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Found 12 papers, 2 papers with code
On the impact of the antenna radiation patterns in passive radio sensing
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.
Full-wave EM simulation analysis of human body blockage by dense 2D antenna arrays
Recently, proposals of human-sensing-based services for cellular and local area networks have brought indoor localization to the attention of several research groups.
Physics-informed generative neural networks for RF propagation prediction with application to indoor body perception
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.
An EM Body Model for Device-Free Localization with Multiple Antenna Receivers: A First Study
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.
A physics-informed generative model for passive radio-frequency sensing
Electromagnetic (EM) body models predict the impact of human presence and motions on the Radio-Frequency (RF) stray radiation received by wireless devices nearby.
On the Energy and Communication Efficiency Tradeoffs in Federated and Multi-Task Learning
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.
An Energy and Carbon Footprint Analysis of Distributed and Federated Learning
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.
Electromagnetic Models for Passive Detection and Localization of Multiple Bodies
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.
A Multisensory Edge-Cloud Platform for Opportunistic Radio Sensing in Cobot Environments
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.
A Framework for Energy and Carbon Footprint Analysis of Distributed and Federated Edge Learning
Recent advances in distributed learning raise environmental concerns due to the large energy needed to train and move data to/from data centers.
Processing of body-induced thermal signatures for physical distancing and temperature screening
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
Federated Learning with Cooperating Devices: A Consensus Approach for Massive IoT Networks
Federated learning (FL) is emerging as a new paradigm to train machine learning models in distributed systems.
