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The challenge of time series forecasting has been the focus of research in recent years, with Transformer-based models using various self-attention mechanisms to uncover long-range dependencies. However, complex trends and nonlinear serial dependencies presented in some specific datasets may not always be captured properly. To address these issues, we present STLformer, a novel Transformer-based m...Show More
Permutation entropy (PE) and its variants are ordinal pattern-based techniques that have become widely used as complexity measures to quantify the degree of disorder or randomness in a time series. Despite their popularity, these techniques have several limitations, such as sensitivity to the embedding dimension, the sampling frequency, and their specific preprocessing strategies. The information ...Show More
Long-term forecasting problem for time series has been actively studied during the last several years, and preceding Transformer-based models have exploited various self-attention mechanisms to discover the long-range dependencies. However, the hidden dependencies required by the forecasting task are not always appropriately extracted, especially the nonlinear serial dependencies in some datasets....Show More
Multi-scale permutation entropy (MPE) is an interesting tool for analyzing signal internal structures and quantifying complexity. The most commonly used MPEs involve a linear preprocessing step applied to the original signal prior to the evaluation of the permutation entropy (PE). However, recent research done by Davalos et al has demonstrated that linear filtering preprocessing significantly modi...Show More
The empirical mode decomposition (EMD) is a well-known data-driven signal decomposition. It produces a finite number of intrinsic mode functions (IMF) well adapted to Hilbert spectral analysis. Since the EMD is not a theoretical approach but rather an iterative algorithm, the question of how the EMD can achieve spectral band separation remains open. Several attempts to experimentally and theoretic...Show More
Multivariate time series forecasting problem has attracted enormous research in recent years, and many deep learning models have been proposed and claimed to be effective in different tasks. We find that many of these models were tested in a simple one-step-ahead strategy, which does not apply to real scenarios requiring multistep forecasting. This paper compares the performance of three well-know...Show More
Multiscale Permutation Entropy (MPE) is one of the most common techniques to assess the ordinal information content within a time series. In the present paper we propose an explicit, deterministic function of the MPE of a general ARMA process, as a function only of its parameters and time scale. We compare our theoretical results with the MPE of corresponding simulated signals, which further suppo...Show More
Multiscale Permutation Entropy (MPE), an extension of Permutation Entropy (PE), was proposed to better capture the information content in long range trends. This technique has been extensively used in biomedical applications for diagnosis purposes. Although PE theory is well established and explored, there is still a lack of theoretical development for MPE. In the present paper, we expand the theo...Show More
Foot ulceration can be prevented by using thermal information of the plantar foot surface. Indeed, important indicators can be provided with a thermal infrared image. As part of a non-constraining acquisition protocol, these images are freehandedly taken with a smartphone equipped by a dedicated thermal camera. A total of 248 images have been obtained from an acquisition campaign composed of contr...Show More
Permutation Entropy has been used as a robust and fast approach to calculate complexity of time series. There have been extensive studies on the properties and behavior of Permutation Entropy on known signals. Similarly, Multiscale Permutation Entropy has been used to analyze the structures at different time scales. Nevertheless, the Permutation Entropy is constrained by signal length, a problem w...Show More
The discrete Teager-Kaiser operator and its generalized versions (GTKO) have found many applications in various fields of signal processing. However, few studies have focused on their statistical properties and most of them were limited to the mean and variance expressed under Gaussian assumptions. We aim at filling the lack of distribution laws of these operators in the case of uniformly distribu...Show More
The aim of the present study is to propose a new joint segmentation method dedicated to plantar foot thermal images. The proposed method is based on a modified active contour method (Snake) that includes a prior shape information, namely an atlas of the plantar foot contour, as an extra term in the Snake energy function. This term guides the Snake to the targeted contours during the deformation pr...Show More
The discrete Teager-Kaiser operator (TKO) was firstly introduced in [1]. Generalized versions of this operator (GTKO) were proposed later in [2]. Both the TKO and GTKO were able to detect instantaneous amplitude changes of signals and they significantly improved the signal to noise ratios (SNR). The TKO, as well as the GTKO, can be viewed as the determinant of an embedding square matrix of size 2 ...Show 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 burst. Those features with their class name ...Show 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. The FLP is applied on real sEMG data record...Show 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 enhances the detection performance. All the obt...Show 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 is assumed and the associated Fisher information ...Show 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 parameters, a Bayesian inference approac...Show 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. Indeed, the PSD Bayesian estimation proposed by...Show More
Heart rate variability (HRV) signals are processed using the new time-frequency representation (PRSA-TFR) defined based on the Phase-Rectified Signal Averaging (PRSA) method. PRSA is a technique which enhances quasi-periodic components in nonstationary signals and thus improves frequency estimation. The PRSA-TFR is obtained by applying the PRSA method to sliding windows along univariate signals. O...Show More
The muscle fiber conduction velocity (CV) is usually used as a muscle fatigue indicator. The CV evaluation can indirectly be performed by estimating the time delay between surface electromyography (sEMG) signals recorded on electrodes aligned with the muscle fiber direction. To take into account the variability of the CV along the fiber and between channel recordings, the recently published method...Show More
The phase rectified signal averaging (PRSA) method is a technique initially developed to evaluate the acceleration and deceleration of the cardiac rhythm when applied to long-term-recordings of heartbeat intervals [1]. Because PRSA enables better cancellation of non-periodic or/and intermittent components, artifacts and impulsive noise, the quasi-periodic components are enhanced compared with clas...Show More
The heart rate variability (HRV) spectral parameters are classically used for studying the autonomic nervous system, as they allow the evaluation of the balance between the sympathetic and parasympathetic influences on heart rhythm. However, this evaluation is based on the definition of frequency bands that are found to be controversial because of possible changes in the frequency boundaries due t...Show 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 frequency estimation is improved. In this ...Show 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 segments whose lengths are suitable for ...Show More