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Conference papers

Model-based adaptive filtering of harmonic perturbations applied to high-frequency noninvasive valvometry

Nelson Barroso 1 Rosane Ushirobira 1 Denis Efimov 1 Mohamedou Sow 2 Jean-Charles Massabuau 2
1 VALSE - Finite-time control and estimation for distributed systems
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
Abstract : In this paper, a model-based adaptive filter is used to suppress electrical noise in a high-frequency noninvasive valvometry device, which is part of an autonomous biosensor system using bivalve mollusks valve-activity measurements for ecological monitoring purposes. The proposed model-based adaptive filter uses the dynamic regressor extension and mixing method to allow a decoupled estimation of the parameters. Once the desired regression form of the output model is obtained, a fixed-time estimation approach is used to identify its parameters. By applying these two techniques, a flexible filter structure is obtained with the property of retaining the major relevant components of interest of the original valve-activity signals, even in the case when the unwanted signal frequency components are in the same frequency range as the useful variables.
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Submitted on : Thursday, July 2, 2020 - 3:52:10 PM
Last modification on : Thursday, March 24, 2022 - 3:42:50 AM
Long-term archiving on: : Thursday, September 24, 2020 - 5:18:49 AM


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Nelson Barroso, Rosane Ushirobira, Denis Efimov, Mohamedou Sow, Jean-Charles Massabuau. Model-based adaptive filtering of harmonic perturbations applied to high-frequency noninvasive valvometry. IFAC 2020 - 21st IFAC World Congress, Jul 2020, Berlin, Germany. ⟨hal-02887927⟩



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