The present invention provides a method for detecting a positional change amount due to a movement of a moving body by reading, with a sensor, a plurality of gradations disposed along the direction of the movement. The method includes: a step for taking, as one period, one gradation among the plurality of gradations and acquiring a pseudo sinusoidal signal in response to the positional change amount; a step for executing a Fourier transform on the pseudo sinusoidal signal within the range of at least one gradation, and calculating, from the spectral intensity of each frequency component obtained by the Fourier transform, the signal intensity of a fundamental wave component and the signal intensity of at least one harmonic component; a step for calculating a gain corresponding to each of the at least one harmonic component by dividing each signal intensity of the at least one harmonic component by the signal intensity of the fundamental wave component; and a step for detecting the positional change amount by subtracting, from the pseudo sinusoidal signal, each harmonic component multiplied by a corresponding gain.

The present invention provides a method for detecting a positional change amount due to a movement of a moving body by reading, with a sensor, a plurality of gradations disposed along the direction of the movement. The method includes: a step for taking, as one period, one gradation among the plurality of gradations and acquiring a pseudo sinusoidal signal in response to the positional change amount; a step for executing a Fourier transform on the pseudo sinusoidal signal within the range of at least one gradation, and calculating, from the spectral intensity of each frequency component obtained by the Fourier transform, the signal intensity of a fundamental wave component and the signal intensity of at least one harmonic component; a step for calculating a gain corresponding to each of the at least one harmonic component by dividing each signal intensity of the at least one harmonic component by the signal intensity of the fundamental wave component; and a step for detecting the positional change amount by subtracting, from the pseudo sinusoidal signal, each harmonic component multiplied by a corresponding gain.

A signal analysis device includes: a plurality of time-frequency conversion units that are each provided corresponding to one of a plurality of sampling sequences and convert a corresponding sampling sequence, the plurality of sampling sequences having been subjected to sampling performed at a sampling rate lower than a Nyquist rate from a plurality of signal systems generated by branching a signal of interest, and having received addition of delay times different from each other; signal processing units that collectively perform a phase compensation process corresponding to a sub-Nyquist zone of the sampling sequence output by a corresponding time-frequency conversion unit and a process of canceling phase rotation caused by a delay time difference between the plurality of sampling sequences; and a frequency estimation unit that estimates a frequency of the signal of interest by determining from which sub-Nyquist zone the signal of interest has been folded.

An analysis system includes inference processer circuitry configured to infer a corresponding classification by inputting part of frequency spectrum data corresponding to reference measurement data to a learned model having learned a relation between part of frequency spectrum data corresponding to sample measurement data and a classification related to noise corresponding to the part, causal component identification processer circuitry configured to identify causal component data of noise from a component data list based on the inferred classification, and a presentation information generator configured to generate presentation information for a user based on the causal component data.

An analysis system includes inference processer circuitry configured to infer a corresponding classification by inputting part of frequency spectrum data corresponding to reference measurement data to a learned model having learned a relation between part of frequency spectrum data corresponding to sample measurement data and a classification related to noise corresponding to the part, causal component identification processer circuitry configured to identify causal component data of noise from a component data list based on the inferred classification, and a presentation information generator configured to generate presentation information for a user based on the causal component data.

A computing device includes a port to allow the computing device to connect to a network, and one or more processors, the one or more processors configured to execute code to cause the one or more processors to determine that a new waveform has been added to a repository connected to the computing device, perform a set of measurements on the new waveform, attach results from the measurements to the new waveform as metadata, and store the new waveform and attached metadata to the repository. A method of managing waveform data includes determining that a new waveform has been added to a repository, performing a set of measurements on the new waveform, attaching results from the measurements to the new waveform as metadata, and storing the new waveform and attached metadata to the repository.

A computing device includes a port to allow the computing device to connect to a network, and one or more processors, the one or more processors configured to execute code to cause the one or more processors to determine that a new waveform has been added to a repository connected to the computing device, perform a set of measurements on the new waveform, attach results from the measurements to the new waveform as metadata, and store the new waveform and attached metadata to the repository. A method of managing waveform data includes determining that a new waveform has been added to a repository, performing a set of measurements on the new waveform, attaching results from the measurements to the new waveform as metadata, and storing the new waveform and attached metadata to the repository.

Disclosed is a device and method for extracting electric network frequency (ENF). An ENF extraction device includes an electric conductor configured to receive an electromagnetic wave generated by an adjacent alternating current (AC) power source; a sampling device configured to sample an output signal of the electric conductor; and a processor configured to extract ENF based on an output signal of the sampling device.

Disclosed is a device and method for extracting electric network frequency (ENF). An ENF extraction device includes an electric conductor configured to receive an electromagnetic wave generated by an adjacent alternating current (AC) power source; a sampling device configured to sample an output signal of the electric conductor; and a processor configured to extract ENF based on an output signal of the sampling device.

A method for measuring electrical response of a DUT includes using a measurement instrument, generating a radio frequency (RF) test signal via the measurement instrument at one or more initial frequencies, propagating the RF test signal at the one or more initial frequencies to the DUT, measuring a response of the DUT at the one or more initial frequencies and aggregating the measured response of the DUT at the one or more initial frequencies as response measurement data. The method then includes iteratively performing, until characterization of the DUT achieves a minimum criterion, the steps of adaptively selecting an additional frequency at which to generate a RF test signal based on the response measurement data based on a predetermined adaptive frequency algorithm, generating the RF test signal at the adaptively selected additional frequency, measuring a response of the DUT at the adaptively selected additional frequency, and adding the measured response of the DUT at the adaptively selected additional frequency to the response measurement data.