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In order to discriminate normal and abnormal heart sounds (HSs) accurately and effectively, a new method for clinical diagnosis of the heart valve diseases is proposed. The method is composed of three stages. The first stage is the preprocessing stage. During the pre-processing stage, the improved wavelet threshold shrinkage denoising algorithm is used for the noise reduction of the measured HSs....
This paper proposes a method which combines Sobel edge detection operator and soft-threshold wavelet de-noising to do edge detection on images which include White Gaussian noises. In recent years, a lot of edge detection methods are proposed. The commonly used methods which combine mean de-noising and Sobel operator or median filtering and Sobel operator can not remove salt and pepper noise very well...
In order to address the unreadable problem of direct unique identification on metal reflective surface during product real-time tracking, in this paper, two different reflective region images are captured for the same identification, the wavelet-based homomorphic filtering is applied to enhance the contrast of reflective region, the zoning Otsu thresholding is used to get the binary image, the characteristic...
We took both of the translation invariant wavelet transform and the bivariate mode under the frame of bayes MAP estimate into account and proposed a new algorithm on image de-noising. Not like the traditional threshold shrink algorithms, our threshold based not on the coefficients independently, but on the intra-scale and inter-scale correlations of TIWT coefficients. We used the relationship to create...
A variety of wavelet transform methods have been introduced to remove noise from images. However, many of these algorithms remove the fine details and smooth the structures of the image when removing noise. The wavelet coefficient magnitude sum (WCMS) algorithm can preserve edges, but it is at the expense of removing noise. The Non-Local means algorithm can removing noise effective. But it tend to...
Classification of textures based on wavelet pattern analysis is one of the most effective methods in texture classification. However using all frequency sub-bands in decomposition for classification may increase time complexity of classification algorithms. To reduce the time complexity, sub-bands with high energy and entropy are selected for classification. Fractal dimension can be used to select...
Mammography is the most effective method for the early diagnosis and treatment of breast Cancer diseases. However, data sets collected by image sensors are generally contaminated by noise. This ensures the need for image enhancement to aid interpretation. This paper introduces an efficient enhancement algorithm of digital mammograms based on wavelet analysis and modified mathematical morphology. In...
It is specially adapted to process the brim signal and the sudden(or quick)variations of signal for the fractal geometry technique, which is a multiresolution analysis and has a good localizing feature. There will be a very good expand outlook in fault detection and diagnosis. This paper takes the wavelet analysis transform as the means to process the fault detection and diagnosis of the jointing...
The recovery can be achieved from the undersampling signal in compressed sensing theory relying on the sparsity and incoherent characteristics of the signal. A data compression algorithm is advanced in this article, based on compressed sensing and wavelet transform. Firstly the framework of the compressed sensing theory is introduced, and then a one-dimension and a two-dimension wavelet transform...
Wavelet packet transform is an efficient method in speech denoising processing. In this paper, we research on various wavelet packet basis, decomposition layers, values of the threshold and threshold functions which are key parameters in wavelet packet denoising. Furthermore, we adopt three methods to evaluate the effects of denoised speech, including signal-noise-ratio(SNR), wavelet spectrum distortion...
This study evaluated the capability of neural classifier to perform the separation between epileptiform and non-epileptiform events. To processing the EEG signals was used the Wavelet Transform through the use of the Coiflet1 function. The main elements present in the EEG signals were separated in five distinct event classes (spikes, sharp waves, blinks, background activity and noise). All the events...
In this paper, the notion of multiresolution matrix-valued multiresolution analysis is introduced. A method for constructing biorthogonal multiple vector-valued multivariate wavelet wraps is put forward and their properties are investigated by means of time-frequency analysis method, matrix theory and operator theory. Three biorthogonality formulas concerning these wavelet packets are obtained. Finally,...
The advantages of wavelets and their promising features in various application have attracted a lot of interest and effort in recent years. In this article, the notion of two-directional biorthogonal finitely supported trivariate wavelet packets with multi-scale is developed. Their properties is investigated by virtue of algebra theory, time-frequency analysis method and functional analysis method...
In this paper, a new image denoising algorithm based on dual-tree complex wavelet transform (DTCWT) is proposed, in which directional windows are chosen as local neighborhood to estimate the variance. For the different subbands within the same scale, better estimation about energy cluster can obtained by ellipse windows than square windows, and the sizes of the ellipse windows for different scales...
The asymptotic waveform evaluation (AWE) technique is proposed with a preprocessing technique which is achieved by the arbitrarily dimensional fast lifting wavelet-like transform. With the new preprocessing technique, the sparse matrix equation in wavelet-domain is formed firstly. The wide band solution of this sparse linear system is obtained by application of AWE technique, and the actual induced...
This paper proposes a novel detection approach for harmonics in strong additive background noise. Firstly, a wavelet packet transform based filter bank was used to decompose the received noisy signal into several orthogonal sub-band signals. And then, combined with adaptive threshold floating technique, a group of sliding power detectors was applied to detect each given sub-band signals respectively...
In this paper, a new parameterized construction method for biorthogonal wavelet is presented. In particular, the attribution of biorthogonal wavelet such as supported interval and symmetry and vanishing moments can be selected in processing of construction. There are two kinds of parameters which are imported in construction process. By choosing different sign, the waveform shape can be changed distinctly...
In this paper a multi-resolution analysis based on Independent Component Analysis (ICA) for face recognition is examined. We extract image features of facial images from various wavelet transforms (Haar, Daubechies, Coiflet, Symlet, Biothogonal and Reverse Biorthogonal) by decomposing face image in subbands 1 to 8. These features are analyzed by ICA and Euclidean distance measure. A series of experiments...
The past decade has witnessed substantial progress towards the application of low-rate speech coders to civilian and military communications as well as computer-related voice applications. Central to this progress has been the development of new speech coders capable of producing high-quality speech at low data rates. Most of these coders incorporate mechanisms to represent the spectral properties...
The algorithm of wavelet analysis can be used to estimate the time delay (TD) and Doppler stretch (DS) of Chirp signals, but it cannot suppress the effect of Gaussian noises. In this paper, a definition of fourth-order Wavelet-Cumulants is given by combining the wavelet and the forth-order cumulant. This method can be used in unknown Gaussian noise environment. The simulation results demonstrate the...
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