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Projecte llegit

Títol: Feasibility Analysis of Neural Network Architectures for Signal Processing in Space Missions

Estudiants que han llegit aquest projecte:

Director/a: TORRES GIL, SANTIAGO

Departament: FIS

Títol: Feasibility Analysis of Neural Network Architectures for Signal Processing in Space Missions

Data inici oferta: 27-01-2025     Data finalització oferta: 31-01-2025


Estudis d'assignació del projecte:
    MU AEROSPACE S&T 21
Tipus: Individual
 
Lloc de realització: EETAC
 
Segon director/a (UPC): GARCÍA ZAMORA, ENRIQUE MIGUEL
 
Paraules clau:
Artificial Intelligence, Neural Networks, Space Mission, Astrophysics
 
Descripció del contingut i pla d'activitats:
Artificial Intelligence has become an essential tool in modern science and technology. In particular, deep learning, based on neural network architectures, is one of the most effective and widely used strategies. Simultaneously, current space missions, particularly those focused on astronomy and astrophysics, generate an overwhelming quantity of data that exceeds human capacity for direct analysis and inspection.

In this project, we aim to analyze the feasibility of neural network architectures in the context of space missions. A variety of archetypal signals, such as Gaussian distributions and blackbody spectra, will be studied, both in the absence and presence of different types of noise. The analysis will focus on the impact of the number of neurons and layers on the networks' performance, as well as the computational efficiency in terms of processing time using GPU-based systems.
 
Overview (resum en anglès):
Artificial Intelligence has become an essential tool in modern science and technology. In particular, deep learning, based on neural network architectures, is one of the most effective and widely used strategies. Simultaneously, current space missions, particularly those focused on astronomy and astrophysics, generate an overwhelming quantity of data that exceeds human capacity for direct analysis and inspection.

In this project, we aim to analyse the feasibility of neural network architectures in the context of space missions. A variety of archetypal signals, such as Gaussian distributions and blackbody spectra, will be studied, both in the absence and presence of different types of noise. The analysis will focus on the impact of the number of neurons and layers on the networks' performance, as well as the computational efficiency in terms of processing time using GPU-based systems.

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