Structure learning of graphical models for task-oriented robot grasping

In the collective imaginaries a robot is a human like machine as any androids in science fiction. However the type of robots that you will encounter most frequently are machinery that do work that is too dangerous, boring or onerous. Most of the robots in the world are of this type. They can be found in auto, medical, manufacturing and space industries. Therefore a robot is a system that contains sensors, control systems, manipulators, power supplies and software all working together to perform a task. The development and use of such a system is an active area of research and one of the main problems is the development of interaction skills with the surrounding environment, which include the ability to grasp objects. To perform this task the robot needs to sense the environment and acquire the object informations, physical attributes that may influence a grasp. Humans can solve this grasping problem easily due to their past experiences, that is why many researchers are approaching it from a machine learning perspective finding grasp of an object using information of already known objects. But humans can select the best grasp amongst a vast repertoire not only considering the physical attributes of the object to grasp but even to obtain a certain effect. This is why in our case the study in the area of robot manipulation is focused on grasping and integrating symbolic tasks with data gained through sensors. The learning model is based on Bayesian Network to encode the statistical dependencies between the data collected by the sensors and the symbolic task. This data representation has several advantages. It allows to take into account the uncertainty of the real world, allowing to deal with sensor noise, encodes notion of causality and provides an unified network for learning. Since the network is actually implemented and based on the human expert knowledge, it is very interesting to implement an automated method to learn the structure as in the future more tasks and object features can be introduced and a complex network design based only on human expert knowledge can become unreliable. Since structure learning algorithms presents some weaknesses, the goal of this thesis is to analyze real data used in the network modeled by the human expert, implement a feasible structure learning approach and compare the results with the network designed by the expert in order to possibly enhance it.

Experimental characterization of backscattering properties of UWB antennas in the presence of close objects

RTLS and RFID systems are becoming more and more important in several fields. When these systems meet the UWB technology, they can take advantage of each other strengths. Since nowadays a strong importance is given to the "green" technology, we chose to adopt a passive solution. In this case the backscattering modulation can be used to carry data. Therefore it is necessary to analyze the behavior of the antennas used as tags, when they are closed to dierent material objects. In particular, the antenna mode part has been deeply observed, as it is the crucial part of the signal regarding the backscatter modulation.

A study of two Languages for Active Objects with Futures

In computer systems, specifically in multithread, parallel and distributed systems, a deadlock is both a very subtle problem - because difficult to pre- vent during the system coding - and a very dangerous one: a deadlocked system is easily completely stuck, with consequences ranging from simple annoyances to life-threatening circumstances, being also in between the not negligible scenario of economical losses. Then, how to avoid this problem? A lot of possible solutions has been studied, proposed and implemented. In this thesis we focus on detection of deadlocks with a static program analysis technique, i.e. an analysis per- formed without actually executing the program. To begin, we briefly present the static Deadlock Analysis Model devel- oped for coreABS−− in chapter 1, then we proceed by detailing the Class- based coreABS−− language in chapter 2. Then, in Chapter 3 we lay the foundation for further discussions by ana- lyzing the differences between coreABS−− and ASP, an untyped Object-based calculi, so as to show how it can be possible to extend the Deadlock Analysis to Object-based languages in general. In this regard, we explicit some hypotheses in chapter 4 first by present- ing a possible, unproven type system for ASP, modeled after the Deadlock Analysis Model developed for coreABS−−. Then, we conclude our discussion by presenting a simpler hypothesis, which may allow to circumvent the difficulties that arises from the definition of the ”ad-hoc” type system discussed in the aforegoing chapter.

Quantum solitons in the XXZ model with staggered external magnetic field

The 1-D 1/2-spin XXZ model with staggered external magnetic field, when restricting to low field, can be mapped into the quantum sine-Gordon model through bosonization: this assures the presence of soliton, antisoliton and breather excitations in it. In particular, the action of the staggered field opens a gap so that these physical objects are stable against energetic fluctuations. In the present work, this model is studied both analytically and numerically. On the one hand, analytical calculations are made to solve exactly the model through Bethe ansatz: the solution for the XX + h staggered model is found first by means of Jordan-Wigner transformation and then through Bethe ansatz; after this stage, efforts are made to extend the latter approach to the XXZ + h staggered model (without finding its exact solution). On the other hand, the energies of the elementary soliton excitations are pinpointed through static DMRG (Density Matrix Renormalization Group) for different values of the parameters in the hamiltonian. Breathers are found to be in the antiferromagnetic region only, while solitons and antisolitons are present both in the ferromagnetic and antiferromagnetic region. Their single-site z-magnetization expectation values are also computed to see how they appear in real space, and time-dependent DMRG is employed to realize quenches on the hamiltonian parameters to monitor their time-evolution. The results obtained reveal the quantum nature of these objects and provide some information about their features. Further studies and a better understanding of their properties could bring to the realization of a two-level state through a soliton-antisoliton pair, in order to implement a qubit.

Multifrequency VLBA observations of two compact symmetric objects

In this thesis the results of the multifrequency VLBA observations of the GPS 1944+5448 and the HFP J0111+3906 are presented. They are compact objects smaller than about 100 pc, completely embedded in the host galaxy. The availability of multi-epoch VLBI observations spanning more than 10 years, allowed us to compute the hot spot advance speed in order to obtain the kinematic age of both sources. Both radio sources are young, in agreement with the idea that they are in an early evolutionary stage. The spectral analysis of each source component, such as the lobes, the hot spots, the core and the jets, making a comparison with the theoretical ones is described. In addition the physical parameters derived from VLBA images as the magnetic field, the luminosity, the energy and the ambient medium density of both sources are discussed.