Laboratory verification of predicted permanent deformation in asphalt materials following climate change.

In the past a change in temperature of 5°C most often occurred over intervals of thousands of years. According to estimates by the IPCC, in the XXI century is expected an increase in average temperatures in Europe between 1.8 and 4.0°C in the best case caused by emissions of carbon dioxide and other GHG from human activities. As well as on the environment and economic context, global warming will have effects even on road safety. Several studies have already shown how increasing temperature may cause a worsening of some types of road surface damages, especially rutting, a permanent deformation of the road structures consisting in the formation of a longitudinal depression in the wheelpath, mostly due to the rheological behavior of bitumen. This deformation evolves during the hot season because of the heating capacity of the asphalt layers, in fact, the road surface temperature is up to 24°C higher than air. In this thesis, through the use of Wheeltrack test, it was studied the behavior of some types of asphalt concrete mixtures subjected to fatigue testing at different temperatures. The objectives of this study are: to determine the strain variation of different bituminous mixture subjected to fatigue testing at different temperature conditions; to investigate the effect of aggregates, bitumen and mixtures’ characteristics on rutting. Samples were made in the laboratory mostly using an already prepared mixtures, the others preparing the asphalt concrete from the grading curve and bitumen content. The same procedure was performed for each specimen: preparation, compaction using the roller compactor, cooling and heating before the test. The tests were carried out at 40 - 50 - 60°C in order to obtain the evolution of deformation with temperature variation, except some mixtures for which the tests were carried out only at 50°C. In the elaboration of the results were considered testing parameters, component properties and the characteristics of the mixture. Among the testing parameters, temperature was varied for each sample. The mixtures responded to this variation with a different behavior (linear logarithmic and exponential) not directly correlated with the asphalt characteristics; the others parameters as load, passage frequency and test condition were kept constant. According to the results obtained, the main contribution to deformation is due to the type of binder used, it was found that the modified bitumen have a better response than the same mixtures containing traditional bitumen; to the porosity which affects negatively the behavior of the samples and to the homogeneity ceteris paribus. The granulometric composition did not seem to have interfered with the results. Overall has emerged at working temperature, a decisive importance of bitumen composition, than the other characteristics of the mixture, that tends to disappear with heating in favor of increased dependence of rutting resistance from the granulometric composition of the sample considered. In particular it is essential, rather than the mechanical characteristics of the binder, its chemical properties given by the polymeric modification. To confirm some considered results, the maximum bulk density and the air voids content were determined. Tests have been conducted in the laboratories of the Civil Engineering Department at NTNU in Trondheim according to European Standards.

Gravitational waves as dark sirens: an astrophysical and cosmological analysis

Despite the success of the ΛCDM model in describing the Universe, a possible tension between early- and late-Universe cosmological measurements is calling for new independent cosmological probes. Amongst the most promising ones, gravitational waves (GWs) can provide a self-calibrated measurement of the luminosity distance. However, to obtain cosmological constraints, additional information is needed to break the degeneracy between parameters in the gravitational waveform. In this thesis, we exploit the latest LIGO-Virgo-KAGRA Gravitational Wave Transient Catalog (GWTC-3) of GW sources to constrain the background cosmological parameters together with the astrophysical properties of Binary Black Holes (BBHs), using information from their mass distribution. We expand the public code MGCosmoPop, previously used for the application of this technique, by implementing a state-of-the-art model for the mass distribution, needed to account for the presence of non-trivial features, i.e. a truncated power law with two additional Gaussian peaks, referred to as Multipeak. We then analyse GWTC-3 comparing this model with simpler and more commonly adopted ones, both in the case of fixed and varying cosmology, and assess their goodness-of-fit with different model selection criteria, and their constraining power on the cosmological and population parameters. We also start to explore different sampling methods, namely Markov Chain Monte Carlo and Nested Sampling, comparing their performances and evaluating the advantages of both. We find concurring evidence that the Multipeak model is favoured by the data, in line with previous results, and show that this conclusion is robust to the variation of the cosmological parameters. We find a constraint on the Hubble constant of H0 = 61.10+38.65−22.43 km/s/Mpc (68% C.L.), which shows the potential of this method in providing independent constraints on cosmological parameters. The results obtained in this work have been included in [1].

Study on the practical realization of a device able to generate an in-space 3D luminous image

Gli ologrammi sono parte integrante della cultura pop a partire dagli anni 50, tanto che ad oggi sentirne parlare non desta più scalpore. Dal lato pratico, invece, solo negli ultimi anni sono state fatte ricerche approfondite con lo scopo di realizzarli. Fra i dispositivi attualmente in commercio, in pochi sono degni di nota e presentano numerose limitazioni, questo perché è molto difficile riuscire a progettare un sistema che permetta di illuminare dei punti specifici in uno spazio tridimensionale per lunghi periodi. In questa tesi si illustrano i principi di funzionamento ed il progetto per un nuovo dispositivo, diverso da quelli fino ad ora realizzati, che sfrutti il decadimento spontaneo di atomi di rubidio eccitati tramite due fasci laser opportunamente incrociati. Nel punto di incrocio si produce luce visibile a 420 nm. Con un opportuno sistema di specchi che muovono velocemente il punto di intersezione tra i due fasci è possibile realizzare un vero ologramma tridimensionale visibile da quasi ogni angolazione.

End-to-end Deep Metric Learning con Vision-Language Model per il Fashion Image Captioning

L'image captioning è un task di machine learning che consiste nella generazione di una didascalia, o caption, che descriva le caratteristiche di un'immagine data in input. Questo può essere applicato, ad esempio, per descrivere in dettaglio i prodotti in vendita su un sito di e-commerce, migliorando l'accessibilità del sito web e permettendo un acquisto più consapevole ai clienti con difficoltà visive. La generazione di descrizioni accurate per gli articoli di moda online è importante non solo per migliorare le esperienze di acquisto dei clienti, ma anche per aumentare le vendite online. Oltre alla necessità di presentare correttamente gli attributi degli articoli, infatti, descrivere i propri prodotti con il giusto linguaggio può contribuire a catturare l'attenzione dei clienti. In questa tesi, ci poniamo l'obiettivo di sviluppare un sistema in grado di generare una caption che descriva in modo dettagliato l'immagine di un prodotto dell'industria della moda dato in input, sia esso un capo di vestiario o un qualche tipo di accessorio. A questo proposito, negli ultimi anni molti studi hanno proposto soluzioni basate su reti convoluzionali e LSTM. In questo progetto proponiamo invece un'architettura encoder-decoder, che utilizza il modello Vision Transformer per la codifica delle immagini e GPT-2 per la generazione dei testi. Studiamo inoltre come tecniche di deep metric learning applicate in end-to-end durante l'addestramento influenzino le metriche e la qualità delle caption generate dal nostro modello.

Image Processing Algorithms applied to Radio SLAM using MIMO Radars

Radio Simultaneous Location and Mapping (SLAM) consists of the simultaneous tracking of the target and estimation of the surrounding environment, to build a map and estimate the target movements within it. It is an increasingly exploited technique for automotive applications, in order to improve the localization of obstacles and the target relative movement with respect to them, for emergency situations, for example when it is necessary to explore (with a drone or a robot) environments with a limited visibility, or for personal radar applications, thanks to its versatility and cheapness. Until today, these systems were based on light detection and ranging (lidar) or visual cameras, high-accuracy and expensive approaches that are limited to specific environments and weather conditions. Instead, in case of smoke, fog or simply darkness, radar-based systems can operate exactly in the same way. In this thesis activity, the Fourier-Mellin algorithm is analyzed and implemented, to verify the applicability to Radio SLAM, in which the radar frames can be treated as images and the radar motion between consecutive frames can be covered with registration. Furthermore, a simplified version of that algorithm is proposed, in order to solve the problems of the Fourier-Mellin algorithm when working with real radar images and improve the performance. The INRAS RBK2, a MIMO 2x16 mmWave radar, is used for experimental acquisitions, consisting of multiple tests performed in Lab-E of the Cesena Campus, University of Bologna. The different performances of Fourier-Mellin and its simplified version are compared also with the MatchScan algorithm, a classic algorithm for SLAM systems.

Application of active and transfer learning to wound image segmentation

Wound management is a fundamental task in standard clinical practice. Automated solutions already exist for humans, but there is a lack of applications on wound management for pets. The importance of a precise and efficient wound assessment is helpful to improve diagnosis and to increase the effectiveness of treatment plans for the chronic wounds. The goal of the research was to propose an automated pipeline capable of segmenting natural light-reflected wound images of animals. Two datasets composed by light-reflected images were used in this work: Deepskin dataset, 1564 human wound images obtained during routine dermatological exams, with 145 manual annotated images; Petwound dataset, a set of 290 wound photos of dogs and cats with 0 annotated images. Two implementations of U-Net Convolutioal Neural Network model were proposed for the automated segmentation. Active Semi-Supervised Learning techniques were applied for human-wound images to perform segmentation from 10% of annotated images. Then the same models were trained, via Transfer Learning, adopting an Active Semi- upervised Learning to unlabelled animal-wound images. The combination of the two training strategies proved their effectiveness in generating large amounts of annotated samples (94% of Deepskin, 80% of PetWound) with the minimal human intervention. The correctness of automated segmentation were evaluated by clinical experts at each round of training thus we can assert that the results obtained in this thesis stands as a reliable solution to perform a correct wound image segmentation. The use of Transfer Learning and Active Semi-Supervied Learning allows to minimize labelling effort from clinicians, even requiring no starting manual annotation at all. Moreover the performances of the model with limited number of parameters suggest the implementation of smartphone-based application to this topic, helping the future standardization of light-reflected images as acknowledge medical images.