4 accepted papers
Federated Graph Learning (FGL) has emerged as a powerful paradigm for decentralized training of graph neural networks while preserving data privacy. However, existing FGL methods are predominantly designed for static graphs and rely on parameter averaging or distribution alignment, which implicitly
This paper proposes to employ a multiple-input multiple-output (MIMO) radar-based quickest change detection (QCD) for non-contact sudden abnormal heart rate (HR) alerting, where the HR changes from a normal to an abnormal value at an unknown change time. By developing a signal model for the non-cont…
Consider a quickest detection problem, where a sudden change of parameters needs to be detected as quickly as possible. When unknown parameters exist in the post-change distribution, generalized likelihood ratio test (GLRT) based method is a straightforward option, which may, however, lead to huge c…
Heart rate (HR) is one of the most important indicators to assess the health condition of a person, so obtaining an accurate HR estimate is crucial. In this paper, a multiple-input multiple-output (MIMO) radar with dispersed antennas is employed to monitor the physiological signals caused by respira…