CEDoc – UM6P – CC: AI-Driven Detection, Localization, and Tracking of Full-time

Mohammed VI Polytechnic University is an institution dedicated to research and innovation in Africa and aims to position itself among world-renowned universities in its fields

The University is engaged in economic and human development and puts research and innovation at the forefront of African development. A mechanism that enables it to consolidate Morocco’s frontline position in these fields, in a unique partnership-based approach and boosting skills training relevant for the future of Africa.

Located in the municipality of Benguerir, in the very heart of the Green City, Mohammed VI Polytechnic University aspires to leave its mark nationally, continentally, and globally.

Description:

The widespread of drone’s technologies has raised significant concerns regarding both public security and individual privacy. As incidents involving drones become more frequent, there is an urgent demand for the implementation of anti-drone systems. Such systems can rapidly identify drones entering restricted areas, detect their positions, and enable the deployment of suitable countermeasures. The aim of this PhD project is to develop sophisticated detection methods that locate malicious drones with precision and predict their flight paths. To achieve this, we will leverage cutting-edge techniques in computer vision and motion detection to develop advanced AI and machine learning models, such as attention networks and transformers. Additionally, we will employ state-of-the-art localization and anomaly detection methods to ensure reliable target tracking. This work will be conducted in collaboration with the Czech Technical University in Prague and the University of New south wales in Australia.

The project will be co-supervised by Profs Lamiae Azizi, Hajar el Hammouti and Daniel Bonilla Licea.

Required documents:

• Transcripts of Bachelor and Masters.
• CV
• Cover letter

References:

– El Hammouti, Hajar, et al. “Learn-as-you-fly: A distributed algorithm for joint 3D placement and user association in multi-UAVs networks.” IEEE Transactions on Wireless Communications 18.12 (2019): 5831-5844.

– El Hammouti, Hajar, et al. “The optimal and the greedy: Drone association and positioning schemes for Internet of UAVs.” IEEE Internet of Things Journal 8.18 (2021): 14066-14079.

– Huttunen, M., 2019. Civil unmanned aircraft systems and security: The European approach. Journal of transportation security, 12(3-4), pp.83-101.

– Yaacoub, J.P., Noura, H., Salman, O. and Chehab, A., 2020. Security analysis of drones systems: Attacks, limitations, and recommendations. Internet of Things, 11, p.100218.

– P Cheemaa, M Alamdari Makki, G Vio, L Azizi, S Luo 2023. On the use of Matrix Profiles and Optimal Transport Theory for Multivariate Time Series Anomaly Detection within Structural Health Monitoring. Mechanical systems and signal processing, Vol 204.

– M. Vrba and M. Saska, “Marker-Less Micro Aerial Vehicle Detection and Localization Using Convolutional Neural Networks,” in IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 2459-2466, April 2020, doi: 10.1109/LRA.2020.2972819.

UM6P.