Systems and methods for determining a swing angle of a swing boom of a vehicle are provided. Sensor data is received from sensors disposed on a swing boom and a body of a vehicle. It is determined whether the swing boom is static or moving relative to the body based on the sensor data. In response to determining that the swing boom is static, the received sensor data is corrected based on an observed swing angle and an estimated swing angle is calculated based on the corrected sensor data. In response to determining that the swing boom is moving, the estimated swing angle is calculated based on the received sensor data. The estimated swing angle is output.

A microelectromechanical system device is described. The microelectromechanical system device can comprise: a proof mass coupled to an anchor via a spring, wherein the proof mass moves in response to an imposition of an external load to the proof mass, and an overtravel stop comprising a first portion and a second portion.

A microelectromechanical system device is described. The microelectromechanical system device can comprise: a proof mass coupled to an anchor via a spring, wherein the proof mass moves in response to an imposition of an external load to the proof mass, and an overtravel stop comprising a first portion and a second portion.

An encoder device for determining a kinematic value of the movement of a first object relative to a second object is provided, wherein the encoder device comprises a standard associated with the first object and at least one scanning unit associated with the second object for producing at least one scanning signal by detection of the standard and a control and evaluation unit that is configured to determine the kinematic value from the scanning signal. The control and evaluation unit is here further configured to determine the kinematic value by an evaluation of the scanning signal using a method of machine learning, with the evaluation being trained with a plurality of scanning signals and associated kinematic values.

Apparatuses and methods for predicting a crash using estimated vehicle speed. A set of sensor measurements are received from a mobile device disposed within a vehicle. A set of contiguous windows based on the sensor measurements may be defined. Each contiguous window represents a contiguous portion of the sensor measurements. A set of sensor measurements may be defined for each contiguous window. A trained neural network may execute, using the set of features, to generate one or more speed predictions. A vehicle crash prediction may be generated using the speed prediction. The vehicle crash prediction may then be transmitted to a remote device.

Apparatuses and methods for predicting a crash using estimated vehicle speed. A set of sensor measurements are received from a mobile device disposed within a vehicle. A set of contiguous windows based on the sensor measurements may be defined. Each contiguous window represents a contiguous portion of the sensor measurements. A set of sensor measurements may be defined for each contiguous window. A trained neural network may execute, using the set of features, to generate one or more speed predictions. A vehicle crash prediction may be generated using the speed prediction. The vehicle crash prediction may then be transmitted to a remote device.

Provided is a physical quantity sensor including: a movable body; a base body; and a lid body, in which the movable body is accommodated in a space between the base body and the lid body, the space is sealed with a melt portion obtained by melting a through hole provided in the lid body, the lid body and the melt portion contain silicon, and the melt portion has a continuous curved surface having unevenness.

A method for classifying a response signal of acceleration data of a structure includes obtaining at least one signal feature of a response signal, inputting the at least one signal feature into an artificial neural network, and classifying, using the artificial neural network, the response signal as an impact event or a non-impact event. One or more signal features may be used, including a response length feature, a number of peaks feature, a spectral energy feature, a dominant frequency feature, a maximum response feature, a center of mass feature, a slope feature, an average peak power feature, a response symmetry feature, or combinations thereof. One or more artificial neural networks may be used. The artificial neural networks may be trained using different combinations of signal features.

A method for classifying a response signal of acceleration data of a structure includes obtaining at least one signal feature of a response signal, inputting the at least one signal feature into an artificial neural network, and classifying, using the artificial neural network, the response signal as an impact event or a non-impact event. One or more signal features may be used, including a response length feature, a number of peaks feature, a spectral energy feature, a dominant frequency feature, a maximum response feature, a center of mass feature, a slope feature, an average peak power feature, a response symmetry feature, or combinations thereof. One or more artificial neural networks may be used. The artificial neural networks may be trained using different combinations of signal features.

Operation of a hubodometer includes orbiting detection and cancelation. In particular, in a hubodometer having a housing that rotates relative to a pendulous assembly, such operation includes detecting, by at least one orbiting sensor operatively connected to the pendulous assembly, an orbiting condition of the pendulous assembly, the orbiting condition being at least partly defined by an orbiting direction. Thereafter, and responsive to the detection of the orbiting condition, operation of the hubodometer further comprises applying, by an electric actuator operatively connected to the pendulous assembly, a countervailing force to the pendulous assembly in a direction opposite the orbiting direction. Through application of the countervailing force, the orbiting condition may be canceled.