Use of self-correlation metrics for evaluation of information properties of binary strings

TL;DR

This paper demonstrates that self-correlation based metrics can effectively evaluate the information content of binary strings, providing a measurable degree of determinism for their segregation.

Contribution

It introduces a novel approach using self-correlation metrics to assess the information properties of binary strings, advancing evaluation techniques.

Findings

Self-correlation metrics can distinguish binary strings by information content.

The metrics provide a quantifiable measure of determinism in binary strings.

The approach enables effective segregation based on information levels.

Abstract

It is demonstrated that appropriately chosen computable metrics based on self-correlation properties provide a degree of determinism sufficient to segregate binary strings by level of information content.