Methods, techniques, apparatus, and algorithms are described for robustly measuring real-world distances using any mobile device equipped with an accelerometer and monocular camera. A general software implementation processes 2D video, precisely tracking points of interest across frames to estimate the unsealed trajectory of the device, which is used to correct the device's inertially derived trajectory. The visual and inertial trajectories are then aligned in scale space to estimate the physical distance travelled by the device and the true distance between the visually tracked points.

Earthquake detector is a solid-state device that detects the motion of a building or structure and initiates an alarm when the motion of a building or structure rises above a certain base level or threshold level of motion that is automatically calibrated or manually entered for the specific building or structure and the specific location of the building or structure. Earthquake detector measures the amplitude of movement and the magnitude of acceleration of the actual building or structure caused by a seismic event, earthquake, or other external force because this is the primary cause of damage to the building or structure and the associated potential for collapse of the building or structure. Earthquake detector has a circuit board; a microprocessor, integrated circuit, or chip; an accelerometer integrated circuit or chip; an alarm module; a connection to a power source; and a calibration control.

Examples of the disclosure describe systems and methods for presenting virtual content on a wearable head device. In some embodiments, a state of a wearable head device is determined by minimizing a total error based on a reduced weight associated with a reprojection error. A view reflecting the determined state of the wearable head device is presented via a display of the wearable head device. In some embodiments, a wearable head device calculates a first preintegration term and second preintegration term based on the image data received via a sensor of the wearable head device and the inertial data received via a first IMU and a second IMU of the wearable head device. The wearable head device estimates a position of the device based on the first and second preintegration terms, and the wearable head device presents the virtual content based on the position of the device.

Examples of the disclosure describe systems and methods for presenting virtual content on a wearable head device. In some embodiments, a state of a wearable head device is determined by minimizing a total error based on a reduced weight associated with a reprojection error. A view reflecting the determined state of the wearable head device is presented via a display of the wearable head device. In some embodiments, a wearable head device calculates a first preintegration term and second preintegration term based on the image data received via a sensor of the wearable head device and the inertial data received via a first IMU and a second IMU of the wearable head device. The wearable head device estimates a position of the device based on the first and second preintegration terms, and the wearable head device presents the virtual content based on the position of the device.

A system estimates the speed of a moving vehicle and hence the driving behavior of an individual driving the vehicle using accelerometer data. To do so, the system analyzes received accelerometer data to find idling points when the vehicle is not moving during a driving session. Based on the idling points, the system may divide the driving session into two or more segments. The system may then determine the speed of the vehicle at one or more boundary points of each segment. For each segment, the system may analyze the accelerometer data to determine the acceleration of the vehicle for points when the vehicle is moving. Subsequently, the system may calculate the speed of the vehicle for the points when the vehicle is moving based on the acceleration of the vehicle at the points when the vehicle is moving and the speed of the vehicle at the boundary points.

Disclosed are a portable surface characteristics measurement device and a control method thereof. The portable surface characteristics measurement device includes: a roughness sensor configured to convert a signal sensed from a surface of an object during movement of the portable surface characteristics measurement device on the surface of the object into an electric vibration signal; a movement measurement sensor configured to measure a movement physical quantity of the portable surface characteristics measurement device; and a processor configured to change a sampling interval depending on the measured movement physical quantity, and sample the vibration signal in real time, wherein the processor is configured to perform Fourier transform on the sampled vibration signal, and identify a peak frequency band shown in the Fourier-transformed vibration signal as surface roughness information of the object.

Disclosed are a portable surface characteristics measurement device and a control method thereof. The portable surface characteristics measurement device includes: a roughness sensor configured to convert a signal sensed from a surface of an object during movement of the portable surface characteristics measurement device on the surface of the object into an electric vibration signal; a movement measurement sensor configured to measure a movement physical quantity of the portable surface characteristics measurement device; and a processor configured to change a sampling interval depending on the measured movement physical quantity, and sample the vibration signal in real time, wherein the processor is configured to perform Fourier transform on the sampled vibration signal, and identify a peak frequency band shown in the Fourier-transformed vibration signal as surface roughness information of the object.

A method of calculating a speed of an aircraft, a method for calculating a protection radius, a positioning system and an associated aircraft are disclosed. In one aspect, the method includes obtaining a measured speed of the aircraft and obtaining a measured position of the aircraft, associated with a reliability protection radius related to position. The method also includes calculating, by a correction loop, a corrected speed, wherein the calculation of the corrected speed includes calculating a calculated position by integration of the corrected speed, and correcting the measured speed as a function of a difference between the calculated position and the measured position. The method further comprising calculating a reliability protection radius related to the corrected speed.

A method of calculating a speed of an aircraft, a method for calculating a protection radius, a positioning system and an associated aircraft are disclosed. In one aspect, the method includes obtaining a measured speed of the aircraft and obtaining a measured position of the aircraft, associated with a reliability protection radius related to position. The method also includes calculating, by a correction loop, a corrected speed, wherein the calculation of the corrected speed includes calculating a calculated position by integration of the corrected speed, and correcting the measured speed as a function of a difference between the calculated position and the measured position. The method further comprising calculating a reliability protection radius related to the corrected speed.

A method includes determining a first speed value based on a first signal from a first data source. The method also includes determining a second speed value based on a second signal from a second data source. The method further includes determining a first likelihood of icing value based on a third signal from a third data source. The method also includes determining a second likelihood of icing value based on a fourth signal from a fourth data source. The method further includes performing a first comparison between the first speed value and the second speed value and performing a second comparison between the first likelihood of icing value and the second likelihood of icing value. The method also includes generating sensor reliability data based on the first comparison and the second comparison and displaying situational awareness data based on the sensor reliability data.