Harmonic wavelet transform

About: Harmonic wavelet transform is a research topic. Over the lifetime, 9602 publications have been published within this topic receiving 247336 citations.

Effectiveness of the continuous wavelet transform in the analysis of some dispersive elastic waves

Abstract: Although there have been many investigations employing the continuous wavelet transform for the analysis of dispersive waves, they seem to lack theoretical justifications for the effectiveness of the continuous wavelet transform (CWT) over other time–frequency analysis tools such as the short-time Fourier transform (STFT). The goal in this paper is to offer theoretical and experimental justifications for its effectiveness by comparing the performance of CWT and STFT in terms of their time–frequency analysis capabilities of certain dispersive elastic waves. The waves in consideration are elastic flexural waves generated by an impact in a solid circular cylinder. The ridge analysis procedure is employed to estimate instantaneous frequencies by CWT and STFT. Although in the present investigation we are focused on a limited class of dispersive waves, it gives an insight into the effectiveness of CWT for the analysis of other types of dispersive wave systems.

Characterization of Visually Similar Diffuse Diseases from B-Scan Liver Images Using Nonseparable

Abstract: This paper describes a new approach for texture characterization, based on nonseparable wavelet decomposition, and its application for the discrimination of visually similar diffuse diseases of liver. The proposed feature-extraction algo- rithm applies nonseparable quincunx wavelet transform and uses energies of the transformed regions to characterize textures. Classification experiments on a set of three different tissue types show that the scale/frequency approach, particularly one based on the nonseparable wavelet transform, could be a reliable method for a texture characterization and analysis of B-scan liver images. Comparison between the quincunx and the traditional wavelet decomposition suggests that the quincunx transform is more appropriate for characterization of noisy data, and practical applications, requiring description with lower rotational sensitivity.

Characterization of visually similar diffuse diseases from B-scan liver images using nonseparable wavelet transform

Abstract: This paper describes a new approach for texture characterization, based on nonseparable wavelet decomposition, and its application for the discrimination of visually similar diffuse diseases of liver. The proposed feature-extraction algorithm applies nonseparable quincunx wavelet transform and uses energies of the transformed regions to characterize textures. Classification experiments on a set of three different tissue types show that the scale/frequency approach, particularly one based on the nonseparable wavelet transform, could be a reliable method for a texture characterization and analysis of B-scan liver images. Comparison between the quincunx and the traditional wavelet decomposition suggests that the quincunx transform is more appropriate for characterization of noisy data, and practical applications, requiring description with lower rotational sensitivity.

Wavelet transform with spectral post-processing for enhanced feature extraction [machine condition monitoring]

Abstract: The quality of machine condition monitoring techniques and their applicability in the industry are determined by the effectiveness and efficiency, with which characteristic signal features are extracted and identified. Because of the weak amplitude and short duration of structural defect signals at the incipient stage, it is generally difficult to extract hidden features from the data measured using conventional spectral techniques. A new approach, based on a combined wavelet and Fourier transformation, is presented in this paper. Experimental studies on a rolling bearing with a localized point defect of 0.25 mm diameter have shown that this new technique provides significantly improved feature extraction capability over the spectral technique.