Добірка наукової літератури з теми "Estimation de séparation"
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Статті в журналах з теми "Estimation de séparation":
Chakir, Hamouch, and Chaaouan Jamal. "Prédétermination des Crues de L’oued Inaouène à L’Aide des Méthodes Statistiques, Maroc Septentrional." European Scientific Journal, ESJ 19, no. 33 (November 30, 2023): 202. http://dx.doi.org/10.19044/esj.2023.v19n33p202.
Chouaf, Seloua, and Youcef Smara. "Méthode de sélection des bandes à base de l'Analyse en Composantes Indépendantes appliquée aux images hyperspectrales de télédétection." Revue Française de Photogrammétrie et de Télédétection, no. 204 (April 8, 2014): 57–62. http://dx.doi.org/10.52638/rfpt.2013.22.
Robin, Marion, Lucile Bonnardel, François Saintoyant, Aziz Essadek, Gérard Shadili, Victoire Peres, and Maurice Corcos. "Facteurs D’adversité Chez des Adolescents Issus de Milieu Aisé Hospitalisés en Psychiatrie." Canadian Journal of Psychiatry 67, no. 4 (December 13, 2021): 319–21. http://dx.doi.org/10.1177/07067437211064593.
Bertrand-Krajewski, Jean-Luc, and Jean-Pascal Bardin. "Estimation des incertitudes de mesure sur les débits et les charges polluantes en réseau d'assainissement : application au cas d'un bassin de retenue-décantation en réseau séparatif pluvial." La Houille Blanche, no. 6-7 (October 2001): 99–108. http://dx.doi.org/10.1051/lhb/2001078.
Taleb, M. "Adversité sociale et troubles psychotiques." European Psychiatry 29, S3 (November 2014): 630–31. http://dx.doi.org/10.1016/j.eurpsy.2014.09.134.
Дисертації з теми "Estimation de séparation":
Ichir, Mahieddine Mehdi. "Estimation bayésienne et approche multi-résolution en séparation de sources." Paris 11, 2005. http://www.theses.fr/2005PA112370.
Mamouni, Nezha. "Utilisation des Copules en Séparation Aveugle de Sources Indépendantes/Dépendantes." Thesis, Reims, 2020. http://www.theses.fr/2020REIMS007.
The problem of Blind Source Separation (BSS) consists in retrieving unobserved mixed signals from unknown mixtures of them, where there is no, or very limited, information about the source signals and/or the mixing system. In this thesis, we present algorithms in order to separate instantaneous and convolutive mixtures. The principle of these algorithms is to minimize, appropriate separation criteria based on copula densities, using descent gradient type algorithms. These methods can magnificently separate instantaneous and convolutive mixtures of possibly dependent source components even when the copula model is unknown
Rafi, Selwa. "Chaînes de Markov cachées et séparation non supervisée de sources." Thesis, Evry, Institut national des télécommunications, 2012. http://www.theses.fr/2012TELE0020/document.
The restoration problem is usually encountered in various domains and in particular in signal and image processing. It consists in retrieving original data from a set of observed ones. For multidimensional data, the problem can be solved using different approaches depending on the data structure, the transformation system and the noise. In this work, we have first tackled the problem in the case of discrete data and noisy model. In this context, the problem is similar to a segmentation problem. We have exploited Pairwise and Triplet Markov chain models, which generalize Hidden Markov chain models. The interest of these models consist in the possibility to generalize the computation procedure of the posterior probability, allowing one to perform bayesian segmentation. We have considered these methods for two-dimensional signals and we have applied the algorithms to retrieve of old hand-written document which have been scanned and are subject to show through effect. In the second part of this work, we have considered the restoration problem as a blind source separation problem. The well-known "Independent Component Analysis" (ICA) method requires the assumption that the sources be statistically independent. In practice, this condition is not always verified. Consequently, we have studied an extension of the ICA model in the case where the sources are not necessarily independent. We have introduced a latent process which controls the dependence and/or independence of the sources. The model that we propose combines a linear instantaneous mixing model similar to the one of ICA model and a probabilistic model on the sources with hidden variables. In this context, we show how the usual independence assumption can be weakened using the technique of Iterative Conditional Estimation to a conditional independence assumption
Arberet, Simon. "Estimation robuste et apprentissage aveugle de modèles pour la séparation de sources sonores." Phd thesis, Rennes 1, 2008. ftp://ftp.irisa.fr/techreports/theses/2008/arberet.pdf.
Blind source separation in the underdetermined case is an ill-posed problem where it is usually assumed that sources are independent and sparse in the time-frequency domain. Separation is then done in two steps : the estimation of the mixture parameters, followed by the estimation of the sources. The assumptions made about the sources are not valid for all the time-frequency points, so that the approaches which naively address all the points identically and independently, are little robust in estimating the mixture parameters and the sources. In this thesis we exploit the local distribution of the mixture in the neighborhood of each time-frequency point, to : - Detect the time-frequency regions where only one source is active and to estimate the direction of the dominant source in these regions; - Estimate the distribution of the sources in each time-frequency point using the knowledge on the mixture parameters. The proposed local approach is supported by a clustering algorithm called DEMIX, which robustly estimates the mixture parameters in the instantaneous and anechoic cases. On the other hand, the local spatial distribution of the sources can be used to learn Spectral-GMM which until now required a learning step with source examples. We show that this approach improve the source estimation performance of some dB in SDR
Arberet, Simon. "Estimation robuste et apprentissage aveugle de modèles pour la séparation de sources sonores." Phd thesis, Université Rennes 1, 2008. http://tel.archives-ouvertes.fr/tel-00564052.
Essebbar, Abderrahman. "Séparation paramétrique des ondes en sismique." Phd thesis, Grenoble INPG, 1992. http://tel.archives-ouvertes.fr/tel-00785644.
Rosier, Julie. "Estimation de fréquences fondamentales multiples : application à la séparation de signaux de parole et musique." Phd thesis, Télécom ParisTech, 2003. http://pastel.archives-ouvertes.fr/pastel-00000723.
RAFI, Selwa. "Chaînes de Markov cachées et séparation non supervisée de sources." Phd thesis, Institut National des Télécommunications, 2012. http://tel.archives-ouvertes.fr/tel-00995414.
Fourt, Olivier. "Traitement des signaux à phase polynomiale dans des environnements fortement bruités : séparation et estimation des paramètres." Paris 11, 2008. http://www.theses.fr/2008PA112064.
The research works of this thesis deal with the processings of polynomial phase signals in heavily corrupted environnements, whatsoever noise with high levels or impulse noise, noise modelled by the use of alpha-stable laws. Noise robustness is a common task in signal processing and if several algorithms are able to work with high gaussian noise level, the presence of impulse noise often leads to a great loss in performances or makes algorithms unable to work. Recently, some algorithms have been built in order to support impulse noise environnements but with one limit: the achievable results decrease with gaussian noise situations and thus needs as a first step to select the good method versus the kind of the noise. So one of the key points of this thesis was building algorithms who were robust to the kind of the noise which means that they have similar performances with gaussian noise or alpha-stable noise. The second key point was building fast algorithms, something difficult to add to robustness
Boudjellal, Abdelouahab. "Contributions à la localisation et à la séparation de sources." Thesis, Orléans, 2015. http://www.theses.fr/2015ORLE2063.
Signal detection, localization, and separation problems date back to the beginning of the twentieth century. Nowadays, this subject is still a hot topic receiving more and more attention, notably with the rapid growth of wireless communication systems that arose in the last two decades and it turns out that many challenging aspects remain poorly addressed by the available literature relative to this subject. This thesis deals with signal detection, localization using temporal or directional measurements, and separation of dependent source signals. The main objective is to make use of some available priors about the source signals such as sparsity, cyclo-stationarity, non-circularity, constant modulus, autoregressive structure or training sequences in a cooperative framework. The first part is devoted to the analysis of (i) signal’s time-of-arrival estimation using a new minimum error rate based detector, (ii) noise power estimation using an improved order-statistics estimator and (iii) side information impact on direction-of-arrival estimation accuracy and resolution. In the second part, the source separation problem is investigated at the light of different priors about the original sources. Three kinds of prior have been considered : (i) separation of constant modulus communication signals, (ii) separation of dependent source signals knowing their dependency structure and (iii) separation of dependent autoregressive sources knowing their autoregressive structure