Spectral analysis of sea level anomalies (SLA) is widely used in the altimetry community to understand the geophysical content of the measured signal, to assess and compare the missions’ performances. Spectral content of SLA is used to characterize the ocean at different scales as well as instrumental noise. Based on the SLA spectrum, one can estimate the spectral slope at medium to large scales (relied to the Surface Quasi-Geostrophic (SQG) ocean dynamics theory) and the measurement noise (observed as a noise plateau at smallest scales). It has already been shown that the spectral slope strongly depends on ocean variability, both in time and space domains [1]. However, spectral analysis based on Fourier transform requires stationary signals and is well-known to suffer from a convolutive bias and a high variance of estimation [2]. Thus, using Fourier transforms for SLA spectral analysis requires mathematical caution and needs to be fully managed. This study aims at reviewing applicability of Fourier transform-based methods to SLA analysis and comparing it to other spectral methods. Such comparison has been performed on both simulated SLA signals obtained from theoretical spectra and real signals from a high-resolution altimeter (Orbit – Range – Mean Sea Surface). Finally, a parametric spectral analysis method is proposed and suggested for use by the wider Cal/Val and altimetry science community. [1] C. Dufau et al., Mesoscale capability of along-track altimeter data in LRM & SARM, OSTST Meeting, 2014. [2] P. Stoica, R. Moses, Introduction to spectral analysis, Prentice Hall, 1997.

Monitoring the drive train of a wind turbine is still a challenge for reducing operationand maintenance costs and therefore decreasing cost of energy. In this paper, astandalone, data-driven and automatic tracking analyzer, entitled AStrion and alreadypresented in this conference, is applied on vibration data acquired during one full yearon a set of sensors located in the nacelle of two wind turbines in a wind farm in thePyrénées (France). These experimentations were realized thanks to KAStrion projectfunded by KIC InnoEnergy program.In the context of a particular case study, the main bearing failure of one of the two windturbines, this paper will highlight three main assets of AStrion strategy. A first asset willbe the application of the data validation module. According to the value of anonstationary index, the data measured on the sensor located on the main bearing closeto the failure have been discarded. This was justified afterwards by a dysfunction of thesensor. Then from the validated data acquired with a more remote sensor, a second assetwill be the trends of global features computed by AStrion which proved a strong linkwith maintenance operations on the mechanical components such as the greasing. Thethird asset will be the reading of other AStrion features associated to one specificcomponent. Indeed the trends of the features of the main bearing show evolutionsthroughout the year. A real time reading would have led to the conclusion of a severeevolution of the condition of this main bearing eight months before the failure and thestop of the machine. This study was carried out thanks to a narrow collaboration withthe operator of the wind farm.

Offering a secured shared hosting environment for web applications is not a trivial task. In addition to a well secured system configuration, up-to-date shared hosting are still exposed to security threats by compromised web applications that serve as spam relay, distributed denial of service actors, phishing page hosting and drive-by download page hosting to name a few. As a result, the availability of the server could suffer from a bad IP address reputation and thus, blocked access to all accounts in the server, not only the compromised account. The emergence of web application firewalls (WAF) manages to close the gap by thoroughly analysing HTTP requests in search of known vulnerabilities. However, as any misuse type mechanism, it falls short at discovering zero-day attacks or already compromised environment. In this paper, an anomaly detection model is proposed as a very helpful tool to start building an efficient intrusion detection system adapted to a specific web application or to assist a forensic analysis. The learning phase does not need past activities nor prior knowledge of the web application and its underlying architecture, making it a very simple yet powerful tool for reducing the access log entries for further analysis.

A detection device comprising a first data processing unit configured to determine a first trend of the control surface control order as a function of time, the first trend being defined according to a reference parameter, a second data processing unit configured to determine at least one second trend of the control surface position value as a function of time, the second trend being also defined according to the reference parameter, and a monitoring unit configured to compare the first and second trends in order to verify the existence of a correlation between these first and second trends so as to detect an oscillatory failure as soon as a loss of correlation between the first and second trends appears, if necessary.

Hyperspectral unmixing is aimed at identifying the reference spectral signatures composing an hyperspectral image and their relative abundance fractions in each pixel. In practice, the identified signatures may vary spectrally from an image to another due to varying acquisition conditions, thus inducing possibly significant estimation errors. Against this background, hyperspectral unmixing of several images acquired over the same area is of considerable interest. Indeed, such an analysis enables the endmembers of the scene to be tracked and the corresponding endmember variability to be characterized. Sequential endmember estimation from a set of hyperspectral images is expected to provide improved performance when compared to methods analyzing the images independently. However, the significant size of hyperspectral data precludes the use of batch procedures to jointly estimate the mixture parameters of a sequence of hyperspectral images. Provided that each elementary component is present in at least one image of the sequence, we propose to perform an online hyperspectral unmixing accounting for temporal endmember variability. The online hyperspectral unmixing is formulated as a two-stage stochastic program, which can be solved using a stochastic approximation. The performance of the proposed method is evaluated on synthetic and real data. A comparison with independent unmixing algorithms finally illustrates the interest of the proposed strategy.

This paper proposes a robust fast multi-band image fusion method to merge a high-spatial lowspectral resolution image and a low-spatial high-spectral resolution image. Following the method recently developed in [1], the generalized Sylvester matrix equation associated with the multi-band image fusion problem is solved in a more robust and efficient way by exploiting the Woodbury formula, avoiding any permutation operation in the frequency domain as well as the blurring kernel invertibility assumption required in [1]. Thanks to this improvement, the proposed algorithm requires fewer computational operations and is also more robust with respect to the blurring kernel compared with the one in [1]. The proposed new algorithm is tested with different priors considered in [1]. Our conclusion is that the proposed fusion algorithm is more robust than the one in [1] with a reduced computational cost.

Texture analysis can be embedded in the mathematical framework of multifractal (MF) analysis, enabling the study of the fluctuations in regularity of image intensity and providing practical tools for their assessment, wavelet leaders. A statistical model for leaders was proposed permitting Bayesian estimation of MF parameters for images yielding improved estimation quality over linear regression based estimation. This present work proposes an extension of this Bayesian model for patch-wise MF analysis of images. Classical MF analysis assumes space homogeneity of the MF properties whereas here we assume MF properties may change between texture elements and we do not know where the changes are located. This paper proposes a joint Bayesian model for patches formulated using spatially smoothing gamma Markov Random Field priors to counterbalance the increased statistical variability of estimates caused by small patch sizes. Numerical simulations based on synthetic multifractal images demonstrate that the proposed algorithm outperforms previous formulations and standard estimators.

This paper addresses the problem of blind deconvolution of medical ultrasound (US) images. Specifically, a parametric model for the point spread function (PSF) established experimentally is used, i.e., the US PSF can be modeled by a Gaussian function modulated by a sinusoidal function. Given this parametric model, the estimation of the PSF in a blind deconvolution problem can be reduced to the estimation of its parameters. Moreover, due to the ill-posedness of blind deconvolution problem, an ℓp-norm (0 <; p ≤ 2) regularization term (including the widely considered ℓ1-norm, ℓ2-norm regularization terms) for the ultrasound tissue reflectivity function (TRF) is employed, based on the assumption of generalized Gaussian distributed US images. An alternating optimization approach is proposed for the estimations of the US PSF and TRF. The behavior of the proposed algorithm is illustrated using simulated and in vivo US data.

Several random access (RA) techniques have been proposed recently for the satellite return link. The main objective of these techniques is to resolve packets collisions in order to enhance the limited throughput of traditional RA schemes. In this context, Multi-Replica Decoding using Correlation based Localisation (MARSALA) has been introduced and has shown good performance with DVB-RCS2 coding scheme and equi-powered transmissions. However, it has been shown in the literature that alternative coding schemes and packets power distributions can have a positive impact on RA performance. Therefore, in this paper, we investigate the behaviour of MARSALA with various coding schemes and various packet power distributions, then we propose a configuration for optimal performance. This paper also introduces the enhancement of MARSALA RA scheme by adding MRC to optimize replicas combination and study the impact on the throughput. We compare two different MRC techniques and we evaluate, via simulations, the gain achieved using MRC with different coding schemes and unbalanced packets. The simulation results demonstrate that the proposed enhancements to MARSALA show substantial performance gain, i.e. throughput achieved for a target Packet Loss Ratio (PLR).

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. Contrary to most methods widely used in the literature, the present work proposes to jointly exploit information from SAR and AIS raw data in order to detect the absence or presence of a ship using a binary hypothesis testing problem. This detection problem is handled by a generalized likelihood ratio detector whose test statistics has a simple closed form expression. The distribution of the test statistics is derived under both hypotheses, allowing the corresponding receiver operational characteristics (ROCs) to be computed. The ROCs are then used to compare the detection performance obtained with different sensors showing the interest of combining information from AIS and radar.