Advanced search
Start date
Betweenand

Adaptive filtering and machine learning: applications in diffusion networks, soft sensors and classification of cardiac arrhythmias

Grant number: 21/02063-6
Support type:Regular Research Grants
Duration: August 01, 2021 - July 31, 2023
Field of knowledge:Engineering - Electrical Engineering - Telecommunications
Principal researcher:Magno Teófilo Madeira da Silva
Grantee:Magno Teófilo Madeira da Silva
Home Institution: Escola Politécnica (EP). Universidade de São Paulo (USP). São Paulo , SP, Brazil
Assoc. researchers: Renato Candido

Abstract

Distributed signal processing has attracted attention in the scientific community due to its several advantages over centralized approaches. In this field, sampling and censoring techniques have been topics of intense research, since the cost associated with measuring, processing and/or transmitting data throughout the entire network may be prohibitive in certain situations. The aim of this project is to deeply study sampling and censoring techniques, by proposing improvements to the algorithms that were recently proposed in the literature. These algorithms will be extended to different diffusion networks, such as multitask, kernel-based, and asynchronous networks. Distributed versions of kernel adaptive filters will also be addressed and compared to distributed neural networks for thesolution of nonlinear problems in diffusion networks. The Gram-Schmidt-based sparsification technique for dictionary will be also applied to kernel principal component analysis.In the machine learning field, we intend to use soft sensors for fault detection and diagnosisin air conditioning systems, enabling corrections before the increase in the consumption of electrical energy. Finally, we indent to use neural networks to classify cardiac arrhythmias, taking into account thethe separation of the patients into training and testing sets. (AU)

Articles published in Agência FAPESP Newsletter about the research grant:
Articles published in other media outlets (0 total):
More itemsLess items
VEICULO: TITULO (DATA)
VEICULO: TITULO (DATA)