
Jacopo PicciniReykjavík University · School of Engineering
Jacopo Piccini
Master of Science
Mathematical modelling, machine learning and signal processing applied to the electrodermal system and sleep.
About
10
Publications
382
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Citations
Introduction
I received a M.Sc. in Aerospace Engineering from the University of Bologna in April 2021. By spending my last year as an Erasmus+ Exchange student at Chalmers Univerisity, I could focus on control theory and dynamical systems.
From November 2021, I am pursuing a PhD in Engineering at Reykjavik University. My current research interest lies in the mathematical modelling of physiological processes, particularly thermoregulation, in addition to control theory.
Education
September 2019 - October 2021
October 2014 - March 2019
Publications
Publications (10)
The use of the electrodermal activity (EDA) signal for health diagnostics is becoming increasingly popular. The increase is due to advances in computational methods such as machine learning (ML) and the availability of wearable devices capable of better measuring EDA signals. One field where work on EDA has significantly increased is sleep research...
Currently, there is significant interest in developing algorithms for processing electrodermal activity (EDA) signals recorded during sleep. The interest is driven by the growing popularity and increased accuracy of wearable devices capable of recording EDA signals. If properly processed and analysed, they can be used for various purposes, such as...
In this paper, we present a novel approach to determine the stability of switched linear and nonlinear systems using Sum of Squares optimisation. Particularly, we use Sum of Squares optimisation to search for a Lyapunov function that defines an absorbing set that confines solution trajectories. For linear systems, we show that this also implies glo...
In this paper, we present a novel approach to determine the stability of switched linear and nonlinear systems using Sum of Squares optimisation. Particularly, we use Sum of Squares optimisation to search for a Lyapunov function that defines an absorbing set that confines solution trajectories. For linear systems, we show that this also implies glo...
It has been recently demonstrated that the artificial neural networks’ (ANN) learning
under gradient descent method, can be studied using neural tangent kernel (NTK).
This thesis’ goal is to show how techniques related to control theory, can be applied
to model and improve the hyperparameters training dynamics. Moreover, it will be proven how by us...