A. Amer, M. Mehndiratta, J. le Fevre Sejersen, H. X. Pham, and E. Kayacan, “Visual Tracking Nonlinear Model Predictive Control Method for Autonomous Wind Turbine Inspection,”10.1109/icar58858.2023.10406329__2024, doi:10.1109/icar58858.2023.10406329 .

A. Amer, O. Álvarez-Tuñón, H. . Ufurlu, J. Le Fevre Sejersen, Y. Brodskiy, and E. Kayacan, “UNav-Sim: A Visually Realistic Underwater Robotics Simulator and Synthetic Data-Generation Framework,”10.1109/icar58858.2023.10406819__2024, doi:10.1109/icar58858.2023.10406819 .

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

O. Álvarez-Tuñón et al., “MIMIR-UW: A Multipurpose Synthetic Dataset for Underwater Navigation and Inspection,”10.1109/iros55552.2023.10341436__2023, doi:10.1109/iros55552.2023.10341436 .

M. Heiß, J. G. Hansen, D. Li, M. Kozowski, and E. Kayacan, “PredictiveSLAM - Robust Visual SLAM Through Trajectory-Aware Object Masking,”10.1109/icuas57906.2023.10155806__2023, doi:10.1109/icuas57906.2023.10155806 .

J. le Fevre Sejersen and E. Kayacan, “CAMETA: Conflict-Aware Multi-Agent Estimated Time of Arrival Prediction for Mobile Robots,”10.1109/iros55552.2023.10341937__2023, doi:10.1109/iros55552.2023.10341937 .

M. Protte, R. Fahr, and D. E. Quevedo, “Behavioral Economics for Human-in-the-Loop Control Systems Design: Overconfidence and the Hot Hand Fallacy,”10.1109/mcs.2020.3019723__IEEE Control Systems, vol. 40, no. 6, pp. 57–76, 2022, doi:10.1109/mcs.2020.3019723 .

D. Weber, S. Heid, H. Bode, J. Lange, E. Hüllermeier, and O. Wallscheid, “Safe Bayesian Optimization for Data-Driven Power Electronics Control Design in Microgrids: From Simulations to Real-World Experiments,”10.1109/ACCESS.2021.3062144__IEEE Access, vol. 9, pp. 35654–35669, 2021, doi:10.1109/ACCESS.2021.3062144 .

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

M. Schenke and O. Wallscheid, “Improved Exploring Starts by Kernel Density Estimation-Based State-Space Coverage Acceleration in Reinforcement Learning,” arXiv preprint arXiv:2105.08990, 2021.

T. Dragicevic et al., “Guest Editorial Model Predictive Control in Energy Conversion Systems,”10.1109/tec.2021.3076279__IEEE Transactions on Energy Conversion, vol. 36, no. 2, pp. 1311–1312, 2021, doi:10.1109/tec.2021.3076279 .

J. Lu and D. E. Quevedo, “A Jointly Optimal Design of Control and Scheduling in Networked Systems  under Denial-of-Service Attacks,” arXiv:2103.05893. 2021.

S. Heid, D. Weber, H. Bode, E. Hüllermeier, and O. Wallscheid, “OMG: A scalable and flexible simulation and testing environment toolbox for intelligent microgrid control,” Journal of Open Source Software, vol. 5, no. 54, p. 2435, 2020.

J. Lu, A. S. Leong, and D. E. Quevedo, “Optimal eventtriggered transmission scheduling for privacypreserving wireless state estimation,”10.1002/rnc.4910__International Journal of Robust and Nonlinear Control, vol. 30, no. 11, pp. 4205–4224, 2020, doi:10.1002/rnc.4910 .

N. Noroozi, R. Jackson, D. E. Quevedo, F. R. Wirth, and R. Findeisen, “On noise-to-state stability of stochastic discrete-time systems via finite-step Lyapunov functions,”10.1109/cdc40024.2019.9030178__2020, doi:10.1109/cdc40024.2019.9030178 .

K. Ding, X. Ren, D. E. Quevedo, S. Dey, and L. Shi, “Defensive deception against reactive jamming attacks in remote state estimation,” Automatica, vol. 113, 2020.

A. S. Leong, A. Ramaswamy, D. E. Quevedo, H. Karl, and L. Shi, “Deep reinforcement learning for wireless sensor scheduling in cyber–physical systems,” Automatica, 2019.

A. S. Leong, A. Ramaswamy, D. E. Quevedo, H. Karl, and L. Shi, “Deep reinforcement learning for wireless sensor scheduling in cyber–physical systems,”10.1016/j.automatica.2019.108759__Automatica, vol. 113, Art. no. 108759, 2019, doi:10.1016/j.automatica.2019.108759 .

K. Ding, X. Ren, D. E. Quevedo, S. Dey, and L. Shi, “Defensive deception against reactive jamming attacks in remote state estimation,”10.1016/j.automatica.2019.108680__Automatica, vol. 113, Art. no. 108680, 2019, doi:10.1016/j.automatica.2019.108680 .

Z. Guo, D. Shi, D. E. Quevedo, and L. Shi, “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, vol. 67, no. 1, pp. 194–207, 2019.