Among biomedical signals, repetitive or quasi-periodic signals are particularly widespread. While the periodic component is still presented these signals are characterized by period variations (fundamental frequency, amplitude, etc.). The lack of synchronization or phase shifts results in variations in similar segments’ durations, nominally identical signals demonstrate a variation at peak retention times, etc. The inverse methods of oscillation theory were proposed recently as a tool to solve the problems of modelling of repetitive signals with phase shift. In the article, the inverse method of oscillation theory is considered as a tool to solve the problems of supervised and non-supervised classification, and filtering of repetitive signals with phase shift. Examples of application are presented.

The article discusses the entropy approach to the analysis of corporate financial system on the basis of the financial coefficients and market share price rates. The uncertainty of the corporate financial attractiveness is assessed using the entropy indicator of a random vector. Financial indicators such as Liquidity Financial Ratios, Operating Financial Ratios, Leverage Financial Ratios, Profitability Financial Ratios and Market Ratios serve here as vector components. Discrete entropy models of random vectors as well as differential entropy models for continuous probability distributions were used. It is shown that special probability distributions, approximately coinciding with the basic statistical ones for the first two moments, set the boundary of the maximum differential entropy. The entropy indicators were calculated based on the probability and entropy measures.