A measurement of phase frequency response of a device under test (DUT), wherein the DUT is characterized by a set of switchable configurations, comprises choosing the steps of a particular configuration of the DUT having nominal parameters as a reference configuration, measuring an amplitude frequency response A.sub.ref (f) and a phase frequency response ϕ.sub.ref(f) of the reference configuration, processing all configurations of the DUT which are different from the reference configuration, one after another, by measuring an amplitude response A(f) of the configuration being processed, calculating a minimum phase difference response Δϕ.sub.min(f); and calculating for each configuration, a phase frequency response ϕ(f) of the respective configuration which is being processed, in accordance with ϕ(f)=ϕ.sub.ref(f)+Δϕ.sub.min(f).

In order to improve the analysis of the RF interferences, there is provided a peak selection assistance method for finding a local peak in an RF spectrum trace. The method provides a window on the spectrum trace display, which can be configured and/or moved by a user from user interaction on said user interface. When the window is defined, the method finds the highest peak within the window and optionally adds a marker on it. The method therefore snaps to the highest peak within the window. This improves the way of precisely detecting the maximum amplitude and the center frequency for any local peak on the spectrum.

Methods and systems are disclosed for analyzing interactions between low-frequency oscillations and high-frequency activity in electromagnetic brain signals such as EEG, MEG, SEEG, and ECoG signals in subjects in real-time that does not depend on the signals being contained within narrow frequency bands, sinusoidal, sustained and monolithic. The disclosed methods and systems can be applied to electromagnetic brain signals to detect brain activity alterations associated with neurological and psychiatric diseases.

Methods and systems are disclosed for analyzing interactions between low-frequency oscillations and high-frequency activity in electromagnetic brain signals such as EEG, MEG, SEEG, and ECoG signals in subjects in real-time that does not depend on the signals being contained within narrow frequency bands, sinusoidal, sustained and monolithic. The disclosed methods and systems can be applied to electromagnetic brain signals to detect brain activity alterations associated with neurological and psychiatric diseases.

A spectrum analyzing device and method include sweeping a first frequency range at least once at a first resolution and performing a Fast Fourier Transform (FFT) on data from the sweeping of the first frequency range to produce first peak hold trace data. A second frequency range is swept at least once at a second resolution and an FFT applied to produce second peak hold trace data based on the sweeping of the second frequency range. The resolution for the sweeps is selected based on a bandwidth of the frequency range to be swept. A composite graphical display is produced based upon the first and second peak hold trace data. By caching the peak hold trace data, the spectrum analyzer preserves detected signal information when zooming from a coarser to a finer frequency resolution while minimizing the processing requirements of the spectrum analysis.

The present disclosure discloses a method of substance identification of an item to be inspected using a multi-energy-spectrum X-ray imaging system, the method comprising: acquiring a transparency related vector consisting of transparency values of the item to be inspected in N energy regions, wherein N is greater than 2; calculating distances between the transparency related vector and transparency related vectors stored in the system consisting of N transparency mean values of multiple kinds of items with multiple thicknesses in the N energy regions; and identifying the item to be inspected as the item corresponding to the minimum distance. The present disclosure is based on a multi-energy-spectrum X-ray imaging system, and proposes a method of substance identification by analyzing the multi-energy-spectrum substance identification issue. Compared with the conventional dual-energy X-ray system, the multi-spectrum imaging can significantly improve the system's ability to identify substances in theory, especially in the field of security applications. The improvement of substance identification is important for contraband inspection, such as, drugs, explosives.

The present disclosure discloses a method of substance identification of an item to be inspected using a multi-energy-spectrum X-ray imaging system, the method comprising: acquiring a transparency related vector consisting of transparency values of the item to be inspected in N energy regions, wherein N is greater than 2; calculating distances between the transparency related vector and transparency related vectors stored in the system consisting of N transparency mean values of multiple kinds of items with multiple thicknesses in the N energy regions; and identifying the item to be inspected as the item corresponding to the minimum distance. The present disclosure is based on a multi-energy-spectrum X-ray imaging system, and proposes a method of substance identification by analyzing the multi-energy-spectrum substance identification issue. Compared with the conventional dual-energy X-ray system, the multi-spectrum imaging can significantly improve the system's ability to identify substances in theory, especially in the field of security applications. The improvement of substance identification is important for contraband inspection, such as, drugs, explosives.

Provided a method comprising: obtaining waveform data sets of a periodic electric waveform signal, with a length set to one cycle time; calculating a frequency spectrum for each waveform data set; extracting and separating odd and even frequency harmonics to create odd and even frequency harmonic matrices on which a canonical correlation analysis (CCA) being applied to obtain CCA features; performing linear transformation on the CCA features to obtain linear transformed features; generating a model based on the linear transformed features; performing magnitude quantization of frequency spectrums of waveform data sets to identify normal and anomalous waveform signals.

Provided a method comprising: obtaining waveform data sets of a periodic electric waveform signal, with a length set to one cycle time; calculating a frequency spectrum for each waveform data set; extracting and separating odd and even frequency harmonics to create odd and even frequency harmonic matrices on which a canonical correlation analysis (CCA) being applied to obtain CCA features; performing linear transformation on the CCA features to obtain linear transformed features; generating a model based on the linear transformed features; performing magnitude quantization of frequency spectrums of waveform data sets to identify normal and anomalous waveform signals.

A system for testing a Nakagami fading channel and a verification method thereof are provided. The testing system includes a signal generator, a Nakagami fading channel simulator, and a computer. The signal generator is used to output a sine wave signal whose frequency is f and transmit the sine wave signal to the Nakagami fading channel simulator and the computer. The Nakagami fading channel simulator is used to generate a Nakagami fading channel. The computer is used to perform data processing and analysis. In the verification method, time domain fading characteristics, first-order statistics characteristics, and second-order statistics characteristics of the Nakagami fading channel are respectively verified. Verifying the time domain fading characteristics is verifying a waveform fluctuation rate and a fluctuation range on a time domain under different Nakagami fading factors. Verifying the first-order statistics characteristics is mainly verifying amplitude and phase distribution statistics characteristics of the Nakagami fading channel by means of Kolmogorov Smirnov (KS) hypothesis test. Verifying the second-order statistics characteristics is mainly verifying the shape and bandwidth of a power spectrum density function. In the present invention, verification on performance of the Nakagami fading channel simulator or a simulation model has features of accuracy and feasibility.