This paper studies a new fusion algorithm for magnetic resonance (MR) and ultrasound (US) images combining two inverse problems for MR image super-resolution and US image despeckling. A polynomial function is used to link the gray levels of the two imaging modalities. Qualitative and quantitative evaluations on experimental phantom data show the interest of the proposed algorithm. The fused image is shown to take advantage of both the good contrast and high signal to noise ratio of the MR image and the good spatial resolution of the US image.

Available super-resolution techniques for 3D images are either computationally inefficient prior-knowledge-based iterative techniques or deep learning methods which require a large database of known low- and high-resolution image pairs. A recently introduced tensor-factorization-based approach offers a fast solution without the use of known image pairs or strict prior assumptions. In this article this factorization framework is investigated for single image resolution enhancement with an off-line estimate of the system point spread function. The technique is applied to 3D cone beam computed tomography for dental image resolution enhancement. To demonstrate the efficiency of our method, it is compared to a recent state-ofthe-art iterative technique using low-rank and total variation regularizations. In contrast to this comparative technique, the proposed reconstruction technique gives a 2-order-of-magnitude improvement in running time – 2 minutes compared to 2 hours for a dental volume of 282×266×392 voxels. Furthermore, it also offers slightly improved quantitative results (peak signalto-noise ratio, segmentation quality). Another advantage of the presented technique is the low number of hyperparameters. As demonstrated in this paper, the framework is not sensitive to small changes of its parameters, proposing an ease of use.

EVM is defined in many communication standards. These definitions are generally procedures to result in a percentage always slightly different in different standards cookbook recipes and not mathematical definitions. Uncertainty on this measurement difficult to assess : problem for evaluation of measurement equipment by users, problem for calibration of measurement equipment.

The effective coverage of satellites and the technology behind have motivated many actors to develop efficient communications for Internet access, television and telephony. As a matter of fact, reservation resources of Demand assigned multiple access (DAMA) techniques have been largely deployed in satellite, occupying most of the frequency bandwidth. However, these resources cannot follow the technological growth with big users communities in applications like the Internet of Things and Machine to Machine communications. Especially because the Round Trip Time is significant in addition to a potential underuse of the ressources. Thus, access protocols based on Aloha took over a big part of the Random Access (RA) research area and have considerably evolved lately. The main goal of this PhD research is to seek for more effective RA techniques with reduced complexity that could operate on a satellite communication return link. More precisely, on how to manage multi-user transmissions and solve interference at reception.

Compressive spectral imagers reduce the number of sampled pixels by coding and combining the spectral information. However, sampling compressed information with simultaneous high spatial and high spectral resolution demands expensive high-resolution sensors. This paper introduces a model allowing data from high spatial/low spectral and low spatial/high spectral resolution compressive sensors to be fused. Based on this model, the compressive fusion process is formulated as an inverse problem that minimizes an objective function defined as the sum of a quadratic data fidelity term and smoothness and sparsity regularization penalties. The parameters of the different sensors are optimized and the choice of an appropriate regularization is studied in order to improve the quality of the high resolution reconstructed images. Simulation results conducted on synthetic and real data, with different compressive sampling imagers, allow the quality of the proposed fusion method to be appreciated.

Le dispositif de détection (1) comprend un module de construction de test (6) configuré pour définir un modèle de la position de la gouverne selon deux hypothèses de test dont une première hypothèse de test pour laquelle il n'existe pas de vibration et une deuxième hypothèse de test pour laquelle il existe une vibration, un capteur (7) pour acquérir une mesure de position de la gouverne, un module de décision (10) pour décider si la première hypothèse doit être rejetée ou retenue à l'aide d'un test statistique, un module d'estimation (14) pour estimer une amplitude et une durée de la vibration, un module de calcul (15) pour calculer un indicateur de confort et de pilotabilité, un module de comparaison (16) pour comparer l'indicateur de confort et de pilotabilité à une échelle de valeurs de criticité pour obtenir un indicateur de criticité et un module d'envoi (17) pour envoyer l'indicateur de criticité à un dispositif utilisateur (18).

Single-photon light detection and ranging (Lidar) data can be used to capture depth and intensity profiles of a 3D scene. In a general setting, the scenes can have an unknown number of surfaces per pixel (semi-transparent surfaces or outdoor measurements), high background noise (strong ambient illumination), can be acquired by systems with a broad instrumental response (non-parallel laser beam with respect to the target surface) and with possibly high attenuating media (underwater conditions). The existing methods generally tackle only a subset of these problems and can fail in a more general scenario. In this paper, we propose a new 3D reconstruction algorithm that can handle all the aforementioned difficulties. The novel algorithm estimates the broadening of the impulse response, considers the attenuation induced by scattering media, while allowing for multiple surfaces per pixel. A series of experiments performed in real long-range and underwater Lidar datasets demonstrate the performance of the proposed method.

The identification of nonlinear Wiener models (NWMs) for deterministic inputs and Gaussian noise is studied. We show that the nonparametric kernel regression estimation of the nonlinearity of a NWM (based on the Nadaraya-Watson kernel estimator) can be formulated as a parametric estimation problem leading to a Gaussian conditional observation model. This property allows us to derive the maximum likelihood estimators of the unknown parameters of the NWM, as well as the associated Cramer-Rao (CR) bounds. We finally derive a CR-like bound on the global mean squared error (MSE) of the estimated nonlinearity of a NWM. Numerical results obtained for a pulse wave input are presented and compared to the ones based on the Nadaraya-Watson kernel estimator.