Zobrazeno 1 - 10
of 96
pro vyhledávání: '"Baydoun, Mohamad"'
Autor:
Kanapram, Divya, Campo, Damian, Baydoun, Mohamad, Marcenaro, Lucio, Bodanese, Eliane L., Regazzoni, Carlo, Marchese, Mario
This paper presents a novel approach to detect abnormalities in dynamic systems based on multisensory data and feature selection. The proposed method produces multiple inference models by considering several features of the observed data. This work f
Externí odkaz:
http://arxiv.org/abs/2010.14900
This paper proposes a method for performing continual learning of predictive models that facilitate the inference of future frames in video sequences. For a first given experience, an initial Variational Autoencoder, together with a set of fully conn
Externí odkaz:
http://arxiv.org/abs/2006.01945
Autor:
Ravanbakhsh, Mahdyar, Baydoun, Mohamad, Campo, Damian, Marin, Pablo, Martin, David, Marcenaro, Lucio, Regazzoni, andCarlo
The technology for autonomous vehicles is close to replacing human drivers by artificial systems endowed with high-level decision-making capabilities. In this regard, systems must learn about the usual vehicle's behavior to predict imminent difficult
Externí odkaz:
http://arxiv.org/abs/2004.10049
Autor:
Slavic, Giulia, Campo, Damian, Baydoun, Mohamad, Marin, Pablo, Martin, David, Marcenaro, Lucio, Regazzoni, Carlo
This paper proposes a method for detecting anomalies in video data. A Variational Autoencoder (VAE) is used for reducing the dimensionality of video frames, generating latent space information that is comparable to low-dimensional sensory data (e.g.,
Externí odkaz:
http://arxiv.org/abs/2003.07623
Autor:
Baydoun, Mohamad
Le but de ce travail de thèse est de démontrer l’intérêt d’un macrophyte aquatique submergé, Myriophyllum alterniflorum pour la détection de contaminants dans l’environnement. Des études in situ ont été réalisées pendant 28 jours sur
Externí odkaz:
http://www.theses.fr/2018LIMO0087
Autor:
Ravanbakhsh, Mahdyar, Baydoun, Mohamad, Campo, Damian, Marin, Pablo, Martin, David, Marcenaro, Lucio, Regazzoni, Carlo S.
In recent years several architectures have been proposed to learn embodied agents complex self-awareness models. In this paper, dynamic incremental self-awareness (SA) models are proposed that allow experiences done by an agent to be modeled in a hie
Externí odkaz:
http://arxiv.org/abs/1806.04012
Autor:
Ravanbakhsh, Mahdyar, Baydoun, Mohamad, Campo, Damian, Marin, Pablo, Martin, David, Marcenaro, Lucio, Regazzoni, Carlo S.
This paper presents a novel approach for learning self-awareness models for autonomous vehicles. The proposed technique is based on the availability of synchronized multi-sensor dynamic data related to different maneuvering tasks performed by a human
Externí odkaz:
http://arxiv.org/abs/1806.02609
Autor:
Baydoun, Mohamad, Ravanbakhsh, Mahdyar, Campo, Damian, Marin, Pablo, Martin, David, Marcenaro, Lucio, Cavallaro, Andrea, Regazzoni, Carlo S.
This paper focuses on multi-sensor anomaly detection for moving cognitive agents using both external and private first-person visual observations. Both observation types are used to characterize agents' motion in a given environment. The proposed met
Externí odkaz:
http://arxiv.org/abs/1803.06579
Akademický článek
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Autor:
Baydoun, Mohamad, Gajtani, Zen, Patton, Michaela, McLennan, Andrew, Cartwright, Stephen, Carlson, Linda E.
Publikováno v:
Frontiers in Pain Research; 2024, p1-11, 11p
