Clean Sensor Data with Filters

Clean Sensor Data with Filters
Read any Noisy Sensor Data and use different types of filters to reduce the noise and convert RAW data to useable data

Read any Noisy Sensor Data and use different types of filters to reduce the noise and convert RAW data to useable data

Sensors and microcontrollers allow us to turn real-life phenomena into simple numerical signals that we can learn from. However, the raw output from the sensor may not be sufficient to extract desired information from. Real hardware is subject to interference and noise from the environment.

Filtering is a simple technique that you can use to smooth out the signal, removing noise and making it easier to learn from the sensor output. This course introduces the concept of filters in different types and how to incorporate them into your design.

Measurements from the real world often contain noise. Loosely speaking, noise is just the part of the signal you didn’t want. Maybe it comes from electrical noise: the random variations you see when calling analogRead on a sensor that should be stable. Noise also arises from real effects on the sensor. Vibration from the engine adds noise .. etc

Filtering is a method to remove some of the unwanted signals to leave a smoother result.

You Will Learn:

  • Why we need to clean noise data
  • What are Filters
  • How to implement Filters using Microcontrollers like Arduino
  • Moving Average Filter
  • Averaging filter
  • Running average filter
  • Exponential filter
  • Turning Filtering equations into actual code
  • Compare results before and after filtering

You will learn as you practice with real-world examples in this course

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