Noise RMS, Noise Power and Noise Peak-to-Peak : Understanding Noise Part III

In the first and second half of the series we discussed about some basic concepts of noise in electronics. It is so often vague and abstract to many, that we often try to avoid dealing with it as much as possible. But this is really something we cannot ignore, and so it is imperative that every engineer understand its nature. Below is an illustration of how we might imagine a noise is. Reality check: it is present even in the most "noiseless" DC signal we can imagine. But we would like to be able to quantify it in terms of RMS, Peak-to-Peak, Power, and Noise Density.

Noise generally can be thought of to be present in all frequencies at different levels, but circuits do have specific bandwidths. And so when we quantify noise, we think in terms of frequency and bandwidth.

Noise Root Mean Square (RMS)
It is basically a measure of one standard deviation of the noise plot distribution. Imagine looking at a noise plot on a time domain, seeing the noise amplitude variations. If you are to plot the distribution on histogram and gets a normal distribution, one standard deviation of this distribution is the rms noise.

What is Noise Power?
In order to quantify noise in power, we make an analogy of the common understanding of power. Power is voltage multiplied by current, or voltage squared divided by the resistance. In noise, we simplify it further by deleting the resistance, and representing noise power in V squared. In fact, squaring the noise rms produces the noise power.

Noise Peak-to-Peak
If plotting the noise on a histogram plot gets us the noise rms, the noise peak-to-peak corresponds to the end-to-end value of the noise distribution, thus the peak-to-peak. A 6-sigma confidence peak-to-peak noise would mean six times the rms.

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Arduino Transistor DC Motor Control

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