(→2026) |
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| (4 versioni intermedie di un altro utente non mostrate) | |||
| Riga 1: | Riga 1: | ||
__NOTOC__ | __NOTOC__ | ||
==2026== | ==2026== | ||
| + | <paper authors="Muhammad Farooq, Gioacchino Manfredi, Maria Martini, Saverio Mascolo, Luca De Cicco" conference="12th International Conference on Control, Decision and Information Technologies (CODIT)" place="Bari, Italy" date="July 2026"> | ||
| + | A Comparison of AI Models for Viewport Prediction in Immersive 360° Video Streaming | ||
| + | </paper> | ||
| + | |||
| + | <paper authors="Nunzio Barone, Danilo Pau, Saverio Mascolo, Luca De Cicco" conference="12th International Conference on Control, Decision and Information Technologies (CODIT)" place="Bari, Italy" date="July 2026"> | ||
| + | Attention-based Neural Networks for Event Cameras Visual-Inertial Odometry on Edge Devices | ||
| + | </paper> | ||
| + | |||
| + | <paper authors="Giada Lops, Vito Andrea Racanelli, Luca De Cicco, Saverio Mascolo" conference="12th International Conference on Control, Decision and Information Technologies (CODIT)" place="Bari, Italy" date="July 2026"> | ||
| + | Risk-Aware Multi-Horizon Glucose Prediction for Type 2 Diabetes Using Temporal Attention and Conformal Prediction | ||
| + | </paper> | ||
| + | |||
| + | <paper authors="Giada Lops, Francesco De Paola, Vito Andrea Racanelli, Gioacchino Manfredi, Luca De Cicco, Saverio Mascolo" conference="IFAC World Congress" place="Busan, South Korea" date="August 2026"> | ||
| + | Data-Driven Control of Type 2 Diabetes Progression via Personalized Physical Activity | ||
| + | </paper> | ||
| + | |||
<paper authors="Giada Lops, Taha Ramdan, Vito Andrea Racanelli, Luca De Cicco, Saverio Mascolo" conference="European Control Conference (ECC)" place="Reykjavík, Iceland" date="July 2026"> | <paper authors="Giada Lops, Taha Ramdan, Vito Andrea Racanelli, Luca De Cicco, Saverio Mascolo" conference="European Control Conference (ECC)" place="Reykjavík, Iceland" date="July 2026"> | ||
Bridging Clinical Knowledge and Reinforcement Learning in Automated Insulin Delivery: An LLM-in-the-Loop Approach | Bridging Clinical Knowledge and Reinforcement Learning in Automated Insulin Delivery: An LLM-in-the-Loop Approach | ||
</paper> | </paper> | ||
| − | <paper authors="Federico Baldisseri, Giada Lops, Mohab Mahdy Helmy Atanasious, Danilo Menegatti, Valentina Becchetti, Francesco Delli Priscoli, Saverio Mascolo, Andrea Wrona" conference="European Control Conference (ECC)" place="Reykjavík, Iceland" date="July 2026"> | + | <paper authors="Federico Baldisseri, Giada Lops, Mohab Mahdy Helmy Atanasious, Danilo Menegatti, Valentina Becchetti, Francesco Delli Priscoli, Saverio Mascolo, Vito Andrea Racanelli, Andrea Wrona" conference="European Control Conference (ECC)" place="Reykjavík, Iceland" date="July 2026"> |
Safe Deep Reinforcement Learning Control of Type 1 Diabetes | Safe Deep Reinforcement Learning Control of Type 1 Diabetes | ||
</paper> | </paper> | ||
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©2007 IEEE. Personal use of this material is permitted.
However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the IEEE.