ALI DABBOUS

This paper presents a neuromorphic computing model that classifies material textures using a neural coding scheme based on threshold encoding. The proposed threshold encoding converts raw tactile data of each texture into an eventbased data highlighting the spatio-temporal features needed to recognize human touch. Achieved results show that the...

Tiny machine learning technologies are bringing intelligence ever closer to the sensor, thus enabling the key benefits of edge computing (e.g., reduced latency, improved data security, higher energy efficiency, and lower bandwidth consumption, also without the need for constant connectivity). This promises to significantly enhance industrial...

Datasets are key to developing new machine learning-based applications but are very costly to prepare, which hinders research and development in the field. We propose an edge-to-cloud end-to-end system architecture optimized for sport activity recognition dataset collection and application deployment. Tests in authentic contexts of use in four...