As part of a satellite communications system, the characteristics of the communication links make it difficult to set up telecommunications systems. For certain applications and protocols (TCP for example), the main problem is the propagation delay which reaches 500 ms for the round trip of the signal via a geostationary satellite. Another problem is the loss of data due to the characteristics of the transmission channel. For these reasons, protocols that ensure the reliability of communications must be set up on a satellite link. The aim of this thesis is to propose a mechanism that ensures the reliability of communication and maximize the utilization efficiency of the available bandwidth. HARQ protocol (Hybrid Automatic Repeat reQuest) is known for its ability to achieve the best compromise reliability/throughput. However, this mechanism which is now used in most terrestrial standards, is not well adapted for a satellite link. First, we propose a reliability method based on static HARQ. This method is specifically for services that tolerate some delay before the reception of the message. It consists in defining the probability of decoding at each transmission, using an optimization algorithm that we propose. The number of bits to be sent is calculated based on these probabilities and the distribution of the mutual information, assuming knowledge of the statistical distribution of the channel attenuation. Secondly, we introduce an adaptive version of the proposed method. Unlike the method proposed previously, this new approach calculates the number of bits to be sent by taking into account variations of the channel state during the communication. In fact, instead of sending a fixed number of bits at each transmission, the receiver calculates the number of bits to be sent depending on the channel state during the current transmission. Finally, we propose a frame structure for a physical layer that implements the proposed mechanisms and evaluate their performance by varying the system parameters. The aim is to find the optimal order of frame sizes and codes to be used and also to define the best strategy of transmission to be adopted by the transmitter.

As part of a satellite communications system, the characteristics of the communication links make it difficult to set up telecommunications systems. For certain applications and protocols (TCP for example), the main problem is the propagation delay which reaches 500 ms for the round trip of the signal via a geostationary satellite. Another problem is the loss of data due to the characteristics of the transmission channel. For these reasons, protocols that ensure the reliability of communications must be set up on a satellite link. The aim of this thesis is to propose a mechanism that ensures the reliability of communication and maximize the utilization efficiency of the available bandwidth. HARQ protocol (Hybrid Automatic Repeat reQuest) is known for its ability to achieve the best compromise reliability/throughput. However, this mechanism which is now used in most terrestrial standards, is not well adapted for a satellite link. First, we propose a reliability method based on static HARQ. This method is specifically for services that tolerate some delay before the reception of the message. It consists in defining the probability of decoding at each transmission, using an optimization algorithm that we propose. The number of bits to be sent is calculated based on these probabilities and the distribution of the mutual information, assuming knowledge of the statistical distribution of the channel attenuation. Secondly, we introduce an adaptive version of the proposed method. Unlike the method proposed previously, this new approach calculates the number of bits to be sent by taking into account variations of the channel state during the communication. In fact, instead of sending a fixed number of bits at each transmission, the receiver calculates the number of bits to be sent depending on the channel state during the current transmission. Finally, we propose a frame structure for a physical layer that implements the proposed mechanisms and evaluate their performance by varying the system parameters. The aim is to find the optimal order of frame sizes and codes to be used and also to define the best strategy of transmission to be adopted by the transmitter.

Mixing phenomena in hyperspectral images depend on a variety of factors such as the resolution of observation devices, the properties of materials, and how these materials interact with incident light in the scene. Different parametric and nonparametric models have been considered to address hyperspectral unmixing problems. The simplest one is the linear mixing model. Nevertheless, it has been recognized that mixing phenomena can also be nonlinear. The corresponding nonlinear analysis techniques are necessarily more challenging and complex than those employed for linear unmixing. Within this context, it makes sense to detect the nonlinearly mixed pixels in an image prior to its analysis, and then employ the simplest possible unmixing technique to analyze each pixel. In this talk, we shall present a technique for detecting nonlinearly mixed pixels. The detection approach is based on the comparison of the reconstruction errors using both a Gaussian process regression model and a linear regression model. The two errors are combined into a detection statistics for which a probability density function can be reasonably approximated. We also propose an iterative endmember extraction algorithm to be employed in combination with the detection algorithm. The proposed detect-then-unmix strategy, which consists of extracting endmembers, detecting nonlinearly mixed pixels and unmixing, is tested with synthetic and real images.

De par leur complexité toujours plus croissante, les systèmes embarqués avioniques récents sont exposés à des menaces externes dont le potentiel de nuisance peut être préoccupant vis-à-vis des enjeux opérationnels. Auparavant restreints à un monde avionique bien délimité et très spécifique, on assiste de plus en en plus à une augmentation des capacités de connectivité de ces systèmes et à des possibilités d’intégration avec des technologies « monde ouvert », par exemple pour interagir avec des équipements passagers.

L'invention a pour objet un procédé d'identification d'émetteurs par un terminal dans un réseau iso-fréquence comprenant une pluralité d'émetteurs. Les émetteurs sont synchronisés et émettent avec un retard artificiel τ propre à chaque émetteur. Le procédé comporte au moins une étape (100) d'acquisition de la position approximative du terminal , de la position p d'une liste d'émetteurs {Tx} au voisinage du terminal et des retards des retard τ leurs étant associés, une étape (101) de mesures de pseudo-distances ρ entre les émetteurs et le terminal et une étape (102) d'association des mesures ρ aux émetteurs de positions connues p en minimisant une fonction de coût, ladite fonction de coût correspondant à la norme de l'erreur entre les mesures ρi et un modèle de mesures des pseudo-distances appliqué à une permutation de la position des émetteurs.

Depending on the congestion level and the network characteristics (e.g., buffer sizes, capacity of the bottleneck, deployment scenario, etc.) a fixed Initial Window (IW) would be either too conservative or too aggressive. This results in low usage of the network resource or damaging high congestion level. This paper presents a sender-side only modification to the slow-start of TCP, SmartIW, that bypasses the limitations and potential issues of a fixed IW. The Round Trip Time (RTT) is estimated during the establishment of the connection and further exploited by SmartIW to pace the transmission of an adequate number of packets during the first RTT. Our simulation results show that, since the IW has been set in adequacy with the available network information, larger IW can be transmitted without increasing the congestion level of the network. SmartIW eventually reduces the RTT dependence of the slow start stage to fairly provide significant performance improvements whatever the network characteristics (RTT and congestion level).

Multifractal (MF) analysis enables the theoretical study of scale invariance models and their practical assessment via wavelet leaders. Yet, the accurate estimation of MF parameters remains a challenging task. For a range of applications, notably biomedical, the performance can potentially be improved by taking advantage of the multivariate nature of data. However, this has barely been considered in the context of MF analysis. This paper proposes a Bayesian model that enables the joint estimation of MF parameters for multivariate time series. It builds on a recently introduced statistical model for leaders and is formulated using a 3D gamma Markov random field joint prior for the MF parameters of the voxels of spatio-temporal data, represented as a multivariate time series, that counteracts the statistical variability induced by small sample size. Numerical simulations indicate that the proposed Bayesian estimator significantly outperforms current state-of-the-art algorithms.

I will describe a projected Nesterov’s proximal-gradient (PNPG) approach for sparse signal reconstruction. The objective function that we wish to minimize is a sum of a convex differentiable data-fidelity (negative log-likelihood (NLL)) term and a convex regularization term. We apply sparse signal regularization where the signal belongs to a closed convex set within the closure of the domain of the NLL; the convex-set constraint facilitates flexible NLL domains and accurate signal recovery. Signal sparsity is imposed using the ℓ₁-norm penalty on the signal's linear transform coefficients or gradient map, respectively. The PNPG approach employs projected Nesterov's acceleration step with restart and an inner iteration to compute the proximal mapping. We propose an adaptive step-size selection scheme to obtain a good local majorizing function of the NLL and reduce the time spent backtracking. Thanks to step-size adaptation, PNPG does not require Lipschitz continuity of the gradient of the NLL. We establish O(k⁻²) convergence of the PNPG scheme; our convergence-rate analysis accounts for inexactness of the iterative proximal mapping. The tuning of PNPG is largely application-independent. Tomographic and compressed-sensing reconstruction experiments with Poisson generalized linear and Gaussian linear measurement models demonstrate the performance of the proposed approach.

Health monitoring is performed on CNES spacecraft using two complementary methods: an utomatic Out-Of-Limits (OOL) checking executed on a set of critical parameters after each new telemetry reception, and a monthly monitoring of statistical features (daily minimum, mean and maximum) of another set of parameters. In this paper we present the limitations of this monitoring system and we introduce an innovative anomaly detection method based on machine-learning algorithms, developed during a collaborative R&D action between CNES and TESA (TElecommunications for Space and Aeronautics). This method has been prototyped and has shown encouraging results regarding its ability to detect actual anomalies that had slipped through the existing monitoring net. An operational-ready software implementing this method, NOSTRADAMUS, has been developed in order to further evaluate the interest of this new type of surveillance, and to consolidate the settings proposed after the R&D action. The lessons learned from the operational assessment of this system for the routine surveillance of CNES spacecraft are also presented in this paper.

This paper presents a new strategy to correct the Earth data corrupted by spurious samples that are randomly included in the multiplexed data stream provided by the MADRAS instrument. The proposed strategy relies on the construction of a trellis associated with each scan of the multi-channel image, modeling the possible occurrences of these erroneous data. A specific weight that promotes the smooth behavior of the signals recorded in each channel is assigned to each transition between trellis states. The joint detection and correction of the erroneous data are conducted using a dynamic programming algorithm for minimizing the overall cost function throughout the trellis. Simulation results obtained on synthetic and real MADRAS data demonstrate the effectiveness of the proposed solution.