Cubo, Rubén

Deep Brain Stimulation (DBS) is an established therapy that is predominantly utilized in treating the symptoms of neurodegenerative diseases such as Parkinson's Disease and Essential Tremor, crippling diseases like Chronic Pain and Epilepsy, and psychiatric diseases such as Schizophrenia and Depression. Due to its invasive nature, DBS is considere...

Russell, Paul Thomas

In this thesis we introduce a framework for parallel MCMC methods which we call parallel adaptive importance sampling (PAIS). At each iteration we have an ensemble of particles, from which PAIS builds a kernel density estimate (KDE). We propose a new ensemble, using this KDE, that is weighted according to standard importance sampling rules. A state...

Cekić, Mihajlo

This thesis is concerned with the inverse problem of determining a unitary connection $A$ on a Hermitian vector bundle $E$ of rank $m$ over a compact Riemannian manifold $(M, g)$ from the Dirichlet-to-Neumann (DN) map $\Lambda_A$ of the associated connection Laplacian $d_A^*d_A$. The connection is to be determined up to a unitary gauge equivalence ...

Tournier, Pierre-Henri Hecht, Frédéric Nataf, Frédéric Bonazzoli, Marcella Rapetti, Francesca Dolean, Victorita Semenov, Serguie El Kanfoud, Ibtissam Aliferis, Ioannis Migliaccio, Claire
...

This paper deals with microwave tomography for brain stroke imaging using state-of-the-art numerical modeling and massively parallel computing. Iterative microwave tomographic imaging requires the solution of an inverse problem based on a minimization algorithm (e.g. gradient or Newton-like methods) with successive solutions of a direct problem. Th...

Mourya, Rahul Ferrari, André Flamary, Rémi Bianchi, Pascal Richard, Cédric

Image deblurring is an economic way to reduce certain degradations (blur and noise) in acquired images. Thus, it has become essential tool in high resolution imaging in many applications, e.g., astronomy, microscopy or computational photography. In applications such as astronomy and satelliteimaging, the size of acquired images can be extremely lar...

Marnissi, Yosra

Bayesian approaches are widely used in signal processing applications. In order to derive plausible estimates of original parameters from their distorted observations, they rely on the posterior distribution that incorporates prior knowledge about the unknown parameters as well as informations about the observations. The posterior mean estimator is...

Barucq, Helene Djellouli, Rabia Estecahandy, Elodie Moussaoui, Mohand

The characterization of the Fréchet derivative of the elasto-acoustic scattered field with respect to Lipschitz continuous polygonal domains is established. The considered class of domains is of practical interest since two-dimensional scatterers are always transformed into polygonal-shaped domains when employing finite element methods for solving ...

Akıncı, Mehmet Nuri

Ph.D.

Ibtissam, Medarhri Rajae, Aboulaich Debit, Naima

This contribution is an extension of the work initiated in [1], presenting a strategy for the calibration of the local volatility. Due to Morozov's discrepancy principle [6], the Tikhonov regularization problem introduced in [7] is understood as an inequality-constrained minimization problem. An Uzawa procedure is proposed to replace this latter by...

Tournier, Pierre-Henri Bonazzoli, Marcella Dolean, Victorita Rapetti, Francesca Hecht, Frédéric Nataf, Frédéric Aliferis, Iannis El Kanfoud, Ibtissam Migliaccio, Claire de Buhan, Maya
...

This paper deals with microwave tomography for brain stroke imaging using state-of-the-art numerical modeling and massively parallel computing. Microwave tomographic imaging requires the solution of an inverse problem based on a minimization algorithm (e.g. gradient based) with successive solutions of a direct problem such as the accurate modeling ...