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Ahouandjinou A.S.R.M.,LISIC | Assogba K.,EPAC UAC | Motamed C.,Laboratoire LISIC
2016 International Conference on Bio-Engineering for Smart Technologies, BioSMART 2016 | Year: 2016

Setting up a smart and pervasive environment is one of the current challenges being investigated in several research topics. Among the panoply of applications enabled by the Internet of Things (IoT), smart and connected health care is a particularly important one. Networked sensors, either worn on the body or embedded in our living environments, make possible the gathering of rich information indicative of our physical and mental health. Design a smart intensive care units is an original idea and a recent research topic which is tackled in this work. First, in this paper, we highlight the opportunities and challenges for IoT in realizing this vision of the future of health care and then, it is devoted to attainment of new patient monitoring intelligent system in ICUs in order to improve medical care service performance. We offer through this work, an hybrid architecture over a single platform for a visual patient monitoring system for Automatic Detection of risk Situations and Alert (ADSA) using a multi-camera system and collaborative medical sensors network. © 2016 IEEE.

Constantin J.,Lebanese University | Bigand A.,LISIC | Constantin I.,Lebanese University
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2017

The generation of photo-realistic images is a major topic in computer graphics. By using the principles of physical light propagation, images that are indistinguishable from real photographs can be generated. However, this computation is a very time-consuming task. When simulating the real behavior of light, individual images can take hours to be of sufficient quality. This paper proposes a bio-inspired architecture with spiking neurons for acceleration of global illumination rendering. This architecture with functional parts of sparse encoding, learning and decoding consists of a robust convergence measure on blocks. Feature, concatenation and prediction pooling coupled with three pooling operators: convolution, average and standard deviation are used in order to separate noise from signal. The pooling spike neural network (PSNN) represents a nonlinear mapping from stochastic noise features of rendering images to their quality visual scores. The system dynamic, that computes a learning parameter for each image based on its level of noise, is a consistent temporal framework where the precise timing of spikes is employed for information processing. The experiments are conducted on a global illumination set which contains diverse image distortions and large number of images with different noise levels. The result of this study is a system composed from only two spike pattern association neurons (SPANs) suitably adopted to the quality assessment task that accurately predict the quality of images with a high agreement with respect to human psycho-visual scores. The proposed spike neural network has also been compared with support vector machine (SVM). The obtained results show that the proposed method gives promising efficiency. © Springer International Publishing AG 2017.

Herbez C.,LISIC | Ramat E.,LISIC | Quesnel G.,French National Institute for Agricultural Research
SIMULTECH 2015 - 5th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, Proceedings | Year: 2015

With the emergence of parallel computational infrastructures at low cost, reducing simulation time becomes again an issue of the research community in modeling and simulation. This paper presents an approach to improve time of discrete event simulations. For that, the Parallel Discrete EVent System formalism is coupled to a partitioning method in order to parallelize the graph of models. We will present the graph partitioning method to realize this cutting and quantify the resulting time savings of parallel implementation. This article highlights the importance of considering the dynamic of the model when partitioning to improve performances. Many tests are performed from graphs with different sizes and shapes on several hardware architectures.

Kalakech M.,Heilongjiang University | Kalakech M.,Lille University of Science and Technology | Biela P.,Heilongjiang University | Biela P.,Lille University of Science and Technology | And 2 more authors.
Pattern Recognition Letters | Year: 2011

Recent feature selection scores using pairwise constraints (must-link and cannot-link) have shown better performances than the unsupervised methods and comparable to the supervised ones. However, these scores use only the pairwise constraints and ignore the available information brought by the unlabeled data. Moreover, these constraint scores strongly depend on the given must-link and cannot-link subsets built by the user. In this paper, we address these problems and propose a new semi-supervised constraint score that uses both pairwise constraints and local properties of the unlabeled data. Experiments using Kendall's coefficient and accuracy rates, show that this new score is less sensitive to the given constraints than the previous scores while providing similar performances.© 2010 Elsevier B.V. All rights reserved.

Bigand A.,LISIC | Colot O.,French National Center for Scientific Research
IS'2012 - 2012 6th IEEE International Conference Intelligent Systems, Proceedings | Year: 2012

Fuzzy sets which capture the meaning representation of linguistic variables have been widely used in image processing in the last decades. Fuzzy sets are associated with vagueness which is type 1 uncertainty. Interval-valued fuzzy sets (IVFS) are associated with type 2 semantic uncertainty. Indeed, the length of the interval provides the non-specifity measure for IVFS. We investigate this particular information measure applied to low-level image processing. This method can be used for both smoothing and noise filtering and applications in speckle noise reduction show the interest of this concept. © 2012 IEEE.

Bourgois L.,Supelec | Roussel G.,LISIC | Benjelloun M.,LISIC
Neural Networks | Year: 2013

This paper deals with the design methodology of an Inverse Neural Network (INN) model. The basic idea is to carry out a semi-physical model gathering two types of information: the a priori knowledge of the deterministic rules which govern the studied system and the observation of the actual conduct of this system obtained from experimental data. This hybrid model is elaborated by being inspired by the mechanisms of a neuromimetic network whose structure is constrained by the discrete reverse-time state-space equations. In order to validate the approach, some tests are performed on two dynamic models. The first suggested model is a dynamic system characterized by an unspecified r-order Ordinary Differential Equation (ODE). The second one concerns in particular the mass balance equation for a dispersion phenomenon governed by a Partial Differential Equation (PDE) discretized on a basic mesh. The performances are numerically analyzed in terms of generalization, regularization and training effort. © 2012 Elsevier Ltd.

Roussel G.,LISIC | Bourgois L.,Supelec | Benjelloun M.,LISIC | Delmaire G.,LISIC
Information Fusion | Year: 2013

In this paper, we present the fusion of two complementary approaches for modeling and monitoring the spatio-temporal behavior of a fluid flow system. We also propose a mobile sensor deployment strategy to produce the most accurate estimate of the true system state. For this purpose, deterministic and statistical information was used. We adopted a filtering method based on a semi-physical model which derives from a fluid flow numerical model known as lattice Boltzmann model (LBM). The a priori physical knowledge was introduced by the Navier-Stokes equations which were discretized by the lattice Boltzmann approach. Moreover, its multiple-relaxation-time (MRT) variant not only improved the stability, but also enabled the introduction of additional degrees of freedom to be estimated like the synaptic weights of a neural network. The statistical knowledge was then introduced into the model by performing a sequential learning of these parameters and an estimation of the speed field of the fluid flow starting from measurements. The low spatial density of measurements, the large amount of data inherent to environmental issues and the nonlinearity of the generalized lattice Boltzmann equations (GLBEs) enjoined us to use the ensemble Kalman filter (EnKF) for the recursive estimation procedure. A dual state-parameter estimation which results in a significantly reduced computation time was used by combining two filters consecutively activated in the same iteration. Finally, we proposed to complete the lack of spatial information of the sparse-observation network by adding a mobile sensor, which was routed to the location where the cell-by-cell output estimation error was the highest. Experimental results in the context of the standard lid-driven cavity problem revealed the presence of few zones of interest, where fixed sensors can be deployed to increase performances in terms of convergence speed and estimation quality. Finally, the study showed the feasibility of introducing some additional parameters which act as degrees of freedom, to perform large-eddy simulation of turbulent flows without numerical instabilities.

Constantin J.,Université Ibn Tofail | Delepoulle S.,LISIC | Bigand A.,LISIC | Renaud C.,LISIC
2013 3rd International Conference on Communications and Information Technology, ICCIT 2013 | Year: 2013

Reduced-reference image quality assessment needs no prior knowledge of reference image but only a minimal knowledge about processed images. A new reduced-reference image quality measure, based on Relevance Vector Machine (RVM), using a supervised learning framework and synthetic images is proposed. This new metric is compared with experimental psycho-visual data. A recently performed psycho-visual experiment provides psycho-visual scores on some synthetic images, and comprehensive testing demonstrates the good consistency between these scores and the quality measures we obtain. The proposed measure has been too compared with close methods like RBF, MLP and SVM and gives satisfactory performance. © 2013 IEEE.

Delepoulle S.,LISIC | Bigand A.,LISIC | Renaud C.,LISIC
IS'2012 - 2012 6th IEEE International Conference Intelligent Systems, Proceedings | Year: 2012

A no-reference image quality metric detecting both blur and noise is proposed in this paper. The proposed metric is based on IFS2 entropy applied on computer-generated images and does not require any edge detection. Its value drops either when the test image becomes blurred or corrupted by random noise. It can be thought of as an indicator of the signal to noise ratio of the image. Experiments using synthetic, natural and computer-generated images are presented to demonstrate the effectiveness and robustness of this metric. The proposed measure has been too compared with full-reference quality measures (or faithfullness measures) like SSIM and gives satisfactory performance. © 2012 IEEE.

Hijazi H.,Université Ibn Tofail | Bazzi O.,Université Ibn Tofail | Bigand A.,LISIC
2013 3rd International Conference on Communications and Information Technology, ICCIT 2013 | Year: 2013

In a previous paper a new version of nonlinear dimensionality reduction algorithm was proposed, the SC-LLE approach. This approach combines a supervised method, linear discriminant analysis (LDA, a simple but widely used algorithm in pattern recognition) with an unsupervised method, local linear embedding (LLE, manifold learning). SC-LLE method can generalize any linear classifier (like LDA) to nonlinear by transforming data into some low-dimensional feature space. This new concept (SC-LLE) applied to nonlinear data projection seems to be promising, and we show in this new paper that semi-supervised learning (SSL) is another interesting property of SC-LLE. Applications on 3D data show the interest of this method. © 2013 IEEE.

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