MicroPython for Data-Driven Engineering
Sensors take measurements of the physical world such as pressure, temperature, position, distance, and sound. These measurements are the basis for most data-driven engineering applications. Raw measurements often require cleansing (filtering) to remove bad samples such as outliers, noise, or drift. Microcontrollers and microprocessors facilitate the collection and processing of data locally to the device. For example, a self-driving car may collect 25 GB/hr of data to enable self driving capabilities. Airplane jet engine collects approximately 50,000 GB/hr of data to monitor the health of the engine and detect early warning signs. Embedded systems use microcontrollers to collect the data and microprocessors analyze the data to extract valuable insights and recommend corrective actions. Cloud computing performs the analysis after the data is transferred to an online repository. Edge computing is a growing focus area to perform most of the calculations locally and send summary information to online repositories.
MicroPython / µPython
Most microcontrollers run custom build C code or similar lower-level programming languages build for limited resources. MicroPython can run on more capable microcontrollers (Arduino, ESP32, Teensy, Raspberry Pi Pico, and others). MicroPython is valuable for quick prototyping and reduced programming complexity. CircuitPython (from AdaFruit) is similar to MicroPython, but with an objective to be compatible with CPython. The community for MicroPython is larger with more supported boards.
Load Firmware
The example in this tutorial is with an Espressif ESP32 board.
Course Website
https://apmonitor.com/dde/index.php/M...
