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In diesem Artikel stellen wir ein System zur Synchronisierung beliebiger, in Java geschriebener, Anwendungen vor. Nach einer kurzen Diskussion der Vor- und Nachteile von replizierter Datenhaltung – wie sie in unserem System verwendet wird – werden wir am Beispiel einer komplexen Diskussions- und Modellierungsumgebung
zeigen, wie man mit unserer Architektur Anwendungen partiell koppeln kann und somit flexible Kooperationsmöglichkeiten technisch unterstützen kann.
Bipolar electrosurgical systems are used for the treatment of benign prostatic hyperplasia (BPH) in urology. In order to analyse electrothermal processes during surgery the power loss density distribution around a bipolar resectoscope is calculated out of the measured potential distribution in isotonic saline solution ex situ. During further analysis power loss density values act as input for the Penne's bioheat equation. To achieve results, which are as realistic as possible, a method to obtain power loss density values, depending on the observed tissue or medium in the operating field, is presented. Applying this method, the power loss density distribution in isotonic saline solution at 25 °C is compared to the distribution calculated for the average conductivity of biological tissue in the region of interest.
In this contribution we present a novel approach to transform data from time-of-flight (ToF) sensors to be interpretable by Convolutional Neural Networks (CNNs). As ToF data tends to be overly noisy depending on various factors such as illumination, reflection coefficient and distance, the need for a robust algorithmic approach becomes evident. By spanning a three-dimensional grid of fixed size around each point cloud we are able to transform three-dimensional input to become processable by CNNs. This simple and effective neighborhood-preserving methodology demonstrates that CNNs are indeed able to extract the relevant information and learn a set of filters, enabling them to differentiate a complex set of ten different gestures obtained from 20 different individuals and containing 600.000 samples overall. Our 20-fold cross-validation shows the generalization performance of the network, achieving an accuracy of up to 98.5% on validation sets comprising 20.000 data samples. The real-time applicability of our system is demonstrated via an interactive validation on an infotainment system running with up to 40fps on an iPad in the vehicle interior.