System and method for separation and classification of signals using cyclic loop images
A system to classify signals includes an input to receive incoming waveform data; a memory, and one or more processors configured to execute code to cause the one or more processors to: generate a ramp sweep signal from the incoming waveform data, locate a data burst in the incoming waveform data using a burst detector, receive a signal from the burst detector to cause the memory to store cyclic loop image data in the form of the incoming waveform data as y-axis data and the ramp sweep signal as x-axis data, and employ a machine learning system to receive the cyclic loop image data and classify the data burst. A method of classifying signals includes generating a ramp sweep signal from incoming waveform data, locating a data burst in the incoming waveform data, storing cyclic loop image data for the data burst in the form of the incoming waveform data as Y-axis data and the ramp sweep signal as X-axis data, and using a machine learning system to receive the cyclic loop image and classify the data burst.
1. A system to classify signals, comprising:
an input to receive incoming waveform data;
a memory; and
one or more processors configured to execute code to cause the one or more processors to:
generate a ramp sweep signal from the incoming waveform data;
locate a data burst in the incoming waveform data using a burst detector;
receive a signal from the burst detector to cause the memory to store cyclic loop image data in the form of the incoming waveform data as y-axis data and the ramp sweep signal as x-axis data; and
employ a machine learning system to receive the cyclic loop image data and classify the data burst.
2. The system as claimed in claim 1 , further comprising a test and measurement instrument to acquire the incoming waveform data from a device under test.
3. The system as claimed in claim 1 , wherein the code to cause the one or more processors to generate the ramp sweep signal from the incoming waveform data further comprises code to cause the one or more processors to:
use a clipper to generate a square pulse signal from the incoming waveform data; and
use an integrator to generate ramp signals based upon the square pulse signal and transmit the ramp signals to the burst gate.
4. The system as claimed in claim 3 , wherein the integrator comprises a boxcar filter.
5. The system as claimed in claim 1 , further comprising a measurement unit to take a measurement using the cyclic loop image data.
6. The system as claimed in claim 5 , wherein the measurement includes one or more of reflection delay, reflection coefficient, intersymbol interference, signal-to-noise ratio, and non-linearity.
7. The system as claimed in claim 5 , wherein the measurement unit sends the measurement to the machine learning system.
8. The system as claimed in claim 1 , further comprising a concatenator to create a multi-burst waveform from the classified data burst from the machine learning system.
9. The system as claimed in claim 8 , further comprising a filter to apply to the multi-burst waveform.
10. The system as claimed in claim 1 , further comprising a system controller to coordinate operations of the system.
11. A method of classifying signals, comprising:
generating a ramp sweep signal from incoming waveform data;
locating a data burst in the incoming waveform data;
storing cyclic loop image data for the data burst in the form of the incoming waveform data as Y-axis data and the ramp sweep signal as X-axis data; and
using a machine learning system to receive the cyclic loop image and classify the data burst.
12. The method as claimed in claim 11 , further comprising receiving system parameters from a user.
13. The method as claimed in claim 11 , wherein generating the ramp sweep signal comprises:
generating a square pulse signal from the incoming waveform data; and
integrating the square pulse signal to produce the ramp sweep signal.
14. The method as claimed in claim 13 , wherein integrating the square pulse signal is performed by a boxcar filter.
15. The method as claimed in claim 11 , further comprising taking a measurement using the cyclic loop image data.
16. The method as claimed in claim 15 , wherein the taking the measurement includes measuring one or more of reflection delay, reflection coefficient, intersymbol interference, signal-to-noise ratio, and non-linearity.
17. The method as claimed in claim 15 , further comprising sending the measurement to the machine learning system, wherein the machine learning system classifies the data burst using the cyclic loop image data and the measurement.
18. The method as claimed in claim 11 , further comprising creating a multiburst waveform from classified data bursts from the machine learning system.
19. The method as claimed in claim 11 , further comprising training the machine learning system, the training comprising:
providing a set of cyclic loop images;
receiving user inputs that classify each cyclic loop image;
testing the machine learning system by having the machine learning system provide a classification and verifying the classification against the user input; and
repeating the testing until the machine learning system correctly classifies the cyclic loop images.
20. A system to classify data signals, comprising:
a ramp generator to generate a ramp sweep signal from incoming waveform data;
a burst detector to locate a data burst in the incoming waveform data;
a burst gate to receive a signal from the burst detector and cause a memory to store cyclic loop image data in the form of the incoming waveform data as y-axis data and the ramp sweep signal as x-axis data; and
a machine learning system to receive the cyclic loop image and classify the data burst.
21. The system as claimed in claim 20 , wherein the incoming waveform data is acquired from a memory device under test, and wherein the machine learning system classifies the data burst as resulting from one of a write operation or a read operation for the memory device under test.