Industry 4.0 introduces modern communication and computation technologies such as cloud computing and Internet of Things to industrial manufacturing systems. As a result, many devices, machines and applications will rely on connectivity, while having different requirements to the network, ranging from high reliability and low latency to high data rates. Furthermore, these industrial networks will be highly heterogeneous as they will feature a number of diverse communication technologies. Current technologies are not well suited for this scenario, which requires that the network is managed at an abstraction level which is decoupled from the underlying technologies. In this paper, we consider network slicing as a mechanism to handle these challenges. We present methods for slicing deterministic and packet-switched industrial communication protocols which simplifies the manageability of heterogeneous networks with various application requirements. Furthermore, we show how to use network calculus to assess the end-to-end properties of the network slices.

Purpose:To compare global functional parameters determined from a stack of cinematographic MR images of mouse heart by a manual segmentation and an automatic segmentation algorithm. Materials and Methods:The manual and automatic segmentation results of 22 mouse hearts were compared. The automatic segmentation was based on propagation of a minimum cost algorithm in polar space starting from manually drawn contours in one heart phase. Intra-and interobserver variability as well as validity of the automatic segmentation was determined. To test the reproducibility of the algorithm the variability was calculated from the intraand interobserver input. Results:The mean time of segmentation for one dataset was around 10 minutes and Ϸ2.5 hours for automatic and manual segmentation, respectively. There were no significant differences between the automatic and the manual segmentation except for the end systolic epicardial volume. The automatically derived volumes correlated well with the manually derived volumes (R 2 ϭ 0.90); left ventricular mass with and without papillary muscle showed a correlation R 2 of 0.74 and 0.76, respectively. The manual intraobserver variability was superior to the interobserver variability and the variability of the automatic segmentation, while the manual interobserver variability was comparable to the variability of the automatic segmentation. The automatic segmentation algorithm reduced the bias of the intra-and interobserver variability. Conclusion:We conclude that automatic segmentation of the mouse heart provides a fast and valid alternative to manual segmentation of the mouse heart.

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Providing secure communication links between devices of low computational power has been increasingly investigated during recent years. The need for fast and easy-to-implement security for computationally weak wireless devices has lead to the development of physical layer based key generation approaches. The generation of symmetric cryptographic keys out of wireless channel properties turned out to be a promising approach comprising advantages of symmetric as well as asymmetric cryptography. Numerous quantization schemes have been proposed in previous works to increase the Key Generation Rate (KGR). Also by increasing the sampling rate, the input data can be generated faster. However, due to fast sampling rates, redundancy of subsequent bits will increase. This lowers the quality, that is, the randomness of the generated secret key and makes the system more vulnerable to bruteforce attacks. We present and analyze different techniques for transforming a temporally correlated sequence into a compressed sequence of decorrelated bits and, therefore, assure non-redundant key sequences.

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