An ultrasound circuit comprising a trans-impedance amplifier (TIA) with built-in time gain compensation functionality is described. The TIA is coupled to an ultrasonic transducer to amplify an electrical signal generated by the ultrasonic transducer in response to receiving an ultrasound signal. The TIA is, in some cases, followed by further analog and digital processing circuitry.

An ultrasound circuit comprising a trans-impedance amplifier (TIA) with built-in time gain compensation functionality is described. The TIA is coupled to an ultrasonic transducer to amplify an electrical signal generated by the ultrasonic transducer in response to receiving an ultrasound signal. The TIA is, in some cases, followed by further analog and digital processing circuitry.

In one embodiment, a device in a network receives a machine learning encoder and decoder trained by a supervisory service. The service trains the encoder and decoder using vibration measurement data sent to the service by a plurality of devices. The device trains, based on the received encoder, a classifier to determine whether vibration measurement data is indicative of a behavioral anomaly. The device receives vibration measurement data captured by a particular set of one or more vibration sensors of a monitored system. The device evaluates, using the trained decoder, the received vibration measurement data to determine whether the data is indicative of a structural anomaly in the monitored system. The device evaluates, using the trained classifier, the received vibration measurement data to determine whether the data is indicative of a behavioral anomaly in the monitored system.

A diagnostic device includes a reception unit and a determination unit. The reception unit is configured to receive context information and sensing information. The context information corresponds to a certain operation of a target item that constitutes a target device. The context information is a piece of a plurality of pieces of context information each describing an operation of the target item determined depending on a type of operation of the target device. The sensing information is on a physical quantity that varies in accordance with the operation of the target item. The determination unit is configured to determine a state of the target item based on the sensing information detected while the target item is performing the certain operation, and based on a model corresponding to the received context information. The model is a model of one or more models respectively defined for one or more pieces of the context information.

A diagnostic device includes a reception unit and a determination unit. The reception unit is configured to receive context information and sensing information. The context information corresponds to a certain operation of a target item that constitutes a target device. The context information is a piece of a plurality of pieces of context information each describing an operation of the target item determined depending on a type of operation of the target device. The sensing information is on a physical quantity that varies in accordance with the operation of the target item. The determination unit is configured to determine a state of the target item based on the sensing information detected while the target item is performing the certain operation, and based on a model corresponding to the received context information. The model is a model of one or more models respectively defined for one or more pieces of the context information.

The measurement system 100 includes: a measurement apparatus 20 that measures vibrations of an object 40; an imaging apparatus 30 that is located so as to capture an image of the measurement apparatus 20; and a correction processing apparatus 10. the correction processing apparatus 10 includes: a displacement calculation unit 11 that calculates a displacement of the measurement apparatus 20 based on time-series images of the measurement apparatus 20 output from the imaging apparatus 30; a movement amount calculation unit 12 that calculates an amount of movement of the measurement apparatus 20 relative to the imaging apparatus 30, based on the displacement; and a correction processing unit 13 that corrects vibrations of the object measured by the measurement apparatus 20, using the calculated amount of movement of the measurement apparatus 20.

The measurement system 100 includes: a measurement apparatus 20 that measures vibrations of an object 40; an imaging apparatus 30 that is located so as to capture an image of the measurement apparatus 20; and a correction processing apparatus 10. the correction processing apparatus 10 includes: a displacement calculation unit 11 that calculates a displacement of the measurement apparatus 20 based on time-series images of the measurement apparatus 20 output from the imaging apparatus 30; a movement amount calculation unit 12 that calculates an amount of movement of the measurement apparatus 20 relative to the imaging apparatus 30, based on the displacement; and a correction processing unit 13 that corrects vibrations of the object measured by the measurement apparatus 20, using the calculated amount of movement of the measurement apparatus 20.

This monitoring device comprises: an acceleration acquisition unit for acquiring acceleration data of a vehicle traveling along a track; an acceleration effective value acquisition unit for acquiring a plurality of acceleration effective values obtained by applying a bandpass filter for each of a plurality of constant specific bandwidths to the acceleration data; a corrected acceleration calculation unit for calculating a corrected acceleration on the basis of each of the acceleration effective values of the respective constant specific bandwidths and predetermined correction coefficients corresponding to the respective constant specific bandwidths; and an abnormality detection unit for detecting an abnormality of the vehicle or of the track on the basis of the magnitude of the corrected acceleration.

A testing procedure including a data collection procedure and a contrastive learning-based approach, for establishing a profile for utility poles surveyed in an embedding space. Unique properties of utility poles are preserved in a low-dimensional feature vector. Similarities between pairs of samples collected at the same or different poles is reflected by the Euclidean distance between the pole embeddings. During data collection—variabilities of excitation signals are manually introduced, e.g. impact strength, impact locations, impact time ambiguity, data collecting location ambiguity on a DFOS/DAS optical sensor fiber/cable. Data so collected provides a learned model learned complete information about a utility pole and is more robust with respect to uncontrollable factors during operation. A model training procedure that effectively extracts a utility pole intrinsic properties (e.g., structure integrity, dimensions, structure variety) and remote extrinsic influence (e.g., excitation strength, weather conditions, road traffic), without knowing the ground truth of these factors. The only identifying label required is an ID of any tested poles, which is readily available. The model is trained adaptively—end-to-end—is advantageously easy-to-implement on modern deep learning frameworks such as PyTorch.

A system includes a vertical molten sulfur pump assembly that includes a top portion adjacent to a first end of the vertical molten sulfur pump assembly and a bottom portion adjacent to a second end of the vertical molten sulfur pump assembly. A pump motor is disposed in the top portion, an impeller is disposed in the bottom portion within an impeller casing, and a shaft is disposed within a central column and connecting the pump motor with the impeller. A pump inlet is disposed at the second end below the impeller casing. The pump inlet and the impeller casing are configured to be immersed in molten sulfur. The vertical molten sulfur pump assembly is configured to pump the molten sulfur into the inlet and upwards through a discharge passageway by rotation of the impeller. A vibration sensor and a temperature sensor are disposed on an external surface of the bottom portion, on or proximate to the impeller casing and the pump inlet. The temperature sensor is configured to measure a temperature of the molten sulfur proximate to the pump inlet. The vibration sensor includes a substrate comprising a polymer and a resonant layer disposed on a surface of the substrate. The resonant layer includes an electrically conductive nanomaterial and is configured to produce a resonant response in response to receiving a radio frequency signal.