We are proud to let you know that we’ve partnered with Edge Impulse and ARM to deliver efficient, power-optimized on-device machine learning solutions so we can increase the added value of our deliveries. We are excited to be able to begin to unlock endless possibilities for our customers through machine learning by making our devices smarter.
IRNAS and Smart Parks have been working on designing the next generation of open-source tracking solutions for national park management and wildlife protection for the past two years and the deployment of these solutions in the field has proven very valuable.
In this blog post, we will show how to design mechanical parts to ensure repeatable and robust automated glue dispensing. This way we can save significant amounts of time on manufacturing and testing.
There is a common problem with having many devices on the same I2C line. In case one of them fails, it can interrupt communication between the master and the other slaves. Rerouting function calls from application to drivers through a special class can help tackle this.
Part 2 of article series about running machine learning algorithms on microcontrollers. In this part, we deploy the machine learning model to a microcontroller and compare it with results in Part 1.
In this two-article series we will see how to train a machine learning model and deploy it to a microcontroller. For that, we will use TensorFlow Lite framework.
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