Quantum computers might revolutionize the world of technology. Classical computers use bits to store information in binary states. Quantum computers use qubits that can exist in entangled multiple states at once. This feature allows quantum computers to be exceedingly more efficient than current computers. With today’s computers, all encryption schemes – incl. cryptocurrencies like Bitcoin – are impossible to break in reasonable time. However, quantum computers may be able to quickly break current encryption schemes in the upcoming future.

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OverviewTechnical maintenance on ticket vending machines requires a high level of expertise. Technicians usually need many weeks to get a good understanding of the vending machines, under the tight supervision of their mentor. The goal of the present study is to design and implement an AR (Augmented Reality) system that will facilitate the work of the technicians and reduce their apprenticeship time. In fact, this AR solution will provide in real-time the necessary information with the corresponding documentation of the component that needs maintenance. For instance, a technician will be able to immediately scan the different components in the TVM and determine which are the cables that must be connected between them. weiterlesen

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Pediatricians and general practitioners use ultrasound for diagnostic purposes in their office. Ultrasound is fast, reliable, safe, and is seen as the visual stethoscope of the 21st century. Standardized, high-quality examinations depend on the exact position of the transducer for transmitting and receiving the ultrasound beam. However, the correct plane defining the image cross section obtained from the patient is often difficult to achieve.Advanced technology might support physicians with feedback based on automated analyses of examinations by ultrasound. We will use the example of ultrasound-based diagnosis of developmental dysplasia of the hip (DDH) in newborns to suggest such an approach using Neural Networks.

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Today’s Machine Learning algorithms rely on large data sets of ground truth. Especially for medical use cases the lack of appropriate data for training can be a limiting factor for successful Deep Learning. For some imaging problems, the creation of synthetic ground truth can effectively overcome the lack of original ground truth and enable successful learning.In this work we aim to explore the potential of synthetic ground truth for tumor segmentation in medical imagery of eyes. The student will work with 3D stacks of optical coherence tomography (OCT) images. The currently implemented Convolutional Neural Networks (CNNs, e.g. U-net) shall be extended to segment tumors using both synthetic ground truth data and state-of-art CNNs.

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Die wachsende Transparenz im Niederspannungsnetz ermöglicht es unter anderem transiente Vorgänge hochauflösend aufzuzeichnen. Somit wird es denkbar, mit geeigneten Algorithmen die Fehlerstelle im Störungsfall zu lokalisieren, wie es heute nur auf höheren Spannungsebenen gemacht wird. Die Kosten für die Suche nach der Fehlerstelle und die Interventionszeiten der Netzbetreiber könnten somit gesenkt werden und damit wird ein Mehrwert geschaffen.Im ersten Schritt soll der Student im Rahmen seiner Arbeit die bestehenden Fehlerortungsalgorithmen auf deren Anwendbarkeit im Niederspannungsnetz untersuchen und eine Evaluation vornehmen. Im zweiten Schritt soll ein geeigneter Algorithmus implementiert und mittels simulierter Daten getestet werden. Zudem sollen Versuche im ewz Labor durchgeführt werden, um echte Messdaten zu gewinnen.

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A large part of the medical imaging data available today is available in 3D. This is true for example for magnetic resonance imaging data, computed tomography scan data and many other techniques. This is advantageous as it allows drawing conclusions based not only on single images but taking into account more information. Many medical images have to be further processed by segmenting different objects in the images such as bones or tissue layers. In the past years, the analysis of 2D medical images with Deep Learning methods has become a standard in research. With more and more computing power available, the focus is moving towards 3D analysis to profit from more spatial context.

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