Described herein are methods and systems for controlling a sensor assembly with a plurality of same type sensors. Sensors are operated in active and inactive states. The activation state of at least one of the sensors is changed based on an operational parameter that relates to an environmental condition differentially affecting the plurality of same type sensors.

A rotational speed sensor, a manufacturing method thereof, a driving method thereof, and an electronic device are provided. The rotational speed sensor includes liquid crystal cell, rotational speed sensing module and rotational speed determining module; rotational speed sensing module is configured to convert rotational speed into voltage signal and apply voltage signal to liquid crystal cell; and at least a part of optical signal propagation module of rotational speed determining module is located in liquid crystal cell. Spectrum drift time of optical signal propagated in optical signal propagation module is variable as refractive index of liquid crystal molecules in liquid crystal cell changes; optical signal transmitting module in rotational speed determining module transmits optical signal to optical signal propagation module; optical signal receiving module in rotational speed determining module receives optical signal propagated by optical signal propagation module and analyzes spectrum to determine rotational speed.

A lens curvature variation apparatus according to an embodiment includes: a liquid lens including a common electrode and a plurality of individual electrodes; a lens driver configured to apply a voltage to the common electrode and the plurality of individual electrodes; a sensor unit configured to sense an interface of the liquid lens; an AD converter configured to convert an analog signal corresponding to the interface output from the sensor unit into a digital signal; and a controller configured to control the lens driver based on a signal output from the AD converter, wherein the plurality of individual electrodes includes a first group and a second group each including two or more individual electrodes, wherein the sensor unit includes a first sensor unit connected to a first individual electrode among individual electrodes of the first group; and a second sensor unit connected to a second individual electrode among individual electrodes of the second group; and wherein the controller includes a controller for controlling the lens driver to adjust the voltage applied to each of the individual electrodes included in the first group and the second group based on the signal output from the AD converter.

A lens curvature variation apparatus according to an embodiment includes: a liquid lens including a common electrode and a plurality of individual electrodes; a lens driver configured to apply a voltage to the common electrode and the plurality of individual electrodes; a sensor unit configured to sense an interface of the liquid lens; an AD converter configured to convert an analog signal corresponding to the interface output from the sensor unit into a digital signal; and a controller configured to control the lens driver based on a signal output from the AD converter, wherein the plurality of individual electrodes includes a first group and a second group each including two or more individual electrodes, wherein the sensor unit includes a first sensor unit connected to a first individual electrode among individual electrodes of the first group; and a second sensor unit connected to a second individual electrode among individual electrodes of the second group; and wherein the controller includes a controller for controlling the lens driver to adjust the voltage applied to each of the individual electrodes included in the first group and the second group based on the signal output from the AD converter.

A platform system receives sensor data describing the state and orientation of a tracked object and models the pose of the tracked object to determine user interactions with the platform system. To ensure that incorrect sensor data due to a saturation event or connection loss does not impact user experience, the platform system identifies regions for correction in sensor data streams based on the sensor data being at or above a saturation limit or not being received. The platform system predicts sensor data for an identified region of correction by applying a fit corresponding to points adjacent to the region for correction and determining predicted sensor data using the applied fit. The predicted sensor data is used to correct the modeled pose for the tracked object.

A platform system receives sensor data describing the state and orientation of a tracked object and models the pose of the tracked object to determine user interactions with the platform system. To ensure that incorrect sensor data due to a saturation event or connection loss does not impact user experience, the platform system identifies regions for correction in sensor data streams based on the sensor data being at or above a saturation limit or not being received. The platform system predicts sensor data for an identified region of correction by applying a fit corresponding to points adjacent to the region for correction and determining predicted sensor data using the applied fit. The predicted sensor data is used to correct the modeled pose for the tracked object.

A physical quantity detection circuit includes a signal conversion circuit configured to output a first differential signal based on an output signal of a physical quantity detection element, an active filter to which a second differential signal based on the first differential signal is input, and an analog/digital conversion circuit configured to sample a third differential signal based on an output signal of the active filter to convert the third differential signal into a digital signal, wherein the active filter includes an operational amplifier, a first chopping circuit disposed in a signal path between the signal conversion circuit and the operational amplifier, and a second chopping circuit disposed in a signal path between the operational amplifier and the analog/digital conversion circuit, and fch<fs/2, the sampling frequency is fs, and the chopping frequency is fch.

A physical quantity detection circuit includes a signal conversion circuit configured to output a first differential signal based on an output signal of a physical quantity detection element, an active filter to which a second differential signal based on the first differential signal is input, and an analog/digital conversion circuit configured to sample a third differential signal based on an output signal of the active filter to convert the third differential signal into a digital signal, wherein the active filter includes an operational amplifier, a first chopping circuit disposed in a signal path between the signal conversion circuit and the operational amplifier, and a second chopping circuit disposed in a signal path between the operational amplifier and the analog/digital conversion circuit, and fch<fs/2, the sampling frequency is fs, and the chopping frequency is fch.

Described herein are systems, methods, and other techniques for determining a period during which an implement of a construction machine is interacting with a ground surface. A vibration signal that is indicative of a movement of the implement is captured. One or more features are extracted from the vibration signal. The one or more features are provided to a machine-learning model to generate a model output. An implement-on-ground (IOG) start time and an IOG end time are predicted based on the model output, the IOG start time and the IOG end time forming the period.

Described herein are systems, methods, and other techniques for determining a period during which an implement of a construction machine is interacting with a ground surface. A vibration signal that is indicative of a movement of the implement is captured. One or more features are extracted from the vibration signal. The one or more features are provided to a machine-learning model to generate a model output. An implement-on-ground (IOG) start time and an IOG end time are predicted based on the model output, the IOG start time and the IOG end time forming the period.