![]() Transport collapse in dynamically evolving networks. ⟨hal-04302096⟩ Accès au texte intégral et bibtex ref_biblio Geoffroy Berthelot, Liubov Tupikina, Min-Yeong Kang, Jérôme Dedecker, Denis S Grebenkov. The Reissner fiber under tension in vivo shows dynamic interaction with ciliated cells contacting the cerebrospinal fluid. ⟨hal-04267199⟩ Accès au texte intégral et bibtex ref_biblio Celine Bellegarda, Guillaume Zavard, Lionel Moisan, Françoise Brochard-Wyart, Jean-François Joanny, et al. ⟨hal-03266676v3⟩ Accès au texte intégral et bibtex ref_biblio Nicholas Barton, Alison Etheridge, Amandine Véber. A refined Weissman estimator for extreme quantiles. ⟨hal-03709864v2⟩ Accès au texte intégral et bibtex ref_biblio Michaël Allouche, Jonathan El Methni, Stéphane Girard. Electron Paramagnetic Resonance Image Reconstruction with Total Variation Regularization. ⟨hal-04236430⟩ Accès au texte intégral et bibtexĢ023 ref_biblio Rémy Abergel, Mehdi Boussâa, Sylvain Durand, Yves-Michel Frapart. Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (AISTATS), In press. Positivity-free Policy Learning with Observational Data. ![]() ⟨hal-03427805⟩ Accès au texte intégral et bibtex ref_biblio Pan Zhao, Antoine Chambaz, Julie Josse, Shu Yang. Spontaneous rotations in epithelia as an interplay between cell polarity and boundaries. ⟨hal-04488229⟩ Accès au texte intégral et bibtex ref_biblio Simon Lo Vecchio, Olivier Pertz, Marcela Szopos, Laurent Navoret, Daniel Riveline. Impact of image registration errors on the quality of hyperspectral images in imaging static Fourier transform spectrometry. Imaging and target recognition through strong turbulence is regarded as one of the most challenging problems in modern turbulence research.2024 ref_biblio Varvara Chiliaeva, Andrés Almansa, Yann Ferrec, Jean-Michel Gaucel, Olivier Gazzano, et al. As the aggregated turbulence distortion inevitably degrades remote targets and makes them less recognizable, both adaptive optics approaches and image correction methods will become less effective in retrieving correct attributes of the target. Meanwhile, machine learning (ML)-based algorithms have been proposed and studied using both hardware and software approaches to alleviate turbulence effects. In this work, we propose a straightforward approach that treats images with turbulence distortion as a data augmentation in the training set, and investigate the effectiveness of the ML-assisted recognition outcomes under different turbulence strengths. Retrospectively, we also apply the recognition outcomes to evaluate the turbulence strength through regression techniques. As a result, our study helps to build a deep connection between turbulence distortion and imaging effects through a standard perceptron neural network (NN), where mutual inference between turbulence levels and target recognition rates can be achieved.Ītmospheric turbulence in free-space will distort the helical phase front of vortex beams (VBs) and cause mode diffusion, seriously hindering the practical application of optical orbital angular momentum (OAM) communications. ![]() ![]() Here, we propose and experimentally investigate a convolutional neural network (CNN) based atmospheric turbulence compensation method for OAM multiplexing communication. Taking the advantage of signal processing, we design a CNN model that can automatically extract the characteristic parameters from the distorted intensity distribution of VBs. Under the influence of the turbulencewith After supervisory training, the CNN model possesses a strong generalization ability and can efficiently predict the equivalent turbulence phase screen. , the mode purity of the distorted VB improves from 26.91% to 93.12% through the compensation. By constructing an OAM multiplexing communication link with the bit-rate of 100Īnd employing the CNN model to equalize the OAM channels, the bit-error-rates are decreased by three orders of magnitude, and the measured crosstalk is reduced from -23.15dB to -29.46dB. Moreover, the constellations converge obviously at the signal-to-noise ratio of 20dB, and the error-vector-magnitude decreases from 0.3337 to 0.1622. These results indicate that the CNN model can well compensate the atmospheric turbulence induced distortion in VBs, and may open new avenues for improving the performance of OAM communications. ![]()
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