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Building better reusable software for Arduino

Tipo di stanza

Permanente

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Pagamento tramite

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Join us live for this Elektor Academy course. The course will take approximately 90 minutes. You can purchase the course via PayPal via either a PayPal account or with a Debit or Credit Card. Contact service@elektor.com if you have any questions after your purchase.


Course Description

You've completed your first few Arduino projects, but don't know how to improve your programming skills? Then this is the course for you! If you are making more complex applications by copying and pasting large sections of previously written code, we probably need to chat about how best to architect software and build reusable code. In this course, we start with a simple traffic light application that we wish to extend to support more traffic lights and even a pedestrian crossing. We'll break the code down into application and hardware interaction so that you can see how your hard work can be used even on non-Arduino platforms. And, as always, the code used will be available on GitHub for you to review.


Prerequisite Knowledge

Before attending this course, we expect that you should:

  • You already use Arduino or microcontrollers
  • You can install and use Arduino IDE
  • You can write and execute your own code
  • You can build a basic circuit
  • You understand C/C++ code 


Course Material
The course is delivered as a walk-through with demonstrations of the examples covered. The code will be available on GitHub after the course. The link will be shared during the event.

Stuart Cording - Elektor

Stuart Cording is an engineer and journalist with more than 25 years of experience in the electronics industry. You can find many of his recent Elektor articles at www.elektormagazine.com/cording. In addition to writing for Elektor, he hosts the monthly livestream, Elektor Engineering Insights (www.elektormagazine.com/elektor-engineering-insights). His previous courses have covered tips on debugging Arduino code and using the platform for simple neural networks.