No description available.

No description available.

No description available.

In this thesis a detailed examination is carried out into the role and impact of the volume of trade in UK futures markets. While the success of a market may be judged by the number of investors that it attracts, how does the behaviour of individuals influence such key variables as price volatility and the cost of trading? The empirical work carried out here allows a unique appreciation of issues that have important implications for policy makers, investors and the practitioner. Motivated by a desire to understand whether volatility is destabilising or a reflection of fundamental factors, as well as the nature of the distribution of price returns, the relationship between volume and price movements is investigated in detail. The preliminary analysis suggests an important role for the flow of information which is confirmed by the rigorous testing of Anderson's (1996) specification of the Mixture of Distributions Hypothesis. The exploitation of this model allows an in-depth analysis of the information process including the identification of the informed and uninformed components of volume. There is also an investigation into the possibility that the volume statistic itself has an informative value. Using the Blume et al. (1994) approach the results suggest that, for a variety of futures contracts, the markets show a high degree of information dispersion. The need to attract investors has never been more acute than in today's competitive financial environment. It is therefore important to obtain a good appreciation of the relationship between volume and the cost of trading. This thesis includes a comprehensive intra-day study of the relation within a simultaneous econometric framework that exploits state-space models to investigate how markets react to unexpected levels of trading. The results question the dominance of inventory cost models and suggest that patterns of trade have become more predictable since contract inception, despite increases in volume.

L'optique adaptative (OA), qui permet de corriger en temps-réel les déformations du front d'onde induites par la turbulence atmosphérique, connaît une limitation fondamentale : l'anisoplanétisme. Pour y remédier, le concept d'OA grand champ (OAGC) a été proposé. La turbulence est mesurée dans plusieurs directions du champ de vue à l'aide d'étoiles guide naturelles et laser, et son impact corrigé sur les images par une commande basée sur une reconstruction tomographique. L'approche linéaire quadratique gaussienne (LQG) est bien adaptée à la conception de lois de commande en OAGC comme en OA classique. Elle permet d'estimer et de prédire la phase à l'aide d'un filtre de Kalman basé sur des a priori spatiaux et temporels. Les modèles d'état et commandes associées sont détaillés. On présente la première mise en oeuvre sur le ciel d'une commande LQG sur tous les modes, en OA classique et multi-objet, à l'aide du démonstrateur CANARY. Ces résultats sont obtenus avec identification du modèle de tip-tilt et filtrage des vibrations, ce qui constitue la première mise en oeuvre ciel de cette stratégie. Les a priori spatiaux de la phase en volume sont identifiés par la méthode LEARN. Des données issues du profilomètre stereoSCIDAR ont aussi été utilisées. Des comparaisons sont proposées avec une commande intégrateur en OA classique, avec un gain significatif en performances pour le LQG. Les comparaisons avec le reconstructeur statique APPLY (moindres carrés régularisés) en OA multi-objet mettent en évidence un gain du LQG dans certains cas (fort bruit en particulier). L'ensemble des résultats confirme la faisabilité et l'intérêt d'une commande LQG pour un instrument d'OA ou d'OAGC. / Adaptive Optics (AO), which enables to correct in real time wavefront deformation induced by atmospheric turbulence, faces a fundamental limitation: anisoplanatism. To counter it, the concept of Wide-Field AO (WFAO) has been proposed. Turbulence is measured in several directions of the field of view, using natural and laser guide stars, and its impact on images is mitigated by a control based on tomographic reconstruction. The Linear Quadratic Gaussian (LQG) approach is well-suited to AO control design in both WFAO and classical AO. LQG enables to estimate and predict the phase with a Kalman filter based on spatial and temporal priors. State-space models and associated controls are laid out. The first on-sky implementation of LQG control on all modes, in classical and multi-object AO, is presented on the CANARY pathfinder. These results have been obtained with identification of tip-tilt models and vibration filtering, which constitutes the first on-sky implementation of this strategy. Spatial priors on the phase in the volume are identified using the LEARN algorithm. Data from the stereoSCIDAR profilometer were also used. Comparisons are provided with integral AO control in standard AO, showing significant gain in performance with LQG. Comparisons with the static reconstructor APPLY (regularized least-squares) in multi-object AO show a gain in performance with LQG in some cases (especially in high-noise conditions). Results confirm feasibility and relevance of LQG control for AO or WFAO instruments.

The multi-scale mixed finite element method (MsMFEM) discussed in this work uses a two-scale approach, where the solutions to independent local flow problems on the fine grid capture the fine-scale variations of the reservoir model, while the coarse grid equations appropriately assimilate this information in the global solution. Temporal changes in porous media flow are relatively moderate when compared to the spatial variations in the reservoir. Hence, approximate global solutions by adaptively solving these local flow problems can be obtained with significant savings in computational time. The ensemble Kalman filter, used for real-time updating of reservoir models, can thus be coupled with the MsMFEM-streamline simulator to speed up the historymatching process considerably.

Abstract A feasible technique, using carrier-phase data from GPS and CCD camera, is presented to identify ground target location as well as azimuth angle of a low altitude aircraft/helicopter without using any gyroscope measurements; the baseline vector can also be identified using GPS compass. The ground target¡¦s image is extracted from background and recorded by image processing technique. By integrating ground target¡¦s location and the recorded GPS data, the designated states can be estimated by using extended Kalman filter technique. Basically, the extended Kalman filter does the state estimation job, and it¡¦s a nonlinear measurement process. By processing these time update and measurement update, the integer ambiguity as well as azimuth angle can be determined. The proposed GPS compass system consists of three componets : pointer, sensor, and controller. By using carrier-phase data from two GPS receivers, we can compute the baseline vector, whose length is equal to one meter, and achieve the direction accuracy within one degree. The integer ambiguity number is resolved by rotating the baseline vector; the conventional antenna swapping technique is a special case of the proposed method. Therefore, the GPS compass may replace these magnetic compass or gyroscope used in navigation system. By continuously snapping ground target image using CCD camera and utilizing the GPS receivers, the coordinate of the ground target can be identified. Simulation justifies the feasibility of the proposed scenario. Simulation has shown that the estimation errors for stationary and traveling with constant velocity ground targets are within 1.8 m and 6 m, respectively.

The Kalman filter algorithm can be applied as a recursive estimator of the state of a dynamic system described by a linear difference equation. Given discrete measurements linearly related to the state of the system, but corrupted by white Gaussian noise, the Kalman filter estimate of the system is statistically optimal with respect to a quadratic function of the estimate error. The first objective of this paper is to give deep enough insight into the mathematics of the Kalman filter algorithm to be able to choose the correct type of algorithm and to set all the parameters correctly in a basic application. This description also includes several examples of different approaches to derive and to explain the Kalman filter algorithm. In addition to the mathematical description of the Kalman filter algorithm this paper also provides an implementation written in MATLAB. The objective of this part is to correctly replicate the target tracker used in the surveillance radar PS-90. The result of the implementation is evaluated using a simulated target programmed to have an aircraft-like behaviour and done without access to the actual source code of the tracker in the PS-90 radar

Stuttgart, Univ., Diplomarb., 2004.

Stuttgart, Univ., Diplomarb., 2004.