The acronym “aka,” short for “also known as,” is used to represent another identity. In more
formal usage, the word “alias” is preferred. According to Merriam-Webster’s online dictionary,
“alias” is defined as otherwise called: otherwise known as. It is from this notion that another word
or name can be used to identify a given subject that we find the word “alias” used in the world of
data conversions, processing, and translations.
More and more, we are becoming immersed in a digital world; nonetheless, parameters of real
world information (temperature, voltage, current, speed, flow, pressure, distance, etc.) used for
process communications and control are ANALOG in nature. Processing this information is
achieved primarily using digital signal processing techniques. The age of microelectronics has
made it possible for digital processing power to be extended into the field where sensors are
located; thus, a lot of preprocessing can be accomplished outside the main computer or PC.
However, there are many issues that can create serious problems along the way, such as EMI
noise, grounding, accuracy, resolution, aging, drift, isolation, and noisy power supplies. In
addition to these issues, there is one very subtle gremlin that often goes unnoticed but can lurk in
the background for months. What is this gremlin and where does it hide? It is known as aliasing,
and it can lurk in isolated signal conditioning modules (SCMs) and anywhere an analog-to-digital
converter is active.
The basic concept of aliasing is this: Converting analog data into digital data requires sampling
the signal at a specific rate, known as the sampling frequency. The result of this conversion
process (time spectrum to frequency spectrum) is a new function, which is the frequency
spectrum of all the frequency components of the original signal time function. The Fourier
transform mathematics of this process shows that this transformed frequency spectrum consists
of the signal frequency spectrum plus this spectrum shifted by all the harmonics of the sampling
frequency. If (a) the original analog signal is sampled in the conversion process at a minimum of
twice the highest frequency component contained in the analog signal, and (b) if the
reconstruction process is limited to the highest frequency of the original signal, then the
reconstructed signal accurately duplicates the original analog signal. Fail (a) or (b) and this
process can give birth to aliasing.
To illustrate, consider a time function signal consisting of two sinusoids, one at 25Hz, the other
expected to be at 180Hz. This sampling process uses a sampling frequency of 400Hz (which is
greater than 2*180Hz) and the reconstruction process has a bandwidth limit of 200Hz. The only
frequency components that will appear in the conversion are 25Hz and 180Hz. Now what if the
original signal’s highest frequency is not the expected 180Hz but 213Hz? This situation results in
a reconstructed signal consisting of 25Hz and 187Hz (400-213). The 213Hz signal component
appears in the output as the 187Hz component – and the aliasing gremlin has arrived (187Hz
“alias” 213Hz). This example is not very practical because sampling frequencies are usually much
higher, but it nonetheless illustrates the aliasing phenomenon associated with sampling
processes.
All signal conditioning modules that provide isolation typically use some form of analog sampling
conversion process to move analog information across an isolation barrier. It is important for
system engineers to carefully examine their application to determine whether aliasing can occur.
Dataforth engineers are very familiar with the aliasing gremlin and have taken precautions to
prevent it in Dataforth’s isolated signal conditioning modules. Dataforth cannot prevent an
undesirable rogue frequency from appearing in a customer’s sensor signal; however, Dataforth’s
design engineers have tailored the sampling frequency and reconstruction process to avoid
aliasing and ensure Dataforth SCMs function within the specified frequency range. One extremely
important special feature in all Dataforth isolating SCMs is the input anti-aliasing filter on these
modules, which eliminates the rogue frequencies that could cause aliasing.
Interested readers can visit the Dataforth website at www.dataforth.com, to download a basic
starter MATLAB program file and interactive Excel spreadsheet to experiment with the effects of
different types of signal and sampling frequencies. Here's a direct link http://dataforth.com/catalog/software/FFT8.zip