Abstract not found

Responses of soil biological processes to elevated atmospheric [CO2] and nitrogen addition in a poplar plantation

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

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

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

Die Theorie an und für sich ist nichts nütze, als insofern sie uns den Zusammenhang der Erscheinungen glauben macht.

First, I would like to thank to Dimka Karastoyanova and Mariano Cilia for all the fruitful discussions, their guidance and support through the course of this project. Thanks also to Prof. Alejandro Buchmann who sponsored my stay at Technische Universität Darmstadt during the spring of 2004. The special thanks go to my advisor, Mariano Cilia, who has made all this possible. Finally, I’d like to thank to my family for all these years of support.

Caractérisation des problèmes conjoints de détection et d'estimation

belowground functioning of tropical biomes

RECHERCHES Elastographie par ondes de cisaillement