Electroretinography is a hightly informative method for diagnosing heterogeneous diseases associated with disorders of the vascular structures. This article describes the biomedical ophthalmic signal of an electroretinogram using wavelet analysis. The spectral characteristics of the signal, the time-frequency picture of the wavelet scalogram are estimated, and approaches to the automation of signal analysis using the available Python libraries are described. The description of the formation of wavelet scalograms of signals using the cwt function of the PyWT library has been formalized. The Gaussian wavelet of the 8th order is chosen as the basic function of the wavelet transform. In order to automate the analysis of wavelet scalograms, the sequence of determining the connectivity of segments using the connectedComponents function from the library of computer vision algorithms, image processing, and general-purpose numerical algorithms with open source OpenCV is described.

Keywords: electrophysiology, epr, electroretinogram, erg, wavelet analysis, decision support system, connected components, Otsu method, retinal dystrophy

This article presents the results of tests of samples on the basis of equiatomic alloy with shape memory effect. The obtained system of dimensionless quantities that adequately describe the elastic-plastic deformation. It is established that the assessment of scale effects in modeling shockproof device-based alloy with shape memory effect leads to the technical ability of extrapolation of experimental results to other objects to generate the initial data of the mathematical model under the condition of similarity of characteristics of external loads.

Keywords: shock proof device, elastic-plastic deformation, shape memory effect, protection of the joints, scale factor