Fakultät Informatik und Mathematik
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The automotive solution for Ethernet is BroadR-Reach, which cannot be found as a common Ethernet-interface in the consumer industry. Hence a media converter from IEEE 802.3 Ethernet to BroadR-Reach is needed to debug and test the communication of automotive devices under test (DUT). If the functionality of a BroadR-Reach connection has to be tested for electromagnetic compatibility (EMC), a BroadR-Reach to optical media converter is needed, which has to comply to the same EMC test specifications as the DUT. This research explains the internal structure of a media converter and defines a test setup for copper bound emission test of BroadR-Reach. By using a standardized stripline measurement like it is common for electromagnetic emission test, it could be shown, that the tested Technica/Tinytron media converter can safely be used inside an EMC chamber.
This paper describes a case study of assessing student's coding behavior and skills in a realistic development setting. Students had to solve typical programming problems in the context of app development for the Android platform using the Eclipse IDE. Data was analyzed using IDE as well as browser interaction logs. In addition, screen recordings of the students' interaction with the IDE provide further insight. In this paper we present the first results of our ongoing work.
We present a paradigm for characterization of artifacts in limited data tomography problems. In particular, we use this paradigm to characterize artifacts that are generated in reconstructions from limited angle data with generalized Radon transforms and general filtered backprojection type operators. In order to find when visible singularities are imaged, we calculate the symbol of our reconstruction operator as a pseudodifferential operator.
In the context of a "Smart Grid" research project, together with industrial partners, the OTH-Regensburg realized an intelligent medium-voltage grid in the local area. Goal was to improve the current voltage regulation and to counter the problems with inconsistent energy feed-in of decentralized renewable energy producers. In this paper we discuss the possibilities of using 3rd generation (3G) cellular networks (UMTS) as basic technology to communicate the voltage-levels within a medium-voltage grid. We build an experimental
hardware setup to generate data-traffic as specified for the smart
grid. By analyzing the performance of 3G cellular networks in
terms of transmission latency and rate of failure, we tried to
evaluate the usability of this technology for such critical data
exchange. Though mobile communication in its structure is not
specified for the use of transmitting such infrastructure critical
data, the results show a promising high reliability with low
transmission latency. The experiments served just to test a
fragment of the conditions of use in a real scenario. An expanded
test scope is needed to further analyze the performance of mobile
radio for automatic control in smart grids. In the end the results
discussed in this paper led to a successful prototype of an
intelligent medium-voltage grid with mobile radio as communication technology
We propose a method for an efficient optimization of experimental designs, using a combination of discrete adjoint computations, Taylor arithmetic and matrix calculus. Compared to the state of the art of using finite differences or the forward mode of automatic differentiation, our proposed approach leads to a reduction of the relative temporal complexity from linear to constant time in the number of control variables and measurement weights. We demonstrate that the advantageous complexity results are not only of theoretical nature, but lead to significant speedups in practice as well. With our implementation we are very close to the theoretical bound of the cheap gradient principle. We present one academic (spatially discretized heat equation) and two industrial application examples (biochemical process/Diesel-oxidation catalysis process) where we achieve speedups that range between 10 and 100. In addition to our core results, we also describe an efficient adjoint approach for the treatment of differential algebraic equations and present adjoint formulas for constrained least-squares problems.
An adequate network management is an essential part for each network permitting to keep the network performance at a suitable level. In this paper, we present our work focusing on an Ethernet network which is used as background interconnection technology for a smart home system in large buildings like tenements or an office block. In this case the network is larger and especially the applications are quite heterogeneous as compared to a private smart home system. Therefore, it is hard to supply a fair and quality of service aware network meeting all the demands. This is especially challenging in the case of an Ethernet network because this technology was intended to use it as a transparent connection system without the feature to support hard quality of service. We present how this problem could be solved by a middleware which best meets the special requirement of the mentioned use case. Finally, we discuss possible design improvements of this solution and present the recent Ethernet standards that could help solve this problem in an alternative way.
Holding non-co-located conversationswhile driving is dangerous (Horrey and- Wickens, 2006; Strayer et al., 2006), much more so than conversations with physically present, “situated” interlocutors
(Drews et al., 2004). In-car dialogue systems typically resemble non-co-located conversations more, and share their negative impact (Strayer et al., 2013). We implemented and tested a simple strategy
for making in-car dialogue systems aware of the driving situation, by giving them the capability to interrupt themselves when a dangerous situation is detected,and resume when over. We show that this improves both driving performance and recall of system-presented information,
compared to a non-adaptive strategy.
When humans speak, they do not plan their full utterance inall detail before beginning to speak, nor do they speak piece-by-piece and ignoring their full message – instead humans usepartial representations in which they fill in the missing partsas the utterance unfolds. Incremental speech synthesizers, incontrast, have not yet made use of partial representations and theinformation contained there-in.We analyze the quality of prosodic parameter assignments(pitch and duration) generated from partial utterance specifi-cations (substituting defaults for missing features) in order todetermine the requirements that symbolic incremental prosodymodelling should meet. We find that broader, higher-level infor-mation helps to improve prosody even if lower-level informationabout the near future is yet unavailable. Furthermore, we findthat symbolic phrase-level or utterance-level information is mosthelpful towards the end of the phrase or utterance, respectively,that is, when this information is becoming available even in theincremental case. Thus, the negative impact of incremental pro-cessing can be minimized by using partial representations thatare filled in incrementally.
Automatic speech recognition (ASR) technology has been developed to such a level that off-the-shelf distributed speech recognition services are available (free of cost), which allow researchers to integrate speech into their applications with little development effort or expert knowledge leading to better results compared with previously used open-source tools.
Often, however, such services do not accept language models or grammars but process free speech from any domain. While results are very good given the enormous size of the search space, results frequently contain out-of-domain words or constructs that cannot be understood by subsequent domain-dependent natural language understanding (NLU) components. We present a versatile post-processing technique based on phonetic distance that integrates domain knowledge with open-domain ASR results, leading to improved ASR performance. Notably, our technique is able to make use of domain restrictions using various degrees of domain knowledge, ranging from pure vocabulary restrictions via grammars or N-Grams to restrictions of the acceptable utterances. We present results for a variety of corpora (mainly from human-robot interaction) where our combined approach significantly outperforms Google ASR as well as a plain open-source ASR solution.