in analyzing a signal, especially a non-stationary signal, it is often necessarythe desired signal to be segmented into small epochs. segmentation can beperformed by splitting the signal at time instances where signal amplitude orfrequency change. in this paper, the signal is initially decomposed into signals withdifferent frequency bands using wavelet transform. then, fractal dimension of thed...

Extended Abstract. Forecasting is one of the most important purposes of time series analysis. For many years, classical methods were used for this aim. But these methods do not give good performance results for real time series due to non-linearity and non-stationarity of these data sets. On one hand, most of real world time series data display a time-varying second order structure. On th...

In the fluorescence detection system, noise will affect the sensitivity of the system. The method using Stationary Wavelet Transform to de-noise fluorescence signals based on adaptive wavelet entropy is studied. The de-noising effects of dbN families are compared, and then the db7 wavelet is chosen as the optimal wavelet. The noised fluorescence signal is decomposed at 5 levels via Discrete Wav...

introduction: one of the major problems in the development of nuclear medicine images is the presence of noise. the noise level in nuclear medicine images is usually reduced by the analysis of imaging data in a fourier transform environment. the main drawback of this environment belongs to low signal to noise ratio in high frequencies because removing noise frequencies may remove data and times...

In this paper, a method of super-resolution image reconstruction based on wavelet transform and edge-directed interpolation is put forward. Firstly the low resolution image is decomposed by the stationary wavelet transform; meanwhile, the image obtained from edge-directed interpolation is decomposed by discrete wavelet transform, then the three high frequency sub bands obtained through stationa...

Image fusion is defined as the process of combining two or more different images into a new single image retaining important features from each image with extended information content. There are two approaches to image fusion, namely Spatial Fusion and Transform fusion. In Spatial fusion, the pixel values from the source images are directly summed up and taken average to form the pixel of the c...

Image fusion is defined as the process of combining two or more different images into a new single image retaining important features from each image with extended information content. There are two approaches to image fusion, namely Spatial Fusion and Transform fusion. In Spatial fusion, the pixel values from the source images are directly summed up and taken average to form the pixel of the c...

Wavelets are of wide potential use in statistical contexts. The basics of the discrete wavelet transform are reviewed using a lter notation that is useful subsequently in the paper. A `stationary wavelet transform', where the coeecient sequences are not decimated at each stage, is described. Two diierent approaches to the construction of an inverse of the stationary wavelet transform are set ou...

Image fusion is defined as the process of combining tw o or more different images into a new single image retaining important features from each image w ith extended information content. There are two approaches to image fusion, namely Spatial Fusion and Transform fusion. In Spatial fusion, the pixel values from the source images are directly summed up and taken average to form the pixel of the...