Experimental report shows generated Gaussian white noise adheres to statistical properties in signal testing, indicating effective mapping theory application.
This experiment aims to generate Gaussian white noise using a custom algorithm based on the Generalized Mapping Theory (GMT) and verify the statistical properties of the generated signal. Through the Central Limit Theorem (CLT), uniform distributions are transformed into a Gaussian distribution, and the Kolmogorov-Smirnov (KS) test and visualization techniques are employed to evaluate the quality of the generated noise. The results demonstrate that the generated signal closely approximates the target Gaussian distribution with a mean of 0 and a standard deviation of 1.
No takes yet. Share an insight, caveat, or question.
洪涛 凌 (2025) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: