Sperimentazione di metodologie innovative di indagine termografica in campo industriale

La termografia è un metodo d’indagine ampiamente utilizzato nei test diagnostici non distruttivi, in quanto risulta una tecnica d’indagine completamente non invasiva, ripetibile nel tempo e applicabile in diversi settori. Attraverso tale tecnica è possibile individuare difetti superficiali e sub–superficiali, o possibili anomalie, mediante la rappresentazione della distribuzione superficiale di temperatura dell’oggetto o dell’impianto indagato. Vengono presentati i risultati di una campagna sperimentale di rilevamenti termici, volta a stabilire i miglioramenti introdotti da tecniche innovative di acquisizione termografica, quali ad esempio la super-risoluzione, valutando un caso di studio di tipo industriale. Si è effettuato un confronto tra gli scatti registrati, per riuscire a individuare e apprezzare le eventuali differenze tra le diverse modalità di acquisizione adottate. L’analisi dei risultati mostra inoltre come l’utilizzo dei dispositivi di acquisizione termografica in modalità super-resolution sia possibile anche su cavalletto.

New markov chain based methods for single and cross-domain sentiment classification

Nowadays communication is switching from a centralized scenario, where communication media like newspapers, radio, TV programs produce information and people are just consumers, to a completely different decentralized scenario, where everyone is potentially an information producer through the use of social networks, blogs, forums that allow a real-time worldwide information exchange. These new instruments, as a result of their widespread diffusion, have started playing an important socio-economic role. They are the most used communication media and, as a consequence, they constitute the main source of information enterprises, political parties and other organizations can rely on. Analyzing data stored in servers all over the world is feasible by means of Text Mining techniques like Sentiment Analysis, which aims to extract opinions from huge amount of unstructured texts. This could lead to determine, for instance, the user satisfaction degree about products, services, politicians and so on. In this context, this dissertation presents new Document Sentiment Classification methods based on the mathematical theory of Markov Chains. All these approaches bank on a Markov Chain based model, which is language independent and whose killing features are simplicity and generality, which make it interesting with respect to previous sophisticated techniques. Every discussed technique has been tested in both Single-Domain and Cross-Domain Sentiment Classification areas, comparing performance with those of other two previous works. The performed analysis shows that some of the examined algorithms produce results comparable with the best methods in literature, with reference to both single-domain and cross-domain tasks, in $2$-classes (i.e. positive and negative) Document Sentiment Classification. However, there is still room for improvement, because this work also shows the way to walk in order to enhance performance, that is, a good novel feature selection process would be enough to outperform the state of the art. Furthermore, since some of the proposed approaches show promising results in $2$-classes Single-Domain Sentiment Classification, another future work will regard validating these results also in tasks with more than $2$ classes.

High resolution study of narrow tailed radio galaxies in clusters

Clusters of galaxies are the most massive and large gravitationally bounded systems in the whole Universe. Their study is of fundamental importance to constrain cosmological parameters and to obtain informations regarding various kind of emission in different wavebands. In particular, in the radio domain, beside the diffuse emission, the study is focused on the radio galaxies emission. Radio galaxies in clusters can have peculiar morphology, since they interact with the intracluster medium (ICM) in which they are embedded. Particularly, in this thesis we focused our attention on the so-called Narrow-Angle Tailed radio galaxies (NAT), which present radio jets that are bent at extreme angle, up to 90 degrees, from their original orientation. Some NAT show a narrow extended structure and the two radio tails are not resolved even with high resolution radio observations. An example is provided by the source IC310, in the Perseus Cluster, whose structure has been recently interpreted as due to Doppler boosting effects of a relativistic jet oriented at a small angle with respect to the line of sight. If the structure is due to relativistic effects, this implies that the jets are relativistic at about 400 kpc from the core, but this is in contrast with unified models, which predict that for low-power radio source (NAT are classified as FRI radio galaxies) the jets decelerate to sub-relativistic speed within a few kpc from the core. To investigate this scientific topic, in this thesis we have analyzed the innermost structure of a sample of eleven radio galaxies showing a very narrow NAT structure. We can conclude that the structure of these radio galaxies is different from that of IC310. These radio galaxies are indeed strongly influenced by environmental effects and are similar to classical NAT sources.