This paper presents a data-driven strategy for the detection of failures impacting the flight control system. Early and robust detection of Oscillatory Failure Case (OFC) allows the aircraft structural design to be optimized, which in turn helps improve the aircraft environmental footprint thanks to weight saving. Compared to existing model-based techniques already used on in-service Airbus aircraft, this paper studies a novel signal processing approach based on distance and correlation. It is shown that a mixed similarity index between Euclidean distance and logarithmic invariant divergence gives promising detection results. This paper details the proposed approach by insisting on practical constraints due to implementation in embedded real-time systems such as the flight control computer. Preliminary results obtained from a Verification & Validation (V&V) on-going campaign are presented.

Linear mixture models are commonly used to represent a hyperspectral data cube as linear combinations of endmember spectra. However, determining the number of endmembers for images embedded in noise is a crucial task. This paper proposes a fully automatic approach for estimating the number of endmembers in hyperspectral images. The estimation is based on recent results of random matrix theory related to the so-called spiked population model. More precisely, we study the gap between successive eigenvalues of the sample covariance matrix constructed from high-dimensional noisy samples. The resulting estimation strategy is fully automatic and robust to correlated noise owing to the consideration of a noise-whitening step. This strategy is validated on both synthetic and real images. The experimental results are very promising and show the accuracy of this algorithm with respect to state-of-the-art algorithms.

Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive technique able to provide the spatial distribu- tion of relevant biochemical compounds commonly used as biomarkers of disease. Information provided by MRSI can be used as a valuable insight for the diagnosis, treatment and follow-up of several diseases such as cancer or neurological disorders. Obtaining accurate metabolite concentrations from in vivo MRSI signals is a crucial require- ment for the clinical utility of this technique. Despite the numerous publications on the topic, accurate quantification is still a challenging problem due to the low signal-to-noise ratio of the data, overlap of spectral lines and the pres- ence of nuisance components. We propose a novel quantification method, which alleviates these limitations by exploiting a spatio-spectral regularization scheme. In contrast to previous methods, the regularization terms are not expressed directly on the parameters being sought, but on appropriate transformed domains. In order to quan- tify all signals simultaneously in the MRSI grid, while introducing prior information, a fast proximal optimization al- gorithm is proposed. Experiments on synthetic MRSI data demonstrate that the error in the estimated metabolite concentrations is reduced by a mean of 41% with the proposed scheme. Results on in vivo brain MRSI data show the benefit of the proposed approach, which is able to fit overlapping peaks correctly and to capture metabolites that are missed by single-voxel methods due to their lower concentrations.

The SPOT 6-7 satellite ground segment includes a systematic and automatic cloud detection step in order to feed a catalogue with a binary cloud mask and an appropriate confidence measure. In order to significantly improve the SPOT cloud detection and get rid of frequent manual re-labelings, we study a new automatic cloud detection technique that is adapted to large datasets. The proposed method is based on a modified distributed boosting algorithm. Experiments conducted using the framework Apache Spark on a SPOT 6 image database with various landscapes and cloud coverage show promising results.

This paper presents a high-resolution hyperspectral image fusion algorithm based on spectral unmixing. The widely used linear observation model (with additive Gaussian noise) is combined with the linear spectral mixture model to form the data terms. The non-negativity and sum-to-one constraints, resulting from the intrinsic physical properties of the abundances (i.e., fractions of the materials contained in each pixel), are introduced to regularize the ill-posed image fusion problem. The joint fusion and unmixing problem is formulated as the minimization of a cost function with respect to the mixing matrix (which contains the spectral signatures of the pure material, referred to as endmembers), and the abundance maps, with non-negativity and sum-to-one constraints. This optimization problem is attacked with an alternating optimization strategy. The two resulting sub-problems are convex and are solved efficiently using the alternating direction method of multipliers. Simulation results, including comparisons with the state-of-the-art, document the effectiveness and competitiveness of the proposed unmixing based fusion algorithm.

Texture analysis can be conducted within the mathematical framework of multifractal analysis (MFA) via the study of the regularity fluctuations of image amplitudes. Successfully used in various applications, however MFA remains limited to the independent analysis of single images while, in an increasing number of applications, data are multi-temporal. The present contribution addresses this limitation and introduces a Bayesian framework that enables the joint estimation of multifractal parameters for multi-temporal images. It builds on a recently proposed Gaussian model for wavelet leaders parameterized by the multifractal attributes of interest. A joint Bayesian model is formulated by assigning a Gaussian prior to the second derivatives of time evolution of the multifractal attributes associated with multi-temporal images. This Gaussian prior ensures that the multifractal parameters have a smooth temporal evolution. The associated Bayesian estimators are then approximated using a Hamiltonian Monte-Carlo algorithm. The benefits of the proposed procedure are illustrated on synthetic data.

This paper addresses the problem of designing efficient sampling moves in order to accelerate the convergence of MCMC methods. The Partially collapsed Gibbs sampler (PCGS) takes advantage of variable reordering, marginalization and trimming to accelerate the convergence of the traditional Gibbs sampler. This work studies two specific moves which allow the convergence of the PCGS to be further improved. It considers a Bayesian model where structured sparsity is enforced using a multivariate Bernoulli Laplacian prior. The posterior distribution associated with this model depends on mixed discrete and continuous random vectors. Due to the discrete part of the posterior, the conventional PCGS gets easily stuck around local maxima. Two Metropolis-Hastings moves based on multiple dipole random shifts and inter-chain proposals are proposed to overcome this problem. The resulting PCGS is applied to EEG source localization. Experiments conducted with synthetic data illustrate the effectiveness of this PCGS with accelerated convergence.

This communication introduces a new framework for incorporating spatial regularization into a nonlinear unmixing procedure dedicated to hyperspectral data. The proposed model promotes smooth spatial variations of the nonlinear component in the mixing model. The spatial regularizer and the nonlinear contributions are jointly modeled by a vector-valued function that lies in a reproducing kernel Hilbert space (RKHS). The unmixing problem is strictly convex and reduces to a quadratic programming (QP) problem. Simulations on synthetic data illustrate the effectiveness of the proposed approach.

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.