We develop novel algorithms for resource-constrained intelligent devices. This involves optimizations at different levels, including:
- data acquisition, by using techniques such as compressed sensing or event-based sampling;
- algorithm execution, by exploiting modern edge/fog/cloud architectures to distribute machine-learning methods in a robust and efficient way; and
- data transmission, by processing the data as close as possible to where it is produced and reducing communications to high-level features and results.
Embedded AI for aerospatial navigation
Energy-efficient acquisition and embedded processing of bio-signals
Federated machine learning over fog/edge/cloud architectures
Multimodal and personalised methods for health and wellness monitoring