Getting rid of noise in derivative?

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SUMMARY

The discussion focuses on techniques for reducing noise in derivative calculations of Current-Voltage data using Excel. The user employs Excel's SLOPE function to compute dV/dI but encounters significant noise, particularly at lower values. Suggestions include fitting a smooth function to the data before differentiation and exploring multi-point methods for numerical differentiation, as outlined in the Wikipedia article on numerical differentiation. The conversation emphasizes the importance of understanding the nature of the noise in the data for effective analysis.

PREREQUISITES
  • Excel SLOPE function for calculating derivatives
  • Understanding of numerical differentiation techniques
  • Fitting functions to data using regression analysis
  • Basic knowledge of noise modeling in data analysis
NEXT STEPS
  • Learn about fitting smooth functions to data using Excel's regression tools
  • Study multi-point methods for numerical differentiation
  • Explore noise modeling techniques in data analysis
  • Investigate advanced smoothing techniques such as Savitzky-Golay filters
USEFUL FOR

Data analysts, electrical engineers, and anyone working with Current-Voltage data who needs to compute derivatives while minimizing noise in their results.

jadi929
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Hey guys, I'm working on some Current-Voltage data in excel. I need to do some analysis with the data, and the first step is to compute dV/dI. I have to do this in excel so decided to use excel's SLOPE function (5 point slope) to compute dV/dI. However, it seems that the derivative has a lot of noise, especially towards the lower values. Since I am writing a program to automate this analysis, I would really like to get rid of this noise.

Another way I thought of was to fit a line to my initial data, and simply differentiate the fitted line's equation. However, I can't seem to get a decent fit for my initial curve.

Pic shows the initial data and my horrible looking derivative.

i_v_curve.jpg

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If you want a mathematically defensible way to smooth the derivative then you need to explain the nature of the "noise". in the data - e.g. can it be modeled as an independent random error at each measurement?

Another thing to try is a "multi-point method" for estimating the derivative. For example, see the "Higher-order methods" section of http://en.wikipedia.org/wiki/Numerical_differentiation. (However, I don't know if that method has any "noise cancelation" properties.)

If you only want a nice pitcure, Is there some reason that you can't fit a smooth function to the data first and then plot the derivative of that function?
 

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