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This paper presents an approach towards a mobile learning environment, which is flexible in terms of supported scenarios, supported devices and input channels. The approach makes use of existing and commonly used channels like SMS, Twitter or Face book to increase acceptance and ease-of-use of mobile devices in learning scenarios. Envisaged application scenarios are described along with technical details for their realization.
The first robots are currently appearing on the consumer market. Initially they are targeted at rather simple applications such as entertainment and home convenience. For more complex areas, these robots will need to collaborate and interactively communicate with their human users, which requires appropriate man-machine interaction technologies and considerable cognitive abilities on the robot's side. Consumer acceptance will strongly depend on the integrated system. Thus, system integration and evaluation of the integrated system is becoming increasingly important. This paper describes our approach to construct a robotic assistance system. We present experience with an integrated technology demonstration and exposure of the integrated system to the public.
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.