Blind Separation of Cyclostationary Sources Sharing Common Cyclic Frequencies Using Joint Diagonalization Algorithm

We propose a new method for blind source separation of cyclostationary sources, whose cyclic frequencies are unknown and may share one or more common cyclic frequencies. The suggested method exploits the cyclic correlation function of observation signals to compose a set of matrices which has a particular algebraic structure. The aforesaid matrices are automatically selected by proposing two new criteria. Then, they are jointly diagonalized so as to estimate the mixing matrix and retrieve the source signals as a consequence. The nonunitary joint diagonalization (NU-JD) is ensured by Broyden-Fletcher-Goldfarb-Shanno (BFGS) method which is the most commonly used update strategy for implementing a quasi-Newton technique. The efficiency of the method is illustrated by numerical simulations in digital communications context, which show good performances comparing to other stateof-the-art methods

Complementary medicine use among Moroccan patients with cancer: A descriptive study

tter inform his patients

Chemical composition, antioxidant, and antimicrobial properties of Mentha subtomentella: in sight in vitro and in silico analysis

Our research focused on assessing essential oils (MSEO) and aqueous extracts (MSAE) derived from M. subtomentella leaves, with a primary focus on evaluating their properties. From 1 kg of leaves, we successfully obtained 18 mL of essential oil. Upon conducting GC/MS analysis, we identified eleven compounds within the oil, collectively accounting for 100% of the constituents identified. Notably, the predominant compounds in the leaf oil were p-Menth-48) -en-3-one (50.48%), 9-Ethylbicyclo (3.3.1) nonan-9-ol (10.04%) (E)-3,3-Dimethyl-delta-1, alpha-cyclohexaneacetaldehyde (8.53%), and D-Limonene (7.22%). Furthermore, utilizing HPLC/DAD, we explored the phenolic profile of MSAE, extracted through decoction. This analysis revealed the presence of fifty-eight compounds, with five major components collectively constituting 61% of the total compounds identified, rosmarinic acid as the major one. We evaluated the antimicrobial effectiveness of the MSEO against ten different strains, observing its notable efficacy against A. Niger (MIC = 0.09%), P. digitatum (MIC = 0.5%), and G. candidum (MIC = 1%). However, the essential oil demonstrated comparatively lower efficacy against bacteria than fungi. In contrast, the MSAE did not exhibit any antimicrobial activity against the tested strains. Regarding antioxidant activity, the aqueous extract displayed a significantly higher antioxidant capacity than the essential oil, which exhibited relatively lower antioxidant activity. The IC50 values were determined to be 0.04 ± 0.01 mg/mL, 0.17 ± 0.01 mg/mL, and 13% ± 0.01% (V/V), for ascorbic acid MSAE and MSEO, respectively. We used a computational method called molecular docking to investigate how certain plant compounds affect antioxidant, antibacterial, and antifungal activities. This involved analyzing the interactions between these compounds and specific protein targets known for their roles in these activities

Contribution à la séparation de sources cyclo-stationnaires : application aux signaux de télécommunications, mécaniques et biomécaniques

In this thesis, we have tackled the problem of blind separation of linear mixtures of sources with cyclo-stationarity properties. Three applications were studied : telecommunications, mechanical vibrations and biomechanics. First, two new methods have been proposed, the first one aims to blindly separate cyclo-stationary sources sharing one or more unknown cyclic frequencies. It combines the joint diagonalization with a new useful point detector (time lag-cyclic frequency) to compose the set of cyclic correlation matrices to be jointly diagonalized. As for the second method, it aims to identify the mixture matrix of cyclo-stationary sources of unknown and different cyclic frequencies. The identification begins with a step of detecting the matrices of rank one, then the product of selected matrices is decomposed into eigen-elements, and finally a hierarchical regrouping method returns the columns of our sought matrix. Both solutions have been applied to telecommunications signals. In a second step, we first applied the first proposed method on mechanical signals coming from a bank of faulty bearings in order to test its ability to separate the sources. Next, we proposed an approach based on sparse component analysis to separate the components of the ground reaction force with cyclo-stationary properties at order 1 and 2Dans cette thèse, nous nous sommes attaqués au problème de séparation aveugle de mélanges linéaires de sources ayant des propriétés de cyclo-stationnarité. Trois applications ont été abordées à savoir : télécommunications, vibrations mécaniques et biomécaniques. Dans un premier temps, deux nouvelles méthodes ont été proposées, la première a pour but de séparer aveuglement des sources cyclo-stationnaires partageant une ou plusieurs fréquences cycliques inconnues. Elle combine la diagonalisation conjointe à un nouveau détecteur de points utiles (retard-fréquence cyclique) permettant de composer l’ensemble de matrices de corrélation cyclique devant être diagonalisées conjointement. Quant à la deuxième méthode, elle vise à identifier la matrice de mélange de sources cyclostationnaires de fréquences cycliques inconnues et différentes. L’identification commence par une étape de détection des matrices de rang un, puis décompose en éléments propres le produit de matrices sélectionnées, enfin une méthode de regroupement hiérarchique restitue les colonnes de notre matrice recherchée. Les deux solutions ont été appliquées aux signaux de télécommunications. Dans un second temps, nous avons appliqué d’abord la première méthode proposée sur des signaux mécaniques issus d’un banc de roulements défaillants afin de tester son aptitude à séparer les sources. Ensuite, nous avons proposé une approche qui s’appuie sur l’analyse en composantes parcimonieuses pour séparer les composantes de la force de réaction au sol ayant des propriétés cyclo-stationnaires à l’ordre 1 et

Contribution to the separation of cyclo-stationary sources : application to telecommunications, mechanical and biomechanical signals

Dans cette thèse, nous nous sommes attaqués au problème de séparation aveugle de mélanges linéaires de sources ayant des propriétés de cyclo-stationnarité. Trois applications ont été abordées à savoir : télécommunications, vibrations mécaniques et biomécaniques. Dans un premier temps, deux nouvelles méthodes ont été proposées, la première a pour but de séparer aveuglement des sources cyclo-stationnaires partageant une ou plusieurs fréquences cycliques inconnues. Elle combine la diagonalisation conjointe à un nouveau détecteur de points utiles (retard-fréquence cyclique) permettant de composer l’ensemble de matrices de corrélation cyclique devant être diagonalisées conjointement. Quant à la deuxième méthode, elle vise à identifier la matrice de mélange de sources cyclostationnaires de fréquences cycliques inconnues et différentes. L’identification commence par une étape de détection des matrices de rang un, puis décompose en éléments propres le produit de matrices sélectionnées, enfin une méthode de regroupement hiérarchique restitue les colonnes de notre matrice recherchée. Les deux solutions ont été appliquées aux signaux de télécommunications. Dans un second temps, nous avons appliqué d’abord la première méthode proposée sur des signaux mécaniques issus d’un banc de roulements défaillants afin de tester son aptitude à séparer les sources. Ensuite, nous avons proposé une approche qui s’appuie sur l’analyse en composantes parcimonieuses pour séparer les composantes de la force de réaction au sol ayant des propriétés cyclo-stationnaires à l’ordre 1 et 2In this thesis, we have tackled the problem of blind separation of linear mixtures of sources with cyclo-stationarity properties. Three applications were studied : telecommunications, mechanical vibrations and biomechanics. First, two new methods have been proposed, the first one aims to blindly separate cyclo-stationary sources sharing one or more unknown cyclic frequencies. It combines the joint diagonalization with a new useful point detector (time lag-cyclic frequency) to compose the set of cyclic correlation matrices to be jointly diagonalized. As for the second method, it aims to identify the mixture matrix of cyclo-stationary sources of unknown and different cyclic frequencies. The identification begins with a step of detecting the matrices of rank one, then the product of selected matrices is decomposed into eigen-elements, and finally a hierarchical regrouping method returns the columns of our sought matrix. Both solutions have been applied to telecommunications signals. In a second step, we first applied the first proposed method on mechanical signals coming from a bank of faulty bearings in order to test its ability to separate the sources. Next, we proposed an approach based on sparse component analysis to separate the components of the ground reaction force with cyclo-stationary properties at order 1 and

An algorithm for non-unitary joint block diagonalization of matrices using BFGS method

International audienceWe consider in this work the problem of joint block diagonalization of a set of complex or real valued matrices which have the same structure, discarding the unitary constraint. It’s an important issue in signal processing which appears in miscellaneous fields such as the problem of arrival’s direction (DOA) estimation, source localization and blind source separation for convolutive mixtures. We introduce in this paper a new iterative algorithm for the non-unitary block diagonalization (NU-JBD) based on the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method which is the most commonly used update strategy for implementing a quasi-newton technique. Numerical simulations are provided in order to illustrate the overall very good performances of the proposed algorithm in comparison to three state-of-the art NU-JBD algorithms. To do so, we study two contexts: first, we consider set of exactly block-diagonal matrices, then we increasingly perturb it by an additive gaussian noise

Une nouvelle solution pour l’identification aveugle de mélanges de sources cyclostationnaires appliquée aux signaux de télécommunication

International audienceIn this communication, we introduce a semi-analytical solution for the blind identification of the mixing matrix in the case of linearly mixed signals from cyclostationnary sources whose cyclic frequencies are unknown and different. The identification combines the eigenvalue decomposition of a set of cyclic autocorrelation matrices of the observation signals constituted by a detector of rank-one matrices with a hierarchical classification method. The proposed approach is applied to telecommunications signals and the theoretical results are supported by numerical simulations in different noise contexts.Dans cette communication, nous introduisons une solution semi-analytique pour l’identification aveugle de la matrice de mélange dans le cas d’un mélange linéaire de sources cyclostationnaires dont les fréquences cycliques sont inconnues et différentes. L’identification combine la décomposition en valeurs propres d’un ensemble de matrices d’autocorrélations cycliques des signaux d’observations constitué grâce à un détecteur de matrices de rang un avec une méthode de classification hiérarchique. L’approche proposée est appliquée aux signaux de télécommunication et les résultats théoriques sont appuyés par des simulations numériques dans différents contextes de bruit

Blind Separation of Cyclostationary Sources Sharing Common Cyclic Frequencies Using Joint Diagonalization Algorithm

We propose a new method for blind source separation of cyclostationary sources, whose cyclic frequencies are unknown and may share one or more common cyclic frequencies. The suggested method exploits the cyclic correlation function of observation signals to compose a set of matrices which has a particular algebraic structure. The aforesaid matrices are automatically selected by proposing two new criteria. Then, they are jointly diagonalized so as to estimate the mixing matrix and retrieve the source signals as a consequence. The nonunitary joint diagonalization (NU-JD) is ensured by Broyden-Fletcher-Goldfarb-Shanno (BFGS) method which is the most commonly used update strategy for implementing a quasi-Newton technique. The efficiency of the method is illustrated by numerical simulations in digital communications context, which show good performances comparing to other state-of-the-art methods