Systems, methods, and computer program products for sound velocity tracking in ultrasonic flow sensors

Abstract

Systems, methods, and computer program products for sound velocity tracking in ultrasonic flow sensors are provided. An example system includes an ultrasonic flow sensor and / or at least one processor. The ultrasonic flow sensor may include a flow tube, a first piezoelectric sensor / transducer, and / or a second piezoelectric sensor / transducer. The at least one processor may be configured to provide an excitation pulse pattern including excitation pulses to the ultrasonic flow sensor such that the ultrasonic flow sensor transmits and receives at least one ultrasonic signal between the first piezoelectric sensor / transducer and the second piezoelectric sensor / transducer, the excitation pulses comprise different pulse widths and / or different voltage levels; receiving a time-series waveform from the ultrasonic flow sensor, the time-series waveform comprising amplitudes of the at least one ultrasonic signal sampled at a plurality of time points; identifying attributes of the time sequence waveform; and determining a propagation time of the at least one ultrasonic signal based on the attribute.

Description

[0001] Cross-references to related applications

[0002] This application claims priority to U.S. Patent Application No. 18 / 777,720, filed July 19, 2024, entitled “System, Method, and Computer Program Product for Speed-Of-Sound Tracking in Ultrasonic Flow Sensors,” the disclosure of which is incorporated herein by reference in its entirety. background Technical Field

[0003] This disclosure generally relates to ultrasonic flow sensors, and in some non-limiting embodiments or aspects, to systems, methods, and computer program products for sound velocity tracking in ultrasonic flow sensors.

[0004] Technical considerations

[0005] Existing ultrasonic flow sensors can use periodic pulse patterns to excite the crystal of a transmitting piezoelectric sensor or transducer to emit ultrasonic signals to a receiving piezoelectric sensor or transducer. The receiving piezoelectric sensor or transducer outputs a time-series waveform, which can be analyzed to calculate the transit time of the ultrasonic signal. For example, Figure 4 This is a graph of an example periodic excitation pulse pattern used to excite a piezoelectric sensor or transducer to emit ultrasonic signals. Figure 5 This is a graph of an example time-series waveform obtained using a periodic excitation pulse pattern.

[0006] The propagation time of ultrasonic signals between a transmitting piezoelectric sensor or transducer and a receiving piezoelectric sensor or transducer can be calculated by analyzing time-series waveforms. For example, the propagation time can be inversely proportional to the speed of sound in the fluid along the flow path of an ultrasonic flow sensor. The propagation time can be calculated by identifying one of the peaks in the waveform (or a point in the graph where the waveform intersects the zero (0) signal line, called a zero-crossing point). However, as... Figure 4 and Figure 5As shown, using a periodic pulse pattern can result in a gradually changing output waveform that includes several cycles, and identifying a peak (or zero crossing) of the same cycle can be difficult. For example, if the amplitude of the output waveform is low or the waveform is slightly changed due to external factors, a wrong peak can be identified or selected. Identifying or selecting a wrong peak (or a wrong zero crossing) can result in a severe flow measurement error and / or limit the types of fluids that the ultrasonic flow sensor can use. SUMMARY

[0007] Accordingly, improved systems, methods, and computer program products for sound velocity tracking in ultrasonic flow sensors are provided.

[0008] According to non-limiting embodiments or aspects, a system is provided that includes: an ultrasonic flow sensor including a flow tube, a first piezoelectric sensor or transducer disposed at an upstream location of the flow tube, and a second piezoelectric sensor or transducer disposed at a downstream location of the flow tube; and at least one processor configured to: provide, to at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, an excitation pulse pattern including a plurality of excitation pulses to cause the at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, to emit at least one ultrasonic signal to another of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, wherein the plurality of excitation pulses of the excitation pulse pattern includes at least one of: a plurality of different pulse widths, a plurality of different voltage levels, or any combination thereof; receive a time series waveform from the ultrasonic flow sensor including a plurality of amplitudes sampled at a plurality of time points of the at least one ultrasonic signal received at the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof; identify a property of the time series waveform; and determine, based on the property of the time series waveform, a propagation time of the at least one ultrasonic signal from the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof.

[0009] In some non-limiting embodiments or aspects, the at least one processor is configured to identify the property of the time series waveform by applying at least one pattern matching algorithm to the time series waveform.

[0010] In some non-limiting embodiments or aspects, the at least one processor is configured to identify the property of the time series waveform by comparing the time series waveform to at least one reference time series waveform, wherein the at least one reference time series waveform is determined based on a plurality of time series waveforms generated by the plurality of ultrasonic flow sensors in response to the excitation pulse pattern.

[0011] In some non-limiting embodiments or aspects, the plurality of excitation pulses of the excitation pulse pattern further comprises a different number of pulses than a previous excitation pulse pattern previously provided to at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, such that the at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof emits at least one previous ultrasonic signal to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof.

[0012] In some non-limiting embodiments or aspects, the at least one processor is further configured to provide an indication associated with a propagation time of the at least one ultrasonic signal.

[0013] In some non-limiting embodiments or aspects, the property of the time series waveform comprises a peak or a zero-crossing of the time series waveform, wherein the at least one processor is configured to identify the property of the time series waveform by analyzing the time series waveform to identify the peak or the zero-crossing of the time series waveform, and wherein the at least one processor is configured to determine the propagation time of the at least one ultrasonic signal from the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof based on the peak or the zero-crossing of the time series waveform.

[0014] In some non-limiting embodiments or aspects, the at least one processor is configured to analyze the time series waveform to identify the peak or the zero-crossing of the time series waveform by providing time series waveform data associated with the time series waveform as an input to a machine learning model trained to identify the peak or the zero-crossing of the time series waveform, and receiving the peak or the zero-crossing of the time series waveform as an output from the machine learning model.

[0015] According to non-limiting embodiments or aspects, an ultrasonic flow sensor is provided, the ultrasonic flow sensor comprising: a flow tube; a first piezoelectric sensor or transducer disposed at an upstream location of the flow tube; a second piezoelectric sensor or transducer disposed at a downstream location of the flow tube; and at least one processor configured to: provide at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof with an excitation pulse pattern comprising a plurality of excitation pulses to cause the at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof to emit at least one ultrasonic signal to another of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, wherein the plurality of excitation pulses of the excitation pulse pattern comprises at least one of: a plurality of different pulse widths, a plurality of different voltage levels, or any combination thereof; receive a time series waveform from the ultrasonic flow sensor, the time series waveform comprising a plurality of amplitudes of the at least one ultrasonic signal sampled at a plurality of time points received at the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof; identify a property of the time series waveform; and determine a propagation time of the at least one ultrasonic signal from the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof based on the property of the time series waveform.

[0016] In some non-limiting embodiments or aspects, the at least one processor is configured to identify the property of the time series waveform by applying at least one pattern matching algorithm to the time series waveform.

[0017] In some non-limiting embodiments or aspects, the at least one processor is configured to identify the property of the time series waveform by comparing the time series waveform to at least one reference time series waveform, wherein the at least one reference time series waveform is determined based on a plurality of time series waveforms generated by a plurality of ultrasonic flow sensors in response to the excitation pulse pattern.

[0018] In some non-limiting embodiments or aspects, the plurality of excitation pulses of the excitation pulse pattern further comprises a different number of pulses than a previous excitation pulse pattern previously provided to at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, such that the at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof emits at least one previous ultrasonic signal to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof.

[0019] In some non-limiting embodiments or aspects, the at least one processor is further configured to provide an indication associated with a time of flight of the at least one ultrasonic signal.

[0020] In some non-limiting embodiments or aspects, the property of the time series waveform comprises a peak or a zero crossing of the time series waveform, wherein the at least one processor is configured to identify the property of the time series waveform by analyzing the time series waveform to identify the peak or the zero crossing of the time series waveform, and wherein the at least one processor is configured to determine the time of flight of the at least one ultrasonic signal from the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof based on the peak or the zero crossing of the time series waveform.

[0021] In some non-limiting embodiments or aspects, the at least one processor is configured to analyze the time series waveform to identify the peak or the zero crossing of the time series waveform by providing time series waveform data associated with the time series waveform as input to a machine learning model trained to identify the peak or the zero crossing of the time series waveform and receiving the peak or the zero crossing of the time series waveform as output from the machine learning model.

[0022] According to non-limiting embodiments or aspects, a method for acoustic velocity tracking in an ultrasonic flow sensor including a flow tube, a first piezoelectric sensor or transducer disposed at an upstream location of the flow tube, and a second piezoelectric sensor or transducer disposed at a downstream location of the flow tube is provided, the method including: providing, using at least one processor, at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, with an excitation pulse pattern including a plurality of excitation pulses to cause the at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, to emit at least one ultrasonic signal to another of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, wherein the plurality of excitation pulses of the excitation pulse pattern includes at least one of a plurality of different pulse widths, a plurality of different voltage levels, or any combination thereof; receiving, using the at least one processor, a time series waveform from the ultrasonic flow sensor, the time series waveform including a plurality of amplitudes sampled at a plurality of time points of the at least one ultrasonic signal received at the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof; identifying, using the at least one processor, a property of the time series waveform; and determining, using the at least one processor, a propagation time of the at least one ultrasonic signal from the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, based on the property of the time series waveform.

[0023] In some non-limiting embodiments or aspects, identifying, using the at least one processor, the property of the time series waveform includes applying, using the at least one processor, at least one pattern matching algorithm to the time series waveform.

[0024] In some non-limiting embodiments or aspects, identifying, using the at least one processor, the property of the time series waveform includes comparing, using the at least one processor, the time series waveform to at least one reference time series waveform, wherein the at least one reference time series waveform is determined based on a plurality of time series waveforms generated by a plurality of ultrasonic flow sensors in response to the excitation pulse pattern.

[0025] In some non-limiting embodiments or aspects, the plurality of excitation pulses of the excitation pulse pattern further comprises a different number of pulses than a previous excitation pulse pattern previously provided to at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, such that the at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof emits at least one previous ultrasonic signal to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof.

[0026] In some non-limiting embodiments or aspects, the method further comprises providing, using the at least one processor, an indication associated with a time of flight of the at least one ultrasonic signal.

[0027] In some non-limiting embodiments or aspects, the property of the time series waveform comprises a peak or a zero crossing of the time series waveform, wherein identifying, using the at least one processor, the property of the time series waveform comprises analyzing, using the at least one processor, the time series waveform to identify the peak or the zero crossing of the time series waveform, wherein the at least one processor determines the time of flight of the at least one ultrasonic signal from the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof based on the peak or the zero crossing of the time series waveform, and wherein analyzing, using the at least one processor, the time series waveform to identify the peak or the zero crossing of the time series waveform comprises providing time series waveform data associated with the time series waveform as input to a machine learning model trained to identify the peak or the zero crossing of the time series waveform and receiving the peak or the zero crossing of the time series waveform as output from the machine learning model.

[0028] According to non-limiting embodiments or aspects, a computer program product is provided that includes a non-transitory computer readable medium including a plurality of program instructions for sound velocity tracking in an ultrasonic flow sensor including a flow tube, a first piezoelectric sensor or transducer disposed at an upstream location of the flow tube, and a second piezoelectric sensor or transducer disposed at a downstream location of the flow tube, the plurality of program instructions, when executed by at least one processor, cause the at least one processor to: provide an excitation pulse pattern including a plurality of excitation pulses to at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, to cause the at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof to emit at least one ultrasonic signal to another of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, wherein the plurality of excitation pulses of the excitation pulse pattern include at least one of a plurality of different pulse widths, a plurality of different voltage levels, or any combination thereof; receive a time series waveform from the ultrasonic flow sensor including a plurality of amplitudes sampled at a plurality of time points of the at least one ultrasonic signal received at the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof; identify a property of the time series waveform; and determine a propagation time of the at least one ultrasonic signal from the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof based on the property of the time series waveform.

[0029] In some non-limiting embodiments or aspects, the plurality of program instructions, when executed by the at least one processor, cause the at least one processor to identify the property of the time series waveform by applying at least one pattern matching algorithm to the time series waveform.

[0030] In some non-limiting embodiments or aspects, the plurality of program instructions, when executed by the at least one processor, cause the at least one processor to identify the property of the time series waveform by comparing the time series waveform to at least one reference time series waveform.

[0031] In some non-limiting embodiments or aspects, the at least one reference time series waveform is determined based on a plurality of time series waveforms generated by a plurality of ultrasonic flow sensors in response to the excitation pulse pattern.

[0032] In some non-limiting embodiments or aspects, the plurality of excitation pulses of the excitation pulse pattern further comprises a different number of pulses than a previous excitation pulse pattern previously provided to at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, such that the at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof emits at least one previous ultrasonic signal to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof.

[0033] In some non-limiting embodiments or aspects, the program instructions, when executed by the at least one processor, further cause the at least one processor to provide an indication associated with a time of flight of the at least one ultrasonic signal.

[0034] In some non-limiting embodiments or aspects, the property of the time series waveform comprises a peak or a zero crossing of the time series waveform, wherein the program instructions, when executed by the at least one processor, cause the at least one processor to identify the property of the time series waveform by analyzing the time series waveform to identify the peak or the zero crossing of the time series waveform, and wherein the program instructions, when executed by the at least one processor, cause the at least one processor to determine the time of flight of the at least one ultrasonic signal from the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof based on the peak or the zero crossing of the time series waveform.

[0035] In some non-limiting embodiments or aspects, the program instructions, when executed by the at least one processor, cause the at least one processor to analyze the time series waveform to identify the peak or the zero crossing of the time series waveform by providing time series waveform data associated with the time series waveform as input to a machine learning model trained to identify the peak or the zero crossing of the time series waveform and receiving the peak or the zero crossing of the time series waveform as output from the machine learning model.

[0036] Other non-limiting embodiments or aspects are set forth in the following numbered clauses:

[0037] CLAIM 1. A system comprising: an ultrasonic flow sensor comprising a flow tube, a first piezoelectric sensor or transducer disposed at an upstream location of the flow tube, and a second piezoelectric sensor or transducer disposed at a downstream location of the flow tube; and at least one processor configured to: provide at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof with an excitation pulse pattern comprising a plurality of excitation pulses to cause the at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof to emit at least one ultrasonic signal to another of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, wherein the plurality of excitation pulses of the excitation pulse pattern comprises at least one of a plurality of different pulse widths, a plurality of different voltage levels, or any combination thereof; receive a time series waveform from the ultrasonic flow sensor comprising a plurality of amplitudes sampled at a plurality of time points of the at least one ultrasonic signal received at the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof; identify a property of the time series waveform; and determine a propagation time of the at least one ultrasonic signal from the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof based on the property of the time series waveform.

[0038] CLAIM 2. The system of claim 1, wherein the at least one processor is configured to identify the property of the time series waveform by applying at least one pattern matching algorithm to the time series waveform.

[0039] CLAIM 3. The system of claim 1 or claim 2, wherein the at least one processor is configured to identify the property of the time series waveform by comparing the time series waveform to at least one reference time series waveform, wherein the at least one reference time series waveform is determined based on a plurality of time series waveforms generated by a plurality of ultrasonic flow sensors in response to an excitation pulse pattern.

[0040] CLAIM 4. The system of any one of claims 1-3, wherein the plurality of excitation pulses of the excitation pulse pattern further comprises a different number of pulses than a previous excitation pulse pattern previously provided to the at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof to cause the at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof to emit at least one previous ultrasonic signal to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof.

[0041] Clause 5. The system of any of clauses 1-4, wherein the at least one processor is further configured to provide an indication associated with a time of flight of the at least one ultrasonic signal.

[0042] Clause 6. The system of any of clauses 1-5, wherein the property of the time series waveform comprises a peak or a zero crossing of the time series waveform, wherein the at least one processor is configured to identify the property of the time series waveform by analyzing the time series waveform to identify the peak or the zero crossing of the time series waveform, and wherein the at least one processor is configured to determine the time of flight of the at least one ultrasonic signal from the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof based on the peak or the zero crossing of the time series waveform.

[0043] Clause 7. The system of any of clauses 1-6, wherein the at least one processor is configured to analyze the time series waveform to identify the peak or the zero crossing of the time series waveform by providing time series waveform data associated with the time series waveform as input to a machine learning model trained to identify the peak or the zero crossing of the time series waveform and receiving the peak or the zero crossing of the time series waveform as output from the machine learning model.

[0044] Clause 8. An ultrasonic flow sensor comprising: a flow tube; a first piezoelectric sensor or transducer disposed at an upstream location of the flow tube; a second piezoelectric sensor or transducer disposed at a downstream location of the flow tube; and at least one processor configured to: provide, to at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, an excitation pulse pattern comprising a plurality of excitation pulses to cause the at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, to emit at least one ultrasonic signal to another of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, wherein the plurality of excitation pulses of the excitation pulse pattern comprise at least one of: a plurality of different pulse widths, a plurality of different voltage levels, or any combination thereof; receive a time series waveform from the ultrasonic flow sensor, the time series waveform comprising a plurality of amplitudes of the at least one ultrasonic signal sampled at a plurality of time points received at the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof; identify a property of the time series waveform; and determine a propagation time of the at least one ultrasonic signal from the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, based on the property of the time series waveform.

[0045] Clause 9. The ultrasonic flow sensor of clause 9, wherein the at least one processor is configured to identify the property of the time series waveform by applying at least one pattern matching algorithm to the time series waveform.

[0046] Clause 10. The ultrasonic flow sensor of clause 8 or 9, wherein the at least one processor is configured to identify the property of the time series waveform by comparing the time series waveform to at least one reference time series waveform, wherein the at least one reference time series waveform is determined based on a plurality of time series waveforms generated by a plurality of ultrasonic flow sensors in response to the excitation pulse pattern.

[0047] Clause 11. The ultrasonic flow sensor of any of clauses 8-10, wherein the plurality of excitation pulses of the excitation pulse pattern further comprises a different number of pulses than a previous excitation pulse pattern previously provided to at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, such that the at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof emits at least one previous ultrasonic signal to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof.

[0048] Clause 12. The ultrasonic flow sensor of any of clauses 8-11, wherein the at least one processor is further configured to provide an indication associated with a propagation time of the at least one ultrasonic signal.

[0049] Clause 13. The ultrasonic flow sensor of any of clauses 8-12, wherein the property of the time series waveform comprises a peak or a zero crossing of the time series waveform, wherein the at least one processor is configured to identify the property of the time series waveform by analyzing the time series waveform to identify the peak or the zero crossing of the time series waveform, and wherein the at least one processor is configured to determine the propagation time of the at least one ultrasonic signal from the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof based on the peak or the zero crossing of the time series waveform.

[0050] Clause 14. The ultrasonic flow sensor of any of clauses 8-13, wherein the at least one processor is configured to analyze the time series waveform to identify the peak or the zero crossing of the time series waveform by providing time series waveform data associated with the time series waveform as input to a machine learning model trained to identify the peak or the zero crossing of the time series waveform and receiving the peak or the zero crossing of the time series waveform as output from the machine learning model.

[0051] Clause 15. A method for acoustic velocity tracking in an ultrasonic flow sensor, the ultrasonic flow sensor comprising a flow tube, a first piezoelectric sensor or transducer disposed at an upstream location of the flow tube, and a second piezoelectric sensor or transducer disposed at a downstream location of the flow tube, the method comprising: providing, using at least one processor, at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, with an excitation pulse pattern comprising a plurality of excitation pulses to cause the at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, to emit at least one ultrasonic signal to another of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, wherein the plurality of excitation pulses of the excitation pulse pattern comprise at least one of a plurality of different pulse widths, a plurality of different voltage levels, or any combination thereof; receiving, using the at least one processor, a time series waveform from the ultrasonic flow sensor, the time series waveform comprising a plurality of amplitudes of the at least one ultrasonic signal sampled at a plurality of time points received at the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof; identifying, using the at least one processor, a property of the time series waveform; and determining, using the at least one processor, a propagation time of the at least one ultrasonic signal from the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, based on the property of the time series waveform.

[0052] Clause 16. The method of clause 15, wherein identifying, using the at least one processor, the property of the time series waveform comprises applying, using the at least one processor, at least one pattern matching algorithm to the time series waveform.

[0053] Clause 17. The method of clause 15 or 16, wherein identifying, using the at least one processor, the property of the time series waveform comprises comparing, using the at least one processor, the time series waveform to at least one reference time series waveform, wherein the at least one reference time series waveform is determined based on a plurality of time series waveforms generated by a plurality of ultrasonic flow sensors in response to the excitation pulse pattern.

[0054] Clause 18. The method of any of clauses 15-17, wherein the plurality of excitation pulses of the excitation pulse pattern further comprises a different number of pulses than a previous excitation pulse pattern previously provided to at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, to cause the at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof to emit at least one previous ultrasonic signal to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof.

[0055] Clause 19. The method of any of clauses 15-18, further comprising providing, using the at least one processor, an indication associated with a time of flight of the at least one ultrasonic signal.

[0056] Clause 20. The method of any of clauses 15-18, wherein the property of the time series waveform comprises a peak or a zero crossing of the time series waveform, wherein identifying, using the at least one processor, the property of the time series waveform comprises analyzing, using the at least one processor, the time series waveform to identify the peak or the zero crossing of the time series waveform, wherein the at least one processor determines, based on the peak or the zero crossing of the time series waveform, a time of flight of the at least one ultrasonic signal from the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, and wherein analyzing, using the at least one processor, the time series waveform to identify the peak or the zero crossing of the time series waveform comprises providing time series waveform data associated with the time series waveform as input to a machine learning model trained to identify the peak or the zero crossing of the time series waveform and receiving, as output from the machine learning model, the peak or the zero crossing of the time series waveform.

[0057] Clause 21. A computer program product comprising a non-transitory computer readable medium including a plurality of program instructions for acoustic velocity tracking in an ultrasonic flow sensor, the ultrasonic flow sensor comprising a flow tube, a first piezoelectric sensor or transducer disposed at an upstream location of the flow tube, and a second piezoelectric sensor or transducer disposed at a downstream location of the flow tube, the plurality of program instructions, when executed by at least one processor, causing the at least one processor to: provide an excitation pulse pattern comprising a plurality of excitation pulses to at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, to cause the at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof to emit at least one ultrasonic signal to another of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, wherein the plurality of excitation pulses of the excitation pulse pattern comprise at least one of a plurality of different pulse widths, a plurality of different voltage levels, or any combination thereof; receive a time series waveform from the ultrasonic flow sensor, the time series waveform comprising a plurality of amplitudes sampled at a plurality of time points of the at least one ultrasonic signal received at the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof; identify a property of the time series waveform; and determine a propagation time of the at least one ultrasonic signal from the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof based on the property of the time series waveform.

[0058] Clause 22. The computer program product of clause 21, wherein the plurality of program instructions, when executed by the at least one processor, cause the at least one processor to identify the property of the time series waveform by applying at least one pattern matching algorithm to the time series waveform.

[0059] Clause 23. The computer program product of clause 21 or 22, wherein the plurality of program instructions, when executed by the at least one processor, cause the at least one processor to identify the property of the time series waveform by comparing the time series waveform to at least one reference time series waveform.

[0060] Clause 24. The computer program product of any one of clauses 21 to 23, wherein the at least one reference time series waveform is determined based on a plurality of time series waveforms generated by a plurality of ultrasonic flow sensors in response to the excitation pulse pattern.

[0061] Clause 25. The computer program product of any of clauses 21-24, wherein the plurality of excitation pulses of the excitation pulse pattern further comprises a different number of pulses than a previous excitation pulse pattern previously provided to at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, to cause the at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof to emit at least one previous ultrasonic signal to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof.

[0062] Clause 26. The computer program product of any of clauses 21-25, wherein the program instructions, when executed by the at least one processor, further cause the at least one processor to provide an indication associated with a time of flight of the at least one ultrasonic signal.

[0063] Clause 27. The computer program product of any of clauses 21-26, wherein the property of the time series waveform comprises a peak or a zero crossing of the time series waveform, wherein the program instructions, when executed by the at least one processor, cause the at least one processor to identify the property of the time series waveform by analyzing the time series waveform to identify the peak or the zero crossing of the time series waveform, and wherein the program instructions, when executed by the at least one processor, cause the at least one processor to determine the time of flight of the at least one ultrasonic signal from the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof based on the peak or the zero crossing of the time series waveform.

[0064] Clause 28. The computer program product of any of clauses 21-27, wherein the program instructions, when executed by the at least one processor, cause the at least one processor to analyze the time series waveform to identify the peak or the zero crossing of the time series waveform by providing time series waveform data associated with the time series waveform as input to a machine learning model trained to identify the peak or the zero crossing of the time series waveform and receiving the peak or the zero crossing of the time series waveform as output from the machine learning model.

[0065] These and other features and characteristics of the present disclosure, as well as the methods of operation and functions of the related elements of structure and the combination of parts and economies of manufacture, will become more apparent upon consideration of the following description and appended claims with reference to the accompanying drawings, all of which form a part of this specification, wherein like reference numerals designate corresponding parts in the various figures. It is to be expressly understood, however, that the drawings are for purposes of illustration and description only and are not intended as a definition of the limits of the disclosed subject matter. BRIEF DESCRIPTION OF DRAWINGS

[0066] Additional advantages and details of implementation will now be described in greater detail with reference to the non-limiting exemplary embodiments illustrated in the drawings, wherein:

[0067] Figure 1A is a schematic diagram of a system for sound velocity tracking in an ultrasonic flow sensor in accordance with some non-limiting embodiments or aspects;

[0068] Figure 1B is a schematic diagram of a system for sound velocity tracking in an ultrasonic flow sensor in accordance with some non-limiting embodiments or aspects; Figure 1A is a perspective view of example components of a flow sensor system of the system for sound velocity tracking in an ultrasonic flow sensor of

[0069] Figure 1C is a perspective view of example components of a flow sensor system of the system for sound velocity tracking in an ultrasonic flow sensor of Figure 1A is a cross-sectional view of example components of an ultrasonic flow sensor of the flow sensor system of the system for sound velocity tracking in an ultrasonic flow sensor of

[0070] Figure 2 is a schematic diagram of example components of one or more devices or systems of Figure 1A

[0071] Figure 3 is a flow diagram of a method of sound velocity tracking in an ultrasonic flow sensor in accordance with some non-limiting embodiments or aspects;

[0072] Figure 4 is a plot of an example periodic excitation pulse pattern for exciting a piezoelectric sensor or transducer to emit ultrasonic signals;

[0073] Figure 5 is a plot of an example time series waveform obtained using the periodic excitation pulse pattern;

[0074] Figure 6 is a plot of an excitation pulse pattern in accordance with some non-limiting embodiments or aspects; and

[0075] Figure 7 is a plot of a waveform obtained using the excitation pulse pattern in accordance with some non-limiting embodiments or aspects.​ Detailed Implementation

[0076] In the following description, for descriptive purposes, the terms “end,” “upper,” “lower,” “right,” “left,” “vertical,” “horizontal,” “top,” “bottom,” “lateral,” “longitudinal,” and their derivatives shall be used in connection with the various embodiments, as these embodiments are oriented in the accompanying drawings. However, it should be understood that this disclosure may take the form of various alternative variations and sequences of steps unless explicitly stated otherwise. It should also be understood that the specific devices and processes shown in the accompanying drawings and described in the following specification are merely exemplary and non-limiting embodiments or aspects of the subject matter of this disclosure. Therefore, specific dimensions and other physical characteristics associated with the embodiments or aspects disclosed herein should not be considered limiting.

[0077] This document describes some non-limiting embodiments or aspects in conjunction with thresholds. As used herein, satisfying a threshold can mean a value that is greater than, more than, higher than, greater than or equal to, less than, less than, lower than, less than or equal to, or equal to a threshold.

[0078] The aspects, components, elements, structures, actions, steps, functions, and / or instructions used herein should not be construed as critical or essential unless explicitly described as such. Furthermore, as used herein, the articles “a” and “an” are intended to include one or more items and may be used interchangeably with “one or more” and “at least one.” Additionally, as used herein, the term “set” is intended to include one or more items (e.g., related items, unrelated items, and / or combinations of related and unrelated items, etc.) and may be used interchangeably with “one or more” or “at least one.” The term “an” or similar language is used where only one item is intended. Furthermore, as used herein, the terms “having,” “having,” “with,” etc., are intended as open-ended terms. Furthermore, unless explicitly stated otherwise, the phrase “based on” is intended to mean “at least partially based on.” Furthermore, an action referring to a condition “based on” can refer to an action “in response to” that condition. For example, in some non-limiting embodiments or aspects, the phrases “based on” and “in response to” can refer to a condition that automatically triggers an action (e.g., a specific operation of an electronic device such as a computing device and / or processor).

[0079] As used herein, the term “communication” can refer to receipt, receipt of, transmission, transfer, and / or provision of data (e.g., information, signals, messages, instructions, and / or commands, etc.). One unit (e.g., a device, a system, a component of a device or system, and / or combinations thereof, etc.) being in communication with another unit means that the one unit is able to directly or indirectly receive a signal from and / or transmit a signal to the other unit. This can refer to a direct or indirect connection (e.g., a direct communication connection and / or an indirect communication connection, etc.) that is wired and / or wireless in nature. Furthermore, two units can be in communication with each other even though the data transmitted can be modified, processed, relayed, and / or routed between the first and second unit. For example, a first unit can be in communication with a second unit even though the first unit passively receives information and does not actively transmit information to the second unit. As another example, a first unit can be in communication with a second unit if at least one intermediary unit processes information received from the first unit and transmits processed information to the second unit. In some non-limiting embodiments or aspects, a message can refer to a network packet (e.g., a data packet, etc.) that includes data. It will be recognized that many other arrangements are possible.

[0080] As used herein, the term “computing device” can refer to one or more electronic devices configured to process data. In some examples, a computing device can include components necessary for receiving, processing, and outputting data, such as a processor, display, memory, input devices, and / or network interface, etc. A computing device can be a mobile device. As an example, a mobile device can include a cellular phone (e.g., a smart phone or a standard cellular phone), a portable computer, a wearable device (e.g., a watch, glasses, lenses, and / or clothing, etc.), a personal digital assistant (PDA), and / or other similar devices. A computing device can also be a desktop computer or other form of non-mobile computer.

[0081] As used herein, the term “server” can refer to or include one or more computing devices operated by or facilitating communication and processing for multiple parties in a network environment (e.g., the Internet), although it will be recognized that communication can also be facilitated over one or more public or private network environments, and other various settings are possible. Furthermore, multiple computing devices (e.g., servers, point-of-sale (POS) devices, mobile devices, etc.) in direct or indirect communication in a network environment can constitute a “system.”

[0082] As used herein, the term "system" can refer to one or more computing devices or a combination of computing devices (e.g., processors, servers, client devices, software applications, and / or components thereof). As used herein, references to "device," "server," and / or "processor," etc., can refer to the previously described device, server, or processor described as performing a previous step or function, different devices, servers, or processors, and / or combinations of multiple devices, combinations of multiple servers, and / or combinations of multiple processors. For example, as used in the specification and claims, a first device, first server, or first processor described as performing a first step or a first function can refer to the same or different device, server, or processor described as performing a second step or a second function.

[0083] Now for reference Figure 1A The diagram shown is a schematic representation of a system for sound velocity tracking in an ultrasonic flow sensor, according to some non-limiting embodiments or aspects. Figure 1A As shown, system 100 may include flow sensor system 102 and / or external computing system 104. The various systems and / or devices of system 100 may be interconnected via wired connection, wireless connection, or a combination of wired and wireless connection.

[0084] The flow sensor system 102 may include one or more devices capable of receiving information and / or data from and / or transmitting information and / or data to the external computing system 104. For example, the flow sensor system 102 may include one or more computing systems, including one or more processors (e.g., one or more computing devices, one or more mobile computing devices, one or more digital signal processors (DSPs), etc.). In some non-limiting embodiments or aspects, the flow sensor system 102 may include the flow sensor system disclosed in U.S. Patent Application Publication No. 2021 / 0231471 or U.S. Patent No. 10,072,959, the contents of which are incorporated herein by reference in their entirety.

[0085] Also refer to Figure 1B , Figure 1B Based on some non-limiting embodiments or aspects Figure 1A A perspective view of example components of a flow sensor system used for sound velocity tracking in ultrasonic flow sensors. Figure 1BAs shown, flow sensor system 102 can include an ultrasonic flow sensor 150 and / or a base 160. For example, ultrasonic flow sensor 150 can be configured to be removably, physically, and / or electrically connected to base 160. A syringe 170 can be configured to be physically connected to flow sensor 150 (e.g., through a fluid injection port, etc.). Syringe 170 can include a marking or label 172, which can include a near field communication (NFC) tag (e.g., a radio frequency identification (RFID) tag, etc.), a barcode, a QR code, or an AprilTag, etc., embedded in marking or label 172. Base 160 can include at least one sensor 162 configured to read and / or decode drug information from marking or label 172 on syringe 170. The drug information can include at least one expected drug type associated with at least one drug contained in syringe 170, such as a drug identifier (e.g., a unique drug identifier associated with a drug contained in syringe 170, etc.). For example, at least one sensor 162 of base 160 can include one or more computing devices, one or more chips, one or more contactless transmitters, one or more contactless transceivers, one or more NFC transmitters / receivers, one or more RFID transmitters / receivers, one or more contact-based transmitters / receivers, one or more optical sensors or scanners, or one or more barcode readers, etc., configured to read and / or decode information stored or encapsulated in marking or label 172.

[0086] Reference is also made to Figure 1C , Figure 1C A flow sensor system for a system for velocity tracking in ultrasonic flow sensors in accordance with some non-limiting embodiments or aspects Figure 1A A cross-sectional view of components of an ultrasonic flow sensor of a flow sensor system for a system for velocity tracking in ultrasonic flow sensors. As shown Figure 1C As shown, ultrasonic flow sensor 150 can include a flow tube 152 defining a fluid flow path of ultrasonic flow sensor 150, a first piezoelectric sensor or transducer 154 disposed at an upstream location of flow tube 152, and / or a second piezoelectric sensor or transducer 156 disposed at a downstream location of flow tube 152.

[0087] The ultrasonic flow sensor 150 can be configured to generate a time series corresponding to (e.g., corresponding to, representative of, quantifying, measuring, etc.) a flow of the at least one fluid through the fluid flow path of the ultrasonic flow sensor 150. For example, the first piezoelectric sensor or transducer 154 and the second piezoelectric sensor or transducer 156 can be configured to generate a time series corresponding to a flow of the at least one fluid through the fluid flow path of the ultrasonic flow sensor 150. As an example, the first piezoelectric sensor or transducer 154 and the second piezoelectric sensor or transducer 156 can each be configured to operate both as an ultrasonic transmitter and as an ultrasonic receiver. In such an example, the ultrasonic flow sensor 150 can be configured to operate by alternately transmitting and receiving bursts of ultrasonic pulses (e.g., “up” and “down” signals, etc.) between the two transducers by measuring the propagation time taken for sound to propagate between the two transducers in both directions. An analog-to-digital converter (ADC) can sample the signal received by the receiving transducer at a plurality of time points to generate a time series comprising a plurality of amplitudes sampled at the plurality of time points, a plurality of phases at the plurality of time points, or at least one of any combination thereof. The measured propagation time difference (e.g., delta time, etc.) can be proportional to the fluid velocity in the fluid flow path. The plurality of propagation time differences (e.g., plurality of delta times, etc.) can be represented as a time series comprising at least one of a plurality of amplitudes at a plurality of time points, a plurality of phases at a plurality of time points, or any combination thereof. For example, an average delta t can be calculated for one or more “up” and “down” signal pairs or one or more cycles, the magnitude of which can be proportional to the flow rate of the fluid through the fluid flow path of the ultrasonic flow sensor 150. As an example, delta t can be converted to the velocity of the fluid in the flow tube 102 using the angle of the ultrasonic signal path, which can be calculated by knowing the flow tube 102 and the speed of sound of the fluid. The angle can be used with trigonometry to convert the ultrasonic path to a straight line component in the flow tube 102, which can be the velocity of the liquid in the flow tube 102. The velocity of the fluid can be converted to flow rate by multiplying the velocity of the fluid by the cross-sectional area of the tube, and the volume of fluid delivered can be calculated by multiplying the flow rate by time.

[0088] The ultrasonic flow sensor 150 can be configured to continuously generate and provide the time series corresponding to the flow of the at least one fluid through the fluid flow path of the ultrasonic flow sensor 150 during (e.g., as the flow of the at least one fluid through the fluid flow path of the ultrasonic flow sensor 150 occurs and proceeds, etc.).

[0089] The flow sensor system 102 and / or the external computing system 104 can be configured to alternately provide the first piezoelectric sensor or transducer 154 and the second piezoelectric sensor or transducer 156 with an excitation pulse pattern including a plurality of excitation pulses such that the first piezoelectric sensor or transducer 154 and the second piezoelectric sensor or transducer 156 alternately emit and receive ultrasonic pulse trains (e.g., ultrasonic signals, etc.) between the two transducers. For example, the first piezoelectric sensor or transducer 154 can be configured to emit a first ultrasonic signal to the second piezoelectric sensor or transducer 156, the second piezoelectric sensor or transducer 156 can be configured to emit a second ultrasonic signal to the first piezoelectric transducer 154, the second piezoelectric sensor or transducer 156 can be configured to receive the first ultrasonic signal emitted by the first piezoelectric sensor or transducer 154 (which can be sampled by the ADC to generate a time series), and / or the first piezoelectric sensor or transducer 154 can be configured to receive the second ultrasonic signal emitted by the second piezoelectric sensor or transducer 156 (which can be sampled by the ADC to generate a time series).

[0090] The external computing system 104 can include one or more devices capable of receiving information and / or data from the flow sensor system 102 and / or transmitting information and / or data to the flow sensor system 102. For example, the external computing system 104 can include one or more computing systems including one or more processors (e.g., one or more computing devices, one or more mobile computing devices, one or more servers, etc.). In some non-limiting embodiments or aspects, the external computing system 104 includes a nurses’ station in a hospital, a hospital information system (HIS), an electronic medical record (EMR) system, a radiology information system (RIS), a picture archiving and communication system (PACS), a laboratory information system (LIS), a smart phone, a tablet computer, and / or any combination thereof, etc.

[0091] Figures 1A-1C The number and arrangement of systems and devices shown in FIG. 1 are provided as an example. There can be additional systems and / or devices, fewer systems and / or devices, different systems and / or devices, or differently arranged systems and / or devices than those shown in FIG. 1. Additionally, Figures 1A-1C There can be additional systems and / or devices, fewer systems and / or devices, different systems and / or devices, or differently arranged systems and / or devices than those shown in FIG. 1. Additionally, Figures 1A-1Ctwo or more systems or devices illustrated in the Figures 1A-1C a single system or device illustrated in the

[0092] Reference is now made to Figure 2 , shown is a schematic diagram of example components of a device 200, in accordance with non-limiting embodiments. By way of example, device 200 can correspond to Figure 1A flow sensor system 102 and / or external computing system 104 in FIG. 1. In some non-limiting embodiments, such a system or device can include at least one device 200 and / or at least one component of device 200. The number and arrangement of components shown are provided as examples. In some non-limiting embodiments, device 200 can include additional components, fewer components, different components, or differently arranged components than those shown. Additionally or alternatively, a group of components (e.g., one or more components) of device 200 can perform one or more functions described as being performed by another group of components of device 200.

[0093] As Figure 2As shown, the device 200 can include a bus 202, a processor 204, a memory 206, a storage component 208, an input component 210, an output component 212, and a communication interface 214. The bus 202 can include a component that permits communication among the components of the device 200. In some non-limiting embodiments, the processor 204 can be implemented in hardware, firmware, or a combination of hardware and software. For example, the processor 204 can include a processor (e.g., a central processing unit (CPU), a graphics processing unit (GPU), an accelerated processing unit (APU), etc.), a microprocessor, a digital signal processor (DSP), or any processing component (e.g., a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), etc.) that can be programmed to perform a function. The memory 206 can include a random access memory (RAM), a read-only memory (ROM), or another type of dynamic or static storage device (e.g., a flash memory, a magnetic storage device, an optical storage device, etc.) that stores information and / or instructions for use by the processor 204.

[0094] With continued reference to Figure 2Storage component 208 may store information and / or software related to the operation and use of device 200. For example, storage component 208 may include a hard disk (e.g., magnetic disk, optical disk, magneto-optical disk, solid-state disk, etc.) or another type of computer-readable medium. Input component 210 may include components that allow device 200 to receive information, for example, through a user input terminal (e.g., touch screen display, keyboard, keypad, mouse, button, switch, microphone, etc.). Additionally or alternatively, input component 210 may include sensors for sensing information (e.g., global positioning system (GPS) component, accelerometer, gyroscope, actuator, etc.). Output component 212 may include components that provide output information from device 200 (e.g., display, speaker, one or more light-emitting diodes (LEDs), etc.). Communication interface 214 may include transceiver-like components (e.g., transceiver, separate receiver and transmitter, etc.) that enable device 200 to communicate with other devices, for example, via wired connection, wireless connection, or a combination of wired and wireless connection. Communication interface 214 allows device 200 to receive information from or provide information to another device. For example, communication interface 214 may include an Ethernet interface, an optical interface, a coaxial interface, an infrared interface, a radio frequency (RF) interface, a universal serial bus (USB) interface, etc. Interfaces or cellular network interfaces, etc.

[0095] Device 200 can perform one or more processes described herein. Device 200 can perform these processes based on processor 204 executing software instructions stored by a computer-readable medium, such as memory 206 or storage component 208. A computer-readable medium includes any non-transitory storage device. A storage device includes memory space located inside a single physical storage device or memory space spread across multiple physical storage devices. Software instructions can be read into memory 206 and / or storage component 208 from another computer-readable medium or from another device via communication interface 214. The software instructions stored in memory 206 or storage component 208, when executed, can cause processor 204 to perform one or more processes described herein. Additionally or alternatively, hardwired circuitry can be used in place of or in combination with software instructions to perform one or more processes described herein. Thus, embodiments described herein are not limited to any specific combination of hardware circuitry and software. The term “configured to” as used herein can refer to a specific arrangement of software, one or more devices, or hardware that is used to perform or cause one or more of the innovative functions (e.g., acts, processes, steps of processes, etc.) described herein. For example, a “processor configured to” can refer to a processor that executes specific software instructions (e.g., program code) that cause the processor to perform one or more functions related to fluid flow detection and / or identification.

[0096] Referring now to the drawing Figure 3 illustrated is a flowchart of a method 300 of sound velocity tracking in an ultrasonic flow sensor, in accordance with some non-limiting embodiments or aspects. Figure 3 The steps shown in FIG. 3 are for exemplary purposes only. It will be appreciated that additional steps, fewer steps, different steps, or steps in a different order can be used in some non-limiting embodiments or aspects. In some non-limiting embodiments or aspects, a certain step can be performed automatically in response to execution or completion of a previous step.

[0097] As Figure 3As shown, in step 302, method 300 includes: providing an excitation pulse pattern comprising a plurality of excitation pulses to at least one of a first piezoelectric sensor or transducer, a second piezoelectric sensor or transducer, or any combination thereof of the ultrasonic flow sensor, such that at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof of the ultrasonic flow sensor transmits at least one ultrasonic signal to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof. For example, flow sensor system 102 and / or external computing system 104 may provide an excitation pulse pattern comprising a plurality of excitation pulses to at least one of a first piezoelectric sensor or transducer 154, a second piezoelectric sensor or transducer 156, or any combination thereof of the ultrasonic flow sensor, such that at least one of the first piezoelectric sensor or transducer 154, the second piezoelectric sensor or transducer 156, or any combination thereof of the ultrasonic flow sensor transmits at least one ultrasonic signal to the other of the first piezoelectric sensor or transducer 154, the second piezoelectric sensor or transducer 156, or any combination thereof of the ultrasonic flow sensor. The excitation pulses of the excitation pulse mode include at least one of the following: multiple different pulse widths; multiple different voltage levels; or any combination thereof. For example, one or more of the multiple excitation pulses have a different pulse width and / or a different voltage level than one or more other excitation pulses in the multiple excitation pulses. As an example, see [reference]. Figure 6 , Figure 6 The excitation pulse pattern is a curve table based on some non-limiting embodiments or aspects, wherein the width of each pulse in the plurality of excitation pulses of the excitation pulse pattern may be different from the width of each other pulse in the plurality of excitation pulses of the excitation pulse pattern.

[0098] In some non-limiting embodiments or aspects, the plurality of excitation pulses of the excitation pulse pattern further includes a different number of pulses than a previous excitation pulse pattern previously provided to at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, such that the at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof emits at least one previous ultrasonic signal to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof. For example, the flow sensor system 102 and / or the external computing system 104 can provide a first excitation pulse pattern including a first number of excitation pulses to at least one of the first piezoelectric sensor or transducer 154, the second piezoelectric sensor or transducer 156, or any combination thereof, such that the at least one of the first piezoelectric sensor or transducer 154, the second piezoelectric sensor or transducer 156, or any combination thereof emits at least one first ultrasonic signal to the other of the first piezoelectric sensor or transducer 154, the second piezoelectric sensor or transducer 156, or any combination thereof, and the flow sensor system 102 and / or the external computing system 104 can provide a second excitation pulse pattern including a second number of excitation pulses different from the first number of excitation pulses to at least one of the first piezoelectric sensor or transducer 154, the second piezoelectric sensor or transducer 156, or any combination thereof, such that the at least one of the first piezoelectric sensor or transducer 154, the second piezoelectric sensor or transducer 156, or any combination thereof emits at least one second ultrasonic signal to the other of the first piezoelectric sensor or transducer 154, the second piezoelectric sensor or transducer 156, or any combination thereof. In this way, a first time series waveform generated based on receipt of the at least one first ultrasonic signal can be different or unique from a second time series waveform generated based on receipt of the at least one second ultrasonic signal.

[0099] Accordingly, non-limiting embodiments or aspects of the present disclosure can provide a customized excitation pattern that produces an output waveform having a unique signature, which can make it easier for peaks or zero-crossings of the waveform to be identified, as described herein (e.g., the uniqueness of the output waveform can be used to consistently identify peaks (or zero-crossings) of the same cycle, etc.).

[0100] As Figure 3As shown, in step 304, method 300 includes: receiving a time-series waveform from an ultrasonic flow sensor, the time-series waveform comprising multiple amplitudes of at least one ultrasonic signal received at another of a first piezoelectric sensor or transducer, a second piezoelectric sensor or transducer, or any combination thereof, sampled at multiple time points. For example, flow sensor system 102 and / or external computing system 104 may receive a time-series waveform from ultrasonic flow sensor 150, the time-series waveform comprising multiple amplitudes of at least one ultrasonic signal received at another of a first piezoelectric sensor or transducer 154, a second piezoelectric sensor or transducer 156, or any combination thereof, sampled at multiple time points. As an example, refer to Figure 7 , Figure 7 It is a graph of a waveform obtained using an excitation pulse pattern according to some non-limiting embodiments or aspects. It can generate a unique time-series waveform in response to an excitation pulse pattern that includes multiple different pulse widths and / or multiple different voltage levels or any combination thereof. This can make it easier to identify the peaks or zero crossings of the waveform, as described herein (e.g., the uniqueness of the output waveform can be used to consistently identify peaks (or zero crossings) of the same period, etc.).

[0101] like Figure 3 As shown, in step 306, method 300 includes: identifying attributes of a time-series waveform. Identifying attributes may include analyzing the time-series waveform to identify peaks or zero-crossings of the time-series waveform. For example, flow sensor system 102 and / or external computing system 104 may identify peaks or zero-crossings (or other attributes of the time-series waveform) of the time-series waveform. As an example, the attributes of the time-series waveform may include peaks or zero-crossings (or other attributes) of the time-series waveform, and flow sensor system 102 and / or external computing system 104 may identify peaks or zero-crossings of the time-series waveform by analyzing the time-series waveform to identify the attributes of the time-series waveform. In some embodiments, analyzing the time-series waveform may include providing time-series waveform data associated with or representing the time-series waveform to a machine learning model, which is trained to identify peaks, zero-crossings, or other attributes of the time-series waveform to provide a characteristic description of the time-series waveform data (e.g., indications of peaks or zero-crossings of the time-series waveform, etc.). For example, the flow sensor system 102 and / or the external computing system 104 can analyze time series waveforms to identify peaks or zero crossings of the time series waveforms by providing time series waveform data associated with the time series waveforms as input to a machine learning model trained to identify peaks or zero crossings of the time series waveforms, and receiving peaks or zero crossings of the time series waveforms from the machine learning model as output.

[0102] In some non-limiting embodiments or aspects, the flow sensor system 102 and / or the external computing system 104 identifies a peak or zero-crossing of the time series waveform by applying at least one pattern matching algorithm to the time series waveform. Peak detection can include identifying a maximum value within the time series. In some implementations, peak detection can include providing an average of a number of highest readings (e.g., an average of the first 3, 4, 5, or 10 readings, etc.). Such sampling can smooth out any outliers or noise within the waveform. In some implementations, pattern matching can look for a particular series of events (e.g., low-high-low, etc.) to determine which value is characterized as a peak or zero-crossing. For example, pattern matching can look for the fifth peak after the time series waveform crosses a threshold. For example, the at least one pattern matching algorithm can utilize the uniqueness of the time series waveform to identify a peak or zero-crossing of the time series waveform.

[0103] In some non-limiting embodiments or aspects, the flow sensor system 102 and / or the external computing system 104 identifies a peak or zero-crossing of the time series waveform by comparing the time series waveform to at least one reference time series waveform. The use of a reference waveform can be used to indicate a time or level at which a peak or zero-crossing is likely to occur.

[0104] In some non-limiting embodiments or aspects, the at least one reference time series waveform is determined based on a plurality of time series waveforms generated by a plurality of ultrasonic flow sensors in response to an excitation pulse pattern. For example, the flow sensor system 102 and / or the external computing system 104 can obtain a plurality of time series waveforms generated by the plurality of ultrasonic flow sensors 150 in response to an excitation pulse pattern and combine (e.g., average, etc.) the plurality of time series waveforms to generate the at least one reference time series waveform, which can be stored in a memory of the flow sensor system 102 or the ultrasonic flow sensors 150 and / or a database of reference waveforms.

[0105] As Figure 3As shown, in step 308, method 300 includes: determining, based on properties of a time-series waveform, the propagation time of at least one ultrasonic signal from a first piezoelectric sensor or transducer, a second piezoelectric sensor or transducer, or any combination thereof, to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof. For example, flow sensor system 102 and / or external computing system 104 may determine, based on properties of a time-series waveform, the propagation time of at least one ultrasonic signal from a first piezoelectric sensor or transducer, a second piezoelectric sensor or transducer, or any combination thereof, to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof. As an example, the properties of the time series waveform may include the peak value or zero-crossing point (or other properties) of the time series waveform, and the flow sensor system 102 and / or the external computing system 104 may determine the propagation time of at least one ultrasonic signal from the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, based on the peak value or zero-crossing point (or other properties) of the time series waveform.

[0106] like Figure 3 As shown, in step 310, method 300 includes providing an indication associated with the propagation time of at least one ultrasonic signal. For example, flow sensor system 102 and / or external computing system 104 can provide the indication associated with the propagation time of at least one ultrasonic signal. This indication can be a human-perceptible output indicating the propagation time of at least one ultrasonic signal and / or a parameter calculated based on that propagation time, such as the flow rate of fluid through ultrasonic flow sensor 150 and / or the volume of fluid delivered by ultrasonic flow sensor 150 (e.g., provided via a speaker and / or display of a reusable base for a flow sensor system, as disclosed in U.S. Patent Application Publication No. 2021 / 0231471). In some embodiments, providing the indication may include storing the value in a location of a storage device for later retrieval (e.g., in the memory of ultrasonic flow sensor 150, in the memory of the flow sensing system, in a database, etc.), directly transmitting the value to a receiver via at least one wired or wireless communication medium, and sending or storing a reference to the value, etc. The provision at step 310 may additionally or alternatively include encoding, decoding, encryption, decryption, verification, and authentication via hardware components.

[0107] In some non-limiting embodiments or aspects, the flow sensor system 102 and / or the external computing system 104 can provide an indication associated with a time of flight of at least one ultrasound signal, the indication being associated with: patient data associated with a patient; procedure data associated with a procedure associated with the patient; caregiver data associated with a caregiver (e.g., a nurse, a doctor, etc.); or any combination thereof, etc. The patient data associated with the patient can include: a patient identifier (e.g., a unique patient identifier, etc.) associated with the patient; patient demographic data (e.g., a name, an age, a gender, a weight, a height, a date of birth, an address, etc.); a list of medications associated with the patient; a list of medication dosages that have been delivered, are being delivered, and / or are to be delivered to the patient; or any combination thereof, etc. The procedure data can include a procedure identifier (e.g., a unique procedure identifier, etc.) associated with the procedure, one or more medical devices associated with the procedure, a name of the procedure, a status of the procedure (e.g., scheduled for a future date and time, currently being performed, a previous date and time of a previous performance, etc.), a caregiver associated with the procedure, a patient associated with the procedure, or any combination thereof, etc. The caregiver data can include a caregiver identifier (e.g., a unique caregiver identifier, etc.) associated with the caregiver, a name of the caregiver, or any combination thereof, etc.

[0108] While embodiments have been described in detail, it will be apparent to those skilled in the art that various modifications can be made within the spirit and scope of the disclosure and thus the disclosure is not intended to be limited to the particular embodiments described by way of illustration. For example, it will be appreciated that the disclosure contemplates, to the extent possible, one or more features of any embodiment or aspect can be combined with one or more features of any other embodiment or aspect.

[0109] The described aspects include artificial intelligence or other operations whereby a system processes inputs and generates outputs with apparent intelligence. Artificial intelligence can be implemented in whole or in part by a model. The model can be implemented as a machine learning model. The learning can be supervised learning, unsupervised learning, reinforcement learning, or hybrid learning that employs multiple learning techniques to generate the model. The learning can be performed as part of training. Training a model can include taking a set of training data and adjusting characteristics of the model to obtain a desired model output. For example, three characteristics can be associated with a desired item location. In this case, training can include receiving the three characteristics as input to the model and adjusting characteristics of the model so that for each set of three characteristics, the output device state matches a desired device state associated with historical data.

[0110] In some embodiments, the training can be dynamic. For example, the system can use a set of events to update the model. Detectable attributes from the events can be used to adjust the model.

[0111] The model can be an equation, an artificial neural network, a recurrent neural network, a convolutional neural network, a decision tree, or other machine-readable artificial intelligence structure. The nature of the structure that can be adjusted during training can vary depending on the model selected. For example, if a neural network is selected as the model, the nature can include input elements, network layers, node density, node activation thresholds, weights between nodes, input or output value weights, etc. If the model is implemented as an equation (e.g., a regression), the nature can include weights of input parameters, thresholds, or limits used to evaluate output values, or criteria for selecting from a set of equations.

[0112] Once the model is trained, retraining can be included to refine or update the model to reflect additional data or specific operating conditions. The retraining can be based on one or more signals detected by the apparatus described herein or as part of the methods described herein. Upon detection of a specified signal, the system can activate a training process to adjust the model as described.

[0113] Further examples of machine learning and modeling features that can be included in the embodiments described above are described in Qu et al., “A survey of machine learning for big data processing,” EURASIP Journal on Advances in Signal Processing (2016), which is incorporated by reference herein in its entirety.

[0114] As used herein, the term “determining” encompasses a wide variety of actions. For example, “determining” can include calculating, computing, processing, deriving, generating, obtaining, looking up (e.g., looking up in a table, a database or another data structure), ascertaining and the like in action with or without one or more user inputs. Also, “determining” can include receiving (e.g., receiving information), accessing (e.g., accessing data in a memory) and the like in action with or without one or more user inputs. “Determining” can also include resolving, selecting, choosing, establishing and the like in action with or without one or more user inputs.

[0115] As used herein, the term "providing" encompasses a wide variety of actions. For example, "providing" can include storing a value in a location of a storage device for subsequent retrieval, transmitting the value directly to a recipient via at least one wired or wireless communication medium, and transmitting or storing a reference to the value, among others. "Providing" can also include encoding, decoding, encrypting, decrypting, authenticating, verifying, and inserting, among others, by a hardware element.

Claims

1. A system comprising: An ultrasonic flow sensor, comprising a flow tube, a first piezoelectric sensor or transducer disposed upstream of the flow tube, and a second piezoelectric sensor or transducer disposed downstream of the flow tube; and At least one processor, said at least one processor being configured to: An excitation pulse pattern comprising a plurality of excitation pulses is provided to at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, such that at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof emits at least one ultrasonic signal to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, wherein the plurality of excitation pulses of the excitation pulse pattern comprises at least one of the following: a plurality of different pulse widths; a plurality of different voltage levels; or any combination thereof; Receive a time-series waveform from the ultrasonic flow sensor, the time-series waveform comprising multiple amplitudes of the at least one ultrasonic signal received at multiple time points from the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof; Identify the properties of the time series waveform; and Based on the properties of the time-series waveform, determine the propagation time of the at least one ultrasonic signal from the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof.

2. The system according to claim 1, wherein, The at least one processor is configured to identify the properties of the time-series waveform in the following manner: At least one pattern matching algorithm is applied to the time series waveform.

3. The system according to claim 1, wherein, The at least one processor is configured to identify the properties of the time-series waveform in the following manner: The time-series waveform is compared with at least one reference time-series waveform, wherein the at least one reference time-series waveform is determined based on multiple time-series waveforms generated by multiple ultrasonic flow sensors in response to the excitation pulse pattern.

4. The system according to claim 1, wherein, The plurality of excitation pulses of the excitation pulse pattern further include a different number of pulses than the previous excitation pulse pattern, which was previously provided to at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, such that at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, transmits at least one previous ultrasonic signal to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof.

5. The system according to claim 1, wherein, The at least one processor is further configured to: Provides an indication associated with the propagation time of the at least one ultrasonic signal.

6. The system according to claim 1, wherein, The attribute of the time-series waveform includes a peak or a zero-crossing point of the time-series waveform, wherein the at least one processor is configured to: identify the attribute of the time-series waveform by analyzing the time-series waveform to identify the peak or the zero-crossing point of the time-series waveform, and wherein the at least one processor is configured to: determine the propagation time of the at least one ultrasonic signal from the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, based on the peak or the zero-crossing point of the time-series waveform.

7. The system according to claim 6, wherein, The at least one processor is configured to analyze the time series waveform to identify the peak or the zero-crossing point of the time series waveform by providing time series waveform data associated with the time series waveform as input to a machine learning model trained to identify the peak or the zero-crossing point of the time series waveform, and receiving the peak or the zero-crossing point of the time series waveform from the machine learning model as output.

8. An ultrasonic flow sensor, comprising: Flow tube; A first piezoelectric sensor or transducer is disposed upstream of the flow tube. A second piezoelectric sensor or transducer is disposed downstream of the flow tube; and At least one processor, said at least one processor being configured to: An excitation pulse pattern comprising a plurality of excitation pulses is provided to at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, such that at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof emits at least one ultrasonic signal to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, wherein the plurality of excitation pulses of the excitation pulse pattern comprises at least one of the following: a plurality of different pulse widths; a plurality of different voltage levels; or any combination thereof; Receive a time-series waveform from the ultrasonic flow sensor, the time-series waveform comprising multiple amplitudes of the at least one ultrasonic signal received at multiple time points from the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof; Identify the properties of the time series waveform; Based on the properties of the time-series waveform, determine the propagation time of the at least one ultrasonic signal from the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof.

9. The ultrasonic flow sensor according to claim 8, wherein, The at least one processor is configured to identify the properties of the time-series waveform in the following manner: At least one pattern matching algorithm is applied to the time series waveform.

10. The ultrasonic flow sensor according to claim 8, wherein, The at least one processor is configured to identify the properties of the time-series waveform in the following manner: The time-series waveform is compared with at least one reference time-series waveform, wherein the at least one reference time-series waveform is determined based on multiple time-series waveforms generated by multiple ultrasonic flow sensors in response to the excitation pulse pattern.

11. The ultrasonic flow sensor according to claim 8, wherein, The plurality of excitation pulses of the excitation pulse pattern further include a different number of pulses than the previous excitation pulse pattern, which was previously provided to at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, such that at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, transmits at least one previous ultrasonic signal to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof.

12. The ultrasonic flow sensor according to claim 8, wherein, The at least one processor is further configured to: Provides an indication associated with the propagation time of the at least one ultrasonic signal.

13. The ultrasonic flow sensor according to claim 8, wherein, The attribute of the time-series waveform includes a peak or a zero-crossing point of the time-series waveform, wherein the at least one processor is configured to: identify the attribute of the time-series waveform by analyzing the time-series waveform to identify the peak or the zero-crossing point of the time-series waveform, and wherein the at least one processor is configured to: determine the propagation time of the at least one ultrasonic signal from the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, based on the peak or the zero-crossing point of the time-series waveform.

14. The ultrasonic flow sensor according to claim 13, wherein, The at least one processor is configured to analyze the time series waveform to identify the peak or the zero-crossing point of the time series waveform by providing time series waveform data associated with the time series waveform as input to a machine learning model trained to identify the peak or the zero-crossing point of the time series waveform, and receiving the peak or the zero-crossing point of the time series waveform from the machine learning model as output.

15. A method for sound velocity tracking in an ultrasonic flow sensor, the ultrasonic flow sensor comprising a flow tube, a first piezoelectric sensor or transducer disposed upstream of the flow tube, and a second piezoelectric sensor or transducer disposed downstream of the flow tube, the method comprising: At least one processor is used to provide at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof with an excitation pulse pattern comprising a plurality of excitation pulses, such that at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof emits at least one ultrasonic signal to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, wherein the plurality of excitation pulses of the excitation pulse pattern comprises at least one of the following: a plurality of different pulse widths; a plurality of different voltage levels; or any combination thereof; The processor receives a time-series waveform from the ultrasonic flow sensor, the time-series waveform comprising multiple amplitudes of the at least one ultrasonic signal received at a plurality of time points from the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof. The at least one processor is used to identify the properties of the time series waveform; and Using the at least one processor, based on the properties of the time-series waveform, determine the propagation time of the at least one ultrasonic signal from the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof.

16. The method according to claim 15, wherein, Identifying the properties of the time series waveform using the at least one processor includes: The at least one processor is used to apply at least one pattern matching algorithm to the time series waveform.

17. The method according to claim 15, wherein, Identifying the properties of the time series waveform using the at least one processor includes: The at least one processor is used to compare the time-series waveform with at least one reference time-series waveform, wherein the at least one reference time-series waveform is determined based on a plurality of time-series waveforms generated by a plurality of ultrasonic flow sensors in response to the excitation pulse pattern.

18. The method according to claim 15, wherein, The plurality of excitation pulses of the excitation pulse pattern further include a different number of pulses than the previous excitation pulse pattern, which was previously provided to at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, such that at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, transmits at least one previous ultrasonic signal to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof.

19. The method of claim 15, further comprising: The at least one processor is used to provide an indication associated with the propagation time of the at least one ultrasonic signal.

20. The method of claim 15, wherein, The attribute of the time-series waveform includes the peak value or zero-crossing point of the time-series waveform, wherein identifying the attribute of the time-series waveform using the at least one processor includes: analyzing the time-series waveform using the at least one processor to identify the peak value or zero-crossing point of the time-series waveform, wherein the at least one processor determines the propagation time of the at least one ultrasonic signal from the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, based on the peak value or zero-crossing point of the time-series waveform, and wherein analyzing the time-series waveform using the at least one processor to identify the peak value or zero-crossing point of the time-series waveform includes: providing time-series waveform data associated with the time-series waveform as input to a machine learning model trained to identify the peak value or zero-crossing point of the time-series waveform, and receiving the peak value or zero-crossing point of the time-series waveform from the machine learning model as output.

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