Publication

Fast and Robust Parametric Estimation of Jointly Sparse Channels

Publications associées (29)

Functional-Basis Analysis of Non-Stationary Signals in Modern Power Grids: Theory and Implementation in Embedded Systems

Situational awareness strategies are essential for the reliable and secure operation of the electric power grid which represents critical infrastructure in modern society. With the rise of converter-interfaced renewable generation and the consequent shift ...

EPFL2024

Robust discrete choice models with t-distributed kernel errors

Michel Bierlaire, Prateek Bansal, Thomas Gasos

Outliers in discrete choice response data may result from misclassification and misreporting of the response variable and from choice behaviour that is inconsistent with modelling assumptions (e.g. random utility maximisation). In the presence of outliers, ...

2023

Traversing Time Dependent Light Fields for Daylight Glare Evaluation

Stephen William Wasilewski

To understand how daylight gives shape and life to architectural spaces, whether existing or imagined, requires quantifying its dynamism and energy. Maintaining these details presents a challenge to simulation and analysis methods that flatten data into di ...

EPFL2023

Regularization for distributionally robust state estimation and prediction

Jean-Sébastien Hubert Brouillon, Giancarlo Ferrari Trecate, Florian Dörfler

The increasing availability of sensing techniques provides a great opportunity for engineers to design state estimation methods, which are optimal for the system under observation and the observed noise patterns. However, these patterns often do not fulfil ...

2023

Microstrip patch antennas with broad beamwidth

Ismael Vico Triviño

Microstrip antennas offer a broad set of advantages such as low profile, light weight, easy fabrication and low cost. As these are desirable or even critical for a broad range of applications, there has been a large interest for these antennas in the anten ...

EPFL2023

Center-aware Adversarial Augmentation for Single Domain Generalization

Mathieu Salzmann, Zhiye Wang

Domain generalization (DG) aims to learn a model from multiple training (i.e., source) domains that can generalize well to the unseen test (i.e., target) data coming from a different distribution. Single domain generalization (SingleDG) has recently emerge ...

IEEE COMPUTER SOC2023

Identification of typical district configurations: A two-step global sensitivity analysis framework

François Maréchal, Jonas Schnidrig, Cédric Terrier

The recent geopolitical conflicts in Europe highlighted the sensibility of the current energy system to the volatility of energy carrier prices. In the prospect of defining robust energy system configurations to ensure energy supply stability, it is necess ...

2023

Small antennas with broad beamwidth integrated on a drone enclosed in a protective structure

Anja Skrivervik, Ismael Vico Triviño

In this paper, we analyze the integration of small antennas in a drone protected by a conductive structure. They must be light and resistant to provide high collision resilience, which is critical for drones flown indoors and in complex environments. To ac ...

IEEE2023

Analysis-aware defeaturing of complex geometries with Neumann features

Ondine Gabrielle Chanon, Pablo Antolin Sanchez

Local modifications of a computational domain are often performed in order to simplify the meshing process and to reduce computational costs and memory requirements. However, removing geometrical features of a domain often introduces a non-negligible error ...

Wiley2023