Enzymatic assaying of soluble sugars was performed to determine concentrations of sugars (glucose, fructose, and sucrose) at different locations in tissue clusters. In both experiments and model simulation, sharp decline in concentrations of sucrose and fructose was observed in the first 12 hours after inoculation in glucose-containing ½ LP gel medium. A significant match between predicted and experimental outputs was observed over the culture period. Both experiments and simulation of the model showed a rapid uptake of glucose from the medium and saturation of PEMs cluster within 12 hours. Hence in a PEMs cluster there was no scarcity of nutrients that would inhibit growth and development of somatic embryos. Though dispersion resulted in a significant increase in development of mature somatic embryos, it might not play the decisive role in synchronized development of embryos. It seems that the major factor in synchronization is the initial developmental stage of immature embryos in a culture and genetic characteristics.

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