High Order Discontinuous Galerkin Method

Standard continuous Galerkin-based finite element methods have poor stability properties when applied to transport-dominated flow problems, so excessive numerical stabilization is needed. In contrast, the Discontinuous Galerkin method is known to have good stability properties when applied to first order hyperbolic problems

Query-Driven Indexing for Scalable Peer-to-Peer Text Retrieval

We present a query-driven algorithm for the distributed indexing of large document collections within structured P2P networks. To cope with bandwidth consumption that has been identified as the major problem for the standard P2P approach with single term indexing, we leverage a distributed index that stores up to top-k document references only for carefully chosen indexing term combinations. In addition, since the number of possible term combinations extracted from a document collection can be very large, we propose to use query statistics to index only such combinations that are indeed frequently requested by the users. Thus, by avoiding the maintenance of superfluous indexing information, we achieve a substantial reduction in bandwidth and storage. A specific activation mechanism is applied to continuously update the indexing information according to changes in the query distribution, resulting in an efficient, constantly evolving query-driven indexing structure. We show that the size of the index and the generated indexing/retrieval traffic remains manageable even for web-size document collections at the price of a marginal loss in precision for rare queries. Our theoretical analysis and experimental results provide convincing evidence about the feasibility of the query-driven indexing strategy for large scale P2P text retrieval. Moreover, our experiments confirm that the retrieval performance is only slightly lower than the one obtained with state-of-the-art centralized query engines

Architecture for Secure and Private Vehicular Communications

The deployment of vehicular communication (VC) systems is strongly dependent on their security and privacy features. In this paper, we propose a security architecture for VC. The primary objectives of the architecture include the management of identities and cryptographic keys, the security of communications, and the integration of privacy enhancing technologies. Our design approach aims at a system that relies on well-understood components which can be upgraded to provide enhanced security and privacy protection in the future. This effort is undertaken by SeVeCom (http://www.sevecom.org), a transversal project providing security and privacy enhancing mechanisms compatible with the VC technologies currently under development by all EU funded projects

Secure Position-Based Routing for VANETs

Vehicular communication (VC) systems have the potential to improve road safety and driving comfort. Nevertheless, securing the operation is a prerequisite for deployment. So far, the security of VC applications has mostly drawn the attention of research efforts, while comprehensive solutions to protect the network operation have not been developed. In this paper, we address this problem: we provide a scheme that secures geographic position-based routing, which has been widely accepted as the appropriate one for VC. Moreover, we focus on the scheme currently chosen and evaluated in the Car2Car Communication Consortium (C2C-CC). We integrate security mechanisms to protect the position-based routing functionality and services (beaconing, multi-hop forwarding, and geo-location discovery), and enhance the network robustness. We propose defense mechanisms, relying both on cryptographic primitives, and plausibility checks mitigating false position injection. Our implementation and initial measurements show that the security overhead is low and the proposed scheme deployable

Introduction to hyperbolic equations and fluid-structure interaction

In this semester project we deal with hyperbolic partial differential equations and Fluid-Structure Interactio

Polarization-dependence of anomalous scattering in brominated DNA and RNA molecules, and importance of crystal orientation in single- and multiple-wavelength anomalous diffraction phasing

In this paper the anisotropy of anomalous scattering at the Br K-absorption edge in brominated nucleotides is investigated, and it is shown that this effect can give rise to a marked directional dependence of the anomalous signal strength in X-ray diffraction data. This implies that choosing the correct orientation for crystals of such molecules can be a crucial determinant of success or failure when using single- and multiple-wavelength anomalous diffraction (SAD or MAD) methods to solve their structure. In particular, polarized absorption spectra on an oriented crystal of a brominated DNA molecule were measured, and were used to determine the orientation that yields a maximum anomalous signal in the diffraction data. Out of several SAD data sets, only those collected at or near that optimal orientation allowed interpretable electron density maps to be obtained. The findings of this study have implications for instrumental choices in experimental stations at synchrotron beamlines, as well as for the development of data collection strategy programs

Efficient and Stable Acoustic Tomography Using Sparse Reconstruction Methods

We study an acoustic tomography problem and propose a new inversion technique based on sparsity. Acoustic tomography observes the parameters of the medium that influence the speed of sound propagation. In the human body, the parameters that mostly influence the sound speed are temperature and density, in the ocean - temperature and current, in the atmosphere - temperature and wind. In this study, we focus on estimating temperature in the atmosphere using the information on the average sound speed along the propagation path. The latter is practically obtained from travel time measurements. We propose a reconstruction algorithm that exploits the concept of sparsity. Namely, the temperature is assumed to be a linear combination of some functions (e.g. bases or set of different bases) where many of the coefficients are known to be zero. The goal is to find the non-zero coefficients. To this end, we apply an algorithm based on linear programming that under some constrains finds the solution with minimum l0 norm. This is actually equivalent to the fact that many of the unknown coefficients are zeros. Finally, we perform numerical simulations to assess the effectiveness of our approach. The simulation results confirm the applicability of the method and demonstrate high reconstruction quality and robustness to noise