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LAST UPDATED: 28 Oct, 2022 
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\$\begingroup\$ Thanks for the linear -to- log pot approximation method. Will use this if I cannot make the "G" taper work. I think the "G" taper in the bourns datasheet means something different from the plots you have shared.
https://electronics.stackexchange.com/questions/258443/convert-reverse-log-taper-potentiometer-to-log-audio-taper-potentiometerThe linear - log converter in its analog form has been extensively used in spectrophotometry for absorbance measurements. In this form advantage is taken of the logarithmic characteristics of the base-emitter junction of a transistor used in the feedback link of an operational amplifier to convert a linear amplifier input into a log output
https://www.freepatentsonline.com/4679162.htmlHey guys, I made an adjustable power supply based in an ATX PSU and a circuit I found on the internet. Works fine but there is a little problem, I only have a logarithmic potentiometer which makes the voltage variation too abrupt. Of course you will say, go and buy a linear one, I will do it, but there are no stores here where I leave, I will have to put an on-line ...
https://forum.arduino.cc/t/log-to-linear-potentiometer/302391The "common" logarithm has 10 as its base and is denoted as “log.” The following formula allows you to take the natural logarithm by using the base-10 logarithm: ln(number) = log(number) ÷ log(2.71828). To convert a number from a natural to a common log, use the equation, ln(x) = log(x) ÷ log(2.71828).
To calculate the antilog using the inverse of log base 10 calculator, follow these steps: Enter the antilog value in the given input box. Enter the antilog base in the next input box. Press the Calculate button to see the antilog. Press the Reset button to reset all values for a new calculation.
Natural Log (ln) is the amount of time needed to reach a certain level of continuous growth. ... The natural log is the inverse of e, a fancy term for opposite.
A log-linear model is a mathematical model that takes the form of a function whose logarithm equals a linear combination of the parameters of the model, which makes it possible to apply (possibly multivariate) linear regression.
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