Clean Sensor Data with Filters

About this course

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.Why you should take this course?Since many sensors produce noisy data, this course will show you how to filter and clean the data so that it can be used for more accurate measurements.You will learn practical ways on how to reduce sensor noise and can perform some of these filters using MATLAB.Nowhere else will you find the information in this course because it is a comprehensive guide.The videos not only teach you about filters but also give exercises that can be done to gain hands-on experience.This course will give you the confidence to know that you can perform different types of filters which are already specified by MATLAB.A wide variety of lessons are available such as signal preprocessing, filtering algorithms, and error models.You Will Learn:Why we need to clean noise dataWhat are FiltersHow to implement Filters using Microc