Self-Supervised Neuromorphic Perception for Autonomous Flying Robots

In the ever-evolving landscape of robotics, the quest for advanced synthetic machines that seamlessly integrate with human lives and society becomes increasingly paramount. At the heart of this pursuit lies the intrinsic need for these machines to perceive, understand, and navigate their surroundings autonomously. Among the senses, vision...

Air–water properties of unsteady breaking bores part 1: Novel Eulerian and Lagrangian velocity measurements using intrusive and non-intrusive techniques

Transient motion, turbulence and bubble dynamics make any velocity quantification extremely difficult in unsteady gas–liquid flows. In the present study, novel Eulerian and Lagrangian techniques of velocimetry were developed, using both intrusive and non-intrusive measurements. The selected unsteady gas–liquid flow was a breaking bore,...

Neuromorphic Computing of Event-Based Data for High-Speed Vision-Based Navigation

The combination of Spiking Neural Networks and event-based vision sensors holds the potential of highly efficient and high-bandwidth optical flow estimation. This thesis presents, to the best of the author’s knowledge, the first hierarchical spiking architecture in which motion (direction and speed) selectivity emerges in a biologically...