Automatic detection of the inferior alveolar nerve in CT scans with CenterNet

The inferior alveolar nerve (IAN) lies within the mandibular canal, named inferior alveolar canal in literature. The detection of this nerve is important during maxillofacial surgeries or for creating dental implants. The poor quality of cone-beam computed tomography (CBCT) and computed tomography (CT) scans and/or bone gaps within the mandible increase the difficulty of this task, posing a challenge to human experts who are going to manually detect it and resulting in a time-consuming task.Therefore this thesis investigates two methods to automatically detect the IAN: a non-data driven technique and a deep-learning method. The latter tracks the IAN position at each frame leveraging detections obtained with the deep neural network CenterNet, fined-tuned for our task, and temporal and spatial information.

Deep learning models for the automatic analysis of volleyball plays

Collecting and analysing data is an important element in any field of human activity and research. Even in sports, collecting and analyzing statistical data is attracting a growing interest. Some exemplar use cases are: improvement of technical/tactical aspects for team coaches, definition of game strategies based on the opposite team play or evaluation of the performance of players. Other advantages are related to taking more precise and impartial judgment in referee decisions: a wrong decision can change the outcomes of important matches. Finally, it can be useful to provide better representations and graphic effects that make the game more engaging for the audience during the match. Nowadays it is possible to delegate this type of task to automatic software systems that can use cameras or even hardware sensors to collect images or data and process them. One of the most efficient methods to collect data is to process the video images of the sporting event through mixed techniques concerning machine learning applied to computer vision. As in other domains in which computer vision can be applied, the main tasks in sports are related to object detection, player tracking, and to the pose estimation of athletes. The goal of the present thesis is to apply different models of CNNs to analyze volleyball matches. Starting from video frames of a volleyball match, we reproduce a bird's eye view of the playing court where all the players are projected, reporting also for each player the type of action she/he is performing.

Analysis and detection of partial discharges in an electric motor of a hybrid supercar.

The objective of this thesis was the development of a new detection method of partial discharge (PD) activity in the stator of an electrical hybrid supercar fed by a silicon carbide converter, for which detection with common methods make it very difficult to separate PD pulses from switching noise. This work focused on the analysis and detection of partial discharges making use of an antenna, a peak detector, and an oscilloscope capable of capturing the electromagnetic pulses emitted during PD activity. Validation of the proposed method was done by comparing the partial discharge inception voltage (PDIV) detected by this system with the one obtained from an optical method of proven accuracy, with different rise times and samples. Further development of this method, if proved successful on a full stator, can help increasing the overall reliability of the car, potentially allowing for real time detection of PD activity and predictive maintenance before failure of the insulation system in a hybrid vehicle.

Construction and detectors’ characterization of a new detection system for FAMU Experiment

From 2010, the Proton Radius has become one of the most interest value to determine. The first proof of not complete understanding of its internal structure was the measurement of the Lamb Shift using the muonic hydrogen, leading to a value 7σ lower. A new road so was open and the Proton Radius Puzzle epoch begun. FAMU Experiment is a project that tries to give an answer to this Puzzle implementing high precision experimental apparatus. The work of this thesis is based on the study, construction and first characterization of a new detection system. Thanks to the previous experiments and simulations, this apparatus is composed by 17 detectors positioned on a semicircular crown with the related electronic circuit. The detectors' characterization is based on the use of a LabView program controlling a digital potentiometer and on other two analog potentiometers, all three used to set the amplitude of each detector to a predefined value, around 1.2 V, set on the oscilloscope by which is possible to observe the signal. This is the requirement in order to have, in the final measurement, a single high peak given by the sum of all the signals coming from the detectors. Each signal has been acquired for almost half of an hour, but the entire circuit has been maintained active for more time to observe its capacity to work for longer periods. The principal results of this thesis are given by the spectra of 12 detectors and the corresponding values of Voltages, FWHM and Resolution. The outcomes of the acquisitions show also another expected behavior: the strong dependence of the detectors from the temperature, demonstrating that an its change causes fluctuations in the signal. In turn, these fluctuations will affect the spectrum, resulting in a shifting of the curve and a lower Resolution. On the other hand, a measurement performed in stable conditions will lead to accordance between the nominal and experimental measurements, as for the detectors 10, 11 and 12 of our system.