Publications related to A Regularized Robust Design Criterion for Uncertain Data

Geometric Variational Models for Inverse Problems in Imaging

This dissertation develops geometric variational models for different inverse problems in imaging that are ill-posed, designing at the same time efficient numerical algorithms to compute their solutions. Variational methods solve inverse problems by the fo ...

EPFL2013

Continuous Localization Using Sparsity Constraints for High-Density Super-Resolution Microscopy

Michaël Unser, Cédric René Jean Vonesch, Hagay Kirshner

Super-resolution localization microscopy relies on sparse activation of photo-switchable probes. Such activation, however, introduces limited temporal resolution. High-density imaging overcomes this limitation by allowing several neighboring probes to be a ...

IEEE2013

Fast Proximal algorithms for Self-concordant function minimization with application to sparse graph selection

Volkan Cevher, Anastasios Kyrillidis

The convex

\ell_1

-regularized

\log\det

divergence criterion has been shown to produce theoretically consistent graph learning. However, this objective function is challenging since the

\ell_1

-regularization is nonsmooth, the

\log\det

objective is n ...

2013

Wavelet-Based Reconstruction for Magnetic Resonance Imaging

Matthieu Guerquin-Kern

Magnetic resonance imaging (MRI) scanners produce raw measurements that are unfit to direct interpretation, unless an algorithmic step, called reconstruction, is introduced. Up to the last decade, this reconstruction was performed by algorithms of moderate ...

EPFL2012

Reduced Models for Optimal Control, Shape Optimization and Inverse Problems in Haemodynamics

Andrea Manzoni

The objective of this thesis is to develop reduced models for the numerical solution of optimal control, shape optimization and inverse problems. In all these cases suitable functionals of state variables have to be minimized. State variables are solutions ...

EPFL2012

Strongly feasible stochastic model predictive control

Milan Korda

In this article we develop a systematic approach to enforce strong feasibility of probabilistically constrained stochastic model predictive control problems for linear discrete-time systems under affine disturbance feedback policies. Two approaches are pre ...

2011

Sparsity-Driven Reconstruction for FDOT With Anatomical Priors

Michaël Unser, Jean-Charles Baritaux, Kai Hassler, Sebanti Sanyal, Martina Bucher

In this paper we propose a method based on (2, 1)-mixed-norm penalization for incorporating a structural prior in FDOT image reconstruction. The effect of (2, 1)-mixed-norm penalization is twofold: first, a sparsifying effect which isolates few anatomical ...

IEEE2011

Multicore thermal management using approximate explicit Model Predictive Control

Giovanni De Micheli, David Atienza Alonso, Colin Neil Jones, Francesco Zanini

Meeting temperature constraints and reducing the hot-spots are critical for achieving reliable and efficient operation of complex multi-core systems. In this paper we aim at achieving an online smooth thermal control action that minimizes the performance l ...

IEEE Press2010

Multicore thermal management using approximate explicit model predictive control

Giovanni De Micheli, David Atienza Alonso, Colin Neil Jones, Francesco Zanini

Meeting temperature constraints and reducing the hot-spots are critical for achieving reliable and efficient operation of complex multi-core systems. In this paper we aim at achieving an online smooth thermal control action that minimizes the performance l ...

IEEE2010

FASTER CADZOW DENOISING BASED ON PARTIAL EIGENVALUE DECOMPOSITION

Yann Barbotin

Certain aspects of the present disclosure relate to a method for speeding up the Cadzow iterative denoising algorithm as a part of the Finite Rate of Innovation (FRI) processing and for decreasing its computational complexity. ...

USPTO2010

A Regularized Robust Design Criterion for Uncertain Data

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Geometric Variational Models for Inverse Problems in Imaging

Continuous Localization Using Sparsity Constraints for High-Density Super-Resolution Microscopy

Fast Proximal algorithms for Self-concordant function minimization with application to sparse graph selection

Wavelet-Based Reconstruction for Magnetic Resonance Imaging

Reduced Models for Optimal Control, Shape Optimization and Inverse Problems in Haemodynamics

Strongly feasible stochastic model predictive control

Sparsity-Driven Reconstruction for FDOT With Anatomical Priors

Multicore thermal management using approximate explicit Model Predictive Control

Multicore thermal management using approximate explicit model predictive control

FASTER CADZOW DENOISING BASED ON PARTIAL EIGENVALUE DECOMPOSITION

Strongly feasible stochastic model predictive control

Multicore thermal management using approximate explicit model predictive control

Multicore thermal management using approximate explicit Model Predictive Control

Fast Proximal algorithms for Self-concordant function minimization with application to sparse graph selection

Reduced Models for Optimal Control, Shape Optimization and Inverse Problems in Haemodynamics

Continuous Localization Using Sparsity Constraints for High-Density Super-Resolution Microscopy

Sparsity-Driven Reconstruction for FDOT With Anatomical Priors

Wavelet-Based Reconstruction for Magnetic Resonance Imaging

Geometric Variational Models for Inverse Problems in Imaging

FASTER CADZOW DENOISING BASED ON PARTIAL EIGENVALUE DECOMPOSITION