The core AI embedded teaching platform, built on a high-compute embedded board integratingmachine vision, voice processing, and multi-sensorsand expansion ports, pre-installed with Linux and AI frameworks, ready out of the box.
Covering teaching, learning, training, assessment, and evaluation — for AI, robotics, and electronic science majors.
Raspberry Pi 5 edge terminal with pre-installed Linux, vision libraries, and inference frameworks
Driver-free USB camera with AI vision/voice, supporting TensorFlow/PyTorch
Plug-and-play USB mic with speech recognition and multimodal interaction
Temp/humidity, ultrasonic, curtain, LED, and fan actuators for multi-module labs
15.6-inch touchscreen, keyboard, and mouse with graphical programming
Lab tools and magnetic mounting for bulk deployment
Supports TensorFlow/PyTorch, open source code and docs for secondary development
Integrates Linux, deep learning, machine vision, speech, and sensors
Integrated vision, voice, sensors, and touchscreen — no extra peripherals
Covers 8+ courses across levels, from basic programming to integrated development
Supports terminal verification, multi-student parallel models, and teacher-defined tasks
Classic algorithm teaching and lab verification — classification, detection, segmentation
End-to-end training from preprocessing to advanced analysis
Embedded Linux administration and environment setup
Lightweight deployment, edge inference, and HRI development
Sensor drivers, peripheral control, and system integration
Multi-sensor acquisition and fusion
Face detection, identity recognition, and classification joint labs
AI models control sensors and actuators for a physical control loop
Industrial defect detection and quality control
Cross-course integrated vision projects
Wake word, command recognition, and synthesis labs
Teacher-defined tasks and code for student innovation
Covering 10+ majors for interdisciplinary teaching
Boost new-engineering and AI programs
Complete AI embedded lab solutions
Support competitions and skill contests
One platform shared across courses
Fill practical teaching gaps and improve curriculum resources
Build integrated hardware-software environments to close the theory-practice gap
Provide platforms for research, curriculum reform, and project development
Support student innovation, competitions, and theses
Match industry AI embedded needs and narrow the talent-industry gap
From hardware to curriculum, from standalone training to full lab construction, tailored AI education solutions.