For the environment of target tracking with highly non-linear models and non-Gaussian noise, the tracking performance of the particle filter is better than extended Kalman filter; in addition, the design of particle filter is simpler, so it is quite suitable for the realistic environment. However, particle filter depends on the probability model of the noise. If the knowledge of the noise is incorrect, the tracking performance of the particle filter will degrade severely. To tackle the problem, cost function-based particle filters have been studied. Though suffering from minor degradation on the performance, the cost function-based particle filters do not need probability assumptions of the noises. The application of cost function-based particle filters will be more robust in any realistic environment. Cost function-based particle filters will enable maneuvering multiple target tracking to be suitable for any environment because it does not depend on the noise model. The difficulty lies in the link between the estimator and data association. The likelihood function are generally obtained from the algorithm of the data association; while cost functions are used in the cost function-based particle filter for moving the particles and update the corresponding weights without probability assumptions on the noises. The thesis is focused on the combination of data association and cost function-based particle filter, in order to make the algorithm of multiple target tracking more robust in noisy environments.

The accuracy of target location and the coverage range of sensor network are two factors that affect each other in target tracking. When the flocking sensor network has a larger coverage area, it can increase the range of detecting target and the scope of environmental information. The network can also pass the information to a query source or other sensors which do not belong to the flocking network. However, the accuracy of measurements at sensors may be affected by the distances between the target and the sensors. We use mobile sensors as agents in flocking algorithm for target tracking. Every mobile sensor exchanges information with its neighbors, and keeps an appropriate separation distance with neighbors to maintain flocking. Flocking algorithm is a distributed control method for mobile sensor which can catch up the target and maintain flocking formation. In the thesis, we derive the cost function based on the accuracy of target positioning and range coverage. The proposed adaptive flocking algorithm combines the amount of information and the distance changes between neighbors based on the cost function. Each mobile sensor adaptively adjusts distance separation with all its neighbors within communication range. Sensors closer to the target shortens the separation distance between neighbors, therefore they will move toward the target and obtain better measurement. Kalman-consensus information filter is used for target positioning. The accuracy of target position can therefore be improved in the overall network. On the other hand, the sensors located far from the target will widen the distance separation between neighbors to expand the overall network area. In the thesis, we use Kalman-consensus information filter to estimate the state of a target, and use adaptive flocking algorithm for maintaining the formation of mobile sensors. Simulations show that adaptive flocking algorithm effectively improves location accuracy while maintaining approximate generally same coverage area when compared with other methods.

In this thesis, we propose a cost-based CLEAN algorithm to accurately find dense multi-path parameters and improve the performance of selective RAKE receiver in indoor UWB systems. RAKE receiver can resolve the dense multi-path interference problems with the multi-path parameters. Because the weak paths are of lower valuable for system performance improvement, selective RAKE receiver combines only the strongest multi-path components and reduce the number of fingers to lower the complexity of RAKE receiver. However, selective RAKE receiver needs accurate multi-path detection to decide the suitable number and parameters of fingers. In order to improve the performance of selective RAKE receiver, the main issue in this thesis is to detect the best paths of channel with the CLEAN algorithm. CLEAN algorithm uses the correlation of the received signal and the template signal as the basis for searching paths. If there are closely adjacent paths, or if one of signal paths is relatively stronger, the detection error of paths may occur and thus affects the performance of the receiver. EP-based CLEAN algorithm uses the cost function and the evolutionary programming (EP) to search the multi-path delay times and gain coefficients for minimizing the cost function. Accurate multi-path detection and high resolution of adjacent paths can be obtained. However, EP-based CLEAN algorithm makes a time-consuming blind search. In the thesis, a CLEAN algorithm based on the cost function is proposed. The proposed cost-based CLEAN algorithm searches the delay times near the peaks of the cross-correlation for local minimum of the cost function, and then uses CLEAN algorithm to extract autocorrelation components and obtain the accurate multi-path detection. By testing the IEEE802.15.3a UWB channel models, and comparing with CLEAN algorithm, the cost-based CLEAN algorithm in the thesis can achieve better detection accuracy in multi-path searching, and improve the performance of selective RAKE receiver.

The EU has been promoting unbundling of the transmission grid from other stages of the electricity supply chain with the aim of fostering competition in the upstream stage of electricity generation. At presence, ownership unbundling is the predominant form of unbundling in Europe. However, the benefits of increased competition from ownership unbundling of the transmission grid may come at the cost of lost vertical synergies between the formerly integrated stages of electricity supply. The policy debate generally neglects such potential costs of unbundling, yet concentrates on its benefits. Therefore European crosscountry evidence may shed some light on this issue. This study helps fill this void by empirically estimating the magnitude of economies of vertical integration (EVI) between electricity generation and transmission based on a quadratic cost function. For this purpose we employ novel firm-level panel data of major European electricity utilities. Our results confirm the presence of substantial EVI, which put the policy measure of transmission ownership unbundling into question. (authors' abstract) / Series: Working Papers / Research Institute for Regulatory Economics

大陸自七零年代以來經濟快速發展，各界對大陸的市場前景和投資環境看好，紛紛登陸設廠。為因應經濟快速發展，中國政府積極發展國內之基礎建設，從八零年代過後加強重點建設，將能源、交通、教育及科學做為經濟發展的重點策略。而值得我們注意的是，公共資本的貢獻不僅僅是帶來經濟成長，也將會為產業帶來正向的外部效果，因此探討公共資本對產業帶來的效應，是否有助於提升產業生產力，進而降低產業生產成本，是個值得我們關注的議題。 　　本研究以中國製造業廠商做為分析對象，將分別區分成不同產業及不同區域之廠商進行實證研究，針對同性質的廠商進行成本函數之建構，用以估計廠商之公共資本成本彈性，進而闡釋究竟哪些公共資本對製造業廠商有正向的外部效應，並更進一步探討公部門及私部門投入要素間的關係。 　　本研究利用1998年至2006年工業企業數據調查資料庫之跨時橫斷面資料進行迴歸檢定分析，其結果發現基礎建設及教育資本會帶來正向的外部效益，可以有效降低製造業之成本，且教育資本比基礎建設對廠商的邊際效益大；而研發資本則無法提升廠商的生產效率。本文推論，中國研發資本投入遠遠低於基礎建設及教育資本，故研發資本並無法顯著提升製造業之生產力。因此中國於公共事業發展上，應以基礎建設及教育為發展要點，且教育方面的公共投資是最具效率的政策工具，將更有益於製造業廠商的生產。

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For accurate prediction of tsunami wave propagation, information on the system of PDEs modelling its evolution and full initial and/or boundary data is required. However the latter is not generally fully available, and so the primary objective becomes to find an optimal estimate of these conditions, using available information. Data Assimilation is a methodology used to optimally integrate observed measurements into a mathematical model, to generate a better estimate of some control parameter, such as the initial condition of the wave, or the sea floor bathymetry. In this study, we considered the shallow water equations in both linear and non-linear form as an approximation for ocean wave propagation, and derived a data assimilation scheme based on the calculus of variations, the purpose of which is to optimise some distorted form of the initial condition to give a prediction closer to the exact initial data. We considered two possible forms of distortion, by adding noise to our initial wave, and by rescaling the wave amplitude. Multiple cases were analysed, with observations measured at different points in our spatial domain, as well as variations in the number of observation points. We found that the error between measurements and observation data was sufficiently minimised across all cases. A relationship was found between the number of measurement points and the error, dependent on the choice of where measurements were taken. In the linear case, since the wave form simply translates a fixed form, multiple measurement points did not necessarily provide more information. In the nonlinear case, because the waveform changes shape as it translates, adding more measurement points provides more information about the dynamics and the wave shape. This is reflected in the fact that in the nonlinear case adding more points gave a bigger decrease in error, and much closer convergence of the optimised guess for our initial condition to the exact initial wave profile. / Thesis / Master of Science (MSc) / In ocean wave modelling, information on the system dynamics and full initial and/or boundary data is required. When the latter is not fully available the primary objective is to find an optimal estimate of these conditions, using available information. Data Assimilation is a methodology used to optimally integrate observed measurements into a mathematical model, to generate a better estimate of some control parameter, such as the initial condition of the wave, or the sea floor bathymetry. In this study, we considered the shallow water equations in both linear and non-linear form as an approximation for ocean wave propagation, and derived a data assimilation scheme to optimise some distorted form of the initial condition to generate predictions converging to the exact initial data. The error between measurements and observation data was sufficiently minimised across all cases. A relationship was found between the number of measurement points and the error, dependent on the choice of where measurements were taken.

The main focus of this present work is to study how mechanical steady state traveling waves can be generated and propagated through one dimensional media by applying forces. By steady state traveling waves we refer to propagating mechanical waves in a finite medium that never exhibit reflections at the boundaries and continuously move from one end of the structure to the other. Mechanical waves can be classified as traveling, standing and hybrid waves that are the results of the interplay of excitation forces, applied force locations, and the boundary conditions of the structure. Traveling waves carry energy through a defined medium while standing waves keep energy at certain areas that are associated with the modes of excitation. To understand the interaction of systems that exhibit traveling waves with their surrounding media (i.e., swimming flagella, manta ray locomotion), it is crucial to first understand the wave propagation and what is desired in these structural systems. The parameters that affect the generation and propagation of waves should be welldefined to control and manipulate the desired system’s response. One-dimensional string and rod equations are studied with various boundary conditions to generate steady-state traveling waves in a string and longitudinal traveling waves in a rod. Two excitation forces are applied to a string and a rod near the boundaries to understand the generation and propagation of traveling and standing waves at various frequencies. The work examines the quality of the wave propagation in a string, and in a rod. A cost function approach is applied to identify the quality of such waves. Furthermore, steady-state square traveling waves are generated in a string and in-plane in a rod, both theoretically and experimentally. To the authors’ knowledge this is the first time this has been attempted in the literature. Determining the quality of traveling waves and understanding the parameters on the wave propagation of a string and rod can lead to further understand and leverage various engineering disciplines such as mechanical actuation mechanisms, propulsion of flagella, and the basilar membrane in the ear’s cochlea. / Master of Science

Master of Science / Department of Agricultural Economics / Timothy Dalton / Previous research in low-income countries reveals that genetically modified (GM) maize has the potential to increase yield and reduce labor use; however, other issues, especially regarding Roundup Ready (RR) maize, remain mostly unexplored. This research examines the impact of GM maize on yield, cost, and risk among 184 smallholders during the 2009-10 maize production season in two regions in KwaZulu-Natal, South Africa; Hlabisa and Simdlangetsha. Two hybrid maize varieties; Pannar and Carnia, and three GM varieties; Bt, RR, and BR (stacked with Bt and RR) are produced. In both regions, producers of RR and BR maize pay 47% more per kilogram of seed and use 44% less labor per hectare compared to other varieties. Due to low labor costs, net returns from RR and BR varieties are 25% and 40% higher than other varieties in Hlabisa and Simdlangetsha, respectively. Stochastic dominance analysis is used to compare net returns of all five varieties in both regions. RR maize is second-degree stochastic dominant to all other varieties in Simdlangetsha, while no variety is stochastically dominant in Hlabisa. Stochastic efficiency with respect to a function (SERF) analysis indicates that RR maize is the preferred variety for producers over the entire range of risk preferences in both regions. While average maize gross returns are $713 per hectare, risk premiums between $18 and $221 must be paid to RR maize producers, depending on region and farmer risk preference, to persuade them to switch to the second-most preferred variety. Econometric analysis indicates significant yield gains of at least 8% from RR maize, although the yield gain varies greatly when input endogeneity is taken into account. Elasticities of output with respect to labor are 0.41 and 0.82 for RR and non-RR maize respectively, and 0.61 and 0.33 with respect to land. A cost function analysis indicates that RR maize has 19% lower costs per maize plot, which increases to at least a 35% advantage when controlling for selectivity bias. Nonparametric kernel density estimation also reveals consistently lower total and average costs of RR maize at most levels of output, suggesting technological benefits to smallholder farmers from RR maize not available through conventionally-bred hybrids.