Oscar Chinellato

Connectivity in the presence of shadowing in 802.11 ad hoc networks

Connectivity is an important property for QoS support in mobile ad hoc networks (MANETs). Recently, there has been a big effort in exploring the critical transmission range (CTR) analytically, based on different network models. While most of these studies rely on a geometric model and come up with asymptotic bounds, their significance regarding finite 802.11 based MANETs is unclear. In this paper, we investigate connectivity in MANETs from a layered perspective. We first point out how the transmission range affects the end-to-end connection probability in a log-normal shadowing model and compare the results to theoretical bounds and measurements in the path loss model. We then show how connectivity issues behave in 802.11 and IP based networks if the fading effect increases. The paper concludes with an analytical model for the link probability in log-normal shadowing environments as a function of the number of nodes, network area, transmission range, path loss exponent and shadowing deviation.

Computation of optical modes in axisymmetric open cavity resonators

The computation of optical modes inside axisymmetric cavity resonators with a general spatial permittivity profile is a formidable computational task. In order to avoid spurious modes the vector Helmholtz equations are discretised by a mixed finite element approach. We formulate the method for first and second order Nedelec edge and Lagrange nodal elements. We discuss how to accurately compute the element matrices and solve the resulting large sparse complex symmetric eigenvalue problems. We validate our approach by three numerical examples that contain varying material parameters and absorbing boundary conditions (ABC).

Using Python for large scale linear algebra applications

Software used in scientific computing is traditionally developed using compiled languages for the sake of maximal performance. However, for most applications, the time-critical portion of the code that requires the efficiency of a compiled language, is confined to a small set of well-defined functions. Implementing the remaining part of the application using an interactive and interpreted high-level language offers many advantages without a big performance degradation tradeoff. This paper describes the Pythonic approach, a mixed language approach combining the Python programming language with near operating-system level languages.We demonstrate the effectiveness of the Pythonic approach by showing a few small examples and fragments of two large scale linear algebra applications.Essential advantages of the Pythonic mixed language approach is the combination of flexible, readable, shorter, and most importantly less error-prone syntax with performance similar to pure Fortran or C/C++ implementations.

Including Covariances in Calibration to Obtain Better Measurement Uncertainty Estimates

Decisions are often based on the results of quantitative analysis. To gain confidence in these results, some indication of their quality is needed. Measurement uncertainty, as proposed by the International Organization for Standardization (ISO), is a way to express this quality. Most measurement techniques compare references and therefore need to be calibrated. Usually least squares methods ignoring uncertainties associated with the calibration references are applied. In this article we show how the measurement uncertainty can be computed in compliance with the ISO proposal, taking into account the covariances of the calibration references. Two new fitting methods, XiP-fit and P-fit, are devised and compared to other fitting methods. For our examples the P-fit method outperforms the other methods in terms of parameter recovery. Additionally, we introduce a solver which is well suited for the class of problems arising from calibration and measurement uncertainty estimation.