Edge Computing and Artificial Intelligence of Things (AIoT) involve performing localized data processing directly on edge devices rather than relying entirely on centralized cloud servers. This architectural approach combines Internet of Things (IoT) hardware with optimized, lightweight machine learning models to enable real-time decision-making, drastically reduce network latency, and improve overall data privacy and bandwidth efficiency.
RT-Thread is an open source IoT Real-Time Operating System (RTOS). https://rt-thread.github.io/rt-thread/
Nuclei Microcontroller Software Interface Standard Development Repo
A Python package with command-line utilities and scripts to aid the development of machine learning models for Silicon Lab's embedded platforms
Provides easy-to-use kits that allow you to quickly create MCP servers Any embedded devices
a lightweight deep learning framework for listenai serial chips
Code samples Machine Learning Tensorflow.js img/video processing ESP32-CAM.
2025嵌赛RT-Thread赛道国二作品 基于PSoC6的多模健康监测台灯系统
A sophisticated AIoT solution designed to assist novice Texas Hold'em players in refining their understanding of pre-flop win rates, complemented by a two-player live broadcast showcasing real-time win rates on a dedicated webpage.
TinyRadar-OL: Ultra-Low Memory Continual Learning for Personalized FMCW Radar Gesture Recognition at the IoT Edge
A tiny robot with IMU (MPU5060) and an ultrasonic sensor (HC-SR04), running on micro-ROS, communicating over UDP.
Smart Door Lock intergrated with Face Recognition. An AIoT project, take advantage of ESP32-S3, ESP32-Cam, Transfer Learning from MobileNetV2.