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

Abstract

SYSTEMS, METHODS, AND COMPUTER PROGRAM PRODUCTS FOR SOUND SPEED TRACKING IN ULTRASONIC FLOW SENSORS SOLUTION: An example system includes an ultrasonic flow sensor / processor. The ultrasonic flow sensor may include a flow tube, a first piezoelectric sensor / transducer, and a second piezoelectric sensor / transducer. The processor may be configured to provide an excitation pulse pattern comprising excitation pulses to the ultrasonic flow sensor to cause the ultrasonic flow sensor to transmit and receive ultrasonic signals between the first and second piezoelectric sensors / transducers comprising excitation pulses comprising different pulse widths / voltage levels, receive, from the ultrasonic flow sensor, a time-series waveform comprising amplitudes of the ultrasonic signals sampled at a plurality of points in time, identify attributes of the time-series waveform, and determine transit times of the ultrasonic signals based on the attributes.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to U.S. patent application Ser. No. 18 / 777,720, filed Jul. 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 technology]

[0002] Technical Field The present disclosure relates generally to ultrasonic flow sensors and, in some non-limiting embodiments or aspects, to systems, methods, and computer program products for speed of sound tracking in ultrasonic flow sensors.

[0003] Technical considerations Existing ultrasonic flow sensors may use a periodic pulse pattern to excite the crystal of a transmitting piezoelectric sensor or transducer to transmit an ultrasonic signal to a receiving piezoelectric sensor or transducer, which outputs a time-series waveform that can be analyzed to calculate the transit times of the ultrasonic signal. For example, FIG. 4 is a graph of an exemplary periodic excitation pulse pattern for exciting the piezoelectric sensor or transducer to transmit an ultrasonic signal, and FIG. 5 is a graph of an exemplary time-series waveform obtained using the periodic excitation pulse pattern. The time-series waveform can be analyzed to calculate the transit time of the ultrasonic signal between the transmitting and receiving piezoelectric sensors or transducers.

[0004] The time-series waveform can be analyzed to calculate the transit time of the ultrasonic signal between the transmitting and receiving piezoelectric sensors or transducers. For example, the transit time can be inversely proportional to the speed of sound of the ultrasonic signal in the fluid flow path of the ultrasonic flow sensor. The transit time can be calculated by identifying one of the waveform's peaks (or one of the locations where the waveform crosses the zero signal line in the graph, called a zero crossing). However, as shown in Figures 4 and 5, using a periodic pulse pattern can result in a gradually changing output waveform that includes several cycles, making it difficult to identify the peak (or zero crossing) of the same cycle. For example, if the output waveform has a low amplitude or if the waveform changes slightly due to external factors, false peaks can be identified or selected. Identifying or selecting false peaks (or false zero crossings) can result in significant flow measurement errors and / or limitations on the types of fluids that can be used with the ultrasonic flow sensor. Summary of the Invention

[0005] Thus, an improved system, method, and computer program product for speed of sound tracking in an ultrasonic flow sensor is provided.

[0006] According to a non-limiting embodiment or aspect, an ultrasonic flow sensor includes a flow tube, a first piezoelectric sensor or transducer disposed at an upstream position of the flow tube, and a second piezoelectric sensor or transducer disposed at a downstream position of the flow tube; and providing 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 transmit 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 a plurality of different pulse widths, ... and at least one processor configured to receive from the ultrasonic flow sensor a time series waveform including multiple amplitudes of the at least one ultrasonic signal received at another of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof sampled at multiple time points, the at least one voltage level being a voltage level associated with the at least one ultrasonic signal received at another of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, based on the attributes of the time series waveform; and

[0007] In certain non-limiting embodiments or aspects, the at least one processor is configured to identify attributes of the time series waveform by applying at least one pattern matching algorithm to the time series waveform.

[0008] In certain non-limiting embodiments or aspects, the at least one processor is configured to identify attributes of the time series waveform by comparing the time series waveform to at least one reference time series waveform, the at least one reference time series waveform being determined based on a plurality of time series waveforms generated by the plurality of ultrasonic flow sensors in response to the excitation pulse pattern.

[0009] In certain non-limiting embodiments or aspects, the plurality of excitation pulses of the excitation pulse pattern further include 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, causing at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof to transmit at least one previous ultrasound signal to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof.

[0010] In certain non-limiting embodiments or aspects, the at least one processor is further configured to provide an indication associated with a transit time of the at least one ultrasound signal.

[0011] In certain non-limiting embodiments or aspects, the attributes of the time series waveform include peaks or zero crossings of the time series waveform, the at least one processor is configured to identify the attributes of the time series waveform by analyzing the time series waveform to identify the peaks or zero crossings of the time series waveform, and the at least one processor is configured to determine a transit time of the at least one ultrasonic signal from a first piezoelectric sensor or transducer, a second piezoelectric sensor or transducer, or any combination thereof, to another of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof based on the peaks or zero crossings of the time series waveform.

[0012] In certain non-limiting embodiments or aspects, the at least one processor is configured to analyze the time series waveform to identify peaks or zero crossings 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 peaks or zero crossings of the time series waveform, and receiving the peaks or zero crossings of the time series waveform as output from the machine learning model.

[0013] According to a non-limiting embodiment or aspect, a flow tube, a first piezoelectric sensor or transducer disposed at an upstream position of the flow tube, and a second piezoelectric sensor or transducer disposed at a downstream position of the flow tube, and providing 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 transmit 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 a plurality of different pulse widths, a plurality of different and at least one processor configured to receive from the ultrasonic flow sensor a time series waveform including multiple amplitudes of the at least one ultrasonic signal received at another of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof sampled at multiple time points, the at least one amplitude being a voltage level, or any combination thereof, identify attributes of the time series waveform, and determine a transit 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 attributes of the time series waveform.

[0014] In certain non-limiting embodiments or aspects, the at least one processor is configured to identify attributes of the time series waveform by applying at least one pattern matching algorithm to the time series waveform.

[0015] In certain non-limiting embodiments or aspects, the at least one processor is configured to identify attributes of the time series waveform by comparing the time series waveform to at least one reference time series waveform, the at least one reference time series waveform being determined based on a plurality of time series waveforms generated by the plurality of ultrasonic flow sensors in response to the excitation pulse pattern.

[0016] In certain non-limiting embodiments or aspects, the plurality of excitation pulses of the excitation pulse pattern further include 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, causing at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof to transmit at least one previous ultrasound signal to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof.

[0017] In certain non-limiting embodiments or aspects, the at least one processor is further configured to provide an indication associated with a transit time of the at least one ultrasound signal.

[0018] In certain non-limiting embodiments or aspects, the attributes of the time series waveform include peaks or zero crossings of the time series waveform, the at least one processor is configured to identify the attributes of the time series waveform by analyzing the time series waveform to identify the peaks or zero crossings of the time series waveform, and the at least one processor is configured to determine a transit time of the at least one ultrasonic signal from a first piezoelectric sensor or transducer, a second piezoelectric sensor or transducer, or any combination thereof, to another of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof based on the peaks or zero crossings of the time series waveform.

[0019] In certain non-limiting embodiments or aspects, the at least one processor is configured to analyze the time series waveform to identify peaks or zero crossings of the time series waveform by providing time series waveform data associated with the time series waveform as input to, and receiving as output from, a machine learning model trained to identify peaks or zero crossings of the time series waveform.

[0020] According to a non-limiting embodiment or aspect, a method is provided for speed of sound tracking in an ultrasonic flow sensor including a flow tube, a first piezoelectric sensor or transducer disposed at an upstream position of the flow tube, and a second piezoelectric sensor or transducer disposed at a downstream position of the flow tube, the method including: using at least one processor, providing 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; causing the at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof to transmit at least one ultrasonic signal to another of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof; and and at least one processor configured to receive from the ultrasonic flow sensor a time series waveform including a plurality of 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 a plurality of time points, identify attributes of the time series waveform, and determine a transit 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 attributes of the time series waveform.

[0021] In certain non-limiting embodiments or aspects, identifying attributes of the time-series waveforms using at least one processor includes applying, using at least one processor, at least one pattern matching algorithm to the time-series waveforms.

[0022] In certain non-limiting embodiments or aspects, identifying attributes of the time series waveform using the at least one processor includes comparing the time series waveform to at least one reference time series waveform using the at least one processor, the at least one reference time series waveform being determined based on a plurality of time series waveforms generated by a plurality of ultrasonic flow sensors in response to the excitation pulse pattern.

[0023] In certain non-limiting embodiments or aspects, the plurality of excitation pulses of the excitation pulse pattern further include 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, causing at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof to transmit at least one previous ultrasound signal to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof.

[0024] In certain non-limiting embodiments or aspects, the method further includes providing, with the at least one processor, an indication associated with a transit time of the at least one ultrasound signal.

[0025] In certain non-limiting embodiments or aspects, the attributes of the time series waveform include peaks or zero crossings of the time series waveform, and identifying the attributes of the time series waveform using the at least one processor includes analyzing the time series waveform to identify peaks or zero crossings of the time series waveform using the at least one processor, wherein the at least one processor determines a transit time of the at least one ultrasonic signal from a first piezoelectric sensor or transducer, a second piezoelectric sensor or transducer, or any combination thereof, to another of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof based on the peaks or zero crossings of the time series waveform, and analyzing the time series waveform to identify peaks or zero crossings of the time series waveform using the at least one processor includes providing the time series waveform data associated with the time series waveform as input to a machine learning model trained to identify peaks or zero crossings of the time series waveform, and receiving the peaks or zero crossings of the time series waveform as output from the machine learning model.

[0026] According to a non-limiting embodiment or aspect, a computer program product is provided that includes a non-transitory computer-readable medium that includes program instructions for speed of sound tracking in an ultrasonic flow sensor that includes a flow tube, a first piezoelectric sensor or transducer disposed at an upstream position of the flow tube, and a second piezoelectric sensor or transducer disposed at a downstream position of the flow tube, which, when executed by at least one processor, causes the at least one processor to provide an excitation pulse pattern to at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, the 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. 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 from the ultrasonic flow sensor a time series waveform comprising a plurality of amplitudes 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 sampled at a plurality of time points; identify attributes of the time series waveform; and determine a transit 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 attributes of the time series waveform.

[0027] In certain non-limiting embodiments or aspects, the program instructions, when executed by the at least one processor, cause the at least one processor to identify attributes of the time-series waveform by applying at least one pattern matching algorithm to the time-series waveform.

[0028] In certain non-limiting embodiments or aspects, the program instructions, when executed by at least one processor, cause the at least one processor to identify attributes of the time series waveform by comparing the time series waveform to at least one reference time series waveform.

[0029] In certain 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.

[0030] In certain non-limiting embodiments or aspects, the plurality of excitation pulses of the excitation pulse pattern further include 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, causing at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof to transmit at least one previous ultrasound signal to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof.

[0031] In certain 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 transit time of the at least one ultrasound signal.

[0032] In certain non-limiting embodiments or aspects, the attributes of the time series waveform include peaks or zero crossings of the time series waveform, and the program instructions, when executed by the at least one processor, cause the at least one processor to identify the attributes of the time series waveform by analyzing the time series waveform to identify the peaks or zero crossings of the time series waveform, and the program instructions, when executed by the at least one processor, cause the at least one processor to determine a transit 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 another of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof based on the peaks or zero crossings of the time series waveform.

[0033] In certain non-limiting embodiments or aspects, the program instructions, when executed by at least one processor, cause the at least one processor to analyze the time series waveform to identify peaks or zero crossings 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 peaks or zero crossings of the time series waveform, and receiving the peaks or zero crossings of the time series waveform as output from the machine learning model.

[0034] Further non-limiting embodiments or aspects are set forth in the following numbered clauses:

[0035] Clause 1. An ultrasonic flow sensor including a flow tube, a first piezoelectric sensor or transducer disposed at an upstream position of the flow tube, and a second piezoelectric sensor or transducer disposed at a downstream position of the flow tube, and providing 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 at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof to transmit 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 a plurality of different pulse widths, a plurality of different voltages, and a plurality of different excitation pulses. and at least one processor configured to: receive from the ultrasonic flow sensor a time series waveform including multiple amplitudes of the at least one ultrasonic signal received at another of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof sampled at multiple time points; identify attributes of the time series waveform; and determine a transit 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 attributes of the time series waveform.

[0036] Clause 2. The system of clause 1, wherein the at least one processor is configured to identify attributes of the time-series waveform by applying at least one pattern matching algorithm to the time-series waveform.

[0037] Clause 3. The system of clause 1 or clause 2, wherein at least one processor is configured to identify attributes of the time series waveform by comparing the time series waveform to at least one reference time series waveform, the at least one reference time series waveform being determined based on a plurality of time series waveforms generated by a plurality of ultrasonic flow sensors in response to the excitation pulse pattern.

[0038] Clause 4. The system of any of clauses 1 to 3, wherein 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, causing at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof to transmit at least one previous ultrasound signal to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof.

[0039] Clause 5. The system of any of clauses 1 to 4, wherein the at least one processor is further configured to provide an indication associated with the transit time of the at least one ultrasound signal.

[0040] Clause 6. The system of any of clauses 1 to 5, wherein the attributes of the time series waveform include peaks or zero crossings of the time series waveform, the at least one processor is configured to identify the attributes of the time series waveform by analyzing the time series waveform to identify the peaks or zero crossings of the time series waveform, and the at least one processor is configured to determine a transit time of the at least one ultrasonic signal from a first piezoelectric sensor or transducer, a second piezoelectric sensor or transducer, or any combination thereof, to another of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, based on the peaks or zero crossings of the time series waveform.

[0041] Clause 7. The system of any of clauses 1 to 6, wherein at least one processor is configured to analyze the time series waveform to identify peaks or zero crossings 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 peaks or zero crossings of the time series waveform, and receiving the peaks or zero crossings of the time series waveform as output from the machine learning model.

[0042] Clause 8. A method for detecting an ultrasonic wave from a flow tube, a first piezoelectric sensor or transducer disposed at an upstream position of the flow tube, and a second piezoelectric sensor or transducer disposed at a downstream position of the flow tube, wherein the method provides 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 transmit 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 a plurality of different pulse widths, a plurality of different voltage levels, or any combination thereof, and at least one processor configured to receive from the ultrasonic flow sensor a time series waveform including multiple amplitudes of at least one ultrasonic signal received at another of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof sampled at multiple time points, identify attributes of the time series waveform, and determine a transit 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 attributes of the time series waveform.

[0043] Clause 9. The ultrasonic flow sensor of clause 9, wherein the at least one processor is configured to identify attributes of the time series waveform by applying at least one pattern matching algorithm to the time series waveform.

[0044] Clause 10. An ultrasonic flow sensor as described in Clause 8 or Clause 9, wherein at least one processor is configured to identify attributes of the time series waveform by comparing the time series waveform to at least one reference time series waveform, the at least one reference time series waveform being determined based on a plurality of time series waveforms generated by the plurality of ultrasonic flow sensors in response to the excitation pulse pattern.

[0045] Clause 11. The ultrasonic flow sensor of any of clauses 8 to 10, wherein 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, causing at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof to transmit 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.

[0046] Clause 12. The ultrasonic flow sensor of any of clauses 8 to 11, wherein the at least one processor is further configured to provide an indication associated with the transit time of the at least one ultrasonic signal.

[0047] Clause 13. An ultrasonic flow sensor as described in any of clauses 8 to 12, wherein the attributes of the time series waveform include peaks or zero crossings of the time series waveform, the at least one processor is configured to identify the attributes of the time series waveform by analyzing the time series waveform to identify the peaks or zero crossings of the time series waveform, and the at least one processor is configured to determine a transit 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 another of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, based on the peaks or zero crossings of the time series waveform.

[0048] Clause 14. An ultrasonic flow sensor as described in any of clauses 8 to 13, wherein at least one processor is configured to analyze the time series waveform to identify peaks or zero crossings of the time series waveform by providing time series waveform data associated with the time series waveform as input to and receiving as output from a machine learning model trained to identify peaks or zero crossings of the time series waveform.

[0049] Clause 15. A method for speed of sound tracking in an ultrasonic flow sensor including a flow tube, a first piezoelectric sensor or transducer disposed at an upstream position of the flow tube, and a second piezoelectric sensor or transducer disposed at a downstream position of the flow tube, the method including: using at least one processor, providing 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 transmit 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 a plurality of different pulses. receiving, with at least one processor, a time series waveform from the ultrasonic flow sensor including a plurality of amplitudes of the at least one ultrasonic signal received at another of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof sampled at a plurality of time points; identifying, with the at least one processor, an attribute of the time series waveform; and determining, with the at least one processor, a transit 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 attribute of the time series waveform.

[0050] Clause 16. The method of clause 15, wherein identifying attributes of the time series waveform using at least one processor includes applying, using at least one processor, at least one pattern matching algorithm to the time series waveform.

[0051] Clause 17. The method of clause 15 or clause 16, wherein identifying attributes of the time series waveform using at least one processor includes comparing the time series waveform with at least one reference time series waveform using at least one processor, the at least one reference time series waveform being determined based on a plurality of time series waveforms generated by a plurality of ultrasonic flow sensors in response to an excitation pulse pattern.

[0052] Clause 18. The method of any of clauses 15 to 17, wherein 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, causing at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof to transmit at least one previous ultrasound signal to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof.

[0053] Clause 19. The method of any of clauses 15 to 18, further comprising using at least one processor to provide an indication associated with the transit time of the at least one ultrasound signal.

[0054] Clause 20. The method of any of clauses 15 to 18, wherein the attributes of the time series waveform include peaks or zero crossings of the time series waveform, and wherein identifying the attributes of the time series waveform using at least one processor includes analyzing the time series waveform to identify peaks or zero crossings of the time series waveform using at least one processor, wherein the at least one processor determines a transit time of the at least one ultrasonic signal from a first piezoelectric sensor or transducer, a second piezoelectric sensor or transducer, or any combination thereof, to another of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof based on the peaks or zero crossings of the time series waveform, and wherein analyzing the time series waveform to identify peaks or zero crossings of the time series waveform using the at least one processor includes providing the time series waveform data associated with the time series waveform as input to a machine learning model trained to identify peaks or zero crossings of the time series waveform, and receiving the peaks or zero crossings of the time series waveform as output from the machine learning model.

[0055] Clause 21. A computer program product including a non-transitory computer-readable medium including program instructions for speed of sound tracking in an ultrasonic flow sensor including a flow tube, a first piezoelectric sensor or transducer disposed at an upstream position of the flow tube, and a second piezoelectric sensor or transducer disposed at a downstream position of the flow tube, which, when executed by at least one processor, causes 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 generate a signal from at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, and to generate a signal from at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, to generate a signal from at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, and to generate a signal from at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, to generate a signal from at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof. transmit at least one ultrasonic signal to another of the first piezoelectric sensor or transducer, the plurality of excitation pulses of the excitation pulse pattern comprising at least one of a plurality of different pulse widths, a plurality of different voltage levels, or any combination thereof; receive from the ultrasonic flow sensor a time series waveform comprising a plurality of amplitudes of the at least one ultrasonic signal received at another of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof sampled at a plurality of time points; identify attributes of the time series waveform; and determine a transit 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 attributes of the time series waveform.

[0056] Clause 22. The computer program product of clause 21, wherein the program instructions, when executed by at least one processor, cause the at least one processor to identify attributes of a time series waveform by applying at least one pattern matching algorithm to the time series waveform.

[0057] Clause 23. The computer program product of clause 21 or clause 22, wherein the program instructions, when executed by at least one processor, cause the at least one processor to identify attributes of the time series waveform by comparing the time series waveform to at least one reference time series waveform.

[0058] Clause 24. The computer program product of any 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 an excitation pulse pattern.

[0059] Clause 25. The computer program product of any of clauses 21 to 24, wherein 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, causing at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof to transmit at least one previous ultrasound signal to the other of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof.

[0060] Clause 26. A computer program product according to any of clauses 21 to 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 the transit time of the at least one ultrasound signal.

[0061] Clause 27. The computer program product of any of clauses 21 to 26. The attributes of the time series waveform include peaks or zero crossings of the time series waveform, the program instructions, when executed by at least one processor, cause the at least one processor to identify the attributes of the time series waveform by analyzing the time series waveform to identify the peaks or zero crossings of the time series waveform, and the program instructions, when executed by the at least one processor, cause the at least one processor to determine a transit 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 another of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, based on the peaks or zero crossings of the time series waveform.

[0062] Clause 28. The computer program product of any of clauses 21 to 27, wherein the program instructions, when executed by at least one processor, cause the at least one processor to analyze the time series waveform to identify peaks or zero crossings 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 peaks or zero crossings of the time series waveform, and receiving the peaks or zero crossings of the time series waveform as output from the machine learning model.

[0063] These and other features and characteristics of the present disclosure, as well as the method of operation and function of the associated elements of structure, combination of parts, and economies of manufacture, will become more apparent from a consideration of the following description and appended claims, with reference to the accompanying drawings, all of which form a part hereof, and in which like reference numerals indicate corresponding parts in the various views. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the subject matter of the present disclosure. [Brief explanation of the drawings]

[0064] Additional advantages and details are explained in more detail below with reference to non-limiting exemplary embodiments shown in the accompanying schematic drawings.

[0065] [Figure 1A] FIG. 1 is a schematic diagram of a system for speed of sound tracking in an ultrasonic flow sensor, in accordance with some non-limiting embodiments or aspects. [Figure 1B] 1B is a perspective view of exemplary components of a flow sensor system of the system for speed of sound tracking in an ultrasonic flow sensor of FIG. 1A, according to some non-limiting embodiments or aspects. [Figure 1C] 1B is a cross-sectional view of exemplary components of an ultrasonic flow sensor of the flow sensor system of the system for speed of sound tracking in the ultrasonic flow sensor of FIG. 1A, in accordance with some non-limiting embodiments or aspects. [Figure 2] FIG. 1B is a schematic diagram of exemplary components of one or more devices or systems of FIG. 1A, according to some non-limiting embodiments or aspects. [Figure 3] 1 is a flow diagram of a method for speed of sound tracking in an ultrasonic flow sensor, in accordance with some non-limiting embodiments or aspects. [Figure 4] 1 is a graph of an exemplary periodic excitation pulse pattern for exciting a piezoelectric sensor or transducer to transmit an ultrasonic signal. [Figure 5] 1 is a graph of an exemplary time series waveform obtained using a periodic excitation pulse pattern. [Figure 6] 1 is a graph of an excitation pulse pattern according to some non-limiting embodiments or aspects. [Figure 7] 1 is a graph of a waveform obtained using an excitation pulse pattern, in accordance with some non-limiting embodiments or aspects. DETAILED DESCRIPTION OF THE INVENTION

[0066] For purposes of the following description, the terms "end," "upper," "lower," "right," "left," "vertical," "horizontal," "top," "bottom," "lateral," "longitudinal," and derivatives thereof, refer to the embodiments as they are oriented in the drawings. However, it should be understood that the present disclosure may contemplate various alternative modifications and step sequences unless expressly specified to the contrary. It should also be understood that the specific devices and processes illustrated in the accompanying drawings and described in the following specification are merely exemplary, non-limiting embodiments or aspects of the presently disclosed subject matter. Accordingly, specific dimensions and other physical characteristics related to the embodiments or aspects disclosed herein are not to be considered limiting.

[0067] Some non-limiting embodiments or aspects are described herein in relation to thresholds. As used herein, meeting a threshold may mean that a value is greater than the threshold, more than the threshold, higher than the threshold, greater than or equal to the threshold, less than the threshold, less than the threshold, lower than the threshold, less than or equal to the threshold, equal to the threshold, etc.

[0068] As used herein, aspects, components, elements, structures, acts, steps, functions, instructions, and / or the like should not be construed as critical or essential unless expressly described as such. Also, 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." Furthermore, as used herein, the term "set" is intended to include one or more items (e.g., related items, unrelated items, combinations of related and unrelated items, etc.) and may be used interchangeably with "one or more" or "at least one." Where only one item is intended, the term "one" or similar language is used. Also, as used herein, the terms "has," "have," "having," etc. are intended to be open-ended terms. Furthermore, the phrase "based on" is intended to mean "based at least in part on," unless expressly specified otherwise. Additionally, references to an action being "based on" a condition may refer to an action being "responsive" to the condition. For example, the phrases "based on" and "responsive to" may, in some non-limiting embodiments or aspects, refer to a condition (e.g., a particular operation of an electronic device, such as a computing device, processor, etc.) for automatically triggering an action.

[0069] As used herein, the term “communication” may refer to the reception, receipt, transmission, transfer, provision, etc. of data (e.g., information, signals, messages, instructions, commands, and / or the like). For one unit (e.g., a device, a system, a component of a device or system, combinations thereof, and / or the like) to communicate with another unit means that the one unit can directly or indirectly receive information from and / or transmit information to the other unit. This may refer to direct or indirect connections (e.g., direct communication connections, indirect communication connections, etc.) that are wired and / or wireless in nature. Additionally, two units may communicate with each other even though the information being transmitted may be modified, processed, relayed, and / or routed between the first and second units. For example, a first unit may communicate with a second unit even if the first unit passively receives information and does not actively transmit information to the second unit. As another example, a first unit may communicate with a second unit if at least one intermediary unit processes information received from the first unit and communicates the processed information to the second unit. In some non-limiting embodiments or aspects, a message may refer to a network packet (e.g., a data packet and / or the like) containing data. It will be understood that numerous other arrangements are possible.

[0070] As used herein, the term "computing device" may refer to one or more electronic devices configured to process data. A computing device, in some examples, may include components necessary to receive, process, and output data, such as a processor, a display, a memory, an input device, a network interface, and / or the like. A computing device may be a mobile device. By way of example, a mobile device may include a mobile phone (e.g., a smartphone or a standard mobile phone), a portable computer, a wearable device (e.g., a watch, eyeglasses, lenses, clothing, etc.), a personal digital assistant (PDA), and / or other similar devices. A computing device may also be a desktop computer or other form of non-mobile computer.

[0071] As used herein, the term "server" may mean or include one or more computing devices that operate or facilitate communications and processing for multiple parties in a network environment, such as the Internet, although it will be understood that communications may be facilitated through one or more public or private network environments, and various other configurations are possible. Furthermore, multiple computing devices (e.g., servers, point-of-sale (POS) devices, mobile devices, etc.) communicating directly or indirectly within a network environment may constitute a "system."

[0072] As used herein, the term "system" may refer to one or more computing devices, or combinations of computing devices (e.g., processors, servers, client devices, software applications, such components, and / or the like). References to "device," "server," "processor," and / or the like as used herein may refer to a previously enumerated device, server, or processor that is enumerated as performing a previous step or function, a different device, server, or processor, and / or combination of devices, servers, and / or processors. For example, as used herein and in the claims, a first device, first server, or first processor that is described as performing a first step or first function may refer to the same or a different device, server, or processor that is described as performing a second step or second function.

[0073] 1A, a schematic diagram of a system for speed of sound tracking in an ultrasonic flow sensor is shown, according to some non-limiting embodiments or aspects. As shown in FIG. 1A, system 100 may include a flow sensor system 102 and / or an external computing system 104. The systems and / or devices of system 100 may be interconnected via wired connections, wireless connections, or a combination of wired and wireless connections.

[0074] The flow sensor system 102 may include one or more devices capable of receiving information and / or data from and / or communicating information and / or data to the external computing device 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 of U.S. Patent Application Publication No. 2021 / 0231471 or U.S. Patent No. 10,072,959, the contents of each of which are incorporated herein by reference in their entirety.

[0075] Also, referring to FIG. 1B, FIG. 1B is a perspective view of exemplary components of a flow sensor system of the system for sound speed tracking in an ultrasonic flow sensor of FIG. 1A, according to some non-limiting embodiments or aspects. As shown in FIG. 1B, the flow sensor system 102 may include an ultrasonic flow sensor 150 and / or a base 160. For example, the ultrasonic flow sensor 150 may be configured to be removably, physically, and / or electrically connected to the base 160. The syringe 170 may be configured to physically connect to the flow sensor 160 (e.g., via a fluid injection port, etc.). The syringe 170 may include a tag or label 172, which may include an NFC tag (e.g., an RFID tag, etc.), a barcode, a QR code, AprilTag, etc., embedded in the tag or label 104. The base 160 may include at least one sensor 162 configured to read and / or decode drug information from the tag or label 172 on the syringe 170. The drug information may 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 the drug contained in syringe 170, etc.). For example, at least one sensor 162 of base 160 may include one or more computing devices, chips, contactless transmitters, contactless transceivers, NFC transmitter / receivers, RFID transmitter / receivers, contact-based transmitter / receivers, optical sensors or scanners, barcode readers, etc. configured to read and / or decode information stored or encapsulated in tag or label 172.

[0076] 1C, which is a cross-sectional view of components of an ultrasonic flow sensor of the flow sensor system for sound speed tracking in an ultrasonic flow sensor of FIG. 1A, according to some non-limiting embodiments or aspects. As shown in FIG. 1C, the ultrasonic flow sensor 150 may include a flow tube 152 defining a fluid flow path of the ultrasonic flow sensor 150, a first piezoelectric sensor or transducer 154 disposed at an upstream position of the flow tube 152, and / or a second piezoelectric sensor or transducer 156 disposed at a downstream position of the flow tube 152.

[0077] The ultrasonic flow sensor 150 may be configured to generate a time series corresponding to (e.g., corresponding to, representative of, quantifying, measuring, etc.) at least one fluid flow 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 may be configured to generate a time series corresponding to at least one fluid flow 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 may each be configured to operate as both an ultrasonic transmitter and an ultrasonic receiver. In such an example, the ultrasonic flow sensor 150 may be configured to operate by alternately transmitting and receiving bursts of ultrasonic waves (e.g., “up” and “down” signals, etc.) between the two transducers by measuring the transit time it takes for sound to travel in both directions between the two transducers. An analog-to-digital converter (ADC) may sample the signal received at the receiving transducer at multiple time points to generate a time series including multiple amplitudes sampled at multiple time points. The difference in measured transit times (e.g., Δtime, etc.) may be directly proportional to the velocity of the fluid within the fluid flow path. Multiple transit time differences (e.g., multiple Δtimes, etc.) may be represented as a time series including at least one of multiple amplitudes at multiple points in time, multiple phases at multiple points in time, or any combination thereof. For example, an average Δt may be calculated over one or more “up” and “down” signal pairs or cycles, the magnitude of which may be proportional to the flow rate of the fluid through the fluid flow path of the ultrasonic flow sensor 150. As an example, Δt may be converted to the velocity of the fluid within the flow tube 102 using the angle of the ultrasonic signal path, which may be calculated by knowing the speed of sound in the flow tube 102 and the fluid. This angle can be used in trigonometry to convert the ultrasonic path to a straight line within the flow tube 102, which may be the velocity of the liquid within the flow tube 102. The velocity of the fluid may be converted to flow rate by multiplying the velocity by the cross-sectional area of ​​the pipe, and the volume of fluid delivered may be calculated by multiplying the flow rate by time.

[0078] The ultrasonic flow sensor 150 may be configured to continuously generate and provide a time series corresponding to the flow of at least one fluid through the fluid flow path of the ultrasonic flow sensor 150 during the flow of at least one fluid through the fluid flow path of the ultrasonic flow sensor 150 (e.g., as the flow of at least one fluid through the fluid flow path of the ultrasonic flow sensor 150 occurs and progresses).

[0079] The flow sensor system 102 and / or the external computing system 104 may be configured to alternately provide an excitation pulse pattern including multiple excitation pulses to the first piezoelectric sensor or transducer 154 and the second piezoelectric sensor or transducer 156, causing the first piezoelectric sensor or transducer 154 and the second piezoelectric sensor or transducer 156 to alternately transmit and receive bursts of ultrasound waves (e.g., ultrasonic signals) between the two transducers. For example, a first piezoelectric sensor or transducer 154 may be configured to transmit a first ultrasonic signal to a second piezoelectric sensor or transducer 156, which may be configured to transmit a second ultrasonic signal to the first piezoelectric transducer 156, which may be configured to receive the first ultrasonic signal transmitted by the first piezoelectric sensor or transducer 154 (which may be sampled by an ADC to generate a time series), and / or the first piezoelectric sensor or transducer 156 may be configured to receive the second ultrasonic signal transmitted by the second piezoelectric sensor or transducer 156 (which may be sampled by an ADC to generate a time series).

[0080] The external computing system 104 may include one or more devices capable of receiving information and / or data from and / or communicating information and / or data to the flow sensor system 102. For example, the external computing system 104 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 servers, etc.). In some non-limiting embodiments or aspects, the external computing system 104 includes a nurses' station within 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 smartphone, a tablet computer, any combination thereof, and / or the like.

[0081] The number and arrangement of systems and devices shown in Figures 1A-1C are provided as an example. There may be additional, fewer, different, or differently arranged systems or devices than those shown in Figures 1A-1C. Furthermore, two or more systems or devices shown in Figures 1A-1C may be implemented within a single system or device, or a single system or device shown in Figures 1A-1C may be implemented as multiple distributed systems or devices. Additionally or alternatively, a set of systems (e.g., one or more systems) or a set of devices (e.g., one or more devices) of system 100 may perform one or more functions described as being performed by another set of systems or another set of devices of system 100.

[0082] 2, a diagram of exemplary components of device 200 is shown, according to a non-limiting embodiment. Device 200 may correspond, by way of example, to flow sensor system 102 and / or external computing system 104 of FIG. 1A. In some non-limiting embodiments, such a system or device may 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 may include additional, fewer, different, or differently arranged components than those shown. Additionally or alternatively, a set of components (e.g., one or more components) of device 200 may perform one or more functions described as being performed by another set of components of device 200.

[0083] 2, device 200 may 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. Bus 202 may include components that enable communication between the components of device 200. In some non-limiting embodiments, processor 204 may be implemented in hardware, firmware, or a combination of hardware and software. For example, processor 204 may 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 functions. Memory 206 may include random access memory (RAM), read-only memory (ROM), or another type of dynamic or static storage device (e.g., flash memory, magnetic memory, optical memory, etc.) that stores information and / or instructions for use by processor 204.

[0084] 2 , storage 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., a magnetic disk, an optical disk, a magneto-optical disk, a solid-state disk, etc.) or another type of computer-readable medium. Input component 210 may include components that enable device 200 to receive information, for example, via user input (e.g., a touchscreen display, a keyboard, a keypad, a mouse, a button, a switch, a microphone, etc.). Additionally or alternatively, input component 210 may include sensors for sensing information (e.g., a global positioning system (GPS) component, an accelerometer, a gyroscope, an actuator, etc.). Output component 212 may include components that provide output information from device 200 (e.g., a display, a speaker, one or more light-emitting diodes (LEDs), etc.). Communications interface 214 may include transceiver-like components (e.g., a transceiver, a separate receiver and transmitter, etc.) that enable device 200 to communicate with other devices, for example, via a wired connection, a wireless connection, or a combination of wired and wireless connections. Communications interface 214 may enable device 200 to receive information from other devices and / or provide information to another device. For example, communications 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, a Wi-Fi interface, a cellular network interface, and / or the like.

[0085] The device 200 may perform one or more processes described herein. The device 200 may perform these processes based on the processor 204 executing software instructions stored by a computer-readable medium, such as the memory 206 and / or the storage component 208. The computer-readable medium may include any non-transitory memory device. The memory device may include memory space located within a single physical storage device or memory space spanning multiple physical storage devices. The software instructions may be loaded into the memory 206 and / or the storage component 208 from another computer-readable medium or from another device via the communication interface 214. When executed, the software instructions stored in the memory 206 and / or the storage component 208 may cause the processor 204 to perform one or more processes described herein. Additionally or alternatively, hardwired circuitry may be used in place of or in combination with software instructions to perform one or more processes described herein. Thus, the embodiments described herein are not limited to any specific combination of hardware circuitry and software. As used herein, the term "configured to" may refer to a particular arrangement of software, devices, or hardware to perform or enable one or more of the innovative functions (e.g., actions, processes, process steps, etc.) described herein. For example, a "processor configured to" may refer to a processor that executes particular software instructions (e.g., program code) that cause the processor to perform one or more functions related to fluid flow detection and / or identification.

[0086] Referring now to Figure 3, a flow diagram of a method 300 for speed of sound tracking in an ultrasonic flow sensor is shown, according to certain non-limiting embodiments or aspects. The steps shown in Figure 3 are for illustrative purposes only. It will be understood that in certain non-limiting embodiments or aspects, additional, fewer, different, or differently ordered steps may be used. In certain non-limiting embodiments or aspects, a step may be performed automatically in response to the execution or completion of a previous step.

[0087] As shown in FIG. 3 , in step 302, the method 300 includes providing an excitation pulse pattern including 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, causing 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 to transmit at least one ultrasonic signal to another 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 may provide an excitation pulse pattern including multiple 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, to cause at least one of the first piezoelectric sensor or transducer 154, the second piezoelectric sensor or transducer 156, or any combination thereof to transmit 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. The multiple excitation pulses of the excitation pulse pattern include at least one of multiple different pulse widths, multiple different voltage levels, or any combination thereof. For example, one or more excitation pulses of the multiple excitation pulses have a different pulse width and / or a different voltage level than one or more other excitation pulses of the multiple excitation pulses. As an example, and with reference to FIG. 6 , which is a graph of an excitation pulse pattern, according to some non-limiting embodiments or aspects, the width of each pulse of the multiple excitation pulses of the excitation pulse pattern may differ from the width of each other pulse of the multiple excitation pulses of the excitation pulse pattern.

[0088] In certain non-limiting embodiments or aspects, the plurality of excitation pulses of the excitation pulse pattern further include 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, causing at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof to transmit at least one previous ultrasound 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 may 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, to cause at least one of the first piezoelectric sensor or transducer 154, the second piezoelectric sensor or transducer 156, or any combination thereof to transmit 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 sub-system 102 and / or the external computing system 104 may 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, to cause at least one of the first piezoelectric sensor or transducer 154, the second piezoelectric sensor or transducer 156, or any combination thereof to transmit 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 manner, a first time series waveform generated based on the reception of at least one first ultrasonic signal may be different or unique from a second time series waveform generated based on the reception of at least one second ultrasonic signal.

[0089] Thus, non-limiting embodiments or aspects of the present disclosure may provide custom excitation patterns that result in an output waveform with a unique signature, such that peaks or zero crossings of the waveform may be more easily identified as described herein (e.g., the uniqueness of the output waveform may be exploited to consistently identify peaks (or zero crossings) of the same cycle).

[0090] 3, in step 304, the method 300 includes receiving, from the ultrasonic flow sensor, a time series waveform including 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, the flow sensor system 102 and / or the external computing system 104 may receive from the ultrasonic flow sensor 102 a time series waveform including 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. A time series waveform including multiple amplitudes of at least one ultrasonic signal received by the other of the first piezoelectric sensor or transducer 154, the second piezoelectric sensor or transducer 156, or any combination thereof, may be received. By way of example, and referring to FIG. 7, which is a graph of a waveform obtained using an excitation pulse pattern, according to some non-limiting embodiments or aspects, a unique time series waveform may be generated in response to an excitation pulse pattern including multiple different pulse widths and / or multiple different voltage levels, or any combination thereof, such that peaks or zero crossings of the waveform may be more easily identified, as described herein (e.g., the uniqueness of the output waveform may be utilized to consistently identify peaks (or zero crossings) of the same cycle).

[0091] As shown in FIG. 3 , in step 306, the method 300 includes identifying attributes of the 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, the flow sensor system 102 and / or the external computing system 104 may identify peaks or zero crossings of the time-series waveform (or other attributes of the time-series waveform). By way of example, the attributes of the time-series waveform may include peaks or zero crossings (or other attributes) of the time-series waveform, and the flow sensor system 102 and / or the external computing system 104 may identify the attributes of the time-series waveform by analyzing the time-series waveform to identify the peaks or zero crossings of the time-series waveform. In some implementations, analyzing the time-series waveform may include providing the time-series waveform data associated with or representing the time-series waveform to a machine learning model trained to identify peaks, zero crossings, or other attributes of the time-series waveform to provide a characterization of the time-series waveform data (e.g., an indication of the peaks or zero crossings of the time-series waveform, etc.). For example, the flow sensor system 102 and / or the external computing system 104 may analyze the time series waveform to identify the peaks or zero crossings 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 peaks or zero crossings of the time series waveform, and receiving the peaks or zero crossings of the time series waveform as output from the machine learning model.

[0092] In some non-limiting embodiments or aspects, the flow sensor system 102 and / or the external computing system 104 identify peaks or zero crossings of the time series waveform by applying at least one pattern matching algorithm to the time series waveform. Peak detection may include identifying a maximum value within the time series. In some implementations, peak detection may include providing an average of several top readings (e.g., an average of the top 3, 4, 5, or 10 readings, etc.). This sampling may smooth out outliers or noise within the waveform. In some implementations, pattern matching may reference a particular series of events (e.g., low-high-low, etc.) to determine a value that characterizes a peak or zero crossing. For example, pattern matching may reference the fifth peak value after the waveform crosses a threshold. For example, at least one pattern matching algorithm may utilize uniqueness of the time series waveform to identify peaks or zero crossings of the time series waveform.

[0093] In some non-limiting embodiments or aspects, the flow sensor system 102 and / or the external computing system 104 identify peaks or zero crossings in the time series waveform by comparing the time series waveform to at least one reference time series waveform. The use of the reference waveform can be used to indicate when or at what level a peak or zero crossing can be expected.

[0094] In some non-limiting embodiments or aspects, the at least one reference time series waveform is determined based on multiple time series waveforms generated by the multiple ultrasonic flow sensors in response to the excitation pulse pattern. For example, the flow sensor system 102 and / or the external computing system 104 acquires the multiple time series waveforms generated by the multiple ultrasonic sensors 102 in response to the excitation pulse pattern and combines (e.g., averages) the multiple time series waveforms to generate at least one reference time series waveform, which can be stored in the memory of the ultrasonic flow sensor 102 and / or in a database of reference waveforms.

[0095] 3, in step 308, the method 300 includes determining a transit 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 another of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, based on attributes of the time series waveform. For example, the flow sensor system 102 and / or the external computing system 104 may determine a transit 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 another of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, based on attributes 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 the flow sensor system 102 and / or the external computing system 104 may determine the transit 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 another of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, based on the peaks or zero crossings (or other attributes) of the time series waveform.

[0096] As shown in FIG. 3 , in step 310, method 300 includes providing an indication associated with the transit time of the at least one ultrasonic signal. For example, flow sensor system 102 and / or external computing system 104 may provide an indication associated with the transit time of the at least one ultrasonic signal. The indication may be a human-perceptible output indicating the transit time of the at least one ultrasonic signal and / or a parameter calculated therefrom, such as the flow rate of fluid through ultrasonic flow sensor 102, the volume of fluid delivered by ultrasonic flow sensor 102, etc. (e.g., provided via a speaker and / or display of the reusable base of the flow sensor system in U.S. Patent Application Publication No. 2021 / 0231471). In some implementations, providing an indication may include storing the value in a storage location (e.g., in a memory of ultrasonic flow sensor 150, in a memory of the flow sensor system, in a database, etc.) for subsequent retrieval, transmitting the value directly to a recipient via at least one wired or wireless communication medium, transmitting or storing a reference to the value, etc. Providing in step 310 may additionally or alternatively include encoding, decoding, encrypting, decrypting, validating, verifying, etc. via hardware elements.

[0097] In some non-limiting embodiments or aspects, the flow sensor system 102 and / or the external computing system 104 may provide an indication associated with the transit time of at least one ultrasound signal associated with patient data associated with a patient, procedure data associated with a patient procedure associated with the patient, caregiver data associated with a caregiver (e.g., nurse, doctor, etc.), any combination thereof, etc. The patient data associated with the patient may include a patient identifier (e.g., a unique patient identifier, etc.) associated with the patient, patient demographics (e.g., name, age, sex, weight, height, date of birth, address, etc.), a list of drug allergies associated with the patient, a list of drug doses delivered, being delivered, and / or pending delivery to the patient, any combination thereof, etc. The procedure data may include a procedure identifier (e.g., a unique procedure identifier, etc.) associated with the procedure, one or more medical devices associated with the procedure, the name of the procedure, the status of the procedure (e.g., scheduled for a future date and time, currently being performed, previously performed date and time, etc.), a caregiver associated with the procedure, a patient associated with the procedure, any combination thereof, etc. The caregiver data may include a caregiver identifier associated with the caregiver (eg, a unique caregiver identifier, etc.), the caregiver's name, any combination thereof, and the like.

[0098] While the embodiments have been described in detail for purposes of illustration, it should be understood that such detail is for that purpose only and that the disclosure is not limited to the disclosed embodiments or aspects, but on the contrary, is intended to cover modifications and equivalent arrangements within the spirit and scope of the appended claims. For example, it should be understood that the present disclosure contemplates that, 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.

[0099] The described aspects include artificial intelligence or other operations in which a system processes inputs and generates outputs with apparent intelligence. The artificial intelligence may be implemented in whole or in part by a model. The model may be implemented as a machine learning model. Learning may be supervised, unsupervised, reinforced, or hybrid learning, in which multiple learning techniques are employed to generate the model. Learning may be performed as part of training. Training a model may include obtaining a set of training data and adjusting model characteristics to obtain a desired model output. For example, three characteristics may be associated with a desired item location. In such an example, training may include receiving three characteristics as inputs to the model and, for each set of three characteristics, adjusting the model characteristics so that the output device state matches a desired device state associated with the historical data.

[0100] In some implementations, training may be dynamic. For example, the system may use a set of events to update the model. Detectable characteristics from the events may be used to adjust the model.

[0101] The model may 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 characteristics of the structure available for adjustment during training may vary based on the model selected. For example, if a neural network is the selected model, the characteristics may include input elements, network layers, node density, node activation thresholds, weights between nodes, weights of input or output values, etc. If the model is implemented as an equation (e.g., regression), the characteristics may include weights of input parameters, thresholds or limits for evaluating output values, or criteria for selecting from a set of equations.

[0102] Once a model has been trained, retraining may be included to refine or update the model to reflect additional data or particular operating conditions. Retraining may be based on one or more signals detected by the devices described herein or as part of the methods described herein. Upon detecting a specified signal, the system may initiate a training process to adjust the model as described.

[0103] Further examples of machine learning and modeling features that may be included in the above-described embodiments are described in "A survey of machine learning for big data processing" by Qiu et al., EURASIP Journal on Advances in Signal Processing (2016), which is incorporated herein by reference in its entirety.

[0104] As used herein, the terms "determine" or "determining" encompass a wide variety of actions. For example, "determining" may include calculating, computing, processing, deriving, generating, obtaining, looking up (e.g., searching a table, database, or another data structure), ascertaining, etc., via a hardware element without user intervention. Also, "determining" may include receiving (e.g., receiving information), accessing (e.g., accessing data in a memory), etc., via a hardware element without user intervention. "Determining" may include resolving, selecting, choosing, establishing, etc., via a hardware element without user intervention.

[0105] As used herein, the terms "providing" or "providing" encompass a wide variety of actions. For example, "providing" may include storing a value in a memory device location for subsequent retrieval, transmitting a value directly to a recipient via at least one wired or wireless communication medium, transmitting or storing a reference to a value, etc. "Providing" may also include encoding, decoding, encryption, decryption, validation, verification, insertion, etc. via a hardware element.

Claims

1. an ultrasonic flow sensor including a flow tube, a first piezoelectric sensor or transducer disposed at an upstream position of the flow tube, and a second piezoelectric sensor or transducer disposed at a downstream position of the flow tube; providing 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 transmit 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 a time series waveform from the ultrasonic flow sensor, the time series waveform including multiple amplitudes 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, sampled at multiple time points; Identifying attributes of the time series waveform; determining a transit 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 another of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, based on the attribute of the time series waveform; at least one processor configured to A system including:

2. The system of claim 1 , wherein the at least one processor is configured to identify the attributes of the time-series waveform by applying at least one pattern matching algorithm to the time-series waveform.

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

4. 10. The system of claim 1, 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, causing at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, to transmit at least one previous ultrasound signal to another of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof.

5. The system of claim 1 , wherein the at least one processor is further configured to provide an indication associated with the transit time of the at least one ultrasound signal.

6. 2. The system of claim 1, wherein the attributes of the time series waveform include peaks or zero crossings of the time series waveform, the at least one processor is configured to identify the attributes of the time series waveform by analyzing the time series waveform to identify the peaks or zero crossings of the time series waveform, and the at least one processor is configured to determine the transit time of the at least one ultrasonic signal based on the peaks or zero crossings of the time series waveform from the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, to another of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof.

7. 7. The system of claim 6, wherein the at least one processor analyzes the time series waveform to identify the peaks or zero crossings 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 peaks or zero crossings of the time series waveform, and receiving the peaks or zero crossings of the time series waveform as output from the machine learning model.

8. A flow tube and a first piezoelectric sensor or transducer disposed at an upstream position of the flow tube; a second piezoelectric sensor or transducer disposed downstream of the flow tube; providing 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 transmit 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 a time series waveform from an ultrasonic flow sensor, the time series waveform including multiple amplitudes 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, sampled at multiple time points; Identifying attributes of the time series waveform; at least one processor that determines a transit 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 another of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, based on the attribute of the time series waveform; 1. An ultrasonic flow sensor comprising:

9. 9. The ultrasonic flow sensor of claim 8, wherein the at least one processor is configured to identify the attributes of the time series waveform by applying at least one pattern matching algorithm to the time series waveform.

10. 9. The ultrasonic flow sensor of claim 8, wherein the at least one processor is configured to identify the attribute of the time series waveform by comparing the time series waveform to at least one reference time series waveform, the at least one reference time series waveform being determined based on a plurality of time series waveforms generated by a plurality of ultrasonic flow sensors in response to the excitation pulse pattern.

11. 9. The ultrasonic flow sensor of claim 8, wherein 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 the at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, causing at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, to transmit at least one previous ultrasonic signal to another of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof.

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

13. 9. The ultrasonic flow sensor of claim 8, wherein the attributes of the time series waveform include peaks or zero crossings of the time series waveform, the at least one processor is configured to identify the attributes of the time series waveform by analyzing the time series waveform to identify the peaks or zero crossings of the time series waveform, and the at least one processor is configured to determine the transit 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 another of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof based on the peaks or zero crossings of the time series waveform.

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

15. 1. A method for speed of sound tracking in an ultrasonic flow sensor including a flow tube, a first piezoelectric sensor or transducer disposed at an upstream position of the flow tube, and a second piezoelectric sensor or transducer disposed at a downstream position of the flow tube, comprising: providing, with at least one processor, 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 transmit at least one ultrasound 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, with the at least one processor, a time series waveform from the ultrasonic flow sensor, the time series waveform including multiple amplitudes 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, sampled at multiple time points; identifying attributes of the time series waveform with the at least one processor; and determining, with the at least one processor, a transit 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 another of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof, based on the attribute of the time series waveform.

16. Identifying the attribute of the time series waveform using the at least one processor includes:

16. The method of claim 15, comprising applying, with the at least one processor, at least one pattern matching algorithm to the time series waveform.

17. Identifying the attribute of the time series waveform using the at least one processor includes:

16. The method of claim 15, further comprising using the at least one processor to compare the time series waveform to at least one reference time series waveform, the at least one reference time series waveform being 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. 16. The method of claim 15, 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, causing at least one of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof to transmit at least one previous ultrasound 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 providing, with the at least one processor, an indication associated with the transit time of the at least one ultrasound signal.

20. 16. The method of claim 15, wherein the attributes of the time series waveform include peaks or zero crossings of the time series waveform, and identifying the attributes of the time series waveform using the at least one processor includes analyzing the time series waveform with the at least one processor to identify the peaks or zero crossings of the time series waveform, the at least one processor determining the transit 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 another of the first piezoelectric sensor or transducer, the second piezoelectric sensor or transducer, or any combination thereof based on the peaks or zero crossings of the time series waveform, and analyzing the time series waveform to identify the peaks or zero crossings of the time series waveform using the at least one processor includes providing time series waveform data associated with the time series waveform as input to a machine learning model trained to identify the peaks or zero crossings of the time series waveform, and receiving the peaks or zero crossings of the time series waveform as output from the machine learning model.

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