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Therefore, through normalization, the normality of the data can be enforced.
After the normalization, the small-signal gain is 1 (0 dB).
Up to a normalization, the first eigenfunction (e_{1}) corresponding to (mu_{1}) is non-negative.
For linear normalization the transformed variance is of the form[35] Σ ̂ = HΣ H T (52).
In Figure 3, because of the normalization, the EE curves of DT under different α overlap.
Weight normalization: The normalization procedure makes the sum of particle weights equal to one.
After the normalization, the deviational sequence is the next step in this grey theory.
Prior to normalization, the raw data is very difficult to interpret.
For normalization, the total protein content was measured using a bicinchoninic acid protein assay kit.
Following normalization, the maximum probability atlas (Hammers N30R83) was applied for VOI definition.
After normalization, the training data are fed into multiple neural networks.
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