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Jean-François Cardoso

Researcher at Institut d'Astrophysique de Paris

Publications -  374
Citations -  127993

Jean-François Cardoso is an academic researcher from Institut d'Astrophysique de Paris. The author has contributed to research in topics: Planck & Cosmic microwave background. The author has an hindex of 145, co-authored 373 publications receiving 115144 citations. Previous affiliations of Jean-François Cardoso include University of Paris & Télécom ParisTech.

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Planck 2015 results. XX. Constraints on inflation

TL;DR: In this article, the authors report on the implications for cosmic inflation of the 2018 Release of the Planck CMB anisotropy measurements, which are fully consistent with the two previous Planck cosmological releases, but have smaller uncertainties thanks to improvements in the characterization of polarization at low and high multipoles.
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Planck 2018 results. VI. Cosmological parameters

Nabila Aghanim, +232 more
TL;DR: In this paper, the cosmological parameter results from the final full-mission Planck measurements of the CMB anisotropies were presented, with good consistency with the standard spatially-flat 6-parameter CDM cosmology having a power-law spectrum of adiabatic scalar perturbations from polarization, temperature, and lensing separately and in combination.
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Blind beamforming for non-gaussian signals

TL;DR: In this paper, a computationally efficient technique for blind estimation of directional vectors, based on joint diagonalization of fourth-order cumulant matrices, is presented for beamforming.
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A blind source separation technique using second-order statistics

TL;DR: A new source separation technique exploiting the time coherence of the source signals is introduced, which relies only on stationary second-order statistics that are based on a joint diagonalization of a set of covariance matrices.
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Blind signal separation: statistical principles

TL;DR: The objectives of this paper are to review some of the approaches that have been developed to address blind signal separation and independent component analysis, to illustrate how they stem from basic principles, and to show how they relate to each other.