| KEYWORD |
Deep neural networks for prediction and detection of solar corona mass ejections
Parole chiave DEEP LEARNING, VIDEO ANALYSIS
Riferimenti ENRICO MAGLI
Gruppi di ricerca CCNE - COMMUNICATIONS AND COMPUTER NETWORKS ENGINEERING, ICT4SS - ICT FOR SMART SOCIETIES, Image Processing Lab (IPL)
Tipo tesi RICERCA
Descrizione This thesis adDresses the problem of prediction and early detection of physical phenomena on the solar surface. Eruptions on the solar surface may give rise to coronal mass ejections, leading to significant magnetic fields that may disrupt the operation of electronic devices on the Earth or in space, including satellites. The activity has the objective of employing artificial intelligence, and in particular deep neural networks, in order to design a system able to predict and detect coronal mass ejections. The detection can be performed using images of the solar corona, as well as measurements from in-situ magnetic sensors. It is foreseen that the activity will be carried out in collaboration with the Astronomic Observatory in Pino Torinese, in order to exploit the large amount of data logged over several years, as well as ground truth data related to observed coronal mass ejections.
Conoscenze richieste Candidate students should have a background in ICT or mathematics. Knowledge of neural networks (including TensorFlow environment and Python programming) is not a prerequisite, although it would help in the initial stage of the activity.
Note We expect to use the TensorFlow environment to set up the prediction/detection system.
Scadenza validita proposta 18/10/2018
PROPONI LA TUA CANDIDATURA