CNRS GIPSA Laboratory

Grenoble, France

CNRS GIPSA Laboratory

Grenoble, France
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Amblard P.-O.,CNRS GIPSA Laboratory | Michel O.J.J.,CNRS GIPSA Laboratory
Journal of Computational Neuroscience | Year: 2011

Directed information theory deals with communication channels with feedback. When applied to networks, a natural extension based on causal conditioning is needed. We show here that measures built from directed information theory in networks can be used to assess Granger causality graphs of stochastic processes. We show that directed information theory includes measures such as the transfer entropy, and that it is the adequate information theoretic framework needed for neuroscience applications, such as connectivity inference problems. © 2010 Springer Science+Business Media, LLC.


Alamir M.,CNRS GIPSA Laboratory
IEEE Transactions on Automatic Control | Year: 2017

In this paper, a state-dependent control updating period strategy is proposed for use in interrupted implementation of real-time Model Predictive Control (MPC). The strategy can be used as soon as a certification bound is available for the underlying optimization algorithm. Moreover, a new fast-Gradient based certifiable algorithm is proposed with the associated certification bounds for convex generally constrained optimization problems. © 2016 IEEE.


Alamir M.,CNRS GIPSA Laboratory
Automatica | Year: 2012

In this paper, a novel approach is proposed to implement low-dimensional parameterized Nonlinear Model Predictive Control (NMPC) schemes for systems showing fast dynamics. The proposed scheme is based on distributing the reconstruction of the cost function over the real lifetime of the controlled system. The framework is particularly suitable for NMPC formulations that use low dimensional control parametrization. The concrete example of a Planar Vertical Take-Off and Landing (PVTOL) aircraft stabilization problem is used to illustrate the efficiency of the proposed formulation. © 2011 Elsevier Ltd. All rights reserved.


Seuret A.,CNRS GIPSA Laboratory
Automatica | Year: 2012

This article proposes a novel approach to assess the stability of continuous linear systems with sampled-data inputs. The method, which is based on the discrete-time Lyapunov theorem, provides easy tractable stability conditions for the continuous-time model. Sufficient conditions for asymptotic and exponential stability are provided dealing with synchronous and asynchronous samplings and uncertain systems. An additional stability analysis is provided for the cases of multiple sampling periods and packet losses. Several examples show the efficiency of the method. © 2011 Elsevier Ltd. All rights reserved.


Comon P.,CNRS GIPSA Laboratory
IEEE Signal Processing Magazine | Year: 2014

Tensor decompositions are at the core of many blind source separation (BSS) algorithms, either explicitly or implicitly. In particular, the canonical polyadic (CP) tensor decomposition plays a central role in the identification of underdetermined mixtures. Despite some similarities, CP and singular value decomposition (SVD) are quite different. More generally, tensors and matrices enjoy different properties, as pointed out in this brief introduction. © 1991-2012 IEEE.


Condat L.,CNRS GIPSA Laboratory
IEEE Signal Processing Letters | Year: 2014

We propose new optimization algorithms to minimize a sum of convex functions, which may be smooth or not and composed or not with linear operators. This generic formulation encompasses various forms of regularized inverse problems in imaging. The proposed algorithms proceed by splitting: the gradient or proximal operators of the functions are called individually, without inner loop or linear system to solve at each iteration. The algorithms are easy to implement and have proven convergence to an exact solution. The classical Douglas-Rachford and forward-backward splitting methods, as well as the recent and efficient algorithm of Chambolle-Pock, are recovered as particular cases. The application to inverse imaging problems regularized by the total variation is detailed. © 2014 IEEE.


Cecotti H.,CNRS GIPSA Laboratory
Journal of Physiology Paris | Year: 2011

Brain-Computer Interfaces (BCIs) have become a large research field that include challenges mainly in neuroscience, signal processing, machine learning and user interface. A non-invasive BCI can allow the direct communication between humans and computers by analyzing electrical brain activity, recorded at the surface of the scalp with electroencephalography. The main purpose for BCIs is to enable communication for people with severe disabilities. Spelling is one of the first BCI application, it corresponds to the main communication mean for people who are unable to speak. While spelling can be the most basic application it remains a benchmark for communication applications and one challenge in the BCI community for some patients. This paper proposes a review of the current main strategies, and their limitations, for spelling words. It includes recent BCIs based on P300, steady-state visual evoked potentials and motor imagery. © 2011 Elsevier Ltd.


Lim L.-H.,University of Chicago | Comon P.,CNRS GIPSA Laboratory
IEEE Transactions on Information Theory | Year: 2014

We discuss a technique that allows blind recovery of signals or blind identification of mixtures in instances where such recovery or identification were previously thought to be impossible. These instances include: 1) closely located or highly correlated sources in antenna array processing; 2) highly correlated spreading codes in code division multiple access (CDMA) radio communication; and 3) nearly dependent spectra in fluorescence spectroscopy. These have important implications. In the case of antenna array processing, it allows for joint localization and extraction of multiple sources from the measurement of a noisy mixture recorded on multiple sensors in an entirely deterministic manner. In the case of CDMA, it allows the possibility of having a number of users larger than the spreading gain. In the case of fluorescence spectroscopy, it allows for detection of nearly identical chemical constituents. The proposed technique involves the solution of a bounded coherence low-rank multilinear approximation problem. We show that bounded coherence allows us to establish existence and uniqueness of the recovered solution. We will provide some statistical motivation for the approximation problem and discuss greedy approximation bounds. To provide the theoretical underpinnings for this technique, we develop a corresponding theory of sparse separable decompositions of functions, including notions of rank and nuclear norm that can be specialized to the usual ones for matrices and operators and also be applied to hypermatrices and tensors. © 1963-2012 IEEE.


Condat L.,CNRS GIPSA Laboratory
IEEE Signal Processing Letters | Year: 2013

A very fast noniterative algorithm is proposed for denoising or smoothing one-dimensional discrete signals, by solving the total variation regularized least-squares problem or the related fused lasso problem. A C code implementation is available on the web page of the author. © 2013 IEEE.


Briat C.,ETH Zurich | Seuret A.,CNRS GIPSA Laboratory
IEEE Transactions on Automatic Control | Year: 2013

An alternative approach for minimum and mode-dependent dwell-time characterization for switched systems is derived. While minimum-dwell time results require the subsystems to be asymptotically stable, mode-dependent dwell-time results can consider unstable subsystems and dwell-times within a, possibly unbounded, range of values. The proposed approach is related to Lyapunov looped-functionals, a new type of functionals leading to stability conditions affine in the system matrices, unlike standard results for minimum dwell-time. These conditions are expressed as infinite-dimensional LMIs which can be solved using recent polynomial optimization techniques such as sum-of-squares. The specific structure of the conditions is finally utilized in order to derive dwell-time stability results for uncertain switched systems. Several examples illustrate the efficiency of the approach. © 2012 IEEE.

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