2. For each group (j = 1,2,..., k) of data points in the spectrum, 2.1 Fit a peak to the data points in component j, using the modified Asymmetric Pseudo-Voigt (mAPV) model.
The main contribution of the new sub-Nyquist sampling approach is that it allows to alleviate the amount of data needed in the spectrum sensing process.
Data encoded in the spectrum of ultrafast optical pulses is mapped into the time domain using dispersion and then mixed with a spectrally engineered broadband pump using four wave mixing to create a narrow bandwidth idler, which is isolated and electronically integrated.
where m is a data point in the spectrum ranging from 0 to M-1, M the total number of points, f m=0 the lowest and f m=M the highest frequency in the measured data and p the power of the transformation required for each mode of vibration to be assigned an equal number of data points.
The intensity of each data point in the spectrum was normalized to the sum of all intensity points prior to spectral analysis.
The cumulative normalized intensity of each data point in the spectrum is defined as the sum of the normalized intensities of all points with intensities smaller than or equal to the intensity of this point.
This paper describes the methodology used to create a spectral index to retrieve vegetation water content from remotely sensed data in the solar spectrum domain.
Such false, randomly matched, identifications are expected to have lower identification scores, because the identification score itself is calculated using the probability that an observed match between experimental data in the MS/MS spectra and a database sequence is random.
Here, N, M, exp, and cal represent the number of data points in the spectra, the number of variable parameters in the model, the experimental data, and the calculated data, respectively.
where N is the number of data points in the spectra, M is the number of variable parameters in the model, and "exp" and "cal" represent the experimental and the calculated data, respectively [27].
