Meryem Jabloun

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In this paper, we propose an original strategy for estimating and reconstructing monocomponent signals having a high nonstationarity and long-time duration. We locally apply to short-time duration intervals the strategy developed in our previous work about nonstationary short-time signals. This paper describes a nonsequential time segmentation that provides(More)
— The problem of estimating nonstationary signals has been considered in many previous publications. In this paper we propose an alternative algorithm in order to accurately estimate AM/FM 1 signals. Only single component signals are considered. We perform local polynomial modeling on short time segments using a nonsequential strategy. The degree of(More)
We consider the modeling of non-stationary discrete signals whose amplitude and frequency are assumed to be nonlinearly modulated over very short-time duration. We investigate the case where both instantaneous amplitude and frequency can be approximated by orthonormal polynomials. Previous works dealing with polynomial approximations refer to orthonormal(More)
In previous published works [8, 3], we have studied the estimation of nonstationary monocomponent signals on short time-windows. Both of the instantaneous amplitude and frequency (IA/ IF) were modeled by polynomial functions. The maximization of the likelihood function was achieved by using a stochastic optimization technique: the Simulated Annealing (SA).(More)
An improved and robust Bayesian method is proposed to estimate the number-weighted Particle Size-Distributions (PSD) from data obtained by Multiangle Dynamic Light Scattering (MDLS). Compared to former approach presented by Clementi, the originality of our method lies in the fact that it is directly applied to raw MDLS data without any preprocessing.(More)
Phase rectified signal averaging (PRSA) is a technique recently introduced that outperforms the classical Fourier analysis when applied to nonstationary signals corrupted by impulsive noise. Indeed, the PRSA helps enhance quasi-periodic components in nonstationary signals while artifacts, intermittent components and high level noise are canceled. Thus the(More)
The inverse problem of estimating the Particle Size Distribution (PSD) from Multiangle Dynamic Light Scattering measurements (MDLS) is considered using a Bayesian inference approach. We propose to model the multimodal PSD as a normal mixture with an unknown number of components (modes or peaks). In order to achieve the estimation of these variable dimension(More)
A new efficient and user-independent technique for the detection of muscle activation (MA) intervals is proposed based on Gaussian Mixture Model (GMM) and Ant Colony Classifier (AntCC). First, time and frequency features are extracted from the surface electromyography (sEMG) signals. Then, GMM is used to cluster these extracted features into burst & non(More)
In this paper, we propose a modeling technique for the surface electromyographic (sEMG) signals based on the fractional linear prediction (FLP). To our knowledge, this is the first time application (use) of the FLP modeling to sEMG Data. This study is motivated by the ability of FLP modeling for characterizing a waveform with a reduced set of parameters.(More)
We propose to apply the Gaussian Mixture Model (GMM) to surface electromyography (sEMG) signals in order to detect the muscular activation (MA) onset, timing off and intervals. First, classical time and frequency features are extracted from the sEMG signals, beside the Teager-Kaiser energy operator (TKEO) is evaluated and added as a new feature which(More)