Improved Denoising Method for Ultrasonic Echo with Mother Wavelet Optimization and Best-Basis Selection

Abstract

Weak features of ultrasonicnondestructive test signals are usually immersed in noisy signals. So, in this paper, we proposed an improved scheme for noise reduction and feature extraction based on discrete wavelet transform. The basis of the mother wavelet was selected to be matched to a given signal. Three different constraints were presented to minimize the error between the denoised and the given signal. It should be mentioned that such an optimum wavelet can represent the signal more compactly with a few large coefficients which can be considered as the signal features. Standard signals and simulated ultrasonic echo were used to evaluate the performance of the presented algorithms. Signal to error ratio was used to compare the designed wavelet performance with that of standard wavelets. Simulation results revealed that the proposed method outperformed the other presented methods and even standard wavelets. The results also has shown that the signal-based noise reduction algorithms make the feature extraction more reliable. Finally, the performance of the proposed algorithm was compared with other methods from different literatures. Keyword:Denoising Discrete wavelet transform Threshold Ultrasonic_NDT

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“…Comparison with model based on signal processing and statistical analysis:The various approaches which use signal processing methods generally use the algorithms like KNN algorithm etc. [15], [16]. These algorithms are very application specific,so suppose if they are designed for a specific application,like for detecting epileptic seizure,then if that same method is used for finding say,Alzheimer,then lot of changes needs to be done in source code and source program.But if we use the synchronization index as a parameter to find the neurological disorder,it will serve as a uniform and generalized method,which can be used to detect any neurological disorder that manifest itself in the synchronization of signals.Similarly,if we compare the proposed algorithm with methods based on statistical analysis,we will see that methods based on statistical analysis use various parameters like determinism,laminarity etc to determine synchronism,while the proposed algorithm only uses a single parameter "synchronization Index",so obviously the latter approach is simpler as compared to the former one.…”