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Waveform Averager Theory - Noise and Interference
Real-world signals are often contaminated with noise, which is any unwanted component of a more-or-less random nature. Often the noise component may be much larger than the desired signal, completely obscuring it when the waveform is viewed. Waveform averaging is a way to extract the desired signal from a noisy background. Of course, if there is a way to reduce the noise directly, that is usually (but not always!) a better approach.
Noise is typically due to many simultaneous uncorrelated processes acting at once, each adding a small component to the whole. In mechanical systems, small thermal motions due to absolute temperature may mask the ability to resolve a desired motion. In acoustics, random motion of air molecules driven by temperature or turbulent air flow may impinge on a microphone diaphragm and generate noise. In electronic circuits, random thermal motion of charged particles in semiconductors and resistors causes noise currents to flow. In biological systems, there is always a level of background activity due to the many neurons or muscle cells that are continuously firing even when not specifically stimulated.
Often, if you look at a fine enough scale, you can find that the noise is made up of components that would look periodic if seen in isolation, and it is only the summation of many of these in an unsynchronized fashion that gives the overall random nature. This is like the difference between hitting one note on a piano, versus having a whole roomful of pianos with squirrels scurrying along the keyboards.
Electronic filters are one common approach to noise rejection, often used in combination with other methods. If you know that the signal of interest has a limited range of frequency components, you may be able to use filters to reduce all other frequencies that are only contributing to the noise, but not to the signal.
For example, in biological recordings of neural signals, you might use a filter that reduces all components below 300 Hz and above 3000 Hz if you know that the neural signal that you want is strongest around 1000 Hz. This same filter might be inappropriate for recording heart rate, since at 60 beats per minute the fundamental rate would be only 1 Hz, which would be blocked by the filter.
CAUTION: Do not connect any electrical equipment to a living subject without proper signal isolation techniques. A lethal shock could result.
However, as well as removing unwanted components, filters may modify the shape of the desired signal through the process of phase shift. This phenomenon is where different frequency components of the signal are delayed by different amounts as they pass through the filter. All else being equal, filters with sharper transitions (cut-offs) between accepted and rejected frequency regions have more severe phase shift. (Note that this does not necessarily apply to digital filters.)
Also, you can't use filters to reduce noise in the same frequency region as the signal.
In addition to random noise, there may be other unwanted signals of a more periodic nature, known as interference. The most common interference source is the 60 Hz power line in the US (or 50 Hz in many other countries). There may also be interference due to harmonic multiples of this, generated by fluorescent lights, motors, and so forth. Spurious signals may arise from nearby radio transmitters as well. These are all best dealt with by arranging to reduce them directly wherever this is feasible, either by avoidance (like using incandescent lighting near the experiment and removing appliances from the area) or by shielding out radio frequencies with a metal cage around the experiment. In addition, careful attention must be paid to equipment grounding and isolation.
Beyond direct reduction, interference may also be reduced by many of the same techniques used for random noise. We will thus lump them together for discussion, noting any differences where appropriate.
Continue to Synchronous Waveform Averaging.
See also Averager.
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