The compressive sensing (CS) framework aims to ease the burden on analog-to-digital converters (ADCs) by exploiting inherent structure in natural and man-made signals. It has been demon-strated that structured signals can be acquired with just a small number of linear measurements, on the order of the signal complexity. In practice, this enables lower sampling rates that can be more easily achieved by current hardware designs. The primary bottleneck that limits ADC sam-pling rates is quantization, i.e., higher bit-depths impose lower sampling rates. Thus, the decreased sampling rates of CS ADCs accommodate the otherwise limiting quantizer of conventional ADCs. In this thesis, we consider a different approach to CS ADC by shifting towards lower quantizer bit-depths rather than lower sampling rates. We explore the extreme case where each measurement is quantized to just one bit, representing its sign. We develop a new theoretical framework to analyze this extreme case and develop new algorithms for signal reconstruction from such coarsely quantized measurements. The 1-bit CS framework leads us to scenarios where it may be more appropriate to reduce bit-depth instead of sampling rate. We find that there exist two distinct regimes of operation that correspond to high/low signal-to-noise ratio (SNR). In the measurement

A particularly important sector for the stability of financial systems is the banking sector. Banks play a central role in the money creation process and in the payment system. Moreover, bank credit is an important factor in the financing of investment and growth. Faltering banking systems have

of scalability and maintainability. Hence, different approaches are based on Peer-to-Peer (P2P) technology. In particular, fast-paced NVEs stress these systems and most likely cause high overlay maintenance overhead. This Master’s Thesis presents a novel approach to spatial Publish/Subscribe (Pub/Sub) in P2P

RECHERCHES Elastographie par ondes de cisaillement

I would like to thank some people who were involved in the creation of this thesis: • Berry Schoenmakers of TU Eindhoven for introducing me to the topic of cryptographic voting protocols. • Evangelos Karatsiolis for his work in guiding me through the process of writing this thesis and his constant motivation. • Roberto Samarone Araujo for hours of discussing various protocols with Evengelos and me. • Warren D. Smith for providing with a preliminary version of his upcoming book »How Mathematics can Improve Democracy«. • Ute Günther and Christian Burgmann for their proof-reading. Nonetheless, any remaing errors shall be blamed on me. • Andrea Peter for her love and for cheering me up when I was down. • Last but not least my parents, Jacqueline Herrnkind and Hans Peter Klink – without their love and continuous support, this thesis would never have been written.

pages 156–175, 2013. [2] Johannes Buchmann, Denise Demirel, and Jeroen van de Graaf. Towards a publicly-verifiable mix-net providing everlasting privacy. In Financial Cryptog-raphy, pages 197–204, 2013. [3] Denise Demirel, Jeroen van de Graaf, and Roberto Araùjo. Improving helios

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.

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Anne SABOURIN Mélanges bayésiens de modèles d'extrêmes multivariés, Application à la prédétermination régionale des crues avec données incomplètes. Sous la direction de: