Disclosed is a method of determining a characteristic of a measurement intermediate layer (220) in a multilayer structure (200) using an ultrasonic transducer (100), wherein the multilayer structure (200) includes a first layer (210), a measurement intermediate layer (220) and a third layer (230) in series abutment. The method comprises transmitting a measurement ultrasonic signal into the first layer (210) towards the measurement intermediate layer (22)0, measuring a measurement reflection of the measurement ultrasonic signal from the multilayer structure (200), determining, using the measurement reflection, a measured frequency response of the measurement intermediate layer (220), determining a plurality of modelled frequency responses of the measurement intermediate layer (220), comparing the measured frequency response to the plurality of modelled frequency responses, and determining the characteristic of the measurement intermediate layer (220) based on the comparison of the measured frequency response and the plurality of modelled frequency responses.

Disclosed is a method of determining a characteristic of a measurement intermediate layer (220) in a multilayer structure (200) using an ultrasonic transducer (100), wherein the multilayer structure (200) includes a first layer (210), a measurement intermediate layer (220) and a third layer (230) in series abutment. The method comprises transmitting a measurement ultrasonic signal into the first layer (210) towards the measurement intermediate layer (22)0, measuring a measurement reflection of the measurement ultrasonic signal from the multilayer structure (200), determining, using the measurement reflection, a measured frequency response of the measurement intermediate layer (220), determining a plurality of modelled frequency responses of the measurement intermediate layer (220), comparing the measured frequency response to the plurality of modelled frequency responses, and determining the characteristic of the measurement intermediate layer (220) based on the comparison of the measured frequency response and the plurality of modelled frequency responses.

Systems and methods for prediction of state of charge (SOH), state of health (SOC) and other characteristics of batteries using acoustic signals, includes determining acoustic data at two or more states of charge and determining a reduced acoustic data set representative of the acoustic data at the two or more states of charge. The reduced acoustic data set includes time of flight (TOF) shift, total signal amplitude, or other data points related to the states of charge. Machine learning models use at least the reduced acoustic dataset in conjunction with non-acoustic data such as voltage and temperature for predicting the characteristics of any other independent battery.

Systems and methods for prediction of state of charge (SOH), state of health (SOC) and other characteristics of batteries using acoustic signals, includes determining acoustic data at two or more states of charge and determining a reduced acoustic data set representative of the acoustic data at the two or more states of charge. The reduced acoustic data set includes time of flight (TOF) shift, total signal amplitude, or other data points related to the states of charge. Machine learning models use at least the reduced acoustic dataset in conjunction with non-acoustic data such as voltage and temperature for predicting the characteristics of any other independent battery.

Systems and methods for prediction of state of charge (SOH), state of health (SOC) and other characteristics of batteries using acoustic signals, includes determining acoustic data at two or more states of charge and determining a reduced acoustic data set representative of the acoustic data at the two or more states of charge. The reduced acoustic data set includes time of flight (TOF) shift, total signal amplitude, or other data points related to the states of charge. Machine learning models use at least the reduced acoustic dataset in conjunction with non-acoustic data such as voltage and temperature for predicting the characteristics of any other independent battery.

Systems and methods for prediction of state of charge (SOH), state of health (SOC) and other characteristics of batteries using acoustic signals, includes determining acoustic data at two or more states of charge and determining a reduced acoustic data set representative of the acoustic data at the two or more states of charge. The reduced acoustic data set includes time of flight (TOF) shift, total signal amplitude, or other data points related to the states of charge. Machine learning models use at least the reduced acoustic dataset in conjunction with non-acoustic data such as voltage and temperature for predicting the characteristics of any other independent battery.

A method of creating and characterizing a representative image of the surface of an object from acoustic emissions of a multimode ultrasonic probe tip and transducer integrated into a micro tool, such as a nano indenter or a nano indenter interfaced with a Scanning Probe Microscope (SPM). The representative image may be utilized to predict mechanical properties or characteristics of the sample, including topography, fracture patterns, indents and artifacts. The tip component is configured to operate at multi-resonant frequencies providing sub-nanometer vertical resolution. The tip component may be quasi-statistically calibrated and deep learning iterative image comparison and characterization may be utilized to derive mechanical properties of a sample.

Techniques, systems, and devices are disclosed for synthetic aperture ultrasound imaging using a beamformer that incorporates a model of the object. In some aspects, a system includes an array of transducers to transmit and/or receive acoustic signals at an object that forms a synthetic aperture of the system with the object, an object beamformer unit to (i) beamform the object coherently as a function of position, orientation, and/or geometry of the transducers with respect to a model of the object, and (ii) produce a beamformed output signal including spatial information about the object derived from beamforming the acoustic echoes; a data processing unit to process data and produce an image of the object based on a rendition of the position, the orientation, the geometry, and/or the surface properties of the object, relative to the coordinate system of the array, as determined by the data processing unit.

The present disclosure provides methods and systems for the characterization of a microtexture of a sample, component, or the like. The methods may include methods of determining a service life limiting region of a component, determining a treatment method for a component, and/or selecting components from a batch of components for use in production. The characterization may include calculating a microtexture level indicator from ultrasonic C-scan images for various samples, regions, components, or the like. The microtexture level indicator may include at least one of an average peak factor, a standard deviation of peak amplitude, and/or a baseband bandwidth.

The present disclosure provides systems and methods for non-destructively estimating the thickness of buried concrete without excavation. An example method may include placing one or more first accelerometers at a plurality of vertical positions below the surface of the ground at an approximate first distance from a vertical edge of the buried concrete each time. The method may further include, for each position in the plurality of vertical positions, generating a dispersive wave in the buried concrete and determining a time of arrival of the dispersive wave at the one or more first accelerometers. The method may further include estimating the thickness of the buried concrete based on at least the times of arrival of the dispersive waves at the one or more first accelerometers.