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Abstract: Online learning initia-tives over the past decade have become increasingly comprehen-sive in their selection of courses and sophisticated in their presen-tation, culminating in the recent announcement of a number of consortium and startup activities that promise to make a university

Abstract—We investigate the problem of a monostatic pulse-Doppler radar transceiver trying to detect targets, sparsely populated in the radar’s unambiguous time-frequency region. Several past works employ compressed sensing (CS) algorithms to this type of problem, but either do not address sample rate reduction, impose constraints on the radar transmitter, propose CS recovery methods with prohibitive dictionary size, or perform poorly in noisy conditions. Here we describe a sub-Nyquist sampling and recovery approach called Doppler focusing which addresses all of these problems: it performs low rate sampling and digital processing, imposes no restrictions on the transmitter, and uses a CS dictionary with size which does not increase with increasing number of pulses P. Furthermore, in the presence of noise, Doppler focusing enjoys a signal-to-noise ratio (SNR) improvement which scales linearly with P, obtaining good detection performance even at SNR as low as-25dB. The recovery is based on the Xampling framework, which allows reducing the number of samples needed to accurately represent the signal, directly in the analog-to-digital conversion process. After sampling, the entire digital recovery process is performed on the low rate samples without having to return to the Nyquist rate. Finally, our approach can be implemented in hardware using a previously suggested Xampling radar prototype. Index Terms—compressed sensing, rate of innovation, radar, sparse recovery, sub-Nyquist sampling, delay-Doppler estimation. I.

-pose estimating it using a reinforcement learning algorithm that approximates optimal stratified sampling. We apply this algo-rithm to a DR program that utilizes the SV for payments and quantify the accuracy of the resulting estimates. Index Terms—Economics, power system economics. I.

Building a more sustainable sensor network via protocol innovation

I would like to thank Prof. Dr. Torsten Braun, head of the Computer Network and Distributed Systems group (RVS), for supervising this work and for his insightful advises. Prof. Dr. Torsten Braun encouraged and motivated me to publish my research results and he provided me the opportunity to present the work on various conferences, for which I thank him. I would also like to thank Prof. Dr. Roger Wattenhofer, responsible for the Koreferat of this work. Also, Prof. Dr. Oscar Nierstrasz who was willing to be the co-examinator of this work deserves many thanks. Many thanks go to my colleagues of the RVS group and of the IAM for our various interesting discussions about all kinds of topics and for making the institute a very pleasant and friendly place to work at. Special thanks go to David Steiner, Marc Steinemann, Matthias Scheidegger, Florian Baumgartner, Ruy De Oliveira, and Attila Weyland. There are many students who worked with me and helped a lot in developing and implementing. Among them I especially thankful to Thomas Bernoulli,

Treatment plan optimization for rotating-shield brachytherapy

Abstract—Causal processing of a signal’s samples is crucial in on-line applications such as audio rate conversion, compression, tracking and more. This paper addresses the problems of predict-ing future samples and causally interpolating deterministic sig-nals. We treat a rich variety of sampling

Massively multiplayer online games (MMOGs) have become increasingly popular in the recent years, particularly in the form of online role-playing games (MMORPGs). These games support up to several ten thousand players interacting in a virtual game world. The current commercially successful games

I herewith certify that all material in this dissertation which is not my own work has been properly acknowledged.