Registrazione e replicazione di procedure Web con una Firefox Extension

In questo lavoro di tesi è stata sviluppata una Firefox Extension per la registrazione e la replicazione di procedure sul Web. Si tratterà a fondo l’ambiente tecnologico nel quale è stata sviluppata l’applicazione e il contesto in cui si inserisce una Firefox Extension. Illustreremo il problema che intendiamo risolvere con la nostra estensione,il contesto applicativo in cui si inserisce e riporteremo una serie di lavori correlati che cercano, con diversi approcci, di risolvere il nostro stesso problema. Illustreremo il lavoro trattando approfonditamente l’approccio da noi utilizzato, mostrandone i vantaggi e i limiti.

Evoluzione delle user interface: dalla metafora del desktop al Wearable Computing

Negli ultimi decenni abbiamo assistito ad una graduale evoluzione delle interfacce utente e della tecnologia. Sono stati introdotti nuovi dispositivi mobile e wearable che negli ultimi anni hanno subito un incremento tecnologico esponenziale arrivando a fondersi con la vita di tutti i giorni. Le classiche interfacce grafiche WIMP, la metafora del desktop e le linee guida di progettazione fino ad ora sviluppate non risultano ideali per la nuova tecnologia di wearable computing. Il proposito che la tesi vuole andare ad affrontare è proprio quello di indagare lo sviluppo di nuove user inteface basate sulla tecnologia wearable ed in particolare per smart glasses.

Deep learning for computer vision: a comparison between convolutional neural networks and hierarchical temporal memories on object recognition tasks

In recent years, Deep Learning techniques have shown to perform well on a large variety of problems both in Computer Vision and Natural Language Processing, reaching and often surpassing the state of the art on many tasks. The rise of deep learning is also revolutionizing the entire field of Machine Learning and Pattern Recognition pushing forward the concepts of automatic feature extraction and unsupervised learning in general. However, despite the strong success both in science and business, deep learning has its own limitations. It is often questioned if such techniques are only some kind of brute-force statistical approaches and if they can only work in the context of High Performance Computing with tons of data. Another important question is whether they are really biologically inspired, as claimed in certain cases, and if they can scale well in terms of "intelligence". The dissertation is focused on trying to answer these key questions in the context of Computer Vision and, in particular, Object Recognition, a task that has been heavily revolutionized by recent advances in the field. Practically speaking, these answers are based on an exhaustive comparison between two, very different, deep learning techniques on the aforementioned task: Convolutional Neural Network (CNN) and Hierarchical Temporal memory (HTM). They stand for two different approaches and points of view within the big hat of deep learning and are the best choices to understand and point out strengths and weaknesses of each of them. CNN is considered one of the most classic and powerful supervised methods used today in machine learning and pattern recognition, especially in object recognition. CNNs are well received and accepted by the scientific community and are already deployed in large corporation like Google and Facebook for solving face recognition and image auto-tagging problems. HTM, on the other hand, is known as a new emerging paradigm and a new meanly-unsupervised method, that is more biologically inspired. It tries to gain more insights from the computational neuroscience community in order to incorporate concepts like time, context and attention during the learning process which are typical of the human brain. In the end, the thesis is supposed to prove that in certain cases, with a lower quantity of data, HTM can outperform CNN.

Tecnologie per la fruizione e rappresentazione di open data

La presente tesi è uno studio sugli strumenti e le tecnologie che caratterizzano l'utilizzo degli open data, in particolare, nello sviluppo di applicazioni web moderne che fanno uso di questo tipo di dati.

Integration of the computer controlled screwdriver for Robotic manufacturing in the ROS environment

The computer controlled screwdriver is a modern technique to perform automatic screwing/unscrewing operations.The main focus is to study the integration of the computer controlled screwdriver for Robotic manufacturing in the ROS environment.This thesis describes a concept of automatic screwing mechanism composed by universal robots, in which one arm of the robot is for inserting cables and the other is for screwing the cables on the control panel switch gear box. So far this mechanism is carried out by human operators and is a fairly complex one to perform, due to the multiple cables and connections involved. It's for this reason that an automatic cabling and screwing process would be highly preferred within automotive/automation industries. A study is carried out to analyze the difficulties currently faced and a controller based algorithm is developed to replace the manual human efforts using universal robots, thereby allowing robot arms to insert the cables and screw them onto the control panel switch gear box. Experiments were conducted to evaluate the insertion and screwing strategy, which shows the result of inserting and screwing cables on the control panel switch gearbox precisely.