In analyzing a signal, especially a non-stationary signal, it is often necessary the desired signal to be segmented into small epochs. Segmentation can be performed by splitting the signal at time instances where signal amplitude or frequency change. In this paper, the signal is initially decomposed into signals with different frequency bands using wavelet transform. Then, fractal dimension of ...

introduction:  breast  cancer  is  a  leading  cause  of  death  among  females  throughout  the  world.  currently,  radiologists are able to detect only 75% of breast cancer cases. making use of computer-aided design (cad)  can play an important role in helping radiologists perform more accurate diagnoses.   material and methods: using our hybrid method, the background and the pectoral muscle...

Precipitation forecasting due to its random nature in space and time always faced with many problems and this uncertainty reduces the validity of the forecasting model. Nowadays nonlinear networks as intelligent systems to predict such complex phenomena are widely used. One of the methods that have been considered in recent years in the fields of hydrology is use of wavelet transform as a moder...

In analyzing a signal, especially a non-stationary signal, it is often necessary the desired signal to be segmented into small epochs. Segmentation can be performed by splitting the signal at time instances where signal amplitude or frequency change. In this paper, the signal is initially decomposed into signals with different frequency bands using wavelet transform. Then, fractal dimension of ...

The wavelet shrinkage denoising approach is able to maintain local regularity of a signal while suppressing noise. However, the conventional wavelet shrinkage based methods are not time-scale adaptive to track the local time-scale variation. In this paper, a new time-scale adaptive denoising method for deterministic signal estimation is presented, based on the wavelet shrinkage. A class of smoo...

An adaptive wavelet method for the chemical master equation is constructed. The method is based on the representation of the solution in a sparse Haar wavelet basis, the time integration by Rothe’s method, and an iterative procedure which in each time-step selects those degrees of freedom which are essential for propagating the solution. The accuracy and efficiency of the approach is discussed,...

in the current iris recognition systems, noise removing step is only used to detect noisy parts of the iris region and features extracted from there will be excluded in matching step. whereas depending on the filter structure used in feature extraction, the noisy parts may influence relevant features. to the best of our knowledge, the effect of noise factors on feature extraction has not been c...

In this paper, we propose a new adaptive wavelet thresholding method for using in speech Enhancement. This modified version of the wavelet thresholding method updates an adaptive threshold in each frame. In the proposed method the selection of the wavelet threshold value depends on the estimates of the clean speech signal features. The evaluation results show that by using the specific features...

An adaptive numerical method for solution of the time-dependent Schrödinger equation is presented. By using an interpolating wavelet transform the number of used points can be reduced, constructing an adaptive sparse point representation. Two di erent discretisation approaches are studied in 1D ; one point-based and one based on equidistant blocks. Due to the nature of the wavelet transform a s...