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Predictive position computations mediated by parietal areas: TMS evidence
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OVIS: ontology video surveillance indexing and retrieval system
No full textInternational audienceNowadays, the diversity and large deployment of video recorders results in a large volume of video data, whose effective use requires a video indexing process. However, this process generates a major problem consisting in the semantic gap between the extracted low-level features and the ground-truth. The ontology paradigm provides a promising solution to overcome this problem. However, no naming syntax convention has been followed in the concept creation step, which constitutes another problem. In this paper, we have considered these two issues and have developed a full video surveillance ontology following a formal naming syntax convention and semantics that addresses queries of both academic research and industrial applications. In addition, we propose an Ontology Video-surveillance Indexing and retrieval System (OVIS) using a set of Semantic Web Rule Language (SWRL) rules that bridges the semantic gap problem. Currently, the existing indexing systems are essentially based on low-level features and the ontology paradigm is used only to support this process with representing surveillance domain. In this paper, we developed the OVIS system based on the SWRL rules and the experiments prove that our approach leads to promising results on the top video evaluation benchmarks and also shows new directions for future developments
Actuator Fault Diagosis of delayed LPV Systems using UIO Observer : descriptor approach
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Consensus Problem with a Reference State for Fractional-Order Multi-Agent Systems
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Sedentary yam-based cropping systems in West Africa: Benefits of the use of herbaceous cover-crop legumes and rotation—lessons and challenges
No full textNew farming technologies such as improved fallow of herbaceous legumes have been developed over the last 20 years as alternatives to traditional yam cropping systems in areas where shifting cultivation is no more possible. One aim of this research was to analyze actual adoption by smallholders of such a technology and influencing factors in the Southern Guinea Savannah in Benin (West Africa). On the other hand, a second objective was to measure agronomic and economic performances of the innovative yam-based cropping systems in comparison with the usual ones under producers’ natural and socioeconomic circumstances in order to discuss the technology potential for large scale adoption. Smallholders with limited land access now develop as usual cropping systems a one-year fallow of Andropogonon gayanus–yam rotation or a maize–yam rotation. Innovative sedentary yam-based systems consist of an Aeschynomene histrix intercropped with maize–yam rotation or a Mucuna pruriens intercropped with maize–yam rotation. Factors potentially affecting adoption were included in a polynomial logit model. Agronomic and economic performances were assessed by the multiple regression and net present value of the 4-years double rotation. Ranking matrix was used to highlight constraints that may impede adoption. Benefits, lessons, and challenges are discussed in this article
Illumination-robust multispectral demosaicing
No full textInternational audienceSnapshot multispectral cameras that are equipped with filter arrays acquire a raw image that represents the radiance of a scene over the electromagnetic spectrum at video rate. These cameras require a demosaicing procedure to estimate a multispectral image with full spatio-spectral definition. Such a procedure is based on spectral correlation properties that are sensitive to illumination. In this paper, we first highlight the influence of illumination on demosaicing performances. Then we propose camera-, illumination-, and raw image-based normalisations that make demosaicing robust to illumination. Experimental results on state-of-the-art demosaicing algorithms show that such normalisations improve the quality of multispectral images estimated from raw images acquired under various illuminations
SIMO communication with impulsive and dependent interference - the Copula receiver
No full textInternational audienceIn this paper, we propose solutions for modelling dependence in impulsive noises. We use the copula framework that allows to represent the upper and lower tail dependencies that can not be captured by classical correlation (which, besides, is not adapted to α-stable distributions often considered in modelling impulsive noise). To illustrate the copula approach we consider a simple communication link with a single transmit antenna and two receive antennas and an adapted receiver architecture. We can derive the likelihood ratio that exhibits two components: one from the marginals and one from the copulas. We can then illustrate the impact of the dependence structure on the decision regions.Dans ce papier, nous proposons une méthode pour modéliser la dépendance entre des bruits impulsifs. Nous utilisons la notion de copule ce qui nous permet de représenter les dépendances d'upper et de lower tail, ce qui n'est pas le cas des coefficients de corrélation classique (qui de plus, ne sont pas adaptés aux lois α-stables, souvent utilisées pour modéliser des bruits impulsifs). Afin d' illustrer l'approche par les copules, nous considérons une configuration de communication simple avec une antenne de transmission et deux antennes de réception. Nous pouvons alors construire un récepteur adapté. Nous déterminons analytiquement le rapport de vraisemblance qui se décompose en deux parties : une dépendant uniquement des marginales et une dépendant de la copule. Nous pouvons ensuite illustrer l'impact de la structure de dépendance sur les régions de décision et les performances du systèmes
On the Troll-Trust Model for Edge Sign Prediction in Social Networks
No full textInternational audienceIn the problem of edge sign prediction, we are given a directed graph (representing a social network), and our task is to predict the binary labels of the edges (i.e., the positive or negative nature of the social relationships). Many successful heuristics for this problem are based on the troll-trust features, estimating at each node the fraction of outgoing and incoming positive/negative edges. We show that these heuristics can be understood, and rigorously analyzed, as approximators to the Bayes optimal classifier for a simple proba-bilistic model of the edge labels. We then show that the maximum likelihood estimator for this model approximately corresponds to the predictions of a Label Propagation algorithm run on a transformed version of the original social graph. Extensive experiments on a number of real-world datasets show that this algorithm is competitive against state-of-the-art classifiers in terms of both accuracy and scalability. Finally, we show that troll-trust features can also be used to derive online learning algorithms which have theoretical guarantees even when edges are adversarially labeled