A method of calculating a motor position uses a hall sensor. The method can include determining a motor position in response to a signal change of a hall sensor installed to a motor, calculating a basic compensation value for compensating a motor position error due to signal measurement delay of the hall sensor, determining whether a current command condition for a constant-speed operation of the motor is satisfied, calculating a first-up compensation value and a first-down compensation value, calculating an average values of the deviation between d-q axes current commands, and correcting the motor position using the basic compensation value when a difference between the average values is less than a reference average value.

A method of calculating a motor position uses a hall sensor. The method can include determining a motor position in response to a signal change of a hall sensor installed to a motor, calculating a basic compensation value for compensating a motor position error due to signal measurement delay of the hall sensor, determining whether a current command condition for a constant-speed operation of the motor is satisfied, calculating a first-up compensation value and a first-down compensation value, calculating an average values of the deviation between d-q axes current commands, and correcting the motor position using the basic compensation value when a difference between the average values is less than a reference average value.

A system includes a plurality of sensors measuring a physical parameter; a processing unit communicatively coupled to the plurality of sensors and configured to receive sensor data from each of the plurality of sensors; wherein the processing unit is further configured to compare rates of change between sensor data for each individual sensor of the plurality of sensors and each of the other individual sensors of the plurality of sensors; wherein the processing unit is further configured to perform a first action when the rate of change of each of the plurality of sensors is within a first threshold of all of the other plurality of sensors; and wherein the processing unit is further configured to perform a second action when the rate of change of at least one of the plurality of sensors is not within the first threshold of at least another of the plurality of sensors.

A system includes a plurality of sensors measuring a physical parameter; a processing unit communicatively coupled to the plurality of sensors and configured to receive sensor data from each of the plurality of sensors; wherein the processing unit is further configured to compare rates of change between sensor data for each individual sensor of the plurality of sensors and each of the other individual sensors of the plurality of sensors; wherein the processing unit is further configured to perform a first action when the rate of change of each of the plurality of sensors is within a first threshold of all of the other plurality of sensors; and wherein the processing unit is further configured to perform a second action when the rate of change of at least one of the plurality of sensors is not within the first threshold of at least another of the plurality of sensors.

An automatic filtering method includes: a step of analyzing an input signal so as to obtain a frequency spectrum of the input signal; and a step of selecting, on the basis of the frequency spectrum, at least one filter from among a plurality of preset filters and filtering the input signal. The step of selecting includes determining, on the basis of the frequency spectrum of, a signal type in the input signal; selecting, on a basis of the signal type, a corresponding filter corresponding to the signal type; filtering the input signal with use of a first parameter of the corresponding filter and setting a second parameter for the corresponding filter on a basis of a result of the filtering; and filtering the input signal with use of the second parameter set for the corresponding filter. The method may ensure a filtering effect against noise.

Disclosed is a system for combining multiple signals or sensor measurements, representing the same physical phenomenon, into a consolidated signal or measurement describing the given physical phenomenon more accurately or precisely, the system comprising a control system to automatically set or adjust the gain of the multiple signals or measurements, and a merging subsystem. The primary application is as an adaptive High Dynamic Range (HDR) sensing system. Disclosed are systems based on it. Such as for the viewing of electric are welding or other phenomena having extreme variation in exposure or signal level. In some embodiments the system includes a machine learning module that adapts to scene or subject matter changes, temporarily, spatially, or spatiotemporally. Coupled dynamic dynamic-range (D.sup.2R) compositing operates by assembling sensor information, such as images or audio, from multiple strong and weak exposures that are allowed to move and change over time, as lighting conditions or sound conditions change over time in their amplitude-domain properties. A feed back-control method automatically adjusts multiple exposure value settings for HDR compositing. To increase the dynamic range of a sensory process, such as video capture. The system is designed to asymptotically approach an optimal distribution of camera exposure control settings, under varying lighting conditions and motion, to capture an extremely high dynamic range for HDR compositing. This exposure array control system is designed to improve the effective dynamic range of cameras, audio recorders, and other sensors. Applications include an audio recorder that can be taken out of one's pocket, and used to record an earthquake or a ballistics test, and then the whispers of a mouse in a quiet room all without adjusting any volume or gain adjustments, and using ordinary sensors and ordinary analog-to-digital converters (ADC's) with a limited inherent dynamic range. Welding vision systems, autonomous robot and spacecraft vision systems, acoustic recorders in geology/mining, and scientific cameras and signal recorders, are all particular applications of this system, requiring extreme dynamic ranges with unpredictable, nonstationary signals. We have devised a new method for automatic exposure-setting control, to enable coupled dynamic dynamic-range (CD.sup.2R) video compositing. Rather than an HDR system that needs to be tuned for each lighting scenario (e.g. indoors with two exposures, and then returned outdoors with three exposures when viewing the sun or welding). The feedback control system adapts to the dynamic histogram of each exposure image to control all the exposure settings in tan

Disclosed is a system for combining multiple signals or sensor measurements, representing the same physical phenomenon, into a consolidated signal or measurement describing the given physical phenomenon more accurately or precisely, the system comprising a control system to automatically set or adjust the gain of the multiple signals or measurements, and a merging subsystem. The primary application is as an adaptive High Dynamic Range (HDR) sensing system. Disclosed are systems based on it. Such as for the viewing of electric are welding or other phenomena having extreme variation in exposure or signal level. In some embodiments the system includes a machine learning module that adapts to scene or subject matter changes, temporarily, spatially, or spatiotemporally. Coupled dynamic dynamic-range (D.sup.2R) compositing operates by assembling sensor information, such as images or audio, from multiple strong and weak exposures that are allowed to move and change over time, as lighting conditions or sound conditions change over time in their amplitude-domain properties. A feed back-control method automatically adjusts multiple exposure value settings for HDR compositing. To increase the dynamic range of a sensory process, such as video capture. The system is designed to asymptotically approach an optimal distribution of camera exposure control settings, under varying lighting conditions and motion, to capture an extremely high dynamic range for HDR compositing. This exposure array control system is designed to improve the effective dynamic range of cameras, audio recorders, and other sensors. Applications include an audio recorder that can be taken out of one's pocket, and used to record an earthquake or a ballistics test, and then the whispers of a mouse in a quiet room all without adjusting any volume or gain adjustments, and using ordinary sensors and ordinary analog-to-digital converters (ADC's) with a limited inherent dynamic range. Welding vision systems, autonomous robot and spacecraft vision systems, acoustic recorders in geology/mining, and scientific cameras and signal recorders, are all particular applications of this system, requiring extreme dynamic ranges with unpredictable, nonstationary signals. We have devised a new method for automatic exposure-setting control, to enable coupled dynamic dynamic-range (CD.sup.2R) video compositing. Rather than an HDR system that needs to be tuned for each lighting scenario (e.g. indoors with two exposures, and then returned outdoors with three exposures when viewing the sun or welding). The feedback control system adapts to the dynamic histogram of each exposure image to control all the exposure settings in tan

Signal processing apparatus includes: an input interface configured to receive an output signal V.sub.a(T) from a sensor; a prediction circuit configured to generate, on the basis of a relationship different depending on each of a plurality of converged values V.sub.c, a plurality of predicted values V.sub.b_T2 corresponding to a value of the output signal that would be obtained at a time T2 after a time T1, in a transition response period before a response time period Tr elapses where Tr denotes a response time period required for a value of the output signal V.sub.a(T) to become a converged value V.sub.c corresponding to a value P of a parameter representing a certain property of an object to be measured, in accordance with a value V.sub.a_T1 of the output signal obtained at the time T1; and an estimation circuit configured to generate, on the basis of the value V.sub.a_T2 of the output signal obtained at the time T2 and the plurality of predicted values V.sub.b_T2, an estimated value Pe of a parameter representing the certain property of the object to be measured.

A system and method for calibrating one or more sensing features for a furniture item detection is provided. More particularly, embodiments relate to determining a first noise state associated with an environment of the furniture item based on a first measured noise received from one or more sensors associated with the furniture item. A first baseline noise level for the furniture item can be generated based on filtering the first noise state associated with the environment of the furniture item. A second noise state associated with the environment of the furniture item can be determined based on a second measured noise received from the one or more sensors associated with the furniture item. A second baseline noise level for the furniture item can be generated based on filtering the second noise state associated with the environment of the furniture item. The first baseline noise level can then be adjusted to the second baseline noise level.

A resolver system includes a rotatable primary winding, a secondary winding fixed relative to the rotatable primary winding, a tertiary winding fixed relative to the rotatable primary winding and positioned /2 radians out of phase with respect to the fixed secondary winding, an excitation module electrically connected to the rotatable primary winding and configured to provide an excitation signal to the rotatable primary winding where the excitation signal is an alternating current waveform having a fundamental frequency, and a controller electrically connected to the secondary winding and configured to sample a voltage across the secondary winding at 18 times the fundamental frequency, sample a voltage across the tertiary winding at 18 times the fundamental frequency, and determine an amplitude of the fundamental frequency based on the sampled voltages across the secondary and tertiary windings, where the alternating current waveform includes a third harmonic frequency.