CentraleSupélec

About: CentraleSupélec is a based out in . It is known for research contribution in the topics: MIMO & Wireless network. The organization has 1330 authors who have published 2344 publications receiving 30533 citations. The organization is also known as: CentraleSupelec & CentraleSupelec of the Paris-Saclay University.

Kriging the fields: a new statistical tool for wave propagation analysis

Abstract: A new statistical method for radiowave propagation analysis is presented based on the spatial statistics tools known as kriging and variographic analysis. In the proposed method, fields are considered as random variables of position. Using a few samples of those variables obtained by numerical or experimental means, unknown field values with confidence intervals are inferred. Validation of the new approach is carried out on measurements in indoor environment.

Deep learning for aircraft classification from VHF radar signatures

Abstract: Radio sources in the Very High Frequency (VHF) band can be seized as opportunity donors in a passive radar configuration such as FM radio stations and VHF omnidirectional range (VOR). A full-wave simulation of three size classes of aeroplanes shows that their bistatic radar cross-section (RCS) are statistically comparable, albeit perform differently in time while the plane is flying. This difference can be exploited to recognize the size of the aeroplanes with respect to these classes. Measurements confirm this possible differentiation between the aeroplanes within the same class. Encouraging initial results were obtained using convolutional or recurrent neural networks to classify aircraft classes, combining simulated bistatic RCS results and real trajectories (collected from automatic dependent surveillance-broadcastdata).

Intuitive facial animation editing based on a generative RNN framework

Abstract: For the last decades, the concern of producing convincing facial animation has garnered great interest, that has only been accelerating with the recent explosion of 3D content in both entertainment and professional activities. The use of motion capture and retargeting has arguably become the dominant solution to address this demand. Yet, despite high level of quality and automation performance-based animation pipelines still require manual cleaning and editing to refine raw results, which is a time- and skill-demanding process. In this paper, we look to leverage machine learning to make facial animation editing faster and more accessible to non-experts. Inspired by recent image inpainting methods, we design a generative recurrent neural network that generates realistic motion into designated segments of an existing facial animation, optionally following user-provided guiding constraints. Our system handles different supervised or unsupervised editing scenarios such as motion filling during occlusions, expression corrections, semantic content modifications, and noise filtering. We demonstrate the usability of our system on several animation editing use cases.

Minimum variance portfolio optimization in the spiked covariance model

Abstract: We study the design of minimum variance portfolio when asset returns follow a low rank factor model. Using results from random matrix theory, an optimal shrinkage approach for the isolated eigenvalues of the covariance matrix is developed. The proposed portfolio optimization strategy is shown to have good performance on synthetic data but not always on real data sets. This leads us to refine the data model by considering time correlation between samples. By updating the shrinkage of the isolated eigenvalues accounting for the unknown time correlation, our portfolio optimization method is shown to have improved performance and achieves lower risk values than competing methods on real financial data sets.

Steering Focusing Waves in a Reverberation Chamber With Generalized Time Reversal

Abstract: Generalized time reversal was introduced in a previous paper from a theoretical point of view. In this paper, experiments are conducted to demonstrate its application to a reverberation chamber, as a method for generating coherent wavefronts in a medium displaying random propagation. Wavefronts thus generated were sampled over a planar surface, confirming that they propagate as if in a free-space environment. The accuracy with which they match their free-space counterpart is not affected by changing their features, e.g., direction of arrival and focus. These results prove that a single excitation antenna can generate complex wavefronts when coupled to diffusive wave propagation.