The instant spatial correlation and the lagged temporal correlation for each sensor is higher with the neighboring sensors and diminishes with distance from the sensor.
In order to calculate the instant coexpression correlation coefficient, we choose t − 1, t, and t + 1 as three time points in a snapshot and at each time point there are three successive expression values serving as replicate samples.
To find time instants of changing correlation directions, key turning points (KTPs) are determined by a piecewise linear representation of multivariate time series.
The noisy acquired images were stopped at three different time instants due to correlation method, EPCM, and the proposed entropy-change based, and the resulting three output diffused images were then compared quantitatively.
However, this scheme has a number of limitations stemming from the use of only one correlation time instant and a single class of adaptive algorithms.
The instant of the peak correlation value obtained for each code corresponds to an estimate of the TOA (see Figure 2).
At these time instants, denoted, the noise correlation matrix is calculated as expressed in the following: (5).
But this is compatible with there being strong correlations between every instant in the past and the future, provided that positive and negative correlations average out.
Even though the phase average of covariance is zero, as also indicated in the previous studies [19, 20], the covariance itself is not zero at every instant of time, causing strong correlation between position and velocity fluctuations in the aqueous Paul trap.
The non-zero correlation between multiple time instants is exploited to improve the robustness, tracking, and convergence performance of existing MMSE schemes.
