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Wireless CAN
(2018)
In modernen elektronischen und mechatronischen Systemen, z. B. im industriellen oder automobil Bereich, kommunizieren eingebettete Steuergeräte und Sensoren vielfach über Bussysteme wie CAN oder LIN. Die Kommunikation findet in der Regel drahtgebunden statt, so dass der Kabelbaum für die Kommunikation sehr groß werden kann. Daher ist es naheliegend, Leitungen und dazugehörige Stecker, z. B. für nicht-sicherheitskritische Komfortsysteme, einzusparen und diese durch gerichtete Funkstrecken für kurze Entfernungen zu ersetzen. Somit könnten Komponenten wie ECUs oder Sensoren kabel- und steckerlos in ein Bussystem integriert werden. Zudem ist eine einfache galvanische und mechanische Trennung zu erreichen. Funkübertragung wird bei diesen Bussystemen derzeit nicht eingesetzt, da insbesondere die Echtzeitfähigkeit und die Robustheit der vorhandenen Funksysteme nicht den Anforderungen der Anwendungen entspricht. Zudem sind bestehende Funksysteme wie WLAN oder Bluetooth im Vergleich zur konventionellen Verkabelung teuer und es besteht hierbei die Möglichkeit, dass sie ausspioniert werden können und so sensible Daten entwendet werden können. In dieser Arbeit wird eine alternative Realisierung zu den bestehenden Funksystemen vorgestellt, die aus wenigen Komponenten aufzubauen ist. Es ist eine protokolllose, echtzeitfähige Übertragung möglich und somit die transparente Integration in ein Bussystem wie CAN.
Zusammenfassung: In der Orthopädie zählt der therapeutische Ultraschall als Mittel zur Prävention und Therapiebegleitung. Er hat mechanische, thermische und physiko-chemische Auswirkungen auf den menschlichen Körper. Um mehr Erkenntnisse über die thermischen Auswirkungen zu erlangen, wurden Versuche an einem Hydrogel-Phantom und an Probanden durchgeführt. Dabei entstand eine signifikante Erwärmung des Gewebes, welche beim Probandenversuch an der Oberfläche und beim Hydrogelversuch in der Tiefe gemessen wurde.
Summary: In orthopaedics, therapeutic ultrasound is a tool of prevention and therapy support. It has mechanical, thermal and physico-chemical effects on the human body. Tests with a hydrogel phantom and with human probands have been performed in order to obtain more knowledge about their thermal effects. Both tests measured temperature increases in cell tissue, on the surface with the human proband test and in depth with the hydrogel phantom test.
Sleep scoring is a necessary and time-consuming task in sleep studies. In animal models (such as mice) or in humans, automating this tedious process promises to facilitate long-term studies and to promote sleep biology as a data-driven f ield. We introduce a deep neural network model that is able to predict different states of consciousness (Wake, Non-REM, REM) in mice from EEG and EMG recordings with excellent scoring results for out-of-sample data. Predictions are made on epochs of 4 seconds length, and epochs are classified as artifactfree or not. The model architecture draws on recent advances in deep learning and in convolutional neural networks research. In contrast to previous approaches towards automated sleep scoring, our model does not rely on manually defined features of the data but learns predictive features automatically. We expect deep learning models like ours to become widely applied in different fields, automating many repetitive cognitive tasks that were previously difficult to tackle.
In this work, we report on our attempt to design and implement an early introduction to basic robotics principles for children at kindergarten age. One of the main challenges of this effort is to explain complex robotics contents in a way that pre-school children could follow the basic principles and ideas using examples from their world of experience. What sets apart our effort from other work is that part of the lecturing is actually done by a robot itself and that a quiz at the end of the lesson is done using robots as well. The humanoid robot Pepper from Softbank, which is a great platform for human–robot interaction experiments, was used to present a lecture on robotics by reading out the contents to the children making use of its speech synthesis capability. A quiz in a Runaround-game-show style after the lecture activated the children to recap the contents they acquired about how mobile robots work in principle. In this quiz, two LEGO Mindstorm EV3 robots were used to implement a strongly interactive scenario. Besides the thrill of being exposed to a mobile robot that would also react to the children, they were very excited and at the same time very concentrated. We got very positive feedback from the children as well as from their educators. To the best of our knowledge, this is one of only few attempts to use a robot like Pepper not as a tele-teaching tool, but as the teacher itself in order to engage pre-school children with complex robotics contents.