Meryem Jabloun

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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 signals. Only single component signals are considered. We perform local polynomial modeling on short time segments using a nonsequential strategy. The degree of polynomial(More)
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)
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)
Parameter estimation for closely spaced or crossing frequency trajectories is a difficult signal processing problem, especially in the presence of both nonlinear amplitude and frequency modulations. In this paper, polynomial models are assumed for the instantaneous frequencies and amplitudes (IF/IA). We suggest two different strategies to process(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)
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)
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)
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)
We derive the Cramér-Rao lower bounds (CRB) for parametric estimation of the number-weighted particle size distribution (PSD) from multiangle Dynamic Light Scattering (DLS) measurements. The CRB is a useful statistical tool to investigate the optimality of the PSD estimators. In the present paper, a Gaussian mixture (GM) model of the multimodal PSD(More)