Provided is a measurement apparatus including a signal source configured to output a binary digital signal configuring a multi-tone waveform, a waveform acquisition unit configured to acquire an analog signal waveform generated in response to application of the digital signal to a device under test, and a computation unit configured to calculate a frequency characteristic of the device under test from the waveform acquired by the waveform acquisition unit, in which the signal source is configured to repeatedly output a signal upconverted by multiplying a pseudo-random binary sequence (PRBS) signal by a repeating rectangular wave with a reference frequency and a reference duty ratio.

The present invention relates to a measuring apparatus, comprising: an arbitrary waveform generator to generate, and inject to a coupling network, a combination of N test signals; the coupling network to couple the N test signals to an EUT, and the responses thereof and those signals generated by the EUT itself, to a measuring unit; the measuring unit to measure the electrical rn signals provided by the coupling network; and—a processing unit to process the N test signals and the measured electrical signals, to obtain: the electromagnetic signals, noise or EMI generated by the EUT; and—the Z, Y or S parameters of the EUT or any other meaningful set of parameters that can be computed from the aforementioned ones or from voltages and currents. The invention also relates to a measuring method adapted to perform method steps with the apparatus of the invention.

The method allows the measurement of the audio signal-to-noise ratio, without having to interrupt the transmission to make the measurements. The method comprises: taking the audio signal processed by the system in a frequency hand (ΔF) for a predefined 5 measurement time period (T1); Fast Fourier Transform the sampled audio signal to obtain a signal power spectrum for the entire frequency band (ΔF); for each frequency (F), detect and store the maximum (PMax) and minimum (PMin) power values relating to each frequency considered with operations of “MaxHold” and “MinHold”, to obtain 10 corresponding sequences, respectively of maximum power (CMax) and minimum power (Cmin) of the audio signal; for each frequency (F), obtain the signal/audio noise ratio (S/N) of the signal as a function of the corresponding values of said sequences (CMax) and (CMin). 15

The method allows the measurement of the audio signal-to-noise ratio, without having to interrupt the transmission to make the measurements. The method comprises: taking the audio signal processed by the system in a frequency hand (ΔF) for a predefined 5 measurement time period (T1); Fast Fourier Transform the sampled audio signal to obtain a signal power spectrum for the entire frequency band (ΔF); for each frequency (F), detect and store the maximum (PMax) and minimum (PMin) power values relating to each frequency considered with operations of “MaxHold” and “MinHold”, to obtain 10 corresponding sequences, respectively of maximum power (CMax) and minimum power (Cmin) of the audio signal; for each frequency (F), obtain the signal/audio noise ratio (S/N) of the signal as a function of the corresponding values of said sequences (CMax) and (CMin). 15

A method for monitoring operation status of an electric motor in real time, including: reading a drive application signal of the electric motor; selecting a frequency to be observed; analyzing the frequency to be observed to obtain a spectrum thereof; analyzing the spectrum; and detecting the operation status of the electric motor on the basis of the spectrum analysis.

A device for the detection of an ultra wide band signal, including a signal reception circuit, a signal divider circuit to divide the received signal into several frequency sub-bands, a circuit to determine the amplitude and duration of the received signal in each frequency sub-band, a circuit to compare the amplitude of the signal received in each frequency sub-band with an amplitude threshold, a circuit to compare the duration of the signal received in each frequency sub-band with a time threshold, and a decision circuit that determines that the received signal is of the ultra wide band type if the amplitude of the signal received in each frequency sub-band is higher than the amplitude threshold and if the duration of the signal received in each frequency sub-band is less than the time threshold.

A device for the detection of an ultra wide band signal, including a signal reception circuit, a signal divider circuit to divide the received signal into several frequency sub-bands, a circuit to determine the amplitude and duration of the received signal in each frequency sub-band, a circuit to compare the amplitude of the signal received in each frequency sub-band with an amplitude threshold, a circuit to compare the duration of the signal received in each frequency sub-band with a time threshold, and a decision circuit that determines that the received signal is of the ultra wide band type if the amplitude of the signal received in each frequency sub-band is higher than the amplitude threshold and if the duration of the signal received in each frequency sub-band is less than the time threshold.

According to one embodiment, a time-series data waveform analysis device implemented by a computer including at least one hardware processor is provided. The hardware processor configured to: add a shapelet being a part of a partial time series included in labeled time-series data to a shapelet set; randomly extract one or more labeled time-series data and calculate a feature value of the shapelet for the extracted labeled time-series data according to a TSS method; update a parameter, which includes the shapelet and a weight coefficient for the shapelet, based on the feature value according to a stochastic gradient descent method; remove the shapelet, the corresponding weight coefficient of which is 0, from the shapelet set; and create an evaluation function based on the shapelet in the shapelet set and the weight coefficient.

According to one embodiment, a time-series data waveform analysis device implemented by a computer including at least one hardware processor is provided. The hardware processor configured to: add a shapelet being a part of a partial time series included in labeled time-series data to a shapelet set; randomly extract one or more labeled time-series data and calculate a feature value of the shapelet for the extracted labeled time-series data according to a TSS method; update a parameter, which includes the shapelet and a weight coefficient for the shapelet, based on the feature value according to a stochastic gradient descent method; remove the shapelet, the corresponding weight coefficient of which is 0, from the shapelet set; and create an evaluation function based on the shapelet in the shapelet set and the weight coefficient.

An arrangement for signal analysis provides at least one central data-processing unit and a screen unit connected to the at least one central data-processing unit, wherein the central data-processing unit calculates a spectrum and a spectrogram from a digitized signal. The at least one central data-processing unit is embodied in such a manner that it controls the screen unit in such a manner that the spectrogram of the digitized signal, the characteristic of the spectrum of the digitized signal and the characteristic of the digitized signal present in the time domain can be displayed together on the screen unit.