Optical ground station optimization for future optical geostationary satellite feeder uplinks

An optical link based on a multiplex of wavelengths at 1.55µm is foreseen to be a valuable alternative to the conventional radio-frequencies for the feeder link of the next-generation of high throughput geostationary satellite. Considering the limited power of lasers envisioned for feeder links, the beam divergence has to be dramatically reduced. Consequently, the beam pointing becomes a key issue. During its propagation between the ground station and a geostationary satellite, the optical beam is deflected (beam wandering), and possibly distorted (beam spreading), by atmospheric turbulence. It induces strong fluctuations of the detected telecom signal, thus increasing the bit error rate (BER). A steering mirror using a measurement from a beam coming from the satellite is used to pre-compensate the deflection. Because of the point-ahead angle between the downlink and the uplink, the turbulence effects experienced by both beams are slightly different, inducing an error in the correction. This error is characterized as a function of the turbulence characteristics as well as of the terminal characteristics, such as the servo-loop bandwidth or the beam diameter, and is included in the link budget. From this result, it is possible to predict intensity fluctuations detected by the satellite, both statistically (mean intensity, scintillation index, probability of fade, etc.) and temporally (power spectral densities, time series). The final objective is to optimize the different parameters of an optical ground station capable of mitigating the impact of atmospheric turbulence on the uplink in order to be compliant with the targeted capacity (1Terabit/s by 2025)

LEGO-Net: Learning Regular Rearrangements of Objects in Rooms

Humans universally dislike the task of cleaning up a messy room. If machines were to help us with this task, they must understand human criteria for regular arrangements, such as several types of symmetry, co-linearity or co-circularity, spacing uniformity in linear or circular patterns, and further inter-object relationships that relate to style and functionality. Previous approaches for this task relied on human input to explicitly specify goal state, or synthesized scenes from scratch -- but such methods do not address the rearrangement of existing messy scenes without providing a goal state. In this paper, we present LEGO-Net, a data-driven transformer-based iterative method for learning regular rearrangement of objects in messy rooms. LEGO-Net is partly inspired by diffusion models -- it starts with an initial messy state and iteratively "de-noises'' the position and orientation of objects to a regular state while reducing the distance traveled. Given randomly perturbed object positions and orientations in an existing dataset of professionally-arranged scenes, our method is trained to recover a regular re-arrangement. Results demonstrate that our method is able to reliably rearrange room scenes and outperform other methods. We additionally propose a metric for evaluating regularity in room arrangements using number-theoretic machinery.Comment: Project page: https://ivl.cs.brown.edu/projects/lego-ne

Topology-Aware Surface Reconstruction for Point Clouds

We present an approach to inform the reconstruction of a surface from a point scan through topological priors. The reconstruction is based on basis functions which are optimized to provide a good fit to the point scan while satisfying predefined topological constraints. We optimize the parameters of a model to obtain likelihood function over the reconstruction domain. The topological constraints are captured by persistence diagrams which are incorporated in the optimization algorithm promote the correct topology. The result is a novel topology-aware technique which can: 1.) weed out topological noise from point scans, and 2.) capture certain nuanced properties of the underlying shape which could otherwise be lost while performing surface reconstruction. We showcase results reconstructing shapes with multiple potential topologies, compare to other classical surface construction techniques, and show the completion of real scan data

Structures pour l'apprentissage profond et l'optimisation de la topologie de fonctions sur les formes 3D

The field of geometry processing is following a similar path as image analysis with the explosion of publications dedicated to deep learning in recent years. An important research effort is being made to reproduce the successes of deep learning 2D computer vision in the context of 3D shape analysis. Unlike images shapes comes in various representations like meshes or point clouds which often lack canonical structure. This makes traditional deep learning algorithms like Convolutional Neural Networks (CNN) non straightforward to apply to 3D data. In this thesis we propose three main contributions:First, we introduce a method to compare functions on different domains without correspondences and to deform them to make the topology of their set of levels more alike. We apply our method to the classical problem of shape matching in the context of functional maps to produce smoother and more accurate correspondences. Furthermore, our method is based on the continuous optimization of a differentiable energy with respect to the compared functions and is applicable to deep learning. We make two direct contributions to deep learning on 3D data. We introduce a new convolution operator over triangles meshes based on local polar coordinates and apply it to deep learning on meshes. Unlike previous works our operator takes all choices of polar coordinates into account without loss of directional information. Lastly we introduce a new rotation invariant convolution layer over point clouds and show that CNNs based on this layer can outperform state of the art methods in standard tasks on un-alligned datasets even with data augmentation.Le domaine du traitement de la géométrie suit un cheminement similaire à celui de l'analyse d'images avec l'explosion des publications consacrées à l'apprentissage profond ces dernières années. Un important effort de recherche est en cours pour reproduire les succès de l'apprentissage profond dans le domaine de la vision par ordinateur dans le contexte de l'analyse de formes 3D. Contrairement aux images, les formes 3D peuvent peuvent être représentées de différentes manières comme des maillages ou des nuages de points souvent dépourvus d'une structure canonique. Les algorithmes d'apprentissage profond traditionnels tels que les réseaux neuronaux convolutifs (CNN) ne sont donc pas faciles à appliquer aux formes 3D. Dans cette thèse, nous proposons trois contributions principales : premièrement, nous introduisons une méthode permettant de comparer des fonctions sur des domaines différents sans correspondances et de les déformer afin de rendre la topologie de leur ensemble de niveaux similaires. Nous appliquons notre méthode au problème classique de la correspondance de formes dans le contexte des applications fonctionnelles (functional maps) afin de produire des correspondances plus lisses et plus précises. Par ailleurs notre méthode reposant sur l'optimisation continue d'une énergie différentiable par rapport aux fonctions comparées elle est applicable à l'apprentissage profond. Nous apportons deux contributions directes à l'apprentissage profond des données 3D. Nous introduisons un nouvel opérateur de convolution sur des maillages triangulaires basés sur des coordonnées polaires locales et l'appliquons à l'apprentissage profond sur les maillages. Contrairement aux travaux précédents, notre opérateur prend en compte tous les choix de coordonnées polaires sans perte d'information directionnelle. Enfin, nous introduisons un nouveau module de convolution invariant par rotation sur les nuages de points et montrons que les CNN basés sur ce dernier peuvent surpasser l'état de l'art pour des tâches standard sur des ensembles de données non alignés même avec augmentation des données

[Identification of three categories of andic horizons in andosols]

At present, both the French Referential pedologique' and the WRB propose that a distinction be made between two categories of andic horizons i.e. the sil-andic and the aluandic horizons. We show that the alternative criteria (Si-o either > or 0.5 or <0.5) selected presently to establish this distinction lead to ambiguous taxonomic placements. Vee present charts allowing estimation of the content of allophanes and Al-humus complexes in these horizons. The utilization of these charts points out that the andic horizons from the TUWAD database can be regrouped into three categories. Such a new regrouping takes much better into account the broad range of allophanes and Al-humus contents present in,Indic horizons. Accordingly, it should improve the schematic description of andosol profiles. (C) 2000 Academie des sciences / Editions scientifiques et medicales Elsevier SAS.Sur l'existence de trois catégories d'horizons de référence dans les andosols. Le référentiel pédologique ainsi que le WRB proposent aujourd'hui que soient distinguées deux catégories d'horizons andiques : l'horizon silandique et l'horizon aluandique. Nous montrons ici que les deux critères alternatifs sur lesquels repose actuellement cette distinction ne conviennent pas : ni la teneur en Si dans le réactif oxalate, ni la valeur du rapport Al extrait par le pyrophosphate sur l'Al extrait par l'oxalate n'indique de façon non ambiguë quel est le constituant secondaire aluminique dominant dans un horizon andique donné. Nous présentons des abaques permettant d'estimer les teneurs en allophane et en complexe organo-aluminique dans ces horizons. Ensuite, nous établissons que, sur la base de la proportion de ces deux familles de constituants dans les horizons de la base de données Tuwad, trois catégories d'horizons andiques peuvent être identifiées. Ce regroupement en trois classes rend mieux compte de la diversité de constitution de ces horizons et facilite la description schématique des profils d'andosols

Characteristics of non-allophanic Andisols with hydric properties from the Ecuadorian páramos.-

International audienc

Directional Enlacement Histograms for the Description of Complex Spatial Configurations between Objects

International audienceThe analysis of spatial relations between objects in digital images plays a crucial role in various application domains related to pattern recognition and computer vision. Classical models for the evaluation of such relations are usually sufficient for the handling of simple objects, but can lead to ambiguous results in more complex situations. In this article, we investigate the modeling of spatial configurations where the objects can be imbricated in each other. We formalize this notion with the term enlacement, from which we also derive the term interlacement, denoting a mutual enlacement of two objects. Our main contribution is the proposition of new relative position descriptors designed to capture the enlacement and interlacement between two-dimensional objects. These descriptors take the form of circular histograms allowing to characterize spatial configurations with directional granularity, and they highlight useful invariance properties for typical image understanding applications. We also show how these descriptors can be used to evaluate different complex spatial relations, such as the surrounding of objects. Experimental results obtained in the different application domains of medical imaging, document image analysis and remote sensing, confirm the genericity of this approach