Quantitative Measurements of CME-driven Shocks from LASCO Observations

In this paper, we demonstrate that CME-driven shocks can be detected in white light coronagraph images and in which properties such as the density compression ratio and shock direction can be measured. Also, their propagation direction can be deduced via simple modeling. We focused on CMEs during the ascending phase of solar cycle 23 when the large-scale morphology of the corona was simple. We selected events which were good candidates to drive a shock due to their high speeds (V>1500 km/s). The final list includes 15 CMEs. For each event, we calibrated the LASCO data, constructed excess mass images and searched for indications of faint and relatively sharp fronts ahead of the bright CME front. We found such signatures in 86% (13/15) of the events and measured the upstream/downstream densities to estimate the shock strength. Our values are in agreement with theoretical expectations and show good correlations with the CME kinetic energy and momentum. Finally, we used a simple forward modeling technique to estimate the 3D shape and orientation of the white light shock features. We found excellent agreement with the observed density profiles and the locations of the CME source regions. Our results strongly suggest that the observed brightness enhancements result from density enhancements due to a bow-shock structure driven by the CME.Comment: to be published in Astrophysical Journa

Offline signature verification using classifier combination of HOG and LBP features

We present an offline signature verification system based on a signature’s local histogram features. The signature is divided into zones using both the Cartesian and polar coordinate systems and two different histogram features are calculated for each zone: histogram of oriented gradients (HOG) and histogram of local binary patterns (LBP). The classification is performed using Support Vector Machines (SVMs), where two different approaches for training are investigated, namely global and user-dependent SVMs. User-dependent SVMs, trained separately for each user, learn to differentiate a user’s signature from others, whereas a single global SVM trained with difference vectors of query and reference signatures’ features of all users, learns how to weight dissimilarities. The global SVM classifier is trained using genuine and forgery signatures of subjects that are excluded from the test set, while userdependent SVMs are separately trained for each subject using genuine and random forgeries. The fusion of all classifiers (global and user-dependent classifiers trained with each feature type), achieves a 15.41% equal error rate in skilled forgery test, in the GPDS-160 signature database without using any skilled forgeries in training

Various L2-signatures and a topological L2-signature theorem

For a normal covering over a closed oriented topological manifold we give a proof of the L2-signature theorem with twisted coefficients, using Lipschitz structures and the Lipschitz signature operator introduced by Teleman. We also prove that the L-theory isomorphism conjecture as well as the C^*_max-version of the Baum-Connes conjecture imply the L2-signature theorem for a normal covering over a Poincar space, provided that the group of deck transformations is torsion-free. We discuss the various possible definitions of L2-signatures (using the signature operator, using the cap product of differential forms, using a cap product in cellular L2-cohomology,...) in this situation, and prove that they all coincide.Comment: comma in metadata (author field) added

Reconstructing CMEs with Coordinated Imaging and In Situ Observations: Global Structure, Kinematics, and Implications for Space Weather Forecasting

See the pdf for detailsComment: 45 pages, 16 figures, ApJ, in pres

A generalization of several classical invariants of links

We extend several classical invariants of links in the 3-sphere to links in so-called quasi-cylinders. These invariants include the linking number, the Seifert form, the Alexander module, the Alexander-Conway polynomial and the Murasugi-Tristram-Levine signatures.Comment: 25 pages, 6 figure

Automatic refocus and feature extraction of single-look complex SAR signatures of vessels

In recent years, spaceborne synthetic aperture radar ( SAR) technology has been considered as a complement to cooperative vessel surveillance systems thanks to its imaging capabilities. In this paper, a processing chain is presented to explore the potential of using basic stripmap single-look complex ( SLC) SAR images of vessels for the automatic extraction of their dimensions and heading. Local autofocus is applied to the vessels' SAR signatures to compensate blurring artefacts in the azimuth direction, improving both their image quality and their estimated dimensions. For the heading, the orientation ambiguities of the vessels' SAR signatures are solved using the direction of their ground-range velocity from the analysis of their Doppler spectra. Preliminary results are provided using five images of vessels from SLC RADARSAT-2 stripmap images. These results have shown good agreement with their respective ground-truth data from Automatic Identification System ( AIS) records at the time of the acquisitions.Postprint (published version