SDA Operator’s Manual
SDA-OM-E Rev H 195
double verResolution; // vertical resolution of the measurement system (also in vertical units)
double horInterval; // scale factor that relates integer sample indices to the horizontal units of the
waveform.
double horOffset; // horizontal offset (in horizontal units, e.g. seconds) of the waveform
double horResolution; // horizontal resolution of the measurement system (also in horizontal
units)
lecTimeStamp trigTime; // trigger time, units of 1ns since 00:00:00 on Jan 1st 2000, 64-bit
unsigned integer
char verUnit[48]; // vertical units of the waveform ("V" for example)
char horUnit[48]; // horizontal units of the waveform ("s" for example)
Data Length Limitations
The size of the memory window is fixed at 80 Mbytes, which equates to 40M samples.
Performance
Under optimal conditions, on a DSO with a 1.7 GHz Celeron processor, rates of up to 75 MS/s
have been observed. Due to the differences between the acquisition and processing hardware in
each of the X-Stream DSOs, this value may vary significantly and therefore cannot be
guaranteed. However, this is by far the fastest way to process data using a user-defined
algorithm on an X-Stream DSO.
Choice of Programming Language
The system was designed for use with the C/C++ programming language, and all furnished
examples use this language. It is theoretically possible, however, for the processing to be
implemented in any language that supports Windows named events (Mutex) and can open a
named memory-mapped file. Nevertheless, no guarantee can be given as to the behavior of the
system using anything but C/C++.
FFT
Why Use FFT?
For a large class of signals, you can gain greater insight by looking at spectral representation
rather than time description. Signals encountered in the frequency response of amplifiers,
oscillator phase noise and those in mechanical vibration analysis, for example, are easier to
observe in the frequency domain.
If sampling is done at a rate fast enough to faithfully approximate the original waveform (usually
five times the highest frequency component in the signal), the resulting discrete data series will
uniquely describe the analog signal. This is of particular value when dealing with transient signals
because, unlike FFT, conventional swept spectrum analyzers cannot handle them.
Spectral analysis theory assumes that the signal for transformation is of infinite duration. Since no
physical signal can meet this condition, a useful assumption for reconciling theory and practice is
to view the signal as consisting of an infinite series of replicas of itself. These replicas are
multiplied by a rectangular window (the display grid) that is zero outside of the observation grid.
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