The specifications of Digital Video Broadcasting - Return Channel via Satellite (DVB-RCS2) state that the satellite gateway could introduce both random and dedicated access methods to distribute the capacity among the different home users. Before starting an engineering process to design an algorithm allowing to combine both methods, it seems necessary to assess the performance of each. This paper compares random and dedicated access methods by measuring their impact on the performance of Transmission Control Protocol (TCP) sessions when the home users exploit the DVB-RCS2 link for regular use (e.g., web browsing or email transmission). In this paper we detail the implementation of an NS-2 module emulating Physical Channel Access (PCA). This module fills a gap in terms of random and deterministic access methods and allows to model various satellite channel access strategies. Based on NS-2 simulations using realistic system parameters of the DVB-RCS2 link, we demonstrate that, compared to dedicated access methods, which generally result in higher levels of transmitted data, random access methods enable faster transmission for short flows. We propose to combine random and dedicated access methods, with the selection of a specific method dependent on the dynamic load of the network and the sequence number of the TCP segments.

This paper presents a Bayesian fusion technique for remotely sensed multi-band images. The observed images are related to the high spectral and high spatial resolution image to be recovered through physical degradations, e.g., spatial and spectral blurring and/or subsampling defined by the sensor characteristics. The fusion problem is formulated within a Bayesian estimation framework. An appropriate prior distribution exploiting geometrical considerations is introduced. To compute the Bayesian estimator of the scene of interest from its posterior distribution, a Markov chain Monte Carlo algorithm is designed to generate samples asymptotically distributed according to the target distribution. To efficiently sample from this high-dimension distribution, a Hamiltonian Monte Carlo step is introduced within a Gibbs sampling strategy. The efficiency of the proposed fusion method is evaluated with respect to several state-of-the-art fusion techniques.

AStrion is an automatic spectrum analyser software, which proposes a new generic and data-driven method without any a priori information on the measured signals. In order to compute some general characteristics and derive the properties of the signal, the first step in this approach is to give some insight into the nature of the signal. This pre-analysis, the so-called data validation, contains a number of tests to reveal some of the properties and characteristics of the data, such as the acquisition validity (the absence of saturation and a posteriori in respect of the sampling theorem), the stationarity (or non-stationarity), the periodicity and the signal-to-noise ratio. Based on these characteristics, the proposed method defines indicators and alarm trigger thresholds and also categorises the signal into three classes, which helps to guide the following spectral analysis. The present paper introduces the four tests of this pre-analysis and the signal categorisation rules. Finally, the proposed approach is validated on a set of wind turbine vibration measurements to demonstrate its applicability for a long-term and continuous monitoring of real-world signals.

This paper proposes an automatic procedure for condition monitoring. It represents a valuable tool for maintenance of expensive and spread systems such as wind turbine farms. Thanks to data-driven signal processing algorithms, the proposed solution is fully automatic for the user. The paper briefly describes all the steps of the processing, from pre-processing of acquired signal to interpretation of generated results. It starts with an angular resampling method with speed measurement correction. Then comes a data validation step, in both time/angular and frequency/order domains. After these preprocessings, the spectral components of the analyzed signal are identified and classified in several classes from sine wave to narrow band components. This spectral peak detection and classification allows extracting the harmonic and side-band series which may be part of the signal spectral content. Moreover, the detected spectral patterns are associated with the characteristic frequencies of the investigated system. Based on the detected side-band series, the full-band demodulation is performed. At each step, the diagnosis features are computed and dynamically tracked signal by signal. Finally, system health indicators are proposed to conclude about the condition of the investigated system. All mentioned steps create a self-sufficient tool for robust diagnosis of mechanical faults. The paper presents the performance of the proposed method on real-world signals from a wind turbine drive train.

Texture characterization is a central element in many image processing applications. Multifractal analysis is a useful signal and image processing tool, yet, the accurate estimation of multifractal parameters for image texture remains a challenge. This is due in the main to the fact that current estimation procedures consist of performing linear regressions across frequency scales of the 2D dyadic wavelet transform, for which only a few such scales are computable for images. The strongly non-Gaussian nature of multifractal processes, combined with their complicated dependence structure, makes it difficult to develop suitable models for parameter estimation. Here, we propose a Bayesian procedure that addresses the difficulties in the estimation of the multifractality parameter. The originality of the procedure is threefold. The construction of a generic semiparametric statistical model for the logarithm of wavelet leaders; the formulation of Bayesian estimators that are associated with this model and the set of parameter values admitted by multifractal theory; the exploitation of a suitable Whittle approximation within the Bayesian model which enables the otherwise infeasible evaluation of the posterior distribution associated with the model. Performance is assessed numerically for several 2D multifractal processes, for several image sizes and a large range of process parameters. The procedure yields significant benefits over current benchmark estimators in terms of estimation performance and ability to discriminate between the two most commonly used classes of multifractal process models. The gains in performance are particularly pronounced for small image sizes, notably enabling for the first time the analysis of image patches as small as 64 × 64 pixels.

Pansharpening first aims at fusing a panchromatic image with a multispectral image to generate an image with the high spatial resolution of the former and the spectral resolution of the latter. In the last decade many algorithms have been presented in the literature for pansharpening using multispectral data. With the increasing availability of hyperspectral systems these methods are now extending to hyperspectral images. In this work, we attempt to compare new pansharpening techniques designed for hyperspectral data with some of the state of the art methods for multispectral pansharpening, which have been adapted for hyperspectral data. Nine methods from different classes are analysed: component substitution, multiresolution analysis, hybrid, Bayesian and matrix decomposition approaches. These techniques are evaluated with the Wald’s Procol on one dataset to characterize their performances and their robustness.

We develop a representation of reverse-time migration (RTM) in terms of Fourier integral operators, the canonical relations of which are graphs. Through the dyadic parabolic decomposition of phase space, we obtain the solution of the wave equation with a boundary source and homogeneous initial conditions using wave packets. On this basis, we develop a numerical procedure for the reverse-time continuation from the boundary of scattering data and for RTM. The algorithms are derived from those we recently developed for the discrete approximate evaluation of the action of Fourier integral operators and inherit their conceptual and numerical properties.

Dictionary learning is a powerful approach for sparse representation. However, the numerical complexity of classical dictionary learning methods restricts their use to atoms with small supports such as patches. In a previous work, we introduced a model based on a composition of convolutions with sparse kernels to build large dictionary atoms with a low computational cost. The subject of this work is to consider this model at the next level, i.e., to build a full dictionary of atoms from convolutions of sparse kernels. Moreover, we further reduce the size of the representation space by organizing the convolution kernels used to build atoms into a tree structure. The performance of the method is tested for the construction of a curvelet dictionary with a known code.

Abstract In this paper, we study the problem of gateway placement in an hybrid mobile ad hoc-satellite network. We propose a genetic algorithm based approach to solve this multi-criteria optimization problem. The analysis of the proposed algorithm is made by means of simulations. Topology dynamics are also taken into account since the node mobility will impact the gateway placement decisions. Our solution shows promising results and displays unmatched flexibility with respect to the optimization criteria.