Introducing our cutting-edge AI-enhanced ECG system designed specifically for electronics engineers! ?

Description

Welcome to our latest project featuring the innovative UNIHIKER Linux Board! , we demonstrate how to use AI to enhance electronics recognition in a real-world factory setting. ✨

What You'll Learn:

AI Integration:See how artificial intelligence is applied to identify electronic components.

Smart Imaging: Watch as our system takes photos and accurately finds component leads.

Efficiency Boost: Discover how this technology streamlines manufacturing processes and reduces errors.

Why UNIHIKER?

The UNIHIKER Linux Board provides a robust platform for running AI algorithms, making it ideal for industrial applications. Its flexibility and power enable precise component recognition, ensuring quality and efficiency in production.

? Applications: Perfect for electronics engineers, factory automation, and anyone interested in the intersection of AI and electronics.

https://community.dfrobot.com/makelog-314441.html

PART:

1.UNIHIKER (DFRobot) x 1

2.USB Camera x 1

3.USB Cable x 1

4.3Dprint Part x 4

Step 1: Test Usb Camera & Test OpenCV

Since I have an old, second-hand USB camera that I bought at a cheap price and haven't used, I decided to test if the UNIHIKER board can work with the camera. This will help determine if I can use it for this project and also test the OpenCV capabilities included with this board.

Step 2: Training Models

https://teachablemachine.withgoogle.com/

TensorFlow is an open-source library developed by Google for building deep learning

and machine learning models.It is well-suited for tasks that

require high computational power and the processing of large datasets.

TensorFlow Lite is a lightweight version of TensorFlow designed for deploying AI models on resource-constrained devices, such as mobile phones, microcontrollers, and IoT devices.

It focuses on high-efficiency performance with low power consumption. TensorFlow Lite is used to deploy models developed with TensorFlow onto end devices with limited resources. It is ideal for

applications that require real-time responses, such as image processing, voice detection, and speech recognition.