Amer, A., Mehndiratta, M., le Fevre Sejersen, J., Pham, H. X., &; Kayacan, E. (2024). Visual Tracking Nonlinear Model Predictive Control Method for Autonomous Wind Turbine Inspection. 2023 21st International Conference on Advanced Robotics (ICAR).https://doi.org/10.1109/icar58858.2023.10406329 

Amer, A., Álvarez-Tuñón, O., Ufurlu, H. ., Le Fevre Sejersen, J., Brodskiy, Y., &; Kayacan, E. (2024). UNav-Sim: A Visually Realistic Underwater Robotics Simulator and Synthetic Data-Generation Framework. 2023 21st International Conference on Advanced Robotics (ICAR).https://doi.org/10.1109/icar58858.2023.10406819 

Álvarez-Tuñón, O., Brodskiy, Y., &; Kayacan, E. (2023). Monocular visual simultaneous localization and mapping: (r)evolution from geometry to deep learning-based pipelines. IEEE Transactions on Artificial Intelligence, 1–21.https://doi.org/10.1109/tai.2023.3321032 

Álvarez-Tuñón, O., Kanner, H., Marnet, L. R., Pham, H. X., le Fevre Sejersen, J., Brodskiy, Y., &; Kayacan, E. (2023). MIMIR-UW: A Multipurpose Synthetic Dataset for Underwater Navigation and Inspection. 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).https://doi.org/10.1109/iros55552.2023.10341436 

Heiß, M., Hansen, J. G., Li, D., Kozowski, M., &; Kayacan, E. (2023). PredictiveSLAM - Robust Visual SLAM Through Trajectory-Aware Object Masking. 2023 International Conference on Unmanned Aircraft Systems (ICUAS).https://doi.org/10.1109/icuas57906.2023.10155806 

le Fevre Sejersen, J., &; Kayacan, E. (2023). CAMETA: Conflict-Aware Multi-Agent Estimated Time of Arrival Prediction for Mobile Robots. 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).https://doi.org/10.1109/iros55552.2023.10341937 

Protte, M., Fahr, R., &; Quevedo, D. E. (2022). Behavioral Economics for Human-in-the-Loop Control Systems Design: Overconfidence and the Hot Hand Fallacy. IEEE Control Systems, 40(6), 57–76.https://doi.org/10.1109/mcs.2020.3019723 

Weber, D., Heid, S., Bode, H., Lange, J., Hüllermeier, E., &; Wallscheid, O. (2021). Safe Bayesian Optimization for Data-Driven Power Electronics Control Design in Microgrids: From Simulations to Real-World Experiments. IEEE Access, 9, 35654–35669.https://doi.org/10.1109/ACCESS.2021.3062144 

Wallscheid, O. (2021). Thermal Monitoring of Electric Motors: State-of-the-Art Review and Future Challenges. IEEE Open Journal of Industry Applications.

Schenke, M., &; Wallscheid, O. (2021). Improved Exploring Starts by Kernel Density Estimation-Based State-Space Coverage Acceleration in Reinforcement Learning. ArXiv Preprint ArXiv:2105.08990.

Dragicevic, T., Parisio, A., Rodriguez, J., Jones, C., Quevedo, D., Ferrarini, L., Preindl, M., Shafiee, Q., &; Morstyn, T. (2021). Guest Editorial Model Predictive Control in Energy Conversion Systems. IEEE Transactions on Energy Conversion, 36(2), 1311–1312.https://doi.org/10.1109/tec.2021.3076279 

Lu, J., &; Quevedo, D. E. (2021). A Jointly Optimal Design of Control and Scheduling in Networked Systems  under Denial-of-Service Attacks. In arXiv:2103.05893.

Heid, S., Weber, D., Bode, H., Hüllermeier, E., &; Wallscheid, O. (2020). OMG: A scalable and flexible simulation and testing environment toolbox for intelligent microgrid control. Journal of Open Source Software, 5(54), 2435.

Lu, J., Leong, A. S., &; Quevedo, D. E. (2020). Optimal eventtriggered transmission scheduling for privacypreserving wireless state estimation. International Journal of Robust and Nonlinear Control, 30(11), 4205–4224.https://doi.org/10.1002/rnc.4910 

Noroozi, N., Jackson, R., Quevedo, D. E., Wirth, F. R., &; Findeisen, R. (2020). On noise-to-state stability of stochastic discrete-time systems via finite-step Lyapunov functions. 2019 IEEE 58th Conference on Decision and Control (CDC).https://doi.org/10.1109/cdc40024.2019.9030178 

Ding, K., Ren, X., Quevedo, D. E., Dey, S., &; Shi, L. (2020). Defensive deception against reactive jamming attacks in remote state estimation. Automatica, 113.

Leong, A. S., Ramaswamy, A., Quevedo, D. E., Karl, H., &; Shi, L. (2019). Deep reinforcement learning for wireless sensor scheduling in cyber–physical systems. Automatica.https://doi.org/10.1016/j.automatica.2019.108759 

Leong, A. S., Ramaswamy, A., Quevedo, D. E., Karl, H., &; Shi, L. (2019). Deep reinforcement learning for wireless sensor scheduling in cyber–physical systems. Automatica, 113, Article 108759.https://doi.org/10.1016/j.automatica.2019.108759 

Ding, K., Ren, X., Quevedo, D. E., Dey, S., &; Shi, L. (2019). Defensive deception against reactive jamming attacks in remote state estimation. Automatica, 113, Article 108680.https://doi.org/10.1016/j.automatica.2019.108680 

Guo, Z., Shi, D., Quevedo, D. E., &; Shi, L. (2019). Secure State Estimation Against Integrity Attacks: A Gaussian Mixture Model Approach Secure State Estimation Against Integrity Attacks: A Gaussian Mixture Model Approach. Trans. Signal Processing, 67(1), 194–207.