Publications related to Support Recovery in Compressed Sensing: An Estimation Theoretic Approach

PMU-based linear state estimation of Lausanne subtransmission network: Experimental validation

Mario Paolone, Asja Derviskadic, Paolo Romano, Marco Pignati, Lorenzo Zanni, Ali Abur

The paper presents the implementation details and the experimental validation of a linear state estimator based on synchrophasor measurements in a real subtransmission network. The data are provided by 15 phasor measurement units (PMUs) installed in the 12 ...

2020

Distributed State Estimation and Cooperative Path-Following Under Communication Constraints

Francisco Fernandes Castro Rego

The main topics of this thesis are distributed estimation and cooperative path-following in the presence of communication constraints, with applications to autonomous marine vehicles. To this end, we study algorithms that take explicitly into account the c ...

EPFL2018

Averaging Stochastic Gradient Descent on Riemannian Manifolds

Nicolas Henri Bernard Flammarion

We propose an estimator for the mean of a random vector in Rd that can be computed in time O(n3.5 + n2d) for n i.i.d. samples and that has error bounds matching the sub-Gaussian case. The only assumptions we make about the data distribution are that it has ...

2018

Residual-based a posteriori error estimation for contact problems approximated by Nitsche's method

Mathieu Jonathan Fabre, Yves Renard

We introduce a residual-based a posteriori error estimator for contact problems in two- and three-dimensional linear elasticity, discretized with linear and quadratic finite elements and Nitsche’s method. Efficiency and reliability of the estimator are pro ...

2018

Parameter Determination of Sensor Stochastic Models under Covariate Dependency

Jan Skaloud, Stéphane Guerrier, Philipp Clausen

The proliferation of (low-cost) sensors provokes new challenges in data fusion. This is related to the correctness of stochastic characterization that is a prerequisite for optimal estimation of parameters from redundant observations. Different (statistica ...

2017

Blowing in the Wind

Marta Martinez-Camara

Every day tons of pollutants are emitted into the atmosphere all around the world. These pollutants are altering the equilibrium of our planet, causing profound changes in its climate, increasing global temperatures, and raising the sea level. The need to ...

EPFL2017

Long-Term Stable Control of Motor-Imagery BCI by a Locked-in User Through Adaptive Assistance

José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Sareh Saeedi

Performance variation is one of the main challenges that BCIs are confronted with, when being used over extended periods of time. Shared control techniques could partially cope with such a problem. In this paper, we propose a taxonomy of shared control app ...

Institute of Electrical and Electronics Engineers2017

Robust compressive sensing of sparse signals: A review

Alonso Ramirez Manzanares, Rafael Eduardo Carrillo Rangel

Compressive sensing generally relies on the L2-norm for data fidelity, whereas in many applications robust estimators are needed. Among the scenarios in which robust performance is required, applications where the sampling process is performed in the prese ...

Springer International Publishing Ag2016

Estimation Error of the Constrained Lasso

Volkan Cevher, Yen-Huan Li, Ya-Ping Hsieh, Nissim Zerbib

This paper presents a non-asymptotic upper bound for the estimation error of the constrained lasso, under the high-dimensional (

n \ll p

) setting. In contrast to existing results, the error bound in this paper is sharp, is valid when the parameter to be e ...

2016

A new robust and efficient estimator for ill-conditioned linear inverse problems with outliers

Martin Vetterli, Ali Zoubir, Marta Martinez-Camara

Solving a linear inverse problem may include difficulties such as the presence of outliers and a mixing matrix with a large condition number. In such cases a regularized robust estimator is needed. We propose a new tau-type regularized robust estimator tha ...

2015

Support Recovery in Compressed Sensing: An Estimation Theoretic Approach

Graph Chatbot

Chat with Graph Search

PMU-based linear state estimation of Lausanne subtransmission network: Experimental validation

Distributed State Estimation and Cooperative Path-Following Under Communication Constraints

Averaging Stochastic Gradient Descent on Riemannian Manifolds

Residual-based a posteriori error estimation for contact problems approximated by Nitsche's method

Parameter Determination of Sensor Stochastic Models under Covariate Dependency

Blowing in the Wind

Long-Term Stable Control of Motor-Imagery BCI by a Locked-in User Through Adaptive Assistance

Robust compressive sensing of sparse signals: A review

Estimation Error of the Constrained Lasso

A new robust and efficient estimator for ill-conditioned linear inverse problems with outliers

Distributed State Estimation and Cooperative Path-Following Under Communication Constraints

Residual-based a posteriori error estimation for contact problems approximated by Nitsche's method

Robust compressive sensing of sparse signals: A review

Blowing in the Wind

Averaging Stochastic Gradient Descent on Riemannian Manifolds

Long-Term Stable Control of Motor-Imagery BCI by a Locked-in User Through Adaptive Assistance

Estimation Error of the Constrained Lasso

Parameter Determination of Sensor Stochastic Models under Covariate Dependency

A new robust and efficient estimator for ill-conditioned linear inverse problems with outliers

PMU-based linear state estimation of Lausanne subtransmission network: Experimental validation