Research, is carried out by a consortium of partners including industrials and research labs. This project aimed at implementing an Augmented Reality system for mobile use in industrial applications, especially train maintenance. There were two objectives for such a system: the first of them

mixed) reality environments often lack a convincing solution for all aspects of sound. The workshop took place at the ZKM, on December, 2nd and 3rd 1999. Five lectures and demonstrations, presented by eRENA partner's collaborators, alternated with discussions among the participants. The deliverable

[ INFORMATION IN-TERFACES AND PRESENTATION (e.g., HCI)]: Multimedia Information Systems—Artificial, augmented, and virtual realities 1

de nationalité suisse et originaire de Zurich (ZH) et Lucerne (LU) acceptée sur proposition du jury:

Wireless sensor networks are used to collect sensor data in different applications such as environmental monitoring, smart building control, and health care applications. Wireless sensor nodes used are typically small, low-cost, and battery powered. The nodes are often hard to access after deployment, for example when they are in remote locations. Another property of wireless sensor networks is that their operation is dependent on the environment they operate in, both due to the specific sensor readings but also due to the effects on communication by factors such as fading and radio interference. This makes it important to evaluate a wireless sensor network in its intendent target environment before final deployment. To enable experiments with wireless sensor networks in their target environment, we have designed and implemented a testbed called Sensei-UU. It is designed to allow WSN experiments to be repeated in different locations, thus exposing effects caused by the environment. To allow this, the testbed is designed to be easily moved between experimental sites. One type of WSN applications Sensei-UU is aimed to evaluate is protocols where nodes are mobile. Mobile testbed nodes are low-cost robots which follow a tape track on the floor. The localization accuracy of the robot approach is evaluated and is accurate enough to expose a

Abstract not found

April 29, 2011Für UnsShe had never before seen a rabbit with either a waistcoat-pocket, or a watch to take out of it, and burning with curiosity, she ran across the field after it Lewis CarrollThe following document, written under the supervision of Dr. reviewed by:

Caractérisation des problèmes conjoints de détection et d'estimation

We explore two fundamental questions at the intersection of sampling theory and information theory: how is channel capacity affected by sampling below the channel’s Nyquist rate, and what sub-Nyquist sampling strategy should be employed to maximize capacity. In particular, we first derive the capacity of sampled analog channels for two prevalent sampling mechanisms: filtering followed by sampling and sampling following filter banks. Connections between sampling and MIMO Gaussian channels are illuminated based on this analysis. Optimal prefilters that maximize capacity are identified for both cases, as well as several kinds of channels for which these sampling mechanisms are optimal to maximize capacity at sub-Nyquist rates. We also highlight connections between sampled analog channel capacity and minimum mean squared error estimation from sampled data. In particular, it is shown that for both filtering and filter-bank sampling strategies, the filters maximizing capacity and minimizing mean squared error are equivalent. We also investigate a more general sampling strategy by adding modulation banks to filter-bank sampling. This general sampling method subsumes most nonuniform sampling techniques applied in both theory and practice. We also show a connection between this general sampling method and MIMO Gaussian channels. We then identify the optimal sampling strategy for piece-wise flat sampled

First, I would like to thank my advisors Prof. Gal A. Kaminka and Dr. David Sarne for their excellent guidance and constant support in the past two years. I learned quite a bit about scientific research, all thanks to their great instruction. Working with them was a real pleasure, not only on the professional level, but on the personal level as well. Thanks to my colleagues and friends from the Bar-Ilan University in general, and MAVERICK lab in particular, for the great time we had together. Special thanks to Gabriella Melamed, MAVERICK lab manager, for assisting with administra-tive issues, and in particular for helping with the arrangements for the experiment. I also wish to thank Tal-Oron Gilad from Ben-Gurion University, for providing us with a He-brew translation of the NASA-TLX questionnaire. Thanks to my dear family for their support and encouragement, and for enabling me to com-plete this work. Thanks to my girlfriend for her patience and understanding. Finally, I would like to thank my grandfather, who never ceased to encourage me throughout this journey.