Publicaciones

Affichage de 3681 à 3690 sur 16561


  • Article dans une revue

MEMS High Temperature Gradient Sensor for Skin-Friction Measurements in Highly Turbulent Flows

Cécile Ghouila-Houri, Abdelkrim Talbi, Romain Viard, Quentin Gallas, Eric Garnier, Pascal Molton, Jerome Delva, Alain Merlen, Philippe Pernod

This paper presents and discusses the results obtained with a MEMS (Micro-Electro-Mechanical System) high temperature gradient sensor for time-averaged and fluctuating skin-friction measurements in highly turbulent flows. Designed as a robust wall-mounted suspended hot-wire structure, the micro-...

IEEE Sensors Journal, 2021, 21 (8), pp.9749-9755. ⟨10.1109/JSEN.2020.2991785⟩. ⟨hal-03229029⟩

  • Communication dans un congrès

Third order notch over multi-bias and temperature in GaN and GaAs HEMTs

M.A. Alim, A.A. Rezazadeh, Christophe Gaquière

A detailed study about the shifting of third-order notch and threshold voltage with temperature for GaN and GaAs FETs were analyzed. For this purpose, a great deal of data over temperature between-40 to 150°C were measured by using two-tone intermodulation distortion set up. It was found that the...

15th European Microwave Integrated Circuits Conference, EuMIC 2020, Jan 2021, Utrecht, Netherlands. pp.277-280, ⟨10.1109/EuMIC48047.2021.00081⟩. ⟨hal-03362263⟩

  • Article dans une revue

Human sensing in reverberant environments: RF-based occupancy and fall detection in ships

Marwan Yusuf, Emmeric Tanghe, Brecht de Beelde, Pierre Laly, Matteo Ridolfi, Eli de Poorter, Luc Martens, Davy Gaillot, M. Lienard, Wout Joseph

In this paper, we explore the possibility of estimating the number of people inside a below-deck ship compartment using the theory of room electromagnetics. Most RF-based occupancy detection solutions focus on residential environments. Confined metallic spaces such in industrial environments are...

IEEE Transactions on Vehicular Technology, 2021, 70 (5), pp.4512-4522. ⟨10.1109/TVT.2021.3069326⟩. ⟨hal-03344319⟩

  • Communication dans un congrès

Securing Deep Spiking Neural Networks against Adversarial Attacks through Inherent Structural Parameters

R. El-Allami, A. Marchisio, M. Shafique, Ihsen Alouani

Deep Learning (DL) algorithms have gained popularity owing to their practical problem-solving capacity. However, they suffer from a serious integrity threat, i.e., their vulnerability to adversarial attacks. In the quest for DL trustworthiness, recent works claimed the inherent robustness of...

2021 Design, Automation and Test in Europe Conference and Exhibition (DATE 2021), Feb 2021, Grenoble, France. pp.774-779, ⟨10.23919/DATE51398.2021.9473981⟩. ⟨hal-03362270⟩