As mentioned in Section 4, NCV is a good statistic for impulse noise detection, whereas the bilateral filter [2] well suppresses Gaussian noise.
We next describe the NCV statistic, the ASTC noise filter, as well as the contrast enhancement procedure.
Accordingly, the accuracy of state model and statistic characters of noise will be incorrect, which consequently increases the error of transfer alignment.
Thirdly, the suboptimal unbiased constant noise statistic estimator based on the principle of maximum a posterior (MAP) is derived; on this basis, the recursive formula of the time variant noise statistic estimator using exponential weighted method is provided and then the implementation process of the ARSCKF algorithm is constructed.
In real applications, there are scenarios where the speech inactive periods are short, which would reduce the reliability of noise statistic estimation.
Based on this assumption, we introduce a novel local statistic for impulse noise detection neighborhood connective value (NCV), which measures the "connective strength" of a pixel to all the other pixels in its neighborhood window.
Unlike the ML approach, the least squares (LS) approach does not assume any characterization of the noise statistic affecting the observations; hence, it is deemed a suboptimal method [13].
With the aim being to identify a molecular signature that might allow discrimination between the two classes of tumor samples (control and TF), a cross-analysis based on three different statistical methods (significance analysis of microarrays, signal-to-noise statistic method, and Mann-Whitney test) was applied to the normalized cDNA array data.
Using either artificial neural networks [ 5, 8] or the signal-to-noise statistic [ 1] and random permutation tests, we were unable to develop a statistically reliable outcome classifier in these data.
Herein, there is one principle for evaluating the monitoring time — T s should be as small as possible to reduce its negative effect on the illumination time, under the condition of effectively estimating the statistic characteristic of interference and noise.
