What Can I Do About Spikes In My Data?
Whether you call them spikes, glitches, anomalies or data dropouts, these phenomena have been a problem to engineers ever since they started recording data. There are any number of reasons…
The Intelligent Way To Sort, Extract & Analyze Signals
This note is based on a real requirement presented to Prosig by a prospective user. It’s the sort of challenge that we relish. This case is a great example of a real-world signal processing requirement and also great test of some of the unique features of Prosig’s DATS software. It also shows the power and flexibility of the new DATS V7.0 worksheets.
Data Smoothing : RC Filtering And Exponential Averaging
[Updated 12th March 2013]
What are RC Filtering and Exponential Averaging and how do they differ? The answer to the second part of the question is that they are the same process! If one comes from an electronics background then RC Filtering (or RC Smoothing) is the usual expression. On the other hand an approach based on time series statistics has the name Exponential Averaging, or to use the full name Exponential Weighted Moving Average. This is also variously known as EWMA or EMA. (more…)
Spectrum Smoothing : Why and How?
Sometimes data has spikes which are clearly artefacts of the processing or are due to some other external source. One is used to seeing these on time series but in some cases there are unrepresentative “spikes” in the frequency analysed data. Here we discuss how we can use spectrum smoothing to alleviate the problem. An example spectrum is shown below.
