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Träfflista för sökning "WFRF:(Maragos N.) "

Search: WFRF:(Maragos N.)

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  • Acharya, B. S., et al. (author)
  • Introducing the CTA concept
  • 2013
  • In: Astroparticle physics. - : Elsevier BV. - 0927-6505 .- 1873-2852. ; 43, s. 3-18
  • Journal article (other academic/artistic)abstract
    • The Cherenkov Telescope Array (CTA) is a new observatory for very high-energy (VHE) gamma rays. CTA has ambitions science goals, for which it is necessary to achieve full-sky coverage, to improve the sensitivity by about an order of magnitude, to span about four decades of energy, from a few tens of GeV to above 100 TeV with enhanced angular and energy resolutions over existing VHE gamma-ray observatories. An international collaboration has formed with more than 1000 members from 27 countries in Europe, Asia, Africa and North and South America. In 2010 the CTA Consortium completed a Design Study and started a three-year Preparatory Phase which leads to production readiness of CTA in 2014. In this paper we introduce the science goals and the concept of CTA, and provide an overview of the project. (C) 2013 Elsevier B.V. All rights reserved.
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2.
  • Katsamanis, N., et al. (author)
  • Audiovisual speech inversion by switching dynamical modeling Governed by a Hidden Markov Process
  • 2008
  • In: Proceedings of EUSIPCO.
  • Conference paper (peer-reviewed)abstract
    • We propose a unified framework to recover articulation from audiovisual speech. The nonlinear audiovisual-to-articulatory mapping is modeled by means of a switching linear dynamical system. Switching is governed by a state sequence determined via a Hidden Markov Model alignment process. Mel Frequency Cepstral Coefficients are extracted from audio while visual analysis is performed using Active Appearance Models. The articulatory state is represented by the coordinates of points on important articulators, e.g., tongue and lips. To evaluate our inversion approach, instead of just using the conventional correlation coefficients and root mean squared errors, we introduce a novel evaluation scheme that is more specific to the inversion problem. Prediction errors in the positions of the articulators are weighted differently depending on their relevant importance in the production of the corresponding sound. The applied weights are determined by an articulatory classification analysis using Support Vector Machines with a radial basis function kernel. Experiments are conducted in the audiovisual-articulatory MOCHA database.
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