DETECTION OF ANOMALIES IN QUASI-PERIODIC PROCESSES, CYLINDRICAL AND CIRCULAR IMAGES AND THEIR SYSTEMS
Many objects in engineering, medicine and other fields can be represented by quasi-periodic processes that have a noticeable periodicity with random variations of quasi-periods for example, speech signals, turbine vibration, etc. The bright cylindrical and circular images alongside their scanning (sweep) along the spiral also has a quasi-periodic nature, for example images of a blood vessel, biological fluid facies, etc. Therefore, models of quasi-periodic processes and their systems can be used to represent these images. The article uses autoregressive models to represent quasi-periodic processes and their systems. Against the background of processes or images, there may be local anomalies (some differences from global characteristics) arising from the influence of internal or external factors.It is required to detect these anomalies. At the same time the characteristics and their ‘anomality’ are not clearly defined, thus there may be assumptions of the most general nature, for example, the difference in brightness, its dispersion, variation, etc. It is possible that the anomaly lies in a complex of several characteristics. The size of the local area and its shape can also be determined very approximately. In this article six local characteristics are simultaneously used: mean value, range, variation, Laplace operator, variance, and nonlinearity. The decision about the anomaly is made by the Mahalanobis distance or by voting. For quasi-periodic processes, cylindrical and circular images, the detection of two types of anomalies is considered: local addition of a constant or white Gaussian noise. The parameters of the developed algorithm are optimized using an adaptive pseudo-gradient procedure. The proposed algorithm is used to detect areas with the possible presence of pathogenic markers on images of biological fluid facies.