Analysis of harmonic parameters and detection of foreign frequencies in diagnostic signals, which are most often interpreted as fault results, may be problematic because of the spectral leakage effect. When the signal contains only the fundamental frequency and harmonics, it is possible to adjust its spectral resolution to eliminate any distortions for regular frequencies. The paper discusses the influence of resampling distortions on the quality of spectral resolution optimization in diagnostic signals, recorded digitally for objects in a steady state. The method effectiveness is measured with the use of a synthetic signal generated from an analog prototype whose parameters are known. In order to achieve low values of harmonic amplitude errors in the diagnostic signal, a high quality resampling algorithm should be used, therefore the analysis of distortions generated by four popular reasampling methods is performed. Errors are measured for test signals containing different spectral structures. Finally, the results of the test of the analyzed method in practical applications are presented.
In this paper a sample rate conversion algorithm which allows for continuously changing resampling ratio has been presented. The proposed implementation is based on a variable fractional delay filter which is implemented by means of a Farrow structure. Coefficients of this structure are computed on the basis of fractional delay filters which are designed using the offset window method. The proposed approach allows us to freely change the instantaneous resampling ratio during processing. Using such an algorithm we can simulate recording of audio on magnetic tape with nonuniform velocity as well as remove such distortions. We have demonstrated capabilities of the proposed approach based on the example of speech signal processing with a resampling ratio which was computed on the basis of estimated fundamental frequency of voiced speech segments.
Image registration is a key component of various image processing operations which involve the analysis of different image data sets. Automatic image registration domains have witnessed the application of many intelligent methodologies over the past decade; however inability to properly model object shape as well as contextual information had limited the attainable accuracy. In this paper, we propose a framework for accurate feature shape modeling and adaptive resampling using advanced techniques such as Vector Machines, Cellular Neural Network (CNN), SIFT, coreset, and Cellular Automata. CNN has found to be effective in improving feature matching as well as resampling stages of registration and complexity of the approach has been considerably reduced using corset optimization The salient features of this work are cellular neural network approach based SIFT feature point optimisation, adaptive resampling and intelligent object modelling. Developed methodology has been compared with contemporary methods using different statistical measures. Investigations over various satellite images revealed that considerable success was achieved with the approach. System has dynamically used spectral and spatial information for representing contextual knowledge using CNN-prolog approach. Methodology also illustrated to be effective in providing intelligent interpretation and adaptive resampling.