Open Access

Die Erfindung betrifft eine Vorrichtung und Verfahren zur Analyse eines Materialstroms S mit einem Einlassbereich E, einem Messbereich M und einen Auslassbereich A sowie mit einer ersten Weiche W1 und einer zweiten Weiche W2 und einem Umlenkbereich U, wobei die beiden Weichen W1, W2 in einem ersten Schaltzustand Z1 einen durchgängigen ersten Materialdurchströmungsraum vom Einlassbereich E über die erste Weiche W1 durch den Messbereich M über die zweite Weiche W2 bis zum Auslassbereich A ausbilden und in einem zweiten Schaltzustand einen durchgängigen zweiten Materialdurchströmungsraum vom Einlassbereich E über die erste Weiche W1 durch den Umkenkbereich U über die zweite Weiche W2 bis zum Auslassbereich A ausbilden.

In the last 20 years there have been major advances in autonomous robotics. In IoT (Industry 4.0), mobile robots require more intuitive interaction possibilities with humans in order to expand its field of applications. This paper describes a user-friendly setup, which enables a person to lead the robot in an unknown environment. The environment has to be perceived by means of sensory input. For realizing a cost and resource efficient Follow Me application we use a single monocular camera as low-cost sensor. For efficient scaling of our Simultaneous Localization and Mapping (SLAM) algorithm, we integrate an inertial measurement unit (IMU) sensor. With the camera input we detect and track a person. We propose combining state of the art deep learning with Convolutional Neural Network (CNN) and SLAM algorithms functionality on the same input camera image. Based on the output robot navigation is possible. This work presents the specification, workflow for an efficient development of the Follow Me application. Our application’s delivered point clouds are also used for surface construction. For demonstration, we use our platform SCITOS G5 equipped with the afore mentioned sensors. Preliminary tests show the system works robustly in the wild.

We present a new method for detecting gait disorders according to their stadium using cluster methods for sensor data. 21 healthy and 18 Parkinson subjects performed the time up and go test. The time series were segmented into separate steps. For the analysis the horizontal acceleration measured by a mobile sensor system was considered. We used dynamic time warping and hierarchical custering to distinguish the stadiums. A specificity of 92% was achieved.

Research and Development (R&D) is crucial for the growth and future success of research-based pharma companies. To maintain their R&D organisations efficient, pharmaceutical companies started to hedge the potential of open innovation to cut R&D costs and to access external knowledge. These new strategies could be divided into several categories: open source, innovation centres, crowd sourcing and virtual R&D.