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The utilisation of vehicle-oriented gasoline in general aviation is very desirable for both ecological and economical reasons, as well as for general considerations of availability. As of today vehicle fuels may be used if the respective engine and cell are certified for such an operation. For older planes a supplementary technical certificate is provided for gasoline mixtures with less than 1 % v/v ethanol only, though. Larger admixtures of ethanol may lead to sudden engine malfunction and should be considered as considerable security risks. Major problems are caused by the partially ethanol non-withstanding materials, a necessarily changed stochiometric adjustment of the engine for varying ethanol shares and the tendency for phase separation in the presence of absorbed water. The concepts of the flexible fuel vehicles are only partially applicable in the view of air security.
Numerical Study on Increased Energy Density for the DLN Micromix Hydrogen Combustion Principle
(2014)
Development and Testing of a Low NOx Micromix Combustion Chamber for an Industrial Gas Turbine
(2015)
GaAs-based Gunn diodes with graded AlGaAs hot electron injector heterostructures have been developed under the special needs in automotive applications. The fabrication of the Gunn diode chips was based on total substrate removal and processing of integrated Au heat sinks. Especially, the thermal and RF behavior of the diodes have been analyzed by DC, impedance and S-parameter measurements. The electrical investigations have revealed the functionality of the hot electron injector. An optimized layer structure could fulfill the requirements in adaptive cruise control (ACC) systems at 77 GHz with typical output power between 50 and 90 mW.
Inference on the basis of high-dimensional and functional data are two topics which are discussed frequently in the current statistical literature. A possibility to include both topics in a single approach is working on a very general space for the underlying observations, such as a separable Hilbert space. We propose a general method for consistently hypothesis testing on the basis of random variables with values in separable Hilbert spaces. We avoid concerns with the curse of dimensionality due to a projection idea. We apply well-known test statistics from nonparametric inference to the projected data and integrate over all projections from a specific set and with respect to suitable probability measures. In contrast to classical methods, which are applicable for real-valued random variables or random vectors of dimensions lower than the sample size, the tests can be applied to random vectors of dimensions larger than the sample size or even to functional and high-dimensional data. In general, resampling procedures such as bootstrap or permutation are suitable to determine critical values. The idea can be extended to the case of incomplete observations. Moreover, we develop an efficient algorithm for implementing the method. Examples are given for testing goodness-of-fit in a one-sample situation in [1] or for testing marginal homogeneity on the basis of a paired sample in [2]. Here, the test statistics in use can be seen as generalizations of the well-known Cramérvon-Mises test statistics in the one-sample and two-samples case. The treatment of other testing problems is possible as well. By using the theory of U-statistics, for instance, asymptotic null distributions of the test statistics are obtained as the sample size tends to infinity. Standard continuity assumptions ensure the asymptotic exactness of the tests under the null hypothesis and that the tests detect any alternative in the limit. Simulation studies demonstrate size and power of the tests in the finite sample case, confirm the theoretical findings, and are used for the comparison with concurring procedures. A possible application of the general approach is inference for stock market returns, also in high data frequencies. In the field of empirical finance, statistical inference of stock market prices usually takes place on the basis of related log-returns as data. In the classical models for stock prices, i.e., the exponential Lévy model, Black-Scholes model, and Merton model, properties such as independence and stationarity of the increments ensure an independent and identically structure of the data. Specific trends during certain periods of the stock price processes can cause complications in this regard. In fact, our approach can compensate those effects by the treatment of the log-returns as random vectors or even as functional data.
Nowadays, the most employed devices for recoding videos or capturing images are undoubtedly the smartphones. Our work investigates the application of source camera identification on mobile phones. We present a dataset entirely collected by mobile phones. The dataset contains both still images and videos collected by 67 different smartphones. Part of the images consists in photos of uniform backgrounds, especially collected for the computation of the RSPN. Identifying the source camera given a video is particularly challenging due to the strong video compression. The experiments reported in this paper, show the large variation in performance when testing an highly accurate technique on still images and videos.
Table of contents 1. Introduction 2. Multi-level Technology Transfer Infrastructure 2.1 Level 1: University Education – Encourage the Idea of becoming an Entrepreneur 2.2 Level 2: Post Graduate Education – Improve your skills and focus it on a product family. 2.3 Level 3: Birth of a Company – Focus your skills on a product and a market segment. 2.4 Level 4: Ready to stand alone – Set up your own business 2.5 Level 5: Grow to be Strong – Develop your business 2.6 Level 6: Competitive and independent – Stay innovative. 3. Samples 3.1 Sample 1: Laser Processing and Consulting Centre, LBBZ 3.2 Sample 2: Prototyping Centre, CP 4. Funding - Waste money or even lost Money? 5. Conclusion