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Notice that in the code in that answer he's using an array of size 4 (or order 4 in signal processing terminology because such filters are called fourth-order filter, it can actually be modeled by a 4th order polynomial equation: ax^4 + bx^3 + cx^2 + dx). Here's some code from SO about a low pass filter which may suit your needs: Low pass filter software?. One thing you can try is to use a cubic spline with interp1 but again given the nature of the data I dont think it will help much. Here's a short wikipedia article about it (with good external links which you should read if you want to go down this path): interp1 will use linear interpolation on your data so it wont help you much. This is basically Digital Signal Processing (with capital DSP) which contrary to its name is more closely related to analog design. If you want fast response but good smoothing anyway then what you'd use is a weighted average of the array. (forgive my ASCII-ART but I'm hoping it's good enough for illustration). Select Smooth Data from the suggested command completions. In a code block in the script, type a relevant keyword, such as smooth or noisy. The larger the array you use, the smoother the result will be but the larger the lag between the result and the actual reading is. To add the Smooth Data task to a live script in the MATLAB Editor: On the Live Editor tab, select Task > Smooth Data.
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Column C performs a 7-point rectangular smooth (1 1 1 1 1 1 1). Something like this (pseudocode): data_X = Smoothing can be done in spreadsheets using the 'shift and multiply' technique described above.In the spreadsheets smoothing.ods and smoothing.xls (screen image) the set of multiplying coefficients is contained in the formulas that calculate the values of each cell of the smoothed data in columns C and E.
#SMOOTHING DATA MATLAB 2008 HOW TO#
The example also shows how to smooth the levels of a clock signal while preserving the. That is, to keep an array of sensor data readings and average them. This example shows how to use moving average filters and resampling to isolate the effect of periodic components of the time of day on hourly temperature readings, as well as remove unwanted line noise from an open-loop voltage measurement. Their method is limited to a univariate di usion property and cannot control for other covariates of interest, such as age, gender and behavioral variables. The simplest is to do a moving average of your data. Functional data analysis methods for the statistical analysis of di usion properties along ber tracts, a \smoothing rst, then estimation' procedure, was also developed (Goodlett et al., 2009).