The fast simulation of large networks of spiking neurons is a major task for the examination of biology-inspired vision systems. Networks of this type label features by synchronization of spikes and there is strong demand to simulate these e ects in real world environments. As the calculations for one model neuron are complex, the digital simulation of large networks is not e cient using existing simulation systems. Consequently, it is necessary to develop special simulation techniques. This article introduces a wide range of concepts for the di erent parts of digital simulator systems for large vision networks and presents accelerators based on these foundations. Pulse-Coded Neural Vision NetworksThe communication in PCNNs is based on spike e x c hange. In contrast to conventional model neurons, e.g. McCulloch & Pitts neurons, the generation of a spike requires high computational e ort in connection with the time behaviour in the biological example. The computational e ort for individual neuron calculations compared to whole network processing is much higher in PCNNs than in conventional ANNs.Common simulation techniques for neural networks make use of vector representations for the neurons and matrix representations for the connection network 11]. These techniques are not suitable for PCNNs because the actual activity of one neuron is not representable by o n l y o n e v alue. Hence, common simulation techniques based on the acceleration of matrix-vector-calculations are not su cient for PCNNs. A new simulation paradigm is required with

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Intelligent Technical Systems are the consequent extension of the concept of Mechatronic Systems into the world of software-intensive, networked systems. The concept enhances the functionality and variability of physical (or electro-mechanical) technical systems by adding embedded electronics and software. This adds degrees of freedom (DoF) and controllability to the technical systems. It enhances the effectivity of integrating such systems into distributed infrastructures (e.g. smart grids) via a public network like the internet. Therefore, it enables them for an effective integration into the internet-of-things (IoT) since such Intelligent Technical Systems can add and draw significant functionality and optimisation potential from networking with other systems. They can become an integral part of a distributed system which is coupled via the IoT. Nevertheless, it is important to leverage between local control and distributed control carefully according to the requirements of the technical application. A smart heating grid is an example of a distributed cyber-physical system (CPS) which can be coupled via IoT. Such systems include components like block heat and power plants which produce electricity (fed into the public smart grid) and heat. Smart heating grids benefit from components which can convert heat into electricity in a flexible and controllable way and which can change fast from heat provision to electricity provision and vice-versa. An Organic Rankine Cycle (ORC) Turbine is such a component since it converts exhaust heat into electricity. This takes heat out of the heating grid and puts electricity into the electricity grid instead. Making such an ORC turbine intelligent means optimizing it for the usage in a smart heating grid. The challenge is to design a control software architecture which allows coupling via IoT on the required interaction level of the distributed system while guaranteeing a safe operation on the local level. The ar-Information Technology and Control 2018/2/47 350 ticle is an extended version of a previous article at the ICIST conference. It presents the software architecture of an ORC turbine based on the architecture of the Operator-Controller-Module (OCM). Compared to the previous publication it provides a more in-depth presentation of the prototype implementation of the ORC turbine system. The OCM provides an architecture pattern which allows a seamless integration into a smart heating grid based on an IoT infrastructure while enabling maximum flexibility and efficiency of the local functionality of the turbine.

Unmanned Aerial Systems (UAS) are becoming increasingly popular in the public safety sector. While some applications have so far only been envisioned, others are regularly performed in real-life scenarios. Many more fall in between and are actively investigated by research and commercial communities alike. This study reviews the maturity levels, or “market-readiness”, of public safety applications for UAS. As individual assessments of all applications suggested in the literature are infeasible due to their sheer number, we propose a novel set of application categories: Remote Sensing, Mapping, Monitoring, Human-drone Interaction, Flying Ad-hoc Networks, Transportation, and Counter UAV Systems. Each category’s maturity is assessed through a literature review of contained applications, using the metric of Application Readiness Levels (ARLs). Relevant aspects such as the environmental complexity and available mission time of addressed scenarios are taken into account. Following the analysis, we infer that improvements in autonomy and software reliability are the most promising research areas for increasing the usefulness and acceptance of UAS in the public safety domain.