no code implementations • 15 May 2024 • Federica Fieramosca, Vittorio Rampa, Michele D'Amico, Stefano Savazzi
Recently, proposals of human-sensing-based services for cellular and local area networks have brought indoor localization to the attention of several research groups.
no code implementations • 15 May 2024 • Federica Fieramosca, Vittorio Rampa, Stefano Savazzi, Michele D'Amico
Electromagnetic (EM) body models based on the scalar diffraction theory allow to predict the impact of subject motions on the radio propagation channel without requiring a time-consuming full-wave approach.
no code implementations • 9 May 2024 • Luca Barbieri, Stefano Savazzi, Monica Nicoli
Bayesian Federated Learning (FL) has been recently introduced to provide well-calibrated Machine Learning (ML) models quantifying the uncertainty of their predictions.
no code implementations • 3 May 2024 • Federica Fieramosca, Vittorio Rampa, Michele D'Amico, Stefano Savazzi
Electromagnetic (EM) body models designed to predict Radio-Frequency (RF) propagation are time-consuming methods which prevent their adoption in strict real-time computational imaging problems, such as human body localization and sensing.
no code implementations • 2 May 2024 • Vittorio Rampa, Federica Fieramosca, Stefano Savazzi, Michele D'Amico
By exploiting the Integrated Sensing and Communication paradigm, DFL networks employ Radio Frequency (RF) nodes to measure the excess attenuation introduced by the subjects (i. e., human bodies) moving inside the monitored area, and to estimate their positions and movements.
no code implementations • 29 Apr 2024 • Usevalad Milasheuski. Luca Barbieri, Bernardo Camajori Tedeschini, Monica Nicoli, Stefano Savazzi
Federated Learning (FL) allows multiple privacy-sensitive applications to leverage their dataset for a global model construction without any disclosure of the information.
no code implementations • 17 Apr 2024 • Antonio Boiano, Marco Di Gennaro, Luca Barbieri, Michele Carminati, Monica Nicoli, Alessandro Redondi, Stefano Savazzi, Albert Sund Aillet, Diogo Reis Santos, Luigi Serio
This paper provides an overview of the TRUSTroke FL network infrastructure.
no code implementations • 12 Oct 2023 • Luca Barbieri, Stefano Savazzi, Sanaz Kianoush, Monica Nicoli, Luigi Serio
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.
no code implementations • 6 Oct 2023 • Stefano Savazzi, Federica Fieramosca, Sanaz Kianoush, Vittorio Rampa, Michele D'Amico
Electromagnetic (EM) body models predict the impact of human presence and motions on the Radio-Frequency (RF) stray radiation received by wireless devices nearby.
no code implementations • 2 Dec 2022 • Stefano Savazzi, Vittorio Rampa, Sanaz Kianoush, Mehdi Bennis
The MTL process is carried out in two stages: the optimization of a meta-model that can be quickly adapted to learn new tasks, and a task-specific model adaptation stage where the learned meta-model is transferred to agents and tailored for a specific task.
no code implementations • 21 Jun 2022 • Stefano Savazzi, Vittorio Rampa, Sanaz Kianoush, Mehdi Bennis
Classical and centralized Artificial Intelligence (AI) methods require moving data from producers (sensors, machines) to energy hungry data centers, raising environmental concerns due to computational and communication resource demands, while violating privacy.
no code implementations • 27 Apr 2022 • Marco Santoboni, Riccardo Bersan, Stefano Savazzi, Alberto Zecchin, Vittorio Rampa Daniele Piazza
The paper targets the problem of human motion detection using Wireless Local Area Network devices (WiFi) equipped with pattern reconfigurable antennas.
no code implementations • 15 Sep 2021 • Dariush Salami, Ramin Hasibi, Stefano Savazzi, Tom Michoel, Stephan Sigg
Since electromagnetic signals, through cellular communication systems, are omnipresent, RF sensing has the potential to become a universal sensing mechanism with applications in smart home, retail, localization, gesture recognition, intrusion detection, etc.
no code implementations • 15 Apr 2021 • Vittorio Rampa, Gian Guido Gentili, Stefano Savazzi, Michele D'Amico
The paper proposes a multi-body electromagnetic (EM) model for the quantitative evaluation of the influence of multiple human bodies in the surroundings of a radio link.
no code implementations • 26 Mar 2021 • Sanaz Kianoush, Muneeba Raja, Stefano Savazzi, Stephan Sigg
Radio sensing and vision technologies allow to passively detect and track objects or persons by using radio waves as probe signals that encode a 2D/3D view of the environment they propagate through.
no code implementations • 26 Mar 2021 • Sanaz Kianoush, Stefano Savazzi, Manuel Beschi, Stephan Sigg, Vittorio Rampa
Worker monitoring and protection in collaborative robot (cobots) industrial environments requires advanced sensing capabilities and flexible solutions to monitor the movements of the operator in close proximity of moving robots.
3 code implementations • 18 Mar 2021 • Stefano Savazzi, Sanaz Kianoush, Vittorio Rampa, Mehdi Bennis
Recent advances in distributed learning raise environmental concerns due to the large energy needed to train and move data to/from data centers.
2 code implementations • 9 Jan 2021 • Stefano Savazzi, Monica Nicoli, Mehdi Bennis, Sanaz Kianoush, Luca Barbieri
Next-generation autonomous and networked industrial systems (i. e., robots, vehicles, drones) have driven advances in ultra-reliable, low latency communications (URLLC) and computing.
no code implementations • 22 Dec 2020 • Stefano Savazzi, Vittorio Rampa, Leonardo Costa, Sanaz Kianoush, Denis Tolochenko
Massive and unobtrusive screening of people in public environments is becoming a critical task to guarantee safety in congested shared spaces, as well as to support early non-invasive diagnosis and response to disease outbreaks.
Human-Computer Interaction
1 code implementation • 27 Dec 2019 • Stefano Savazzi, Monica Nicoli, Vittorio Rampa
Federated learning (FL) is emerging as a new paradigm to train machine learning models in distributed systems.