DC7: Light-weight JCAS analytics for resource-constrained embedded devices

Task: Light-weight JCAS analytics for resource-constrained embedded devices (WP3)

Host institution: TU Delft

Country: The Netherlands

Supervisors: Prof. Q. Wang [TU Delft]

Co-supervisors: Prof. J. Yang [TU Delft], Dr. A. Mueller [Bosch]

Objectives: To design self-optimising algorithms for allocation of on-board computational and communication resources for JCAS; To employ knowledge distillation techniques to create performant and low-complexity models for embedded devices; To implement and evaluate the proposed solutions using facilities of the embedded-ML lab at TUDelft.

Expected Results: Learning algorithms that can adapt the allocation of resources on embedded devices for different JCAS requirements; Learning algorithms that are significantly compressed but incur negligible performance loss.

PhD enrolment: Doctoral School of TU Delft

Planned secondments: 

  • Bosch (3 months, M10-M12): Design of low-complexity ML algorithm for sensor fusion, with A. Mueller (KPI: joint conference paper)

  • UNITN (3 months, M18-M20): Model compression for high-speed object detection tasks, with P. Casari (KPI: joint conference paper)

Candidate profile: Telecommunications engineering, Embedded systems, Computer engineering, or equivalent disciplines

Desirable skills/interests: Deep learning, tiny machine learning (TinyML), TensorFlow Lite micro, embedded systems, wireless communications (the applicant should be proficient in at least two of the skills)