US 12,146,914 B2
Bit error ratio estimation using machine learning
Maria Agoston, Beaverton, OR (US); John J. Pickerd, Hillsboro, OR (US); and Kan Tan, Portland, OR (US)
Assigned to Tektronix, Inc., Beaverton, OR (US)
Filed by Tektronix, Inc., Beaverton, OR (US)
Filed on May 16, 2022, as Appl. No. 17/745,797.
Claims priority of provisional application 63/189,886, filed on May 18, 2021.
Prior Publication US 2022/0373597 A1, Nov. 24, 2022
Int. Cl. G01R 31/319 (2006.01); G01R 31/26 (2020.01); H04B 17/00 (2015.01); H04L 1/20 (2006.01)
CPC G01R 31/31908 (2013.01) [G01R 31/26 (2013.01); H04B 17/0085 (2013.01); H04L 1/203 (2013.01)] 19 Claims
OG exemplary drawing
 
1. A test and measurement system, comprising:
a machine learning system;
a test and measurement device including a port configured to connect the test and measurement device to a device under test (DUT); and
one or more processors, and a memory for storing code, which when executed by the one or more processors, causes the one or more processors to:
acquire one or more waveforms from the DUT by capturing one or more signals from the DUT and generating the one or more waveforms from the one or more signals;
transform the one or more waveforms into a composite waveform image, the composite waveform image based on at least two images from the one or more waveforms; and
send the composite waveform image to the machine learning system to obtain a bit error ratio (BER) value for the DUT.