A position sensing device for measuring a position, comprises a position sensing device for measuring a position; a plurality of sensors arranged to produce sense signals each being a function of an input phase representative of a position to be measured; a combiner circuit arranged to generate an error signal by combining the sense signals according to an array of weight factors; a processing block including a loop filter to filter the error signal and arranged to output a phase value representative of the position; and a feedback loop comprising a feedback signal unit arranged for receiving the output phase value and for adjusting based on the received output phase value of the array of weight factors.

Provided herein is a system and method for determining whether a sensor is calibrated and recalibrating of an uncalibrated sensor. The system comprises a sensor system comprising a sensor and an analysis engine configured to determine whether the sensor is uncalibrated. The system further comprises an error handling system configured to determine whether to perform a recalibration in response to the sensor system determining that the sensor is uncalibrated. The error handling system further comprises a recalibration engine configured to perform a recalibration.

A method for automatic calibration of a camshaft sensor for a motor vehicle engine. The sensor includes a processing module configured to generate, from a raw signal indicative of the variations in a magnetic field which are caused by a rotation of a target and measured by a primary cell, an output signal indicative of the moments at which teeth of the target pass past the primary cell. The sensor further includes two secondary measurement cells. The calibration method therefore makes it possible to determine two different switching thresholds for each tooth from a differential signal indicative of a difference in magnetic field measurement by the secondary cells. Also disclosed are a camshaft sensor implementing such a method, and a motor vehicle including such a sensor.

A method for calibrating a linearization function for correcting an output of a position sensor providing a continuous output position signal is described. The method adds a new linearization point to the linearization function by detecting the maximum error at the output of the position sensor and applying the new linearization function to the output of the position sensor and repeat the adding new linearization points until all available linearization points have been defined.

A method for measuring a coil property, modeled as a parallel-circuit including a capacitance with a series-circuit including a DC-voltage and frequency-dependent resistance, and an inductance and current-voltage converter series connected, by: applying an AC-voltage, having a first-frequency and a DC-voltage component, to the coil and a voltage at the current-voltage converter is captured at a second-frequency, and the impedance and phase-angle at the first-frequency are derived from n-measured values; and applying an AC-voltage, having a third-frequency differing from the first and having a DC-voltage component, to the coil and voltage at the current-voltage converter is captured at the second or fourth-frequencies, in which the impedance and phase-angle at the third-frequency are derived from m-measured values for the DC-voltage resistance.

The present invention provides a method for measuring an optical sensor system comprising an array of intrinsic fiber optic sensors at an interrogator comprising an optical source and an optical detector. The method comprises the steps of emitting an optical signal to an array of intrinsic fiber optic sensors; detecting optical responses to the emitted signal from the sensors; associating each detected optical response with an individual sensor by determining within which region among a plurality of detection regions assigned to the individual sensors the optical response is detected wherein each detection region corresponds to a wavelength range in the bandwidth of the optical sensor system; and performing signal processing on each optical response to measure the value of the physical parameter detected by its associated sensor. A calibration of the detection region assigned to each sensor is performed at predetermined intervals.

This disclosure is directed to methods, computer program products, and systems for calibrating one or more remote sensing devices in an environment. The disclosed technology relates to a calibration device configured to determine measurement data within an environment. The calibration device may transmit the measurement values, or other calibration data items, to a remote sensing device via a wireless link while the remote sensing device stays with a structure in which the remote sensing device is commissioned to operate. In response to receiving the calibration data items, the remote sensing device may adjust one or more settings of the remote sensing device in order to satisfy a calibration threshold.

Methods and apparatus are disclosed for automated acquisition of moisture readings using a handheld moisture meter. In automated mode, a succession of moisture content readings at successive positions can be acquired without any user interface input, by moving the moisture meter to successive positions on a sample, and holding steady at each position. Moisture readings stable for a time period (e.g. one second) are indicative of moisture content of a sample at a stationary position and are collected. Moisture readings varying in time are indicative of motion of the moisture meter and are not collected. Statistics can be performed on the collected readings. Notifications of stable readings and alerts for out-of-range readings can be provided. Hardware and software architectures are disclosed. The innovative technology is suitable for wood, concrete, and other materials at any stage of manufacturing or product lifecycle.

Systems and methods are described for estimating a state of a process and an input to the process using a sensor network. Each sensor node in the sensor network is directly to one or more adjacent sensor nodes and indirectly coupled to the remaining sensor nodes through the one or more adjacent sensor nodes. Each sensor node iteratively calculates a new estimated state based on estimations of the state and the input to the process calculated by the sensor node in a previous iteration. The new estimated state is then adjusted based on a difference between a predicted and actual output of a sensor and is further adjusted based on differences between a previous estimated state calculated by the sensor node and estimated states calculated by adjacent sensor nodes.

Systems and methods are described for estimating a state of a process and an input to the process using a sensor network. Each sensor node in the sensor network is directly to one or more adjacent sensor nodes and indirectly coupled to the remaining sensor nodes through the one or more adjacent sensor nodes. Each sensor node iteratively calculates a new estimated state based on estimations of the state and the input to the process calculated by the sensor node in a previous iteration. The new estimated state is then adjusted based on a difference between a predicted and actual output of a sensor and is further adjusted based on differences between a previous estimated state calculated by the sensor node and estimated states calculated by adjacent sensor nodes.