In recent years, deep learning (DL) has attracted a signi cant attention in the research community working on microwave imaging (MWI). In fact, DL can provide extremely powerful tools to successfully solve complex classi cation or regression tasks. As such, its use can open new ways to develop accurate and reliable algorithms able to overcome the di culties arising from the non-linear and ill-posed nature of the inverse scattering problem underlying MWI.
Physics-assisted Deep-learning for Microwave Tomography: Merging Inverse Scattering Techniques with Artificial Intelligence / Yago Ruiz, A.; Scapaticci, R.; Palmeri, R.; Cavagnaro, M.; Crocco, L.. - (2023). (Intervento presentato al convegno PhotonIcs & Electromagnetics Research Symposium tenutosi a Prague (CZE)).
Physics-assisted Deep-learning for Microwave Tomography: Merging Inverse Scattering Techniques with Artificial Intelligence
M. Cavagnaro;
2023
Abstract
In recent years, deep learning (DL) has attracted a signi cant attention in the research community working on microwave imaging (MWI). In fact, DL can provide extremely powerful tools to successfully solve complex classi cation or regression tasks. As such, its use can open new ways to develop accurate and reliable algorithms able to overcome the di culties arising from the non-linear and ill-posed nature of the inverse scattering problem underlying MWI.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
