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Favrat Pierre

Professeur HES associé

Main skills

Professeur HES associé

Phone: +41 24 557 27 77

Desktop: C0.03

Haute Ecole d'Ingénierie et de Gestion du Canton de Vaud
Route de Cheseaux 1, 1400 Yverdon-les-Bains, CH

Institute
IICT - Institut des Technologies de l'Information et de la Communication

MSc HES-SO en Engineering - HES-SO Master

Systèmes embarqués

BSc HES-SO en Génie électrique - Haute Ecole d'Ingénierie et de Gestion du Canton de Vaud

Completed

Tyxit_Chèque d'innovation

AGP

Role: Main Applicant

Financement: Innosuisse IRIS

Description du projet : Chèque d'innovation pour une étude préliminaire d'un projet "Radio multi-point à faible latence pour transfert audio haute fidélité"

Research team within HES-SO: Favrat Pierre , Hochet Bertrand , Brunner Nicolas , Nicoulaz Didier

Partenaires académiques: IICT; Favrat Pierre, IICT

Durée du projet: 18.08.2019 - 23.07.2020

Montant global du projet: 15'000 CHF

Statut: Completed

Développement d'un capteur MEMS smart IoT

AGP

Role: Main Applicant

Financement: CTI

Description du projet : Développement d'un capteur MEMS smart IoT pour applications industrielles.

Research team within HES-SO: Favrat Pierre , Messerli Etienne , Hochet Bertrand , Brunner Nicolas , Nicoulaz Didier

Partenaires académiques: IICT; Favrat Pierre, IICT

Durée du projet: 10.02.2019 - 10.05.2020

Montant global du projet: 15'000 CHF

Statut: Completed

MEMS scanning mirror-based ultra-miniature projection system for HUD and mobile applications

AGP

Role: Main Applicant

Financement: EPFL-LEG

Description du projet : LEMOPTIX develops miniature laser microprojector modules destined for integration into industrial display systems and mobile devices. A MEMS micromirror-based projector prototype has already been demonstrated with the following performances: VGA resolution, 16 million colors, VGA connectivity, video projection, yet several challenges related to miniaturization and new functionalities requested by the market, of the microprojector system as a whole need to be solved. The aim of this project is to reach the next level of miniaturization of the microprojection system and add laser eye safety and tactile functionality to the device. The objective is the validation of the color microprojector product in industrial and consumer applications. Meanwhile, all the industrialization and subsequently the production and commercialization will be done by Lemoptix.

Research team within HES-SO: Favrat Pierre , Hochet Bertrand

Partenaires académiques: SIM; Favrat Pierre, SIM

Durée du projet: 02.01.2012 - 28.06.2013

Montant global du projet: 40'000 CHF

Statut: Completed

2020

Localization of electromagnetic interference sources using a time reversal cavity

Scientific paper ArODES

Hamidreza Karami, Mohammad Azadifar, Amirhossein Mostajabi, Pierre Favrat, Marcos Rubinstein, Farhad Rachidi

IEEE Transactions on Industrial Electronics, 2021, vol. 68, no. 1, pp. 654-662

Link to the publication

Summary:

In this paper, we propose and implement a novel technique to locate Electromagnetic Interference (EMI) sources using the concept of time reversal cavity. We show in an intuitive manner that reflections from the surfaces of a cavity can emulate an infinite number of sensors in the Time Reversal (TR) method. In order to locate EMI sources, the equipment under test (EUT) is placed in a rectangular metallic cavity, of suitable dimensions according to the considered frequency band and the EUT size, equipped with a simple monopole or dipole antenna. We demonstrate that, by using only one sensor, we are able to locate EMI sources by taking advantage of the focusing properties of a time reversal cavity. The entropy criterion is applied to obtain the focusing time slice in which the maximum electric field determines the location of the EMI source. Both two- and three-dimensional numerical simulation schemes are deployed to demonstrate the ability of the proposed technique. The validity of numerical simulations is tested against frequency domain measurements. Compared to conventional EMI tests in anechoic chambers and scanning methods, the proposed technique represents a simpler and cost-effective test method requiring only one sensor (a monopole or dipole antenna).

2025

Optimum clustering for multi-border router wireless mesh networks

Conference ArODES

Mahboob Karimian, Yann Charbon, Pierre Favrat

Proceedings of the 2025 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA)

Link to the conference

Summary:

Low-power wireless networks constructed of thousands of nodes forming a mesh topology are becoming increasingly common. Various standards notably Wi-SUN tried to provide a solid frame for the networking part of these networks. However, as the network size grows, congested nodes become a bottleneck and make the network unreliable. Many routing algorithms, including RPL used by most mesh network standards, are based on the local decision of each node without having a global prevision of the ongoing traffic in other links. Additionally, the propagation of traffic information from each link creates an extra load in the network and increases management complexity. To tackle this problem, one solution is to optimize the network for high Quality of Service (QoS) by adding multiple border routers. It allows splitting a large network into manageable sub networks. This remains a cheaper and simpler solution because instead of creating many control packets, we only redirect traffic to a new sink which has high bandwidth and reliable connection towards the cloud/destination. Still, adding border routers needs an arrangement of the whole network so that only the minimum number of border routers for the level of QoS requested by the service user is used. This arrangement consists of finding the best location for the new border routers. This is done in two essential steps: i) Clustering the current big network into small sub-networks ii) Finding the nodes in the cluster that are optimal to be a new border router.

Wireless mesh networks routing optimization using machine learning and deep learning

Conference ArODES

Yann Charbon, Eric Tran, Mahboob Karimian, Pierre Favrat

Proceedings of the 2025 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA)

Link to the conference

Summary:

Wireless mesh networks (WMNs) are increasingly deployed in large-scale infrastructures, where efficient and reliable routing is essential to maximise performance. Conventional protocols such as RPL rely on local decision-making and often produce globally suboptimal Directed Acyclic Graphs (DAGs), particularly in dense or complex topologies. This work investigates whether machine learning (ML) and deep learning (DL) techniques can be used to globally optimise DAG-based routing in WMNs. To support this exploration, we developed a high-performance simulator that evaluates the routing efficiency of arbitrary spanning trees using realistic link quality metrics derived from physical parameters such as RSSI. This simulator generates a large labelled dataset of optimal and suboptimal trees across randomly generated topologies, which is then used to train various ML and DL models. We formulate the optimisation task as a multi-label classification problem, where models predict the set of edges forming the optimal DAG. Despite extensive experimentation with classical classifiers, MLPs, and RNNs, our results show that standard models fail to outperform RPL, primarily due to the difficulty of learning under strict DAG constraints, such as acyclicity and single-parent-per-node rules, which render the optimisation problem non-differentiable. However, the simulator consistently identifies DAGs that outperform RPL by up to 16% in terms of routing efficiency. These findings underscore the limitations of unconstrained learning in combinatorial domains and point toward future research into constraint-aware, structure-driven learning frameworks for global WMN optimisation.

2021

Wireless communication network for big data capture and predictive maintenance in the context of Industry 4.0

Conference

Favrat Pierre

Industrie 2025, 03.02.2021 - 03.02.2021, Online

Link to the conference

Achievements

PEOPLE@HES-SO
Directory and Skills inventory

Favrat Pierre

Professeur HES associé

Main skills

Wireless Communication

IoT Internet Of Things

Embedded Systems

Professeur HES associé

PEOPLE@HES-SO Directory and Skills inventory

Favrat Pierre

Professeur HES associé

Main skills

Wireless Communication

IoT Internet Of Things

Embedded Systems

Professeur HES associé

PEOPLE@HES-SO
Directory and Skills inventory