Using Smartphones as time-bound IoT setup, can save huge capital investments. However, sensor data of a $10 phone is not always be as reliable as the one from a $500 phone. Differences arise in quality of data, frequency of data acquisition, consistency of data correctness or incorrectness, and due to calibration issues.
This is a third article from a series which identifies how smartphone sensors can be used reliably to produce scientific data for IoT applications. Previous article can be found here
In previous Parts (Part-1 – Data Regression & Part-2 – Data Smoothening) we have learned about Data Regression to predict the dependent variable when independent variable is known and Data Smoothening to remove noise from data set. Let’s learn about Digital Filtering.
Data Filtering Basic
Data Filtering is a system that performs mathematical operations on a sampled, discrete-time signal to reduce or enhance certain aspects of that signal. Unwanted frequency components from the signal are removed to enhance wanted ones. Electronic filters can be: passive or active. analog or digital.
There are 4 basic types of filters.
Low-Pass: This filter is designed to pass low frequency from zero to certain cutoff frequency and to block high frequencies
High-Pass: This filter is designed to pass high frequency from a certain cutoff frequency to π and to block low frequencies.
Band-Pass: This filter is designed to pass certain frequency range which does not include zero and to block other frequencies.
Band-Stop: This filter is designed to block certain frequency range which does not include zero and to allow other frequencies.
There are below 4 classifications of filters
Digital Filter vs Analog Filter
Digital Vs Analog Filters
High Accuracy vs Less Accuracy
Liner Phase vs Non-Liner Phase
Flexible, Adaptive Filtering possible vs Adaptive Filtering difficult
Easy to simulate and design vs Difficult to simulate and design
Requires high performance ADC, DAC & DSP vs No ADC, DAC & DSP required
No drift due to component variation vs Drift due to component variation