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Facilitating learning progress in a first control course via Matlab apps

, , , and . 21st IFAC World Congress, 53, 2, page 17356-17361. Elsevier, (2020)
DOI: 10.1016/j.ifacol.2020.12.2086

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Maurice Koch University of Stuttgart

Dataset for "How Deep Is Your Gaze? Leveraging Distance in Image-Based Gaze Analysis", , , and . Dataset, (2024)Related to: Maurice Koch, Nelusa Pathmanathan, Daniel Weiskopf, and Kuno Kurzhals.2024. How Deep Is Your Gaze? Leveraging Distance in Image-Based GazeAnalysis. In 2024 Symposium on Eye Tracking Research and Applications(ETRA ’24), June 04–07, 2024, Glasgow, United Kingdom. ACM, New York,NY, USA, 7 pages. doi: 10.1145/3649902.3653349.
 

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Technology Integration into Pre-service Teacher Training., , and . IJICTE, 8 (1): 1-14 (2012)Robust and optimal predictive control of the COVID-19 outbreak, , , , , and . Annual reviews in control, (2020)arXiv preprint arXiv:2005.03580.Sampling Strategies for Data-Driven Inference of Input–Output System Properties, , and . IEEE Transactions on Automatic Control, 66 (3): 1144-1159 (2021)Verifying dissipativity properties from noise-corrupted input-state data, , and . 2020 59th IEEE Conference on Decision and Control (CDC), page 616-621. IEEE, (2021)Provably Robust Verification of Dissipativity Properties from Data, , and . IEEE transactions on automatic control, 67 (8): 4248-4255 (2022)Data-Driven Controller Design via Finite-Horizon Dissipativity, , , and . Proceedings of the 3rd Conference on Learning for Dynamics and Control, volume 144 of Proceedings of Machine Learning Research, page 287--298. PMLR, (2021)Robust and optimal predictive control of the COVID-19 outbreak, , , , , and . Annual Reviews in Control, (2021)Model-Free Practical Cooperative Control for Diffusively Coupled Systems, , , and . IEEE transactions on automatic control, 67 (2): 754-766 (2022)Data-Driven Reachability Analysis From Noisy Data, , , and . IEEE transactions on automatic control, 68 (5): 3054-3069 (2023)Data-Driven Reachability Analysis Using Matrix Zonotopes, , , and . Learning for Dynamics and Control, 144, page 163-175. PMLR, (2021)