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.

The arbitrary waveform generator is characterised by its flexible signal generation, high frequency resolution and rapid frequency switching speed and is wildly used in fields like communication, radar systems, quantum control, astronautics and biomedicine.With continuous development of technology, higher requirements are placed on to the arbitrary waveform generator. Sampling rate determines the bandwidth of the output signal, spurious-free dynamic range determines the quality of generated signal. Due to above, these two indicators’ improvement is vital. However, the existing waveform generation methods cannot generate signals with quality good enough due to their technical limitations, and in order to realize a high system sampling rate, to accomplish waveform generation process in FPGA, multipath parallel structure is needed. Therefore, we proposed a parallel waveform synthesis structure based on digital resampling, which fixed the problems existing in the current methods effectively and achieved a high sampling rate as well as high quality arbitrary waveform synthesis. We also built up an experimental test bench to validate the proposed structure.

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.