Auto-regressive model based polarimetric adaptive detection scheme part I: Theoretical derivation and performance analysis

This paper deals with the problem of target detection in coherent radar systems exploiting polarimetric diversity. We resort to a parametric approach and we model the disturbance affecting the data as a multi-channel autoregressive (AR) process. Following this model, a new polarimetric adaptive detector is derived, which aims at improving the target detection capability while relaxing the requirements on the training data size and the computational burden with respect to existing solutions. A complete theoretical characterization of the asymptotic performance of the derived detector is provided, using two different target fluctuation models. The effectiveness of the proposed approach is shown against simulated data, in comparison with alternative existing solutions

Pricing financial and insurance products in the multivariate setting

In finance and insurance there is often the need to construct multivariate distributions to take into account more than one source of risk, where such risks cannot be assumed to be independent. In the course of this thesis we are going to explore three models, namely the copula models, the trivariate reduction scheme and mixtures as candidate models for capturing the dependence between multiple sources of risk. This thesis contains results of three different projects. The first one is in financial mathematics, more precisely on the pricing of financial derivatives (multi-asset options) which depend on multiple underlying assets, where we construct the dependence between such assets using copula models and the trivariate reduction scheme. The second and the third projects are in actuarial mathematics, more specifically on the pricing of the premia that need to be paid by policyholders in the automobile insurance when more than one type of claim is considered. We do the pricing including all the information available about the characteristics of the policyholders and their cars (i.e. a priori ratemaking) and about the numbers of claims per type in which the policyholders have been involved (i.e. a posteriori ratemaking). In both projects we model the dependence between the multiple types of claims using mixture distributions/regression models: we consider the different types of claims to be modelled in terms of their own distribution/regression model but with a common heterogeneity factor which follows a mixing distribution/regression model that is responsible for the dependence between the multiple types of claims. In the second project we present a new model (i.e. the bivariate Negative Binomial-Inverse Gaussian regression model) and in the third one we present a new family of models (i.e. the bivariate mixed Poisson regression models with varying dispersion), both as suitable alternatives to the classically used bivariate mixed Poisson regression models

On the properties of point defects in silicon nanostructures from ab initio calculations

In this thesis we apply a variety of computational methods based on density-functional theory (DFT) to the study of defect centres in bulk silicon and silicon nanostructures. Firstly, we discuss the system-size convergence of point defect properties in the supercell method for deep-level defects in bulk silicon; we consider both the vacancy and gold impurity. For the vacancy, we investigate systematically the main contributions to the finite size error that lead to the well-known slow convergence with respect to system size of defect properties, and demonstrate that different properties of interest can benefit from the use of different k-point sampling schemes. We also present a simple and accurate method for calculating the potential alignment correction to the valence band maximum of charged defect supercells by using maximally-localised Wannier functions, and show that the localised view of the electronic structure provided by them gives a clear description of the nature of the electronic bonding at the defect centre. For the gold impurity, we show that the system becomes a non-spin-polarised negative-U centre due to the effect of Jahn-Teller distortion, thus providing a simple explanation for the absent electron paramagnetic resonance signal for gold in silicon. The calculated transition levels are found to be in excellent agreement with experimental measurements. We then investigate the segregation of arsenic impurities in silicon close to an interface with amorphous silica. We employ a multiscale approach, generating a realistic disordered interface structure from Monte Carlo simulation, with a continuous random network model of the system parametrised from DFT. We calculate the segregation energy using DFT for a large number of substitutional sites encompassing all the oxidation states of silicon, and show that the results can be understood with a minimal model based only on the local strain and volume of the defect site.Open Acces

On the properties of point defects in silicon nanostructures from ab initio calculations

In this thesis we apply a variety of computational methods based on density-functional theory (DFT) to the study of defect centres in bulk silicon and silicon nanostructures. Firstly, we discuss the system-size convergence of point defect properties in the supercell method for deep-level defects in bulk silicon; we consider both the vacancy and gold impurity. For the vacancy, we investigate systematically the main contributions to the finite size error that lead to the well-known slow convergence with respect to system size of defect properties, and demonstrate that different properties of interest can benefit from the use of different k-point sampling schemes. We also present a simple and accurate method for calculating the potential alignment correction to the valence band maximum of charged defect supercells by using maximally-localised Wannier functions, and show that the localised view of the electronic structure provided by them gives a clear description of the nature of the electronic bonding at the defect centre. For the gold impurity, we show that the system becomes a non-spin-polarised negative-U centre due to the effect of Jahn-Teller distortion, thus providing a simple explanation for the absent electron paramagnetic resonance signal for gold in silicon. The calculated transition levels are found to be in excellent agreement with experimental measurements. We then investigate the segregation of arsenic impurities in silicon close to an interface with amorphous silica. We employ a multiscale approach, generating a realistic disordered interface structure from Monte Carlo simulation, with a continuous random network model of the system parametrised from DFT. We calculate the segregation energy using DFT for a large number of substitutional sites encompassing all the oxidation states of silicon, and show that the results can be understood with a minimal model based only on the local strain and volume of the defect site.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Nineteenth International Cosmic Ray Conference. Conference Papers: Invited Rapporteur, Highlight, Miscellaneous, Volume 9

Invited talks, rapporteur talks, and highlight talks are included. Topics of the invited and highlight talks include astrophysical jets, gamma-ray line astronomy, cosmic rays and gamma rays in astrophysics, the early universe, elementary particle physics, solar flares and acceleration of energetic particles, cosmogenic nuclei, extragalactic astronomy, composition of solar flare particles, very high energy gamma ray sources, gamma-ray bursts, shock acceleration in the solar wind, cosmic rays in deep underground detectors, spectrum of cosmic rays at 10 to the 19th power eV, and nucleus-nucleus interactions