# A question on aliasing and sampling in a measurement system

Before asking my question here is the scenario how the voltage signals are used:

Where I work some are performing data acquisition of four DC signals through four channels by a daq device at a particular sampling rate. The DC signals are coming from transducers devices measuring temperature, air pressure, force transducer ect. All of these transducers have calibration certificates which have linear calibration equations for voltage to temperature conversion as Temperature = A*Voltage + B where A and B are the slope and offset of the calibration equation. So by looking at a certificate one can say "I measure this voltage so this corresponds to this temperature" ect. After sampling and logging the data, the engineers use software like MATLAB where they can do several transforms like FFT ect.

Here comes my question:

Imagine an analog DC signal coming from a force transducer in a time interval as below: And imagine the maximum frequency of interest is 50Hz. So according to Nyquist theorem the sampling frequency should be at least 100Hz.

But if this signal has many frequency components higher than 50Hz we will be face to face with a situation called aliasing. So we will be undersampling some of the frequency components. So far this is what understand form aliasing.

And below represents this: I encounter some tutorials which suggest anti-aliasing filters like RC low-pass filters between the transducer and the daq so that we remove high freq. components and avoid undersampling and aliasing.

1-) Do you think in my case this type of filtering is necessary if the tools like MATLAB can do post signal processing like low-pass filtering? Or an analog anti-aliasing filter is still needed?

2-) If they don't do any FFT and filtering, is there a possibility the signal's mean values or low freq. components might be affected significantly by aliasing?

3-) How can I know that if a filter is needed or not? (I know how to use a scope)

My concern is if I use a low pass filter I don't want to affect the calibration equation of the transducer.

• Your understanding of aliasing seems to be correct. Whether filtering is required depends on whether the input to the ADC contains noise or undesired signals whose frequency is above the Nyquist cutoff. If the ADC input does contain such noise, then it could definitely effect mean values and low frequency components. Post-capture digital signal processing cannot remove aliased noise from the data. – mkeith May 20 '17 at 16:21
• "Post-capture digital signal processing cannot remove aliased noise from the data." This is the heart of the confusion I have. Is it possible to explain this in a simple way by an example or pictorially? – atmnt May 20 '17 at 16:24
• In this type of situation, what would be very helpful would be to start by determining the signal bandwidth first, then choosing a sample rate, then adding an anti-aliasing filter. The physical variables you are measuring have an inherent bandwidth. And the transducers also have an inherent bandwidth. Any signal content with a frequency higher than these bandwidths is noise. So you should use your anti-aliasing filter to block any such signal. And sample fast enough to capture the desired signal. – mkeith May 20 '17 at 16:28
• How about not using a filter but sampling with lets say 5000Hz for a signal of interest's max freq. component is 50Hz and then post-process? – atmnt May 20 '17 at 16:40
• I cannot find a tutorial on these issues – atmnt May 20 '17 at 16:41