A spectrum processing apparatus includes: a spectrum obtainer configured to obtain an optical spectrum from a light that is scattered or reflected from a subject; and a processor configured to split the optical spectrum into a plurality of bands, determine, based on a predetermined measurement accuracy for measuring a biosignal from the light, one or more key bands from the plurality of bands, and obtain the biosignal from the determined key bands.

A spectrum processing apparatus includes: a spectrum obtainer configured to obtain an optical spectrum from a light that is scattered or reflected from a subject; and a processor configured to split the optical spectrum into a plurality of bands, determine, based on a predetermined measurement accuracy for measuring a biosignal from the light, one or more key bands from the plurality of bands, and obtain the biosignal from the determined key bands.

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.

To facilitate disturbance measurements within a WLAN network, a portable measuring device is introduced. The portable measuring device comprises at least a casing, one or more interfaces to detachably connect an external device to the portable measuring device, a processor placed in the casing, one or more antennas, two simultaneously operating radios placed in the casing, one or more power sources placed in the casing, and a power switch.

To facilitate disturbance measurements within a WLAN network, a portable measuring device is introduced. The portable measuring device comprises at least a casing, one or more interfaces to detachably connect an external device to the portable measuring device, a processor placed in the casing, one or more antennas, two simultaneously operating radios placed in the casing, one or more power sources placed in the casing, and a power switch.

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.

A visualization method for screening electronic devices is provided. In accordance with the disclosed method, a probe is applied to a grid of multiple points on the circuit, and an output produced by the circuit in response to the stimulus waveform is monitored for each of multiple grid points where the probe is applied. A power spectrum analysis (PSA) produces a power spectrum amplitude, in each of one or more frequency bins, on the monitored output for each of the multiple grid points. The PSA provides a respective pixel value for each of the multiple grid points. An image is displayed, in which image portions representing the multiple grid points are displayed with the respective pixel values.

A method of detecting states of an electrical load from an electrical signal. This involves first providing a mathematical model for the electrical signal. A sliding window can then be used to es-timate parameters of the model. In doing this, a plurality of windows are determined for the electrical signal, a window function is applied for each of the windows, and parameters of the model are determined by interpola-tion. The waveform can then be reconstructed from the determined para-meters and subtracted from the reconstructed waveform from the original signal to obtain a residual. State transitions can then be determined where a difference between the reconstructed waveform and the original signal exceeds a threshold. States are detected and determined as existing for the time period between successive state transitions. Methods of monitoring an electrical system using this approach are also described, as are electrical devices adapted to perform such methods.

A method of detecting states of an electrical load from an electrical signal. This involves first providing a mathematical model for the electrical signal. A sliding window can then be used to es-timate parameters of the model. In doing this, a plurality of windows are determined for the electrical signal, a window function is applied for each of the windows, and parameters of the model are determined by interpola-tion. The waveform can then be reconstructed from the determined para-meters and subtracted from the reconstructed waveform from the original signal to obtain a residual. State transitions can then be determined where a difference between the reconstructed waveform and the original signal exceeds a threshold. States are detected and determined as existing for the time period between successive state transitions. Methods of monitoring an electrical system using this approach are also described, as are electrical devices adapted to perform such methods.