Structured-light velocimeter and velocimetry method

Patent metadata

Field	Value
Publication number	US-12313648-B2
Application number	US-202117998262-A
Country	US
Kind code	B2
Filing date	May 10, 2021
Priority date	May 10, 2020
Publication date	May 27, 2025
Grant date	May 27, 2025

Abstract

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.

First claim

What is claimed is:

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.

Assignees

• Univ Colorado Regents
• Harvard College

Inventors

• Strong Elizabeth F
• Rieker Gregory B
• Gopinath Juliet T
• Anderson Alexander
• Brenner Michael P

Classifications

• G01S17/58

  Velocity or trajectory determination systems; Sense-of-movement determination systems · CPC title

• G01P5/20

  using particles entrained by a fluid stream (G01P5/22 takes precedence) · CPC title

• G01P1/06

  Indicating or recording devices, e.g. for remote indication (indicating or recording in general G01D; registering or indicating working conditions of vehicles G07C5/00) · CPC title

• G01P1/026

  for speed measuring devices, e.g. pulse generator · CPC title

• G01P3/481

  of pulse signals · CPC title