Raspberry Pi Pico OLED Display (SSD1306)

This is the third tutorial in a series dedicated to exploring the Raspberry Pi Foundation's groundbreaking new microcontroller: the Raspberry Pi Pico. The first entry centered on the basic principles of interfacing with the Pico and programming with Thonny and MicroPython, while the second entry focused on emulating the Google Home and Amazon Alexa LED animations with a WS2812 RGB LED array. In this tutorial, an SSD1306 organic light emitting diode (OLED) display will be controlled using the Pico microcontroller. A MicroPython library will be used as the base class for interfacing with the SSD1306, while custom algorithms are introduced to create data displays. Additionally, a custom Python3 algorithm will be given that allows users to show a custom image on the display. Lastly, a real-time plot will be created that shows an audio signal outputted by a MEMS microphone, emulating a real-time graph display. The SSD1306 is a useful tool for smaller scale projects that require real-time data displays, control feedback, and IoT testing. The power of the Pico microcontroller makes interfacing with the SSD1306 fast and easy, which will be evident when working with the Pico and SSD1306.

Thermal Camera Analysis with Raspberry Pi (AMG8833)

The AMG8833 infrared thermopile array is a 64-pixel (8x8) detector that approximates temperature from radiative bodies. The module is wired to a Raspberry Pi 4 computer and communicates over the I2C bus at 400kHz to send temperature from all 64 pixels at a selectable rate of 1-10 samples per second. The temperature approximation is outputted at a resolution of 0.25°C over a range of 0°C to 80°C. A real-time infrared camera (IR camera) was introduced as a way of monitoring temperature for applications in person counting, heat transfer of electronics, indoor comfort monitoring, industrial non-contact temperature measurement, and other applications where multi-point temperature monitoring may be useful. The approximate error of the sensor over its operable range is 2.5°C, making is particularly useful for applications with larger temperature fluctuations. This tutorial is meant as the first in a series of heat transfer analyses in electronics thermal management using the AMG8833.

Calibration of an Inertial Measurement Unit (IMU) with Raspberry Pi - Part I

Inertial measurement units (IMUs) can consist of a single sensor or collection of sensors that capture data meant to measure inertial movements in a given reference frame. Acceleration, speed of rotation, and magnetic field strength are examples of sensors contained in an IMU. IMUs can be found in applications ranging smart devices, medical rehabilitation, general robotics, manufacturing control, aviation and navigation, sports learning, and augmented and virtual reality systems. Inertial measurement units have become increasingly popular as their form factors shrink and computational power increases. The ability to use IMUs for indoor/outdoor tracking, motion detection, force estimation, orientation detection, among others has caused the use and availability of inertial sensors to become nearly widespread in smart phones, smart watches, drones, and other common electronic devices. The internet is full of projects involving accelerometers, gyroscopes, and magnetometers, but few cover the full calibration of all three sensors. In this project, the manual calibration of a nine degree-of-freedom (9-DoF) IMU is explored. A common MPU9250 IMU is attached to a cube to manually find the calibration coefficients of the three sensors contained within the IMU: accelerometer, gyroscope, and magnetometer. The IMU is wired to a Raspberry Pi - which will allow for high-speed data acquisition rates of all nine components of the IMU.

Accelerometer, Gyroscope, and Magnetometer Analysis with Raspberry Pi Part I: Basic Readings

A Raspberry Pi will be used to read the MPU9250 3-axis acceleration, 3-axis angular rotation speed, and 3-axis magnetic flux (MPU9250 product page can be found here). The output and limitations of the MPU9250 will be explored, which will help define the limitations of applications for each sensor. This is only the first entry into the MPU9250 IMU series, where in the breadth of the articles we will apply advanced techniques in Python to analyze each of the 9-axes of the IMU and develop real-world applications for the sensor, which may be useful to engineers interested in vibration analysis, navigation, vehicle control, and many other areas.

MPU6050 Arduino High-Frequency Accelerometer and Gyroscope Data Saver

The MPU6050 is a 6-DoF (degree of freedom) accelerometer and gyroscope that is designed for inexpensive, small-scale, and efficient approximation of motion. Accelerometers and gyroscopes are used in smart phones for orientation detection, vibration analysis in vehicles and machines, and even camera stabilization and motion tracking. There are countless applications for accelerometers and gyroscopes, and with devices as accessible as the MPU6050, we can really test the limits of the technology.

Arduino I2C OLED Display - Temperature and Humidity Display (SSD1306)

How to print temperature and humidity readings onto a 0.96 inch I2C OLED display. The device is DIYMall's inexpensive, high resolution (128x64 pixels), yellow and blue organic LED display that is designed for use with the Arduino platform. Together with a DHT22 temperature sensor, the tiny OLED screen will display real-time humidity and temperature data using an Adafruit library and an Arduino Uno board. This project can be expanded upon to print data from a wide array of sensors, and even grab data from the internet to print values for a smart and interactive display.