In the community of remote sensing, nonlinear mixture models have recently received particular attention in hyperspectral image processing. In this paper, we present a novel nonlinear spectral unmixing method following the recent multilinear mixing model of Heylen and Scheunders, which includes an infinite number of terms related to interactions between different endmembers. The proposed unmixing method is unsupervised in the sense that the endmembers are estimated jointly with the abundances and other parameters of interest, i.e., the transition probability of undergoing further interactions. Nonnegativity and sum-to-one constraints are imposed on abun- dances while only nonnegativity is considered for endmembers. The resulting unmixing problem is formulated as a constrained nonlinear optimization problem, which is solved by a block coordinate descent strategy, consisting of updating the end- members, abundances, and transition probability iteratively. The proposed method is evaluated and compared with existing linear and nonlinear unmixing methods for both synthetic and real hyperspectral data sets acquired by the airborne visible/infrared imaging spectrometer sensor. The advantage of using nonlinear unmixing as opposed to linear unmixing is clearly shown in these examples.

This paper studies a data-driven approach to detect faults in flight control systems of civil aircraft. A particular class of failures, referred to as Oscillatory Failure Cases (OFC), impacting the actuator servo loop has motivated the authors to consider a data-driven approach based on distance and correlation measures (see reference [Goupil et al.(2016). A data-driven approach to detect faults in the Airbus flight control system. IFAC-PapersOnLine, 49(17), 52-57] of this paper) leading to promising results compared to the state-of-the-art methods based on analytical redundancy. The present paper extends the formulation and the results of the considered OFC detection approach investigating Support Vector Machine (SVM) techniques to define a more accurate detector based on distance and correlation measures.

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 condence measure. However, current approaches for cloud detection, that are mostly based on machine learning and hand crafted features, have shown lack of robustness. In other tasks such as image recognition, deep learning methods have shown outstanding results outperforming many state-of-the-art methods. These methods are known to produce a powerful representation that can capture texture, shape and contextual information. This paper studies the potential of deep learning methods for cloud detection in order to achieve state-of-the-art performance. A comparison between deep learning methods used with classical handcrafted features and classical convolutional neural networks is performed for cloud detection. Experiments are conducted on a SPOT 6 image database with various landscapes and cloud coverage and show promising results.

This paper studies a data-driven approach to detect faults in flight control systems of civil aircraft. A particular class of failures, referred to as Oscillatory Failure Cases (OFC), impacting the actuator servo loop has motivated the authors to consider a data-driven approach based on distance and correlation measures (see reference [Goupil et al.(2016). A data-driven approach to detect faults in the Airbus flight control system. IFAC-PapersOnLine, 49(17), 52-57] of this paper) leading to promising results compared to the state-of-the-art methods based on analytical redundancy. The present paper extends the formulation and the results of the considered OFC detection approach investigating Support Vector Machine (SVM) techniques to define a more accurate detector based on distance and correlation measures.

Acquisition devices play an important role in digital signal processing. The possibility of a perfect reconstruction is demonstrated in regular as well as irregular sampling when the number of samples in the observation interval is high enough in function of the bandwidth of the sampled signal (length of the support of the spectrum). In the case of high sampling rates, imperfections of acquisition devices can introduce non negligible errors (when the acquisition duration of a given sample becomes not negligible in comparison with the sampling period (or mean sampling period in the case of irregular sampling). In this paper, explicit method is proposed to take into account imperfections of the sampling device in order to improve the reconstruction of the signal. The proposed method is applicable for deterministic functions and random processes in the case of regular sampling, as well as irregular sampling.

In this paper, we consider a bit-interleaved coded modulation scheme (BICM) composed of an error correcting code serially concatenated with a M-ary non linear modulation with memory. We first compare demodulation strategies for both the coherent and the non coherent cases. Then, we perform an asymptotic analysis and try to show that the design of coding schemes performing well for both the coherent and the non coherent regimes should be done carefully when considering sparse graph based codes such as low-density parity-check (LDPC) codes. It will be shown that optimized coding schemes for the non coherent setting can perform fairly well when using coherent demodulation, while on the contrary, optimized coding schemes for the coherent setting may lead to ”non stable” coding schemes in the non coherent setting.

This paper studies a maritime vessel detection method based on the fusion of data obtained from two different sensors, namely a synthetic aperture radar (SAR) and an automatic identification system (AIS) embedded in a satellite. In this work we propose a detector that uses the vessel position provided by the AIS system to improve the radar detection performance. The problem is handled by a generalized likelihood ratio test leading to a detector whose test statistics has a simple closed form expression. The distribution of the test statistics under the hypotheses is also determined, allowing theoretical and simulated receiver operational characteristics (ROCs) to be compared. Our results indicate that the proposed method improves detection performance and motivates the joint use of raw radar data with AIS demodulated information for ship detection.

Deriving a meaningful functional brain parcellation is a very challenging issue in task-related fMRI analysis. The joint parcellation detection estimation model addresses this issue by inferring the parcels from fMRI data. However, it requires a priori fixing the number of parcels through an initial mask for parcellation. Hence, this difficult task generally depends on the subject. The proposed automatic parcellation approach in this paper overcomes this limitation at the subject-level relying on a Dirichlet process mixture model combined with a hidden Markov random field to estimate the parcels and their number online. The proposed method adopts a variational expectation maximization strategy for inference. Compared to the model selection procedure in the joint parcellation detection estimation framework, our method appears more efficient in terms of computational time and does not require finely tuned initialization. Synthetic data experiments show that our method is able to estimate the right model order and an accurate parcellation. Real data results demonstrate the ability of our method to aggregate parcels with similar hemodynamic behaviour in the right motor and bilateral occipital cortices while its discriminating power is increased compared to its ancestors. Moreover, the obtained HRF estimates are close to the canonical HRF in both cortices.

This paper addresses the problem of estimating the roll-off factor of a received communication signal. We study two new statistical estimation methods that determine the roll-off factor by minimizing the difference between the theoretical and empirical power or power spectral density of the received signal. Another interesting contribution is the derivation of the roll-off Cramér–Rao bound which provides a reference in terms of estimation variance. Simulation results conducted on synthetic data allow the performance of the proposed methods to be evaluated. They are compared to a recent technique based on the amplitude fluctuations of the power spectral density associated with the received communication signal. The estimation methods are shown to be robust to channel impairments (including white Gaussian noise and synchronization errors). The proposed strategies are finally tested on real signals with known ground truth showing their possible application to digital communication problems.