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where y n is the offset-free noiseless data sample given as y n = 1 N ∑ k = 0 N - 1 H k X k e j 2 π nk / N. (4).

A N point inverse discrete Fourier transform (IDFT) of X m [q,k], 0 ≤ k ≤ N - 1, is then computed to generate the q th SC-FDMA training symbol in time domain with n th sample given as s m q, n = 1 N ∑ k = 0 N - 1 X m q, k e j 2 π N kn, 0 ≤ n ≤ N - 1. (2).

To evaluate each algorithm, we calculated the correction rate (CR), which we defined as the difference between the total number of true positives and the total number of false positives divided by the total number peptides in the sample, given as (9) where N is the total number of peptides in the sample.

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A training performance is for N training samples given as the sum of square errors (7) Q epoch = ∑ k = 1 N e k 2,   where     e k = y k − y ~ k.

For the removal of small fibers, the mean residual error in γ increased for all data from 3% to 7%, and also showed an increased error associated with high scattering samples, given as scattering set 3; this is visualized in Fig. 5d.

Thus, random sampling gives AS rate estimates that are comparable between species.

A set of 100 randomly generated accuracy test samples (hereinafter given as synthetic samples in this publication) was generated for blind testing of the system for accuracy.

A hydrated and unexposed sample is given as a reference.

The luciferase activity given in light counts per second was normalized against the protein concentration in each sample and given as relative light units (RLU).

The relative quantitation values with and without the additional sample is given as Additional File 5.

After performing discrete Fourier transform (DFT) on the received samples, frequency domain-received samples are given as Y_{m}[!k]=C_{m} epsilon,k)*(H_{m}[!k] X_{m}[!k])+Z_{m}[!k], (4).

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