In this project, you will search the suitable current measurement methods and design the measurement system for strand current. You will validate your design with simulation tools like FEM. Finally you will implemented and test your design. - Electrical Engineering
- Master Thesis
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Development of a software tool which visualises the geometry of a basic switching cell based on geometrical parameters derived by converter optimisation procedures - Electrical Engineering
- Master Thesis, Semester Project
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Development of switching cell mechanical models in the X3D format based on geometrical parameters derived by converter optimisation procedures - Electrical Engineering
- Master Thesis, Semester Project
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In this project you will model the 3D geometry of litz wire by a mathematical algorithm. Furthermore, the current density distribution and the eddy current losses should be calculated for different termination strategies and for different litz wire geometries by FEM. The impact of improper terminations should be qualitatively/quantitatively analysed. - Electrical and Electronic Engineering
- Semester Project
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The project focuses on optimizing the compact Marx generator, which charges capacitors in parallel and discharges in series to produce high-voltage pulses. Through FEM simulations, the objective is to design capacitor arrangements that limit the electrical field, mitigating equipment failures, and extracting parasitic elements. - Electrical Engineering
- Master Thesis
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In this thesis the student is provided the opportunity to optimize and build a medium frequency transformer - Electrical and Electronic Engineering
- Master Thesis, Semester Project
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In this thesis the student will investigate different methods to measure the partial discharge inception of transformer winding geometries. - Electrical Engineering
- Master Thesis
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This project aims to revolutionize the analysis of electroencephalography (EEG) data by developing a specialized foundational model utilizing the principles of artificial intelligence. Despite the critical role of EEG in diagnosing and treating neurological disorders, challenges such as low signal-to-noise ratios and complex signal patterns hinder practical analysis. By adapting strategies from successful domains like natural language processing and computer vision, this project will build a machine learning model tailored for EEG signals. The model will undergo extensive pre-training on diverse EEG datasets to establish a robust understanding of neural activities, followed by fine-tuning for specific clinical tasks such as seizure detection and sleep stage classification. Our approach promises to enhance the accuracy, efficiency, and accessibility of EEG diagnostics, paving the way for improved patient outcomes. Validation and testing using standard performance metrics will measure the model's efficacy, setting a new standard in EEG analysis. - Electrical Engineering, Neural Networks, Genetic Alogrithms and Fuzzy Logic, Pattern Recognition, Signal Processing, Simulation and Modelling
- ETH Zurich (ETHZ), Master Thesis, Semester Project
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The aim of the project is to develop a deep learning model capable of performing 3D semantic segmentation of the different features of the middle ear, from synchrotron-based X-ray microtomography 3D volumes. Challenges include developing a high-performance algorithm for handling large data sizes (20 GB per volume). The project consists in a first phase about dataset generation followed by a second phase of development/selection of the most appropriate DL model and metrics. - Computer Vision, Image Processing, Neural Networks, Genetic Alogrithms and Fuzzy Logic, Otorhinolaryngology
- Collaboration, Master Thesis, Semester Project
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Open vocabulary video semantic segmentation (OV-VSS) aims to assign a semantic label to each pixel of each frame of the video given an arbitrary set of open-vocabulary category names. There are a number of attempts on open vocabulary image semantic segmentation (OV-ISS). However, OV-VSS does not get enough attention due to the difficulty of video understanding tasks in modeling local redundancy and global correlation. In this master thesis project, we plan to fill the gap by extending existing OV-ISS methods to OV-VSS. Specifically, we aim to develop a OV-VSS method which achieves high accuracy by using temporal information and keeps high efficiency.
- Artificial Intelligence and Signal and Image Processing
- Master Thesis
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