We have used the correlation coefficient to measure the correlation between sample vectors between newborn EEG signals in the time domain, and also between their corresponding T-F representations using the MBD.
If correlation coefficient f2 is adopted as the similarity function between measured phase difference vector and sample vector, then f 2 ϕ, θ 1 i = Φ ˜ T ϕ, θ 1 Φ i Φ ˜ T ϕ, θ 1 Φ ˜ ϕ, θ 1 Φ T i Φ i (11) f 2 ϕ, θ 2 i = Φ ˜ T ϕ, θ 2 Φ i Φ ˜ T ϕ, θ 2 Φ ˜ ϕ, θ 2 Φ T i Φ i (12).
Let and be the sample vectors of and.
According to the cosine similarity function, the similarity between measured vector and sample i in the database is calculated as f 1 i = ∑ n = 1 N cos Φ ˜ n − Φ n i (10).
The coherence metric measures the largest correlation between any wavelet-sampling vector pair.
Combining with the estimated angle pair, a small sample database with nine sample vectors can be formulated as shown in Figure 5, where the similarities between measured phase difference vector and the vectors in small sample database are denoted as s i, i = 0, 1, …, 8.
In addition, we determined the differences in gene expression between vector and naïve samples, which generated a list of candidate genes that are potentially involved in the symbiosis of L. striatellus and RSV.
The difference between the air vector and the ground vector is the drift caused by the wind.
