A measuring device (1) for measuring a preload of a frameless door windowpane (2) of a vehicle (3) includes: a holding device (4) for affixing the measuring device (1) to a door windowpane (2) to be checked; an acceleration measuring unit (5) for recording spatial acceleration data (6, 7, 8) about the movement of the measuring device (1); and a communications interface (9) for operating the measuring device (1) and for reading acceleration data (6, 7, 8).

A method at a sensor apparatus, the method including calculating a value for a target function based on at least one sensor of the sensor apparatus; determining that the value of the target function is within a defined threshold range for a defined time period, thereby finding an in-flight state for the sensor apparatus; and turning off transmission from a radio of the sensor apparatus based on the in-flight state.

A method at a sensor apparatus, the method including calculating a value for a target function based on at least one sensor of the sensor apparatus; determining that the value of the target function is within a defined threshold range for a defined time period, thereby finding an in-flight state for the sensor apparatus; and turning off transmission from a radio of the sensor apparatus based on the in-flight state.

An imaging system is described for generating an estimate for a virtual horizon for a moving vehicle. The estimate is based on a lidar point cloud and on pitch rate data from a gyroscope. The lidar point cloud data estimates the horizon based on lidar scans that are updated at a first rate. The gyroscope data is updated at a second rate that is faster than the first rate and therefore can be used to augment the point cloud based horizon estimation to produce a more accurate horizon estimation when the vehicle is pitching at a high rate. The gyroscope data can also be used to correct individual point clouds, which may be distorted if the vehicle is pitching or rolling during an individual scan.

An imaging system is described for generating an estimate for a virtual horizon for a moving vehicle. The estimate is based on a lidar point cloud and on pitch rate data from a gyroscope. The lidar point cloud data estimates the horizon based on lidar scans that are updated at a first rate. The gyroscope data is updated at a second rate that is faster than the first rate and therefore can be used to augment the point cloud based horizon estimation to produce a more accurate horizon estimation when the vehicle is pitching at a high rate. The gyroscope data can also be used to correct individual point clouds, which may be distorted if the vehicle is pitching or rolling during an individual scan.

Systems and methods are disclosed herein for blind frequency synchronization. In one embodiment, a synthetic inertial measurement unit (IMU) is disclosed, comprising: a plurality of nodes wirelessly coupled to each other, each The method may further comprise: a wireless transceiver at a particular node for providing wireless communications with at least one other node of the plurality of nodes, configured to receive I and Q radio samples from the other node, and to determine a frequency offset of the other node based on the received I and Q radio samples, and to synchronize a clock at the particular node, a frequency offset synchronization module at the particular node coupled to the wireless transceiver, at the particular node, and an IMU sensor for determining rotation, acceleration, and speed of the particular node; and an IMU location estimation module for using time of arrival information assuming that the clock may be synchronized at the node, the determined distance, and the rotation, acceleration, and speed of the particular node received from the IMU sensor to determine the location of the nodes, thereby providing enhanced determination of location of the plurality of nodes.

Systems and methods are disclosed herein for blind frequency synchronization. In one embodiment, a synthetic inertial measurement unit (IMU) is disclosed, comprising: a plurality of nodes wirelessly coupled to each other, each The method may further comprise: a wireless transceiver at a particular node for providing wireless communications with at least one other node of the plurality of nodes, configured to receive I and Q radio samples from the other node, and to determine a frequency offset of the other node based on the received I and Q radio samples, and to synchronize a clock at the particular node, a frequency offset synchronization module at the particular node coupled to the wireless transceiver, at the particular node, and an IMU sensor for determining rotation, acceleration, and speed of the particular node; and an IMU location estimation module for using time of arrival information assuming that the clock may be synchronized at the node, the determined distance, and the rotation, acceleration, and speed of the particular node received from the IMU sensor to determine the location of the nodes, thereby providing enhanced determination of location of the plurality of nodes.

A method of monitoring accelerations on a vessel includes measuring acceleration on the vessel using one or more sensors. The one or more sensors are communicatively coupled to a computing unit. Real-time acceleration information representative of an acceleration on the vessel based at least in part on the measured acceleration from the one or more sensors is generated. Acceleration prediction information representative of predicted wave slam using the computing unit is generated. Using the acceleration prediction information, automatic control of trim, steering, or throttle controls of the vessel is performed in a fashion computed to reduce the effects of the predicted wave slam.

A method of monitoring accelerations on a vessel includes measuring acceleration on the vessel using one or more sensors. The one or more sensors are communicatively coupled to a computing unit. Real-time acceleration information representative of an acceleration on the vessel based at least in part on the measured acceleration from the one or more sensors is generated. Acceleration prediction information representative of predicted wave slam using the computing unit is generated. Using the acceleration prediction information, automatic control of trim, steering, or throttle controls of the vessel is performed in a fashion computed to reduce the effects of the predicted wave slam.

An MEMS device includes a package (1), a bottom plate (2), and a first inertial component (3). The first inertial component (3) is located in packaging space (4) formed by the bottom plate (2) and the package (1). There is a first alignment part (21) on a surface that is of the bottom plate (2) and that faces the packaging space (4), and the first inertial component (3) has a first mounting part (31). A shape of the first mounting part (31) matches a shape of the first alignment part (21). The MEMS device is equipped with a mounting alignment reference, the first mounting part (31) is connected to the first alignment part (21), and the first inertial component is mounted on the bottom plate at a preset angle. In addition, a bottom part of the first inertial component is not directly connected to the bottom plate.