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Hadj Slimane Z.-E.,Abou Bekr Belkaid University Tlemcen | Nait-Ali A.,CNRS Laboratory of Image Signal and Intelligent Systems
Digital Signal Processing: A Review Journal | Year: 2010

In this paper, we present a new Empirical Mode Decomposition based algorithm for the purpose of QRS complex detection. This algorithm requires the following stages: a high-pass filter, signal Empirical Mode Decomposition, a nonlinear transform, an integration and finally, a low-pass filter is used. In order to evaluate the proposed technique, the well known ECG MIT-BIH database has been used. Moreover it is compared to a reference technique, namely "Christov's" detection method. As it will be shown later, the proposed algorithm allows to achieve high detection performances, described by means both the sensitivity and the specificity parameters. © 2009 Elsevier Inc. All rights reserved. Source


Harnrnouche K.,Mouloud Mammeri University | Diaf M.,Mouloud Mammeri University | Siarry P.,CNRS Laboratory of Image Signal and Intelligent Systems
Engineering Applications of Artificial Intelligence | Year: 2010

The multilevel thresholding problem is often treated as a problem of optimization of an objective function. This paper presents both adaptation and comparison of six meta-heuristic techniques to solve the multilevel thresholding problem: a genetic algorithm, particle swarm optimization, differential evolution, ant colony, simulated annealing and tabu search. Experiments results show that the genetic algorithm, the particle swarm optimization and the differential evolution are much better in terms of precision, robustness and time convergence than the ant colony, simulated annealing and tabu search. Among the first three algorithms, the differential evolution is the most efficient with respect to the quality of the solution and the particle swarm optimization converges the most quickly. © 2009 Elsevier Ltd. All rights reserved. Source


Nait-Ali A.,CNRS Laboratory of Image Signal and Intelligent Systems
7th International Workshop on Systems, Signal Processing and their Applications, WoSSPA 2011 | Year: 2011

When dealing with biometrics, we generally refer to security biometrics which is a set of techniques used to identify an individual using his biological or behavioral features. But sometimes, biometrics, in particular medical biometrics, refers to some specific methods that are used to quantify or to measure some parameters extracted from medical data. In this paper, we bridge the gap between the security biometrics and the medical biometrics and we try to discuss and highlight the idea which consists in using medical data, such as biosignals, MRI images and X-Ray images for the purpose of individual identification or verification. This is what we call the "Hidden biometrics" or "Intrinsic biometrics". As we will see, some of the techniques using biosignals are suited for applications requiring frequent up-dates and other approaches which use medical images are particularly robust regarding any potential forgery. © 2011 IEEE. Source


Boussaid I.,University of Science and Technology Houari Boumediene | Chatterjee A.,Jadavpur University | Siarry P.,CNRS Laboratory of Image Signal and Intelligent Systems | Ahmed-Nacer M.,University of Science and Technology Houari Boumediene
Computers and Operations Research | Year: 2012

Biogeography-based optimization (BBO) has been recently proposed as a viable stochastic optimization algorithm and it has so far been successfully applied in a variety of fields, especially for unconstrained optimization problems. The present paper shows how BBO can be applied for constrained optimization problems, where the objective is to find a solution for a given objective function, subject to both inequality and equality constraints. To solve such problems, the present work proposes three new variations of BBO. Each new version uses different update strategies, and each is tested on several benchmark functions. A successful implementation of an additional selection procedure is also proposed in this work which is based on the feasibility-based rule to preserve fitter individuals for subsequent generations. Our extensive experimentations successfully demonstrate the usefulness of all these modifications proposed for the BBO algorithm that can be suitably applied for solving different types of constrained optimization problems. © 2012 Elsevier Ltd. All rights reserved. Source


Boussad I.,University of Science and Technology Houari Boumediene | Chatterjee A.,Jadavpur University | Siarry P.,CNRS Laboratory of Image Signal and Intelligent Systems | Ahmed-Nacer M.,University of Science and Technology Houari Boumediene
Computers and Operations Research | Year: 2011

The present paper proposes a new stochastic optimization algorithm as a hybridization of a relatively recent stochastic optimization algorithm, called biogeography-based optimization (BBO) with the differential evolution (DE) algorithm. This combination incorporates DE algorithm into the optimization procedure of BBO with an attempt to incorporate diversity to overcome stagnation at local optima. We also propose to implement an additional selection procedure for BBO, which preserves fitter habitats for subsequent generations. The proposed variation of BBO, named DBBO, is tested for several benchmark function optimization problems. The results show that DBBO can significantly outperform the basic BBO algorithm and can mostly emerge as the best solution providing algorithm among competing BBO and DE algorithms. © 2010 Elsevier Ltd. Source

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