We demonstrate that eigenmodes of a metamaterial structure at terahertz (THz) frequencies can be excited by photo-generation of localized transient dipoles in the semiconductor substrate. We apply this technique to map the coupling of these dipoles to the resonator's near-field. The characteristic metamaterial resonances appear as peaks in the spectrum of the THz radiation emitted from the resonant structures into the far-field. Recording two-dimensional THz emission maps allows us to reproduce the frequency-dependent spatial profiles of the metamaterial's eigenmodes.

Large area terahertz emitters based on the lateral photo-Dember effect in InN (indium nitride) are presented. The formation of lateral photo-Dember currents is induced by laser-illumination through a microstructured metal cover processed onto the InN substrate, causing an asymmetry in the lateral photogenerated charge carrier distribution. Our design uses simple metal structures, which are produced by conventional two-dimensional micro-structuring techniques. Having favoring properties as a photo-Dember material InN is particularly well-suited as a substrate for our emitters. We demonstrate that the emission intensity of the emitters can be significantly influenced by the structure of the metal cover leaving room for improvement by optimizing the masking structures

Followed by the 9/11 attacks in 2001 and the subsequent events, terrorism and other asymmetrical threat situations became increasingly important for security-related efforts of most western societies. In a similar period, the development of data gathering and analysis techniques especially using the methods of machine learning has made rapid progress. Aiming to utilize this development, this paper employs artificial neural networks (ANN) for longterm time series prediction of terrorist event data. A major focus of the paper lies on the specific use of convolutional neural networks (CNN) for this task, as well as the comparison to the performance of classical methods for (long-term) time series prediction. As the database like Global terrorist database (GTD) and Fraunhofer's terrorist event database (TED) are not extensive enough to train a deep learning method, a simple toy model for the generation of time-series data from one or more terrorist groups with defined properties is established. Metrics for comparison of the different approaches are collected and discussed, and a customized sliding window metric (SWM) is introduced. The study shows the principle applicability of CNNs for this task and offers constraints as well as possible extensions for future studies. Based on these results, continuation and further extension of data collection efforts and ML optimization techniques are encouraged.