621.3 Elektrotechnik, Elektronik
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GELaZ folgte dem Förderaufruf „Intelligente Netzanbindung von Parkhäusern und Tiefgaragen“ (INPUT), mit dem Pilotprojekte unterstützt werden sollten, bei denen aufgrund des Einbaus von Ladeinfrastruktur für Elektromobilität in Parkhäuser, Parkplätze und Tiefgaragen (PPT) die Anbindung an das Stromnetz beispielhaft aufgezeigt und intelligent gelöst wird. Das Gesamtziel des Vorhabens umfasste deshalb: Errichtung öffentlichkeitswirksamer Demonstrationsanlagen mit vielen Ladeanschlüssen an drei verschiedenen Standorten in Baden-Württemberg: Ludwigsburg, Reutlingen und Konstanz. Es sollte auf bereits erprobte Hardware- und Softwarelösungen zurückgegriffen werden, die auf die örtlichen Gegebenheiten zum gleichzeitigen Laden mehrerer Fahrzeuge angepasst wurden.
Accurate and safe neurosurgical intervention can be affected by intra-operative tissue deformation, known as brain-shift. In this study, we propose an automatic, fast, and accurate deformable method, called iRegNet, for registering pre-operative magnetic resonance images to intra-operative ultrasound volumes to compensate for brain-shift. iRegNet is a robust end-to-end deep learning approach for the non-linear registration of MRI-iUS images in the context of image-guided neurosurgery. Pre-operative MRI (as moving image) and iUS (as fixed image) are first appended to our convolutional neural network, after which a non-rigid transformation field is estimated. The MRI image is then transformed using the output displacement field to the iUS coordinate system. Extensive experiments have been conducted on two multi-location databases, which are the BITE and the RESECT. Quantitatively, iRegNet reduced the mean landmark errors from pre-registration value of (4.18 ± 1.84 and 5.35 ± 4.19 mm) to the lowest value of (1.47 ± 0.61 and 0.84 ± 0.16 mm) for the BITE and RESECT datasets, respectively. Additional qualitative validation of this study was conducted by two expert neurosurgeons through overlaying MRI-iUS pairs before and after the deformable registration. Experimental findings show that our proposed iRegNet is fast and achieves state-of-the-art accuracies outperforming state-of-the-art approaches. Furthermore, the proposed iRegNet can deliver competitive results, even in the case of non-trained images as proof of its generality and can therefore be valuable in intra-operative neurosurgical guidance.
Stress is recognized as a predominant disease with raising costs for rehabilitation and treatment. Currently there are several different approaches that can be used for determining and calculating the stress levels. Usually the methods for determining stress are divided in two categories. The first category do not require any special equipment for measuring the stress. This category useless the variation in the behaviour patterns that occur while stress. The core disadvantage for the category is their limitation to specific use case. The second category uses laboratories instruments and biological sensors. This category allow to measure stress precisely and proficiently but on the same time they are not mobile and transportable and do not support real-time feedback. This work presents a mobile system that provides the calculation of stress. For achieving this, the of a mobile ECG sensor is analysed, processed and visualised over a mobile system like a smartphone. This work also explains the used stress measurement algorithm. The result of this work is a portable system that can be used with a mobile system like a smartphone as visual interface for reporting the current stress level.