Vol 29, No 7 (2023)

The paper presents an equalizer of the amplitude-frequency response of the acoustic wave propagation channels in the rooms. The considered equalizer uses the so-called modified architecture based on the adaptive filter with the input signal filtering, which weights are calculated using the Recursive Least Squares (RLS) algorithm. The equalizer of the acoustic channels amplitude-fre­quency response usually requires the usage of the adaptive filters with the large number of weighs that means the large computational complexity of the adaptive filter. The adaptive filters based on the fast RLS algorithms are efficient in the terms of their comp­lexity but often unstable if the number of adaptive filter weights is large. Therefore, the algorithms are poorly suited for usage in the amplitude-frequency response equalizers of the acoustic channels. Adaptive filters based on the RLS algorithm, which use the Matrix Inversion Lemma (MIL), are stable, but computationally complex. The equalizer presented in this paper uses a simplified adaptive filter based on the MIL RLS algorithm with a diagonalized correlation matrix of the filter input signal. In addition, in the filter, each of the square submatrices on the simplified correlation matrix diagonal is inverted using a computationally efficient version of the MIL that takes into account the symmetrical structure of the mentioned submatrices relatively their main diagonals. Computer simu­lation confirms the performance of the proposed equalizer and demonstrates its efficiency comparing to the unsimplified equalizer. The demo includes the graphs of the equalizer steady-state amplitude-frequency response, the acoustic channel amplitude-frequency responses before and after the equalization, and the power spectral density plots of the equalizer input signal and the signals passed through the equalizer and the acoustic medium. The equalizer can be used in the equipment for the high-quality voice and music reproduction in the conditions of the limited computing resources for the implementation of the equalizer.

This article is devoted to development of an algorithm for automated analysis and transformation of a log message into a list of features in the form of a fixed-length vector and accumulation of the obtained vectors into a single dataset. The resulted dataset is proposed to be used in machine learning based anomaly detection systems. An additional requirement for the algorithm being developed is the diversity of protocols used to collect log messages in a computer system. These goals were achieved by develop of the software package. The software package collect and parse data from log messages in order to isolate and encode the features from log messages. The software package is enable to collect log messages by several protocols: syslog, SNMP, SQL, reading text and binary files. The data extracted from the log messages of the computing system is considered. The support of LUA scripts for data enrichment is applied. The list of features is generated. The method to encode text data extracted from log messages is proposed. The transformation algorithm of an arbitrary log message into a features vector of fixed dimension is proposed. A methodology for the formation of a dataset for subsequent use in machine learning of the anomaly detection system in a computing system is provided. An example of a dataset storage structure is given.

The diagnosis of many diseases is largely possible thanks to MRI(Magnetic Resonance Imaging). This technology allows to study internal organs of the patient: the brain, spine, bones, joints, vessels and etc. The resolution of the MRI image is limited due to various factors: movement of the patient during the scan, the continuous movement of internal organs. The higher the quality of the MRI image, the longer it takes to scan. For more accurate diagnostics it is possible to increase resolution of the yielded images. This is achieved by using SISR(Single Image Super Resolution) algorithms, which allow you to obtain images with increased resolution from a single input image. In this paper the idea of the image super-resolution algorithms is presented, various forms of the problem and solutions to it are provided. The advantages of the SISR algorithms are described. The relevance of this task in the field of medical MRI images is explained. Metrics for comparing image quality PSNR and SSIM are given and described. A dataset for testing is presented. The stage of data preparation is described: the principle of selecting images from a set of datasets, converting data into the required format, compressing images to obtain input data for selected neural network models. The PSNR, SSIM metrics of two neural network models mDCSRN and FAWDN are measured on equally prepared input data. The comparison results are presented in the form of images and averaged data for the entire sample is stored in the table.

The paper describes: a structural scheme for representing knowledge about a historically significant territory, providing a formalized informational description of a three-dimensional space model; procedural model for determining the values of the characteristics of a virtual space object; the procedure for creating a realistic virtual model of a historically significant territory, according to which conclusions should be drawn about the development of its development; carried out informational analysis of inaccurate data from various primary sources using fuzzy set theory. The technology of creating a virtual historical space using the developed information-logical model and software environments 3DVista Virtual Tour Pro, Twinmotion is described. As a result, a virtual tour has been obtained, which can be used for various purposes: remote consideration of issues related to objects of historical reconstruction; teaching schoolchildren, students; passing educational quests.

The article is devoted to the issues of developing the information support system of innovative enterprises based on modern digital platforms. The relevance of effective development of the economy of the current era and its various fields with the application of digital innovations and high technologies has been shown. The importance of improving the information support system of innovation enterprises, its architectural-technological structure model based on Industry 4.0 technologies, and other modern platforms is substantiated. At the same time, directions for improving the information support of enterprise management activities based on the Open Group Architecture Framework, its main blocks, and elements are also given. The main content and elements of the enterprise architecture are defined.

The directions for the formation of the concept of the effective architecture of the enterprise have been determined. A conceptual structural model of the information support system of innovative enterprises based on modern platforms has been proposed. The interconnected levels of the Open Group Architecture structure are shown. Based on the Information Management Technical Architecture Platform, the concept of Information Systems Architecture was developed and its advantages were determined. Taking into account the components of the Industry 4.0 platform, relevant proposals and recommendations for the modernization of the information support system of innovative enterprises have been developed.