Introductory AIoT programming equipment for learning IoT, Data Science, and Machine Learning
It consists of a high-performance, low-power ARM quad core processor module and various input/output devices optimized for learning as a single board so that sensor data can be collected and analyzed in real time or predicted through model definition in an AIoT environment
Provides various input devices such as ADC-based distance measurement, illumination level, noise level, and motion detection and I2C-based proximity, ambient light, color, gesture, temperature and humidity, etc. in order to improve understanding of embedded interface
Provided various output devices such as from basic LED to OLED that can output text or simple graphic and RGB Type 16×16 Pixel Display with colorful lighting effects in order to support visualization of analyzed or predicted sensor data
Provides Gigabit Ethernet, dual-band Wi-Fi (2.4GHz, 5GHz), and Bluetooth 5.0 in order to remotely control the training equipment with a smartphone or tablet in an IoT connectivity environment
Provides a dedicated learning environment based on a web browser that supports both Python and Google block coding platform, Blockly in order to increase the convenience of AIoT programming and use on PC and tablet
Provides OS that optimizes Debian Linux for ARM-based IoT, data science, and machine learning and Pop library supporting reliable hardware abstraction
Supports for open integrated development environment based on Visual Studio Code for professional application development
Provides dedicated learning content to implement IoT, data science, and machine learning