3 resultados para wavefront height

em AMS Tesi di Dottorato - Alm@DL - Università di Bologna


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The Adaptive Optics is the measurement and correction in real time of the wavefront aberration of the star light caused by the atmospheric turbulence, that limits the angular resolution of ground based telescopes and thus their capabilities to deep explore faint and crowded astronomical objects. The lack of natural stars enough bright to be used as reference sources for the Adaptive Optics, over a relevant fraction of the sky, led to the introduction of artificial reference stars. The so-called Laser Guide Stars are produced by exciting the Sodium atoms in a layer laying at 90km of altitude, by a powerful laser beam projected toward the sky. The possibility to turn on a reference star close to the scientific targets of interest has the drawback in an increased difficulty in the wavefront measuring, mainly due to the time instability of the Sodium layer density. These issues are increased with the telescope diameter. In view of the construction of the 42m diameter European Extremely Large Telescope a detailed investigation of the achievable performances of Adaptive Optics becomes mandatory to exploit its unique angular resolution . The goal of this Thesis was to present a complete description of a laboratory Prototype development simulating a Shack-Hartmann wavefront sensor using Laser Guide Stars as references, in the expected conditions for a 42m telescope. From the conceptual design, through the opto-mechanical design, to the Assembly, Integration and Test, all the phases of the Prototype construction are explained. The tests carried out shown the reliability of the images produced by the Prototype that agreed with the numerical simulations. For this reason some possible upgrades regarding the opto-mechanical design are presented, to extend the system functionalities and let the Prototype become a more complete test bench to simulate the performances and drive the future Adaptive Optics modules design.

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The surface electrocardiogram (ECG) is an established diagnostic tool for the detection of abnormalities in the electrical activity of the heart. The interest of the ECG, however, extends beyond the diagnostic purpose. In recent years, studies in cognitive psychophysiology have related heart rate variability (HRV) to memory performance and mental workload. The aim of this thesis was to analyze the variability of surface ECG derived rhythms, at two different time scales: the discrete-event time scale, typical of beat-related features (Objective I), and the “continuous” time scale of separated sources in the ECG (Objective II), in selected scenarios relevant to psychophysiological and clinical research, respectively. Objective I) Joint time-frequency and non-linear analysis of HRV was carried out, with the goal of assessing psychophysiological workload (PPW) in response to working memory engaging tasks. Results from fourteen healthy young subjects suggest the potential use of the proposed indices in discriminating PPW levels in response to varying memory-search task difficulty. Objective II) A novel source-cancellation method based on morphology clustering was proposed for the estimation of the atrial wavefront in atrial fibrillation (AF) from body surface potential maps. Strong direct correlation between spectral concentration (SC) of atrial wavefront and temporal variability of the spectral distribution was shown in persistent AF patients, suggesting that with higher SC, shorter observation time is required to collect spectral distribution, from which the fibrillatory rate is estimated. This could be time and cost effective in clinical decision-making. The results held for reduced leads sets, suggesting that a simplified setup could also be considered, further reducing the costs. In designing the methods of this thesis, an online signal processing approach was kept, with the goal of contributing to real-world applicability. An algorithm for automatic assessment of ambulatory ECG quality, and an automatic ECG delineation algorithm were designed and validated.

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This thesis tackles the problem of the automated detection of the atmospheric boundary layer (BL) height, h, from aerosol lidar/ceilometer observations. A new method, the Bayesian Selective Method (BSM), is presented. It implements a Bayesian statistical inference procedure which combines in an statistically optimal way different sources of information. Firstly atmospheric stratification boundaries are located from discontinuities in the ceilometer back-scattered signal. The BSM then identifies the discontinuity edge that has the highest probability to effectively mark the BL height. Information from the contemporaneus physical boundary layer model simulations and a climatological dataset of BL height evolution are combined in the assimilation framework to assist this choice. The BSM algorithm has been tested for four months of continuous ceilometer measurements collected during the BASE:ALFA project and is shown to realistically diagnose the BL depth evolution in many different weather conditions. Then the BASE:ALFA dataset is used to investigate the boundary layer structure in stable conditions. Functions from the Obukhov similarity theory are used as regression curves to fit observed velocity and temperature profiles in the lower half of the stable boundary layer. Surface fluxes of heat and momentum are best-fitting parameters in this exercise and are compared with what measured by a sonic anemometer. The comparison shows remarkable discrepancies, more evident in cases for which the bulk Richardson number turns out to be quite large. This analysis supports earlier results, that surface turbulent fluxes are not the appropriate scaling parameters for profiles of mean quantities in very stable conditions. One of the practical consequences is that boundary layer height diagnostic formulations which mainly rely on surface fluxes are in disagreement to what obtained by inspecting co-located radiosounding profiles.