Delaunay sur Matchless [concurrent de la course motocycliste Paris-Nice] : [photographie de presse] / [Agence Rol]

Référence bibliographique : Rol, 57946

Georges Delaunay [n°39], Isodi [n°38], Felix Delaunay [n°127] sur Stucchi [concurrents de la course motocycliste Paris-Nice] : [photographie de presse] / [Agence Rol]

Référence bibliographique : Rol, 57964

d'Arcolo [ou d'Argollo] sur American Excelsior [1000 cmc, concurrent de la course motocycliste Paris-Nice] : [photographie de presse] / [Agence Rol]

Référence bibliographique : Rol, 57963

[A.] Galien sur Benoit Gonin [500 cmc, concurrent de la course motocycliste Paris-Nice] : [photographie de presse] / [Agence Rol]

Référence bibliographique : Rol, 57965

[Max Cyril] Morris sur Matchless [1000 cmc, concurrent de la course motocycliste Paris-Nice] : [photographie de presse] / [Agence Rol]

Référence bibliographique : Rol, 57950

Psalty sur Rover [500 cmc, concurrent de la course motocycliste Paris-Nice] : [photographie de presse] / [Agence Rol]

Référence bibliographique : Rol, 57947

Moto Royal Enfield, Oblin sur Royal Enfield, sidecar Diane [catégorie 1000 cmc, concurrent de la course motocycliste Paris-Nice] : [photographie de presse] / [Agence Rol]

Référence bibliographique : Rol, 57948

Guibert sur BSA [750 cmc, concurrent de la course motocycliste Paris-Nice] : [photographie de presse] / [Agence Rol]

Référence bibliographique : Rol, 57968

Etave sur Indian [1000 cmc, concurrent de la course motocycliste Paris-Nice] : [photographie de presse] / [Agence Rol]

Référence bibliographique : Rol, 57969

Harold Petty [n°116], [V. E.] Horsman [n°117], Stanley Parker [n°122] sur Norton [750 cmc, concurrents de la course motocycliste Paris-Nice] : [photographie de presse] / [Agence Rol]

Référence bibliographique : Rol, 57962

P. W. White sur Sunbeam [500 cmc, concurrent de la course motocycliste Paris-Nice] : [photographie de presse] / [Agence Rol]

Référence bibliographique : Rol, 57961

Multisensor change detection with nonlinear canonical correlation

The analysis of multi-modal and multi-sensor images is nowadays of paramount importance for Earth Observation (EO) applications. There exist a variety of methods that aim at fusing the different sources of information to obtain a compact representation of such datasets. However, for change detection existing methods are often unable to deal with heterogeneous image sources and very few consider possible nonlinearities in the data. Additionally, the availability of labeled information is very limited in change detection applications. For these reasons, we present the use of a semi-supervised kernel-based feature extraction technique. It incorporates a manifold regularization accounting for the geometric distribution and jointly addressing the small sample problem. An exhaustive example using Landsat 5 data illustrates the potential of the method for multi-sensor change detection.

Multi-scale label interaction to improve object detection and segmentation

The project aims at advancing the state of the art in the use of context information for classification of image and video data. The use of context in the classification of images has been showed of great importance to improve the performance of actual object recognition systems. In our project we proposed the concept of Multi-scale Feature Labels as a general and compact method to exploit the local and global context. The feature extraction from the discriminative probability or classification confidence label field is of great novelty. Moreover the use of a multi-scale representation of the feature labels lead to a compact and efficient description of the context. The goal of the project has been also to provide a general-purpose method and prove its suitability in different image/video analysis problem. The two-year project generated 5 journal publications (plus 2 under submission), 10 conference publications (plus 2 under submission) and one patent (plus 1 pending). Of these publications, a relevant number make use of the main result of this project to improve the results in detection and/or segmentation of objects.

Impact of antenna correlation on the capacity of multiantenna channels

This paper applies random matrix theory to obtain analytical characterizations of the capacity of correlated multiantenna channels. The analysis is not restricted to the popular separable correlation model, but rather it embraces a more general representation that subsumesmost of the channel models that have been treated in the literature. For arbitrary signal-to-noise ratios (SNR), the characterization is conducted in the regime of large numbers of antennas. For the low- and high-SNR regions, in turn, we uncover compact capacity expansions that are valid for arbitrary numbers of antennas and that shed insight on how antenna correlation impacts the tradeoffs between power, bandwidth and rate.

Aerodynamic drag modeling of alpine skiers performing giant slalom turns.

PURPOSE: Aerodynamic drag plays an important role in performance for athletes practicing sports that involve high-velocity motions. In giant slalom, the skier is continuously changing his/her body posture, and this affects the energy dissipated in aerodynamic drag. It is therefore important to quantify this energy to understand the dynamic behavior of the skier. The aims of this study were to model the aerodynamic drag of alpine skiers in giant slalom simulated conditions and to apply these models in a field experiment to estimate energy dissipated through aerodynamic drag. METHODS: The aerodynamic characteristics of 15 recreational male and female skiers were measured in a wind tunnel while holding nine different skiing-specific postures. The drag and the frontal area were recorded simultaneously for each posture. Four generalized and two individualized models of the drag coefficient were built, using different sets of parameters. These models were subsequently applied in a field study designed to compare the aerodynamic energy losses between a dynamic and a compact skiing technique. RESULTS: The generalized models estimated aerodynamic drag with an accuracy of between 11.00% and 14.28%, and the individualized models estimated aerodynamic drag with an accuracy between 4.52% and 5.30%. The individualized model used for the field study showed that using a dynamic technique led to 10% more aerodynamic drag energy loss than using a compact technique. DISCUSSION: The individualized models were capable of discriminating different techniques performed by advanced skiers and seemed more accurate than the generalized models. The models presented here offer a simple yet accurate method to estimate the aerodynamic drag acting upon alpine skiers while rapidly moving through the range of positions typical to turning technique.