University of Rennes

About: University of Rennes is a education organization based out in Rennes, France. It is known for research contribution in the topics: Population & Crystal structure. The organization has 18404 authors who have published 40374 publications receiving 995327 citations.

Robust identification of a nonminimum phase system: Blind adjustment of a linear equalizer in data communications

Abstract: Consider an unknown linear time-invariant system without control, driven by a white noise with known distribution. We are interested in the identification of this system, observing only the output. This problem is well known under the major assumption: the system is minimum (or maximum!) phase, in which the very popular least squares method gives an identification of the system in an autoregressive form. However, we are Interested in the case where the system is nonminimum (nor maximum!) phase, i.e., we want identification of both gain and phase of the system. The literature gives only a negative result: the idenfication of the phase of the system is impossible in the case of a Gaussian driving noise (hence, second-order statistics are irrelevant to our problem). For a large class of other input distributions, we present an identification procedure, and give some numerical results for a concrete case origin of our study: the blind adjustment of a transversal equalizer without any startup period prior to data transmission.

Transdimensional inverse thermal history modeling for quantitative thermochronology

Abstract: [1] A new approach for inverse thermal history modeling is presented. The method uses Bayesian transdimensional Markov Chain Monte Carlo and allows us to specify a wide range of possible thermal history models to be considered as general prior information on time, temperature (and temperature offset for multiple samples in a vertical profile). We can also incorporate more focused geological constraints in terms of more specific priors. The Bayesian approach naturally prefers simpler thermal history models (which provide an adequate fit to the observations), and so reduces the problems associated with over interpretation of inferred thermal histories. The output of the method is a collection or ensemble of thermal histories, which quantifies the range of accepted models in terms a (posterior) probability distribution. Individual models, such as the best data fitting (maximum likelihood) model or the expected model (effectively the weighted mean from the posterior distribution) can be examined. Different data types (e.g., fission track, U-Th/He, 40Ar/39Ar) can be combined, requiring just a data-specific predictive forward model and data fit (likelihood) function. To demonstrate the main features and implementation of the approach, examples are presented using both synthetic and real data.

Plume-lithosphere interaction in generation of the Emeishan flood basalts at the Permian-Triassic boundary

Abstract: The Emeishan flood volcanism that erupted at Permian-Triassic boundary time produced a large igneous province of at least 2.5 X 10 5 km 2 in the western margin of the Yangtze craton, southwestern China. The volcanic successions, suggested to have resulted from a starting mantle plume, comprise thick piles of basaltic flows and subordinate picrites and pyroclastics. The picrites, which have high magnesian contents (MgO ≊ 20–16 wt%), variable degrees of light rare earth element enrichment [(Ce/Yb) N ≊ 4–25] and heterogeneous isotope ratios [ϵ Nd ≊ (T) +4 to −4], are proposed to have been generated by mixing between the dominant plume-derived magmas and small amounts of lamproitic liquids from the continental lithospheric mantle.

Pose Estimation for Augmented Reality: A Hands-On Survey

Abstract: Augmented reality (AR) allows to seamlessly insert virtual objects in an image sequence. In order to accomplish this goal, it is important that synthetic elements are rendered and aligned in the scene in an accurate and visually acceptable way. The solution of this problem can be related to a pose estimation or, equivalently, a camera localization process. This paper aims at presenting a brief but almost self-contented introduction to the most important approaches dedicated to vision-based camera localization along with a survey of several extension proposed in the recent years. For most of the presented approaches, we also provide links to code of short examples. This should allow readers to easily bridge the gap between theoretical aspects and practical implementations.