To uphold the highest standards, we subject Nefeli to rigorous scrutiny through peer-reviewed publications. This meticulous process ensures that every component of our system undergoes thorough examination and validation, thus guaranteeing reliability and trustworthiness. Our commitment to transparency and peer review instills full confidence in the integrity and quality of the Nefeli solution.

Arsenos, Anastasios and Petrongonas, Evangelos and Filippopoulos, Orfeas and Skliros, Christos and Kollias, Dimitrios and Kollias, Stefanos, Nefeli: A Deep-Learning Detection and Tracking Pipeline for Enhancing Autonomy in Advanced Air Mobility. Available at SSRN: or

A. Arsenos, D. Kollias, E. Petrongonas, C. Skliros and S. Kollias, "Uncertainty-Guided Contrastive Learning For Single Source Domain Generalisation," ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Seoul, Korea, Republic of, 2024, pp. 6935-6939, doi: 10.1109/ICASSP48485.2024.10448096.

A. Arsenos et al., "Common Corruptions for Evaluating and Enhancing Robustness in Air-to-Air Visual Object Detection," in IEEE Robotics and Automation Letters, doi: 10.1109/LRA.2024.3408485.

Vasileios Karampinis, Anastasios Arsenos, Orfeas Filippopoulos, Evangelos Petrongonas, Christos Skliros, Dimitrios Kollias, Stefanos Kollias, Athanasios Voulodimos, “Ensuring UAV Safety: A Vision-Only and Real-Time Framework for Collision Avoidance through Object Detection, Tracking, and Distance Estimation”, (Accepted at ICUAS 2024)