Methods of processing a data signal obtained from a sensor sensing a dynamic signal to detect a structural anomaly event are disclosed. In one embodiment, a method includes obtaining signal components attributable to fluid flow at a location within an operational pipeline network; processing the data signal to extract one or more features; characterising the one or more extracted features; and detecting an indication of a structural anomaly event proximal the location depending on the characterisation; wherein the structural anomaly event includes an occurrence and/or further development of a structural anomaly.

An IoT Smart Sound Level Meter that is connected through the internet via ethernet cable, wifi or cellular network. The IoT Smart Sound Level Meter measures and monitors sound pressure levels. Sound pressure levels are measured in decibels. The sound pressure level data that is sent via the internet through ethernet, wifi or cellular network to cloud servers where the data is stored and saved. The cloud servers are connected to Application Programming Interfaces (API) connected to web-based software that converts the sound pressure level data into decibel measurements. These measurements are displayed to the end-user through the web-based software and/or application providing real-time decibel level measurements and historic sound pressure level recordings. The web-based software and/or application provides access to view decibel measurement data remotely from a computer, laptop, tablet, smart watch, or mobile device.

A system for measuring sound levels is provided. The system comprises a processor, a memory, and an application stored in the memory that when executed on the processor receives sound levels recorded by each of a plurality of sound sensors located in a coverage area. The application also samples noise levels from the received sound levels at least one location within the coverage area. The application also derives values based at least on the samples and on estimates of sound attenuation at the at least one location. The application also creates a heat map based at least on the derived values, the heat map representing at least noise levels experienced within the coverage area.

An infrasound detector comprises an infrasound transducer, signal feedback path, and feedback force transducer. The infrasound transducer is configured to transduce an infrasound signal to an electrical signal. The signal feedback path is arranged to feed a feedback signal from the infrasound transducer to a feedback force transducer. The feedback force transducer is configured to transduce a feedback electrical signal to a feedback force signal and arranged to provide the feedback force signal as input to the infrasound transducer.

In an abnormal noise evaluation system, running noise of a vehicle is acquired, and the running noise is analyzed to generate analysis data. A rotational order component of the running noise of the vehicle is then extracted from the analysis data, and features for each rotational order of the running noise of the vehicle is generated based on the extracted rotational order component. Subsequently, a learning model is generated using as training data a combination of the features for each rotational order generated for a learning object that is of the same type as an object to be evaluated and an evaluation result given in advance to the learning object. Whether there is abnormal noise from the object to be evaluated is evaluated by applying the features for each rotational order generated for the object to be evaluated to the learning model, and an evaluation result is output.

A driving control apparatus, a device, an optical module, and a driving control method will be provided, the driving control apparatus including: an acquiring unit to acquire a detection signal depending on a detection result of sensing a position of a driving target object; a driving control unit to generate a driving signal to move the driving target object to a target position based on the detection signal; an oscillation detecting unit to detect oscillation in a signal at a predetermined object point on a signal path from the detection signal to the driving signal; and an oscillation suppressing unit to suppress oscillation of the signal path according to detection of the oscillation.

A driving control apparatus, a device, an optical module, and a driving control method will be provided, the driving control apparatus including: an acquiring unit to acquire a detection signal depending on a detection result of sensing a position of a driving target object; a driving control unit to generate a driving signal to move the driving target object to a target position based on the detection signal; an oscillation detecting unit to detect oscillation in a signal at a predetermined object point on a signal path from the detection signal to the driving signal; and an oscillation suppressing unit to suppress oscillation of the signal path according to detection of the oscillation.

Devices and methods are disclosed for monitoring at fishing vessels to detect blast fishing activity. A blast sensor is mountable to a vessel such that it produces a signal in response to pressure waves in water. A controller receives the signal and is operable to analyze the signal to detect blast events based on signal characteristics. A position measurement device measures a position of the vessel. A transmitter is provided to wirelessly transmit status information including a unique identifier of the monitoring unit and indicative of the position of the monitoring unit and to send a wireless alarm signal in response to a detected blast event.

A method for eliminating pump noise by empirical mode decomposition and particle swarm optimization is provided. In the method, based on a hypothesis of the pump noise being a linear combination of a group of bases, an extracted pump noise sample is decomposed into a group of signals as bases by the empirical mode decomposition. Coefficients of the optimized linear combination of the group of bases is determined by the particle swarm optimization, thus updating the pump noise sample and improving a noise elimination effect. During a limited number of noise elimination periods, a current pump noise sample is modified by weighting, such that in a limited number of iterations, the current pump noise sample gradually converges to the pump noise waveform in the unit of a varied period, so as to be applicable to a slow variation of the pump noise during a long-time operation of the system.

A method for eliminating pump noise by empirical mode decomposition and particle swarm optimization is provided. In the method, based on a hypothesis of the pump noise being a linear combination of a group of bases, an extracted pump noise sample is decomposed into a group of signals as bases by the empirical mode decomposition. Coefficients of the optimized linear combination of the group of bases is determined by the particle swarm optimization, thus updating the pump noise sample and improving a noise elimination effect. During a limited number of noise elimination periods, a current pump noise sample is modified by weighting, such that in a limited number of iterations, the current pump noise sample gradually converges to the pump noise waveform in the unit of a varied period, so as to be applicable to a slow variation of the pump noise during a long-time operation of the system.