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Saucan A.-A.,CNRS Communication and Information Sciences Laboratories
IEEE Aerospace and Electronic Systems Magazine | Year: 2015

In this article, we highlight recent advances in adaptive sonar-array processing for three-dimensional (3-D) depth map reconstruction, i.e., bathymetry. Bathymetry reconstruction of underwater environments is of great importance for applications ranging from infrastructure inspection to intrusion detection. Side-scan images, that is, two-dimensional (2-D) images of the backscattered signal amplitude are affected by the layover phenomenon, i.e., superposition of echoes. This phenomenon appears in the presence of tall objects, whenever the pulse emitted by the sonar ensonifies the top of the object before the bottom. In such, the area around the object is not entirely observable and a full 3-D bathymetry reconstruction is necessary. For sidelooking sonar arrays, bathymetry is obtained by triangulating the scatter-ers corresponding to each range bin, or equivalently, estimating the direction of arrival (DOA) of backscattered echoes. © 1986-2012 IEEE. Source


Masse D.,CNRS Communication and Information Sciences Laboratories
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2014

Termination analyzers generally synthesize ranking functions or relations, which represent checkable proofs of their results. In [23], we proposed an approach for conditional termination analysis based on abstract fixpoint computation by policy iteration. This method is not based on ranking functions and does not directly provide a ranking relation, which makes the comparison with existing approaches difficult. In this paper we study the relationships between our approach and ranking functions and relations, focusing on extensions of linear ranking functions. We show that it can work on programs admitting a specific kind of segmented ranking functions, and that the results can be checked by the construction of a disjunctive ranking relation. Experimental results show the interest of this approach. © 2014 Springer-Verlag Berlin Heidelberg. Source


Bouchikhi A.,IRENav | Bouchikhi A.,CNRS Communication and Information Sciences Laboratories | Boudraa A.-O.,IRENav
Signal Processing | Year: 2012

In this paper a signal analysis framework for estimating time-varying amplitude and frequency functions of multicomponent amplitude and frequency modulated (AM-FM) signals is introduced. This framework is based on local and non-linear approaches, namely Energy Separation Algorithm (ESA) and Empirical Mode Decomposition (EMD). Conjunction of Discrete ESA (DESA) and EMD is called EMD-DESA. A new modified version of EMD where smoothing instead of an interpolation to construct the upper and lower envelopes of the signal is introduced. Since extracted IMFs are represented in terms of B-spline (BS) expansions, a closed formula of ESA robust against noise is used. Instantaneous Frequency (IF) and Instantaneous Amplitude (IA) estimates of a multicomponent AM-FM signal, corrupted with additive white Gaussian noise of varying SNRs, are analyzed and results compared to ESA, DESA and Hilbert transform-based algorithms. SNR and MSE are used as figures of merit. Regularized BS version of EMD-ESA performs reasonably better in separating IA and IF components compared to the other methods from low to high SNR. Overall, obtained results illustrate the effectiveness of the proposed approach in terms of accuracy and robustness against noise to track IF and IA features of a multicomponent AM-FM signal. © 2012 Elsevier B.V. All rights reserved. Source


Masse D.,CNRS Communication and Information Sciences Laboratories
Electronic Notes in Theoretical Computer Science | Year: 2012

In this paper, we explore the adaptation of policy iteration techniques to compute greatest fixpoint approximations, in order to find sufficient conditions for program termination. Restricting ourselves to affine programs and the abstract domain of template constraint matrices, we show that the abstract greatest fixpoint can be computed exactly using linear programming, and that strategies are related to the template constraint matrix used. We also present a first result on the relationships between this approach and methods which use ranking functions. © 2012 Elsevier B.V. Source


Liu Z.-G.,Northwestern Polytechnical University | Liu Z.-G.,CNRS Communication and Information Sciences Laboratories | Pan Q.,Northwestern Polytechnical University | Dezert J.,ONERA | Mercier G.,CNRS Communication and Information Sciences Laboratories
Pattern Recognition | Year: 2014

In this paper we present a new credal classification rule (CCR) based on belief functions to deal with the uncertain data. CCR allows the objects to belong (with different masses of belief) not only to the specific classes, but also to the sets of classes called meta-classes which correspond to the disjunction of several specific classes. Each specific class is characterized by a class center (i.e. prototype), and consists of all the objects that are sufficiently close to the center. The belief of the assignment of a given object to classify with a specific class is determined from the Mahalanobis distance between the object and the center of the corresponding class. The meta-classes are used to capture the imprecision in the classification of the objects when they are difficult to correctly classify because of the poor quality of available attributes. The selection of meta-classes depends on the application and the context, and a measure of the degree of indistinguishability between classes is introduced. In this new CCR approach, the objects assigned to a meta-class should be close to the center of this meta-class having similar distances to all the involved specific classes centers, and the objects too far from the others will be considered as outliers (noise). CCR provides robust credal classification results with a relatively low computational burden. Several experiments using both artificial and real data sets are presented at the end of this paper to evaluate and compare the performances of this CCR method with respect to other classification methods. © 2014 Elsevier Ltd. All rights reserved. Source

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