Structured-light velocimeter and velocimetry method
A structured-light-velocimetry method includes extracting one or more bursts from a time-varying signal generated by detecting scattered light from a tracer particle passing through a structured optical beam; fitting each of the one or more bursts to a multi-variable model to extract a plurality of fitted parameters; and executing a machine-learning model with the plurality of fitted parameters to predict an angular velocity of the tracer particle.
1. A structured-light-velocimetry method, comprising:
extracting one or more bursts from a time-varying signal by:
(i) cross-correlating the time-varying signal with a reference function to obtain a cross-correlation signal;
(ii) comparing the cross-correlation signal to a threshold to identify one or more burst start times and one or more corresponding burst end times; and
(iii) cropping the time-varying signal based on the one or more burst start times and the one or more corresponding burst end times;
the time-varying signal having been generated by detecting scattered light from a tracer particle passing through a structured optical beam;
fitting each of the one or more bursts to a multi-variable model to extract a plurality of fitted parameters; and
executing a machine-learning model with the plurality of fitted parameters to predict an angular velocity of the tracer particle.
2. The structured-light-velocimetry method of claim 1 , further comprising outputting the predicted angular velocity.
3. The structured-light-velocimetry method of claim 1 , the reference function being either a rectangular function or a triangular function.
4. The structured-light-velocimetry method of claim 1 , wherein the multi-variable model includes one or more peaks, and the plurality of fitted parameters include a center, a width, and an amplitude for each of the one or more peaks.
5. The structured-light-velocimetry method of claim 4 , the plurality of fitted parameters further including a single offset.
6. The structured-light-velocimetry method of claim 1 , the machine-learning model being a neural network.
7. The structured-light-velocimetry method of claim 1 , further comprising generating the time-varying signal by detecting the scattered light from the tracer particle.
8. The structured-light-velocimetry method of claim 7 , further comprising injecting the tracer particle into the structured optical beam.
9. The structured-light-velocimetry method of claim 7 , further comprising generating the structured optical beam by interfering Laguerre-Gauss beams with orbital angular mode numbers +l and −l.
10. A structured-light velocimeter, comprising:
a processor; and
a memory communicatively coupled with the processor and storing machine-readable instructions that, when executed by the processor, control the structured-light velocimeter to:
extract one or more bursts from a time-varying signal by:
(i) cross-correlating the time-varying signal with a reference function to obtain a cross-correlation signal,
(ii) comparing the cross-correlation signal to a threshold to identify one or more burst start times and one or more corresponding burst end times, and
(iii) cropping the time-varying signal based on the one or more burst start times and the one or more corresponding burst end times,
the time-varying signal having been generated by detecting scattered light from a tracer particle passing through a structured optical beam,
fit each of the one or more bursts to a multi-variable model to extract a plurality of fitted parameters, and
execute a machine-learning model with the plurality of fitted parameters to predict an angular velocity of the tracer particle.
11. The structured-light velocimeter of claim 10 , the memory storing additional machine-readable instructions that, when executed by the processor, control the structured-light velocimeter to output the predicted angular velocity.
12. The structured-light velocimeter of claim 10 , the reference function being either a rectangular function or a triangular function.
13. The structured-light velocimeter of claim 10 , the multi-variable model including one or more peaks, and the plurality of fitted parameters include a center, a width, and an amplitude for each of the one or more peaks.
14. The structured-light velocimeter of claim 13 , wherein the plurality of fitted parameters further includes a single offset.
15. The structured-light velocimeter of claim 10 , the machine-learning model being a neural network.
16. The structured-light velocimeter of claim 10 ,
further comprising an optical detector configured to detect the scattered light;
the memory further storing additional machine-readable instructions that, when executed by the processor, control the structured-light velocimeter to receive the time-varying signal from the optical detector.
17. The structured-light velocimeter of claim 10 , further comprising optics configured to transform an output of a laser into the structured optical beam by interfering Laguerre-Gauss beams with orbital angular mode numbers +l and −l.
18. The structured-light velocimeter of claim 17 , further comprising the laser.