Stimulus signals vs response signals

[mub.re]

Hi,

can anyone explain me what does stimulus signals and response signals mean, and also the differences? There are these two inputs and outputs and I don't know what they are.

Thanks

[Bob_Schor]

Suppose you want to stimulate something and get a response, and you are using LabVIEW and "discrete" hardware (such as A/D converters to sample data, and D/A converters to generate a stimulus signal).  You might have a situation where you generate, say, a stimulus at 100 kHz, and simultaneously sample at 100 kHz, which means that every second, you have a Stimulus Signal of 100,000 points, and a Response Signal of 100,000 points.  That's a lot of points!

You might realize that you need "quick processing" of these data, but in the interest of speeding up the processing time (at the possible expense of getting the timing down to the nearest 10 µs), you can get a rough idea (certainly good enough for a quick plot) by using only 1% of the data, decimating both the Stimulus and the Response Signals equally, then using the (now 1000-point) decimated Signal and Response and having the code run at least 100 times faster.  That is what (I assume) the SI Down Sampling VI is doing.

Were you able to open and read the Detailed Help?  (On my machine, the Help for this function doesn't have a "Detailed Help" option).  Did you look at the Example that ships with LabVIEW to see if it helps you to understand this function?

Bob Schor

[JÞB]

Quote from pg. 2-7 of the User's Manual Archived Here 

Filtering and Downsampling
You might be interested in only a specific frequency range of the frequency
response for a model. You can filter and enhance the data in the frequency
range to improve the fit in the regions of interest. If the sampling
frequency is much higher than the bandwidth of the system, the sampling
frequency might substantially increase the computation burden for
complicated identification algorithms. You can decrease the sampling
frequency by taking every nth sample to construct a new downsampled data
set. Applying an anti-alias filter on the data before downsampling prevents
corruption of the downsampled data set.
You can use the SI Lowpass Filter VI or the SI Bandpass Filter VI to apply
a lowpass or bandpass filter, respectively, to the data from the system. You
then can use the SI Downsampling VI to reduce the number of samples in
the data set.
After preprocessing the data you acquired from a dynamic system, the
result is a data set that you can use to estimate a model that reflects the
system dynamics. Refer to Chapter 3, Nonparametric Model Estimation
Methods, for more information about the nonparametric model estimation......