Leo D'Amato

Titolo della tesi

Efficient Neural Coding Under Resource Constraints: A Rate-Distortion Theory Perspective

Tutori

• Giovanni Pezzulo
• Aldo Gangemi

• Melchiorre, Jonathan; Agostini, Federico; D'Amato, Leo; Rosso, Marco M. (2025)
  Onset Time Detection of Acoustic Emission Signals for Structural Monitoring with Deep Learning. In: Smart Innovation, Systems and Technologies / S.N., S.L., Springer Science and Business Media Deutschland GmbH, pp. 255-265. ISBN: 9789819609932
  Contributo in Volume
• Pezzulo, Giovanni; D'Amato, Leo; Mannella, Francesco; Priorelli, Matteo; Van de Maele, ... (2024)
  Neural representation in active inference: Using generative models to interact with—and understand—the lived world. In: ANNALS OF THE NEW YORK ACADEMY OF SCIENCES, vol. 1534, pp. 45-68. ISSN 0077-8923
  Contributo su Rivista
• D'Amato, Leo; Naldini, Federico; Tibaldo, Valentina; Trianni, Vito; Pellegrini, Paola (2024)
  Towards self-organizing railway traffic management: concept and framework. In: JOURNAL OF RAIL TRANSPORT PLANNING & MANAGEMENT, vol. 29. ISSN 2210-9706
  Contributo su Rivista
• Melchiorre, J.; D'Amato, L.; Agostini, F.; Manuello, A. (2024)
  Deep-Learning-Based Onset Time Precision in Acoustic Emission Non-Destructive Testing. In: 2024 IEEE International Workshop on Metrology for Living Environment, MetroLivEnv 2024, Chania (GRC), 12-14 June 2024, pp. 367-372. ISBN: 979-8-3503-8501-4
  Contributo in Atti di Convegno (Proceeding)
• Melchiorre, Jonathan; D'Amato, Leo; Agostini, Federico; Rizzo, Antonino Maria (2024)
  Acoustic emission onset time detection for structural monitoring with U-Net neural network architecture. In: DEVELOPMENTS IN THE BUILT ENVIRONMENT, vol. 18, pp. 1-13. ISSN 2666-1659
  Contributo su Rivista