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Publications associées (26)

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Learning approaches to high-fidelity optical diffraction tomography

Joowon Lim

Optical diffraction tomography (ODT) provides us 3D refractive index (RI) distributions of transparent samples. Since RI values differ across different materials, they serve as endogenous contrasts. It, therefore, enables us to image without pre-processing ...

EPFL2020

Learning Feature Maps of the Koopman Operator: A Subspace Viewpoint

Colin Neil Jones, Yingzhao Lian

The Koopman operator was recently shown to be a useful method for nonlinear system identification and controller design. However, the scalability of current data-driven approaches is limited by the selection of feature maps. In this paper, we present a new ...

IEEE2019

Design and Implementation of a Neural Network Based Predistorter for Enhanced Mobile Broadband

Alexios Konstantinos Balatsoukas Stimming

Digital predistortion is the process of using digital signal processing to correct nonlinearities caused by the analog RF front-end of a wireless transmitter. These nonlinearities contribute to adjacent channel leakage, degrade the error vector magnitude o ...

IEEE2019

Shape constrained splines as transparent black-box models for bioprocess modeling

Dominique Bonvin, Julien Léo Billeter, Sriniketh Srinivasan

Empirical model identification for biological systems is a challenging task due to the combined effects of complex interactions, nonlinear effects, and lack of specific measurements. In this context, several researchers have provided tools for experimental ...

Elsevier2017

Sensorless Method with Parameter Identification Applied to a Bistable Fast Linear Switched Reluctance Actuator for Textile Machine

Yves Perriard, Douglas Martins Araujo

This paper provides a sensorless method to control the position of keys passing through a linear actuator used in an industrial textile machine. The presented method describes how to find out the position of the key using two cascading discrete Kalman filt ...

2017

Parameter identification of a lithium-ion cell single-particle model through non-invasive testing

Mario Paolone, Sidi-Rachid Cherkaoui, Dimitri Torregrossa, Emil Namor

Physics-based models of electrochemical cells are of great interest for the future battery management systems (BMSs), due to their accuracy and capability to predict cell physical states. One of their main disadvantages, when compared to equivalent circuit ...

Elsevier Science Bv2017