Geometric versus Model Predictive Control based guidance algorithms for fixed-wing UAVs in the presence of very strong wind fields.

The recent years have witnessed increased development of small, autonomous fixed-wing Unmanned Aerial Vehicles (UAVs). In order to unlock widespread applicability of these platforms, they need to be capable of operating under a variety of environmental conditions. Due to their small size, low weight, and low speeds, they require the capability of coping with wind speeds that are approaching or even faster than the nominal airspeed. In this thesis, a nonlinear-geometric guidance strategy is presented, addressing this problem. More broadly, a methodology is proposed for the high-level control of non-holonomic unicycle-like vehicles in the presence of strong flowfields (e.g. winds, underwater currents) which may outreach the maximum vehicle speed. The proposed strategy guarantees convergence to a safe and stable vehicle configuration with respect to the flowfield, while preserving some tracking performance with respect to the target path. As an alternative approach, an algorithm based on Model Predictive Control (MPC) is developed, and a comparison between advantages and disadvantages of both approaches is drawn. Evaluations in simulations and a challenging real-world flight experiment in very windy conditions confirm the feasibility of the proposed guidance approach.

Climatological analysis of temperature and salinity fields in the Mediterranean sea

A climatological field is a mean gridded field that represents the monthly or seasonal trend of an ocean parameter. This instrument allows to understand the physical conditions and physical processes of the ocean water and their impact on the world climate. To construct a climatological field, it is necessary to perform a climatological analysis on an historical dataset. In this dissertation, we have constructed the temperature and salinity fields on the Mediterranean Sea using the SeaDataNet 2 dataset. The dataset contains about 140000 CTD, bottles, XBT and MBT profiles, covering the period from 1900 to 2013. The temperature and salinity climatological fields are produced by the DIVA software using a Variational Inverse Method and a Finite Element numerical technique to interpolate data on a regular grid. Our results are also compared with a previous version of climatological fields and the goodness of our climatologies is assessed, according to the goodness criteria suggested by Murphy (1993). Finally the temperature and salinity seasonal cycle for the Mediterranean Sea is described.

Regularization methods for the solution of a nonlinear least-squares problem in tomography

In this work we study a polyenergetic and multimaterial model for the breast image reconstruction in Digital Tomosynthesis, taking into consideration the variety of the materials forming the object and the polyenergetic nature of the X-rays beam. The modelling of the problem leads to the resolution of a high-dimensional nonlinear least-squares problem that, due to its nature of inverse ill-posed problem, needs some kind of regularization. We test two main classes of methods: the Levenberg-Marquardt method (together with the Conjugate Gradient method for the computation of the descent direction) and two limited-memory BFGS-like methods (L-BFGS). We perform some experiments for different values of the regularization parameter (constant or varying at each iteration), tolerances and stop conditions. Finally, we analyse the performance of the several methods comparing relative errors, iterations number, times and the qualities of the reconstructed images.

The molecular gas content of star-forming galaxies in the ALPINE fields.

In this thesis, I aim to study the evolution with redshift of the gas mass fraction of a sample of 53 sources (from z ∼ 0.5 to z > 5) serendipitously detected in ALMA band 7 as part of the ALMA Large Program to INvestigate C II at Early Times (ALPINE). First, I used SED-fitting software CIGALE, which is able to implement energy balancing between the optical and the far infrared part, to produce a best-fit template of my sources and to have an estimate of some physical properties, such as the star formation rate (SFR), the total infrared luminosity and the total stellar mass. Then, using the tight correlation found by Scoville et al. (2014) between the ISM molecular gas mass and the rest-frame 850 μm luminosity, I used the latter, extrapolating it from the best-fit template using a code that I wrote in Python, as a tracer for the molecular gas. For my sample, I then derived the most important physical properties, such as molecular gas mass, gas mass fractions, specific star formation rate and depletion timescales, which allowed me to better categorize them and find them a place within the evolutionary history of the Universe. I also fitted our sources, via another code I wrote again in Python, with a general modified blackbody (MBB) model taken from the literature (Gilli et al. (2014), D’Amato et al. (2020)) to have a direct method of comparison with similar galaxies. What is evident at the end of the paper is that the methods used to derive the physical quantities of the sources are consistent with each other, and these in turn are in good agreement with what is found in the literature.