Ricerc@Sapienza

Autonomous robotic systems are currently being addressed as a critical element in the development of present and future on-orbit operations. Modern missions are calling for systems capable of reproducing human’s decision-making process thus enhancing their performance. Generally, space manipulators are mounted on a floating spacecraft in a microgravity environment, consequently leading to a mutual influence between the robotic arms and the platform dynamics, thus making the motion planning and control design more challenging than those of terrestrial robots.

Autonomous robotic systems are currently being addressed as a critical element in the development of present and future on-orbit operations. Modern missions are calling for systems capable of reproducing human’s decision-making process, thus enhancing their performance. Generally, space manipulators are mounted on a floating spacecraft in a microgravity environment, consequently leading to a mutual influence between the robotic arms and the platform dynamics, thus making the motion planning and control design more challenging than those of terrestrial robots.

In this paper, a Machine Learning (ML), and more specifically, a Deep Learning (DL) approach, is applied to the resolution of a typical electromagnetic problem such as the analysis and classification of Ground Penetrating Radar (GPR) radargrams. In particular, the study employs a DL architecture, known as DenseNet, to classify a set of radargrams, generated through the gprMax simulation software, and representing the scattering from perfect electric conductor (PEC) cylinders of infinite length, buried in various media, at different depths, and with different radius amplitudes.

The aim of this work is to exploit Machine Learning (ML) for the analysis of Georadar (or Ground Penetrating Radar, GPR) images. In particular, the objective is to apply a Deep Learning (DL) architecture to extract from B-scan images of infinite buried Perfect Electric Conductor (PEC) cylinders: the cylinder radius, the depth with respect to the ground, and the relative dielectric permittivity εr of the medium in which the cylinder is immersed. The architecture chosen is the DenseNet.

Recognizing different visual signatures of people across non-overlapping cameras is still an open problem of great interest for the computer vision community, especially due to its importance in automatic video surveillance on large-scale environments. A main aspect of this application field, known as person re-identification (re-id), is the feature extraction step used to define a robust appearance of a person. In this paper, a novel two-branch Convolutional Neural Network (CNN) architecture for person re-id in video sequences is proposed.

In this paper, a fully immersive serious game system that combines two Natural User Interfaces (NUIs) and a Head Mounted Display (HMD) to provide an interactive Virtual Environment (VE) for patient rehabilitation is proposed. Patients\textquotesingle~data are acquired in real-time by the NUIs, while by the HMD the VE is shown to them, thus allowing the interaction.

Strokes, surgeries, or degenerative diseases can impair motor abilities and balance. Long-term rehabilitation is often the only way to recover, as completely as possible, these lost skills. To be effective, this type of rehabilitation should follow three main rules. First, rehabilitation exercises should be able to keep patient's motivation high. Second, each exercise should be customizable depending on patient's needs. Third, patient's performance should be evaluated objectively, i.e., by measuring patient's movements with respect to an optimal reference model.

In this paper we consider the binary similarity problem that consists in determining if two binary functions are similar only considering their compiled form. This problem is know to be crucial in several application scenarios, such as copyright disputes, malware analysis, vulnerability detection, etc. The current state-of-the-art solutions in this field work by creating an embedding model that maps binary functions into vectors in .