Zobrazeno 1 - 10
of 196
pro vyhledávání: '"Nicoli, Monica"'
Autor:
Santos, Diogo Reis, Aillet, Albert Sund, Boiano, Antonio, Milasheuski, Usevalad, Giusti, Lorenzo, Di Gennaro, Marco, Kianoush, Sanaz, Barbieri, Luca, Nicoli, Monica, Carminati, Michele, Redondi, Alessandro E. C., Savazzi, Stefano, Serio, Luigi
The rapid evolution of artificial intelligence (AI) technologies holds transformative potential for the healthcare sector. In critical situations requiring immediate decision-making, healthcare professionals can leverage machine learning (ML) algorit
Externí odkaz:
http://arxiv.org/abs/2410.13869
Bayesian Federated Learning (FL) has been recently introduced to provide well-calibrated Machine Learning (ML) models quantifying the uncertainty of their predictions. Despite their advantages compared to frequentist FL setups, Bayesian FL tools impl
Externí odkaz:
http://arxiv.org/abs/2405.05855
Autor:
Milasheuski, Usevalad, Barbieri, Luca, Tedeschini, Bernardo Camajori, Nicoli, Monica, Savazzi, Stefano
Federated Learning (FL) allows multiple privacy-sensitive applications to leverage their dataset for a global model construction without any disclosure of the information. One of those domains is healthcare, where groups of silos collaborate in order
Externí odkaz:
http://arxiv.org/abs/2404.18519
Autor:
Boiano, Antonio, Di Gennaro, Marco, Barbieri, Luca, Carminati, Michele, Nicoli, Monica, Redondi, Alessandro, Savazzi, Stefano, Aillet, Albert Sund, Santos, Diogo Reis, Serio, Luigi
Federated Learning (FL) has emerged as a promising approach for privacy-preserving machine learning, particularly in sensitive domains such as healthcare. In this context, the TRUSTroke project aims to leverage FL to assist clinicians in ischemic str
Externí odkaz:
http://arxiv.org/abs/2404.11698
Publikováno v:
IEEE Transactions on Signal Processing, 2024
Accurate positioning is known to be a fundamental requirement for the deployment of Connected Automated Vehicles (CAVs). To meet this need, a new emerging trend is represented by cooperative methods where vehicles fuse information coming from navigat
Externí odkaz:
http://arxiv.org/abs/2402.16656
Autor:
Italiano, Lorenzo, Tedeschini, Bernardo Camajori, Brambilla, Mattia, Huang, Huiping, Nicoli, Monica, Wymeersch, Henk
Publikováno v:
IEEE Communications Surveys & Tutorials 2024
The widespread adoption of the fifth generation (5G) of cellular networks has brought new opportunities for the development of localization-based services. High-accuracy positioning use cases and functionalities defined by the standards are drawing t
Externí odkaz:
http://arxiv.org/abs/2311.10551
Publikováno v:
2023 IEEE 28th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)
Federated Learning (FL) methods adopt efficient communication technologies to distribute machine learning tasks across edge devices, reducing the overhead in terms of data storage and computational complexity compared to centralized solutions. Rather
Externí odkaz:
http://arxiv.org/abs/2310.08087
Publikováno v:
2023 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023)
Bayesian Federated Learning (FL) offers a principled framework to account for the uncertainty caused by limitations in the data available at the nodes implementing collaborative training. In Bayesian FL, nodes exchange information about local posteri
Externí odkaz:
http://arxiv.org/abs/2210.10502
Autor:
Manzoni, Marco, Tagliaferri, Dario, Rizzi, Marco, Tebaldini, Stefano, Monti-Guarnieri, Andrea Virgilio, Prati, Claudio Maria, Nicoli, Monica, Russo, Ivan, Duque, Sergi, Mazzucco, Christian, Spagnolini, Umberto
With the advent of self-driving vehicles, autonomous driving systems will have to rely on a vast number of heterogeneous sensors to perform dynamic perception of the surrounding environment. Synthetic Aperture Radar (SAR) systems increase the resolut
Externí odkaz:
http://arxiv.org/abs/2201.10504
Autor:
Manzoni, Marco, Rizzi, Marco, Tebaldini, Stefano, Monti-Guarnieri, Andrea Virgilio, Prati, Claudio Maria, Tagliaferri, Dario, Nicoli, Monica, Russo, Ivan, Mazzucco, Christian, Biarge, Sergi Duque, Spagnolini, Umberto
This paper deals with the analysis, estimation, and compensation of trajectory errors in automotive-based Synthetic Aperture Radar (SAR) systems. First of all, we define the geometry of the acquisition and the model of the received signal. We then pr
Externí odkaz:
http://arxiv.org/abs/2110.14995