A sound leakage suppression apparatus for suppressing sound produced in a room from leaking outside thereof, includes a microphone, a display, and a processor configured to acquire information indicating sound insulation property of the room, based on the acquired information, determine a maximum volume level of sound that is permitted for each of a plurality of predetermined frequencies, control the microphone to collect sound produced in the room at a first time and determine a current volume level of the sound produced at the first time separately for each of the predetermined frequencies, and control the display to display both the maximum volume level and the current volume level for each of the predetermined frequencies.

A rail-guided cart system includes a traveling rail and a traveling cart to travel along the traveling rail. The rail-guided cart system includes a measurement sensor and a diagnosis processor. The measurement sensor is fixed with respect to the traveling rail to measure vibration or sound. The diagnosis processor diagnoses a condition of the traveling cart that passed through a measurement point on the traveling rail corresponding to a location of the measurement sensor. based on measurement data measured by the measurement sensor.

A method for automatic diagnosis of a mechanical system of a group of mechanical systems sharing mechanical characteristics includes obtaining data relating to a vibration. The vibration-related data is acquired by a portable communications device configured to communicate with a remote processor. The processor automatically diagnoses the mechanical system by applying a relationship to the obtained vibration-related data. The relationship is based on sets of vibration-related data previously obtained from the mechanical systems. Each set of vibration-related data relates to vibrations of a mechanical system. The relationship is further based on sets of operation data previously obtained for mechanical systems of the group. Each set of operation data indicates a previous state of operation of a mechanical system. Each of the previous states of operation is associated with at least one of the previously obtained sets of vibration-related data.

A method for monitoring an aircraft engine vane wheel (22), which includes: acquiring at least one time signal relative to moments when the vane wheel blades (23) pass in front of a sensor (21); determining a common flight phase of the aircraft; for each flight in a series of flights of the aircraft, correlating at least part of each time signal with a predetermined flight phase; and for each blade (23), each flight, and each predetermined flight phase, measuring the mean position (24C), the so-called “balance position”, of the top of the blade. The invention also relates to a device for implementing such a method. One advantage of the invention is providing a diagnosis of the blades using a small number of sensors and low computing power.

A method for monitoring an aircraft engine vane wheel (22), which includes: acquiring at least one time signal relative to moments when the vane wheel blades (23) pass in front of a sensor (21); determining a common flight phase of the aircraft; for each flight in a series of flights of the aircraft, correlating at least part of each time signal with a predetermined flight phase; and for each blade (23), each flight, and each predetermined flight phase, measuring the mean position (24C), the so-called “balance position”, of the top of the blade. The invention also relates to a device for implementing such a method. One advantage of the invention is providing a diagnosis of the blades using a small number of sensors and low computing power.

Disclosed is a piezoelectric voice recognition sensor, which includes a flexible thin film, a piezoelectric material layer laminated on the flexible thin film, and an electrode laminated on the piezoelectric material layer, wherein the electrode includes a plurality of frequency separation channels arranged in a row, and the plurality of frequency separation channels have different lengths from each other. The piezoelectric voice recognition sensor separates a voice, recognized using a plurality of frequency separation channels having a trapezoidal shape, through the plurality of channels depending on frequencies, and simultaneously converts the separated voice signals from mechanical vibration signals into electric signals by means of the flexible piezoelectric element so that the converted electric signals are recognized.

Apparatuses and methods are described herein for identifying an Unmanned Aerial Vehicle (UAV) by a central server connected to a first detection device and a plurality of detection devices, including, but not limited to, receiving, by the central server, information related to the UAV from the first detection device, selecting, by the central server, a second detection device from a plurality of detection devices connected to the central server, and sending, by the central server, the information to the second detection device.

Apparatuses and methods are described herein for identifying a Unmanned Aerial Vehicle (UAV), including, but not limited to, determining a first maneuver type, determining a first acoustic signature of sound captured by a plurality of audio sensors while the UAV performs the first maneuver type, determining a second acoustic signature of sound captured by the plurality of audio sensors while the UAV performs a second maneuver type different from the first maneuver type, determining an acoustic signature delta based on the first acoustic signature and the second acoustic signature, and determining an identity of the UAV based on the acoustic signature delta.

Apparatuses and methods are described herein for identifying a Unmanned Aerial Vehicle (UAV), including, but not limited to, determining a first maneuver type, determining a first acoustic signature of sound captured by a plurality of audio sensors while the UAV performs the first maneuver type, determining a second acoustic signature of sound captured by the plurality of audio sensors while the UAV performs a second maneuver type different from the first maneuver type, determining an acoustic signature delta based on the first acoustic signature and the second acoustic signature, and determining an identity of the UAV based on the acoustic signature delta.

An advanced acoustic awareness platform configured to classify environmental sounds. The advanced acoustic awareness platform comprises an audio signal acquisition sensor array and one or more processors programmed to augment the audio signal, extract features for the audio signal, visualize, and classify the signal using one or more labels. In at least one embodiment, the signal is classified based on transfer learning associated with a pretrained machine learning model.