Presence of the noise was simulated by adding to each object (synthetic or real) randomly generated noise of increasing amplitude.
For strongly pseudo-convex domains, this goal is achieved by adding to each of the (locally holomorphic) Cauchy Fantappié forms that were produced in Sect.
The 4-option test was constructed by adding to each 3-option item, the third most popular incorrect response and the 5-option item also included the fourth most popular incorrect response from the short answer test.
In many practical applications, additive Gaussian noise, which is characterized by adding to each image pixel a value from a zero-mean Gaussian distribution, can be systematically introduced into image during acquisition.
In particular, image noise having a Gaussian-like distribution is quite often encountered, and it is characterized by adding to each pixel a random value obtained from a zero-mean Gaussian distribution, whose variance determines the magnitude of the corrupting noise.
Then consider the two halves in which E is divided by H: (Ecap H^+) and (Ecap H^-) and the sets obtained by adding to each half its symmetral with respect to H, i.e., begin{aligned} E^+:= Ecap H^+ cup r_H Ecap H^+),quad E^- = Ecap H^-)cup r_H(E^- = Ecap
