An inflation system for an inflatable includes a readiness indicator module; a stretch sensor configured for mounting to the inflatable and to provide a real-time stretch data of an elastic deformation of the inflatable; and a controller configured to receive the real-time stretch data and to transmit a control signal to the readiness indicator module to indicate a deployed status of the inflatable.

An inflation system for an inflatable includes a readiness indicator module; a stretch sensor configured for mounting to the inflatable and to provide a real-time stretch data of an elastic deformation of the inflatable; and a controller configured to receive the real-time stretch data and to transmit a control signal to the readiness indicator module to indicate a deployed status of the inflatable.

A method 300 for deploying an aircraft landing gear including: receiving an aircraft landing gear deployment signal 310, receiving an aircraft position signal indicative of a distance of the aircraft from an aircraft landing site 320, receiving one or more flight signals indicating one or more dynamic conditions or parameters relating to the flight of the aircraft 330, determining, based at least on the one or more flight signals, a first aircraft position, relative to the aircraft landing site, at which the landing gear deployment should commence 340, and deploying the landing gear (a) when the aircraft reaches the first aircraft position, in the event that the deployment signal is received before the aircraft reaches the first aircraft position, or (b) immediately, in the event that the deployment signal is received when the aircraft has passed the first aircraft position 350.

A denoising apparatus, including a Micro-Electro-Mechanical System, MEMS, sensor circuit, which is configured to generate a measurement signal in response to a physical quantity. The measurement signal includes a useful signal component indicative of the physical quantity and an attack signal component due to an attack on the MEMS sensor circuit. The denoising apparatus further includes a machine learning circuitry, which is configured to estimate the useful signal component based on the measurement signal. The machine learning circuitry is trained based on training signals comprising known useful signal components and known attack signal components.

A denoising apparatus, including a Micro-Electro-Mechanical System, MEMS, sensor circuit, which is configured to generate a measurement signal in response to a physical quantity. The measurement signal includes a useful signal component indicative of the physical quantity and an attack signal component due to an attack on the MEMS sensor circuit. The denoising apparatus further includes a machine learning circuitry, which is configured to estimate the useful signal component based on the measurement signal. The machine learning circuitry is trained based on training signals comprising known useful signal components and known attack signal components.

A patient transfer device. According to one embodiment, there is provided a patient transfer device: a support part including a patient seating surface on which a patient is supported; a plurality of propeller parts, connected to the support part, for moving the support part; a power supply unit to be transported while being borne by the user, the power supply unit serving to supply power to the plurality of propeller parts; and a connection member connecting the power supply unit and the support part, wherein the support part is configured to move to follow the power supply unit.

A system may include a user interface and a processor. The processor may be configured to (a) pre-arm multiple actions according to a conditional timeline and any associated trigger events, and (b) output commands to cause the multiple actions to be completed at times consistent with the conditional timeline and any associated trigger events.

A system may include a user interface and a processor. The processor may be configured to (a) pre-arm multiple actions according to a conditional timeline and any associated trigger events, and (b) output commands to cause the multiple actions to be completed at times consistent with the conditional timeline and any associated trigger events.

A method is provided for use in maintenance of a vehicle. The method includes an onboard computer including an onboard reasoner diagnosing a failure mode onboard the vehicle using an onboard diagnostic model. The onboard reasoner further determines a service recommendation of a service action to address the failure mode. The method also includes an off-board computer including an off-board copy of the onboard reasoner receiving a measure of fix effectivity of the service action as performed to address the failure mode. The off-board copy diagnoses the failure mode or an alternate failure mode, from the measure of fix effectivity of the service action, and using an off-board copy of the onboard diagnostic model. Responsive to diagnosis of the alternate failure mode, the off-board copy determines a maintenance recommendation of a maintenance action to address the alternate failure mode, and generating a maintenance message including the maintenance recommendation.

A shield system for an unmanned arial vehicle (AUV) is provided. The shield system includes inner shield members rigidly mounted to at least the rotor arms of the AUV and outer shield members shock mounted to the inner shield members. The shield system defines opening for sensors, payload or mechanical portions of the UAV.