ETH ZurichAcronym | ETHZ | Homepage | http://www.ethz.ch/ | Country | Switzerland | ZIP, City | | Address | | Phone | | Type | Academy | Current organization | ETH Zurich | Child organizations | | Members | | Memberships | | Partners | |
Open OpportunitiesDrying (e.g. Pasta drying) is the most energy intensive process step, sometimes taking up more than 50% of the total energy consumption of a plant. Superheated steam drying could present an energy efficient alternative to classical hot-air drying systems used today. This new technology could have a massive impact on the carbon-footprint and sustainability of food-drying; making it a highly future-oriented and potentially impactful innovation. - Interdisciplinary Engineering, Manufacturing Engineering, Mechanical and Industrial Engineering
- ETH Zurich (ETHZ), Master Thesis, Semester Project
| This project aims at automatically learning problem-dependent uncertainty sets by exploiting available data on the uncertain parameters, hence surpassing the limitations of traditional methods such as robust and stochastic optimization approaches that assume the exact knowledge of the support set and of the probability distribution respectively. - Information, Computing and Communication Sciences, Optimisation, Systems Theory and Control
- Master Thesis, Semester Project
| Bühler, a leading industry manufacturer in Uzwil, is partnering with ETH Zürich's Feasibility Lab to offer a unique master thesis opportunity. Throughout your thesis, you'll work hand-in-hand with a team of like-minded peers, following the principles of cross-functional teamwork and agile project planning. You can explore your interests in AI/Machine Learning, Robotics, UX, Additive Manufacturing, Food Science and more and actively define your own project scope. - Digital Systems, Environmental Technologies, Industrial Biotechnology and Food Sciences, Interdisciplinary Engineering, Manufacturing Engineering, Mechanical and Industrial Engineering
- ETH Zurich (ETHZ), Master Thesis, Semester Project
| While camera-based capture systems remain the gold standard for recording human motion, learning-based tracking systems based on sparse wearable sensors are gaining popularity. Most commonly, they use inertial sensors, whose propensity for drift and jitter have so far limited tracking accuracy. Ultra-wideband (UWB) is a radio technology that offers precise ranging capabilities and is integrated into modern smart devices such as iPhones and Apple AirTags. Our recent work, Ultra Inertial Poser, accepted by SIGGRAPH'24, has shown great potential to combine IMU with UWB sensors to constrain drift and jitter in inertial tracking via inter-sensor distances. As we are in the early stages of development, there is still significant room for improvement in the methodology. In this project, we aim to design a deep learning model to improve our dataset's human motion tracking results.
Note: This project will focus on developing a novel supervised learning-based method. We have a clean and synchronized dataset ready (UIP-DB) for training and testing alongside ground-truth tracking data for all joints. - Engineering and Technology, Information, Computing and Communication Sciences
- Master Thesis, Semester Project
| Evaluation of different inverter topologies and different modulation schemes using a multi-objective optimization procedure for a given target drive system. Optimization with respect to different objectives, e.g., efficiency, power density, costs. An existing toolchain provides a framework which can serve as a starting basis for your models and optimization algorithms. Analysis and validation of the design based on vehicle drive cycles. - Electrical Engineering
- Master Thesis
| This project aims to investigate different energy storage technologies for application in the railway sector. - Electrical Engineering
- Master Thesis, Semester Project
| This project aims to analyse the implementation of a smart grid starting from the traditional railway electric grid. - Electrical Engineering
- Master Thesis, Semester Project
| 3D hand pose forecasting is a new benchmark introduced by HoloAssist [1]. Existing action forecasting work mostly focuses on providing semantic labels of future actions and does not provide explicit 3D guidance on hand poses. Predicting 3D hand poses can be useful for various applications, and it can augment instructions and spatially guide users in different tasks. In this benchmark, we take 3 seconds inputs similar to other 3D body location forecasting literature and forecast the continuous 3D hand poses for the next 0.5, 1.0, and 1.5 seconds. The evaluation metric is the average of mean per joint position error over time in centimeters compared to ground truth. To have a proper evaluation metric that can help 3D action guidance, we remove the mistakes from the action sequences and only forecast 3D hand pose for the correct labels.
[1] Wang, X., Kwon, T., Rad, M., Pan, B., Chakraborty, I., Andrist, S., ... & Pollefeys, M. (2023). Holoassist: an egocentric human interaction dataset for interactive ai assistants in the real world. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 20270-20281). - Computer Vision, Virtual Reality and Related Simulation
- ETH Zurich (ETHZ), Master Thesis, Semester Project
| Action recognition is an essential task in computer vision and has numerous applications in various fields, including robotics, surveillance, and healthcare. The recognition of actions involves the analysis of temporal and spatial information within a video sequence. Current state-of-the-art methods use 3D hand and object poses for action recognition, where the object's corners are commonly used for representation. However, this approach has limitations in accurately modeling the hand-object interaction. In [1], we show that leveraging hand-object contact-map representation helps improve action recognition. However, this representation can be learned implicitly for the task of action recognition.
[1] https://arxiv.org/pdf/2309.10001.pdf - Computer Vision, Virtual Reality and Related Simulation
- ETH Zurich (ETHZ), Master Thesis, Semester Project
| The recent development of LLMs (Large Language Models), such as ChatGPT and Llama, opens up new possibilities for understanding procedural actions. In the past, action recognition was restricted to the classification of visual frames. However, with LLMs, the model can observe the whole action sequence in a more effective way and even predict the future actions [1]. In this project, students will explore how LLMs can improve action recognition in procedural tasks. Specifically, given a high-level procedural task (e.g., making coffee, copying a paper), students will use existing pretrained action recognition models to predict the top 5 actions for each clip and feed them into the LLMs to refine and correct the predicted actions. As a comparison, students will also establish a baseline using simple machine learning and statistical methods to correct actions.
[1] Palm: Predicting Actions through Language Models @ Ego4D Long-Term Action Anticipation Challenge 2023, CVPR'23 workshop
- Computer Vision, Text Processing
- ETH Zurich (ETHZ), Master Thesis, Semester Project
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