Resources of our survey paper "A Comprehensive Survey on AI Integration at the Edge: Techniques, Applications, and Challenges"

This project explores Li-Fi technology for wireless data transmission. Using Arduino Nano devices, text is encoded, transmitted through LED light signals, received by a photodiode, and displayed on an LCD. Li-Fi offers high-speed, secure, and efficient communication through light, with potential applications in various domains.

L'objectif de ce challenge est de proposer une méthode de classification, basée sur des réseaux de neurones, permettant de classer des images issues de Google Quickdraw (https://quickdraw.withgoogle.com/data). Le jeu de données proposé comprend 5 classes balancées, avec 15000 exemples d'apprentissage et 5000 exemples de validation.

Many tasks of pytorch for Network-Model-Design

Explore Model Design Space by using the Fisher-Information

Semantic Segmentation Using PSPnet model architecture on CamVid dataset