Wavelet Function
Wavelet Neural Network with Random Wavelet Function Parameters
... the wavelet function parameters are initialized and do not change during the training process, but weights between the hidden layer and the output layer of WNN are adjusted ...for wavelet ...
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COMPARATIVE ANALYSIS OF WAVELET TRANSFORM WITH INBUILT WAVELET FUNCTION AND WAVELET TRANSFORM WITHOUTINBUILT WAVELET FUNCTION FOR IMAGE COMPRESSION
... Daubechies wavelet of Discrete wavelet transform (DWT) with and without inbuilt ...Daubechies wavelet transform using image compression on “Barbara (256 x 256) JPEG 2000 ...
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Multimodal Biometric System for Person Identification using Wavelet Function
... J.G.Daugman [3] proposed automated iris recognition system in which to extract texture phase structure information of the iris, the concept of multi-scale quardrature wavelets is used, XOR operator is used to generate a ...
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Selection of Wavelet Families for Biomedical Image Compression
... of wavelet function to compress different types of medical ...specific wavelet function cannot give optimum result for each type of ...appropriate wavelet function for a given ...
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Wavelet Neural Network for Classification of Bundle Branch Blocks
... activation function is one of important parameters in achieving better performance of neural ...activation function (for example, it is [0 1] for logarithmic sigmoid, but it is [-1 1] for tangent ...
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Reference signal extraction from corrupted ECG using wavelet decomposition for MRI sequence triggering: application to small animals
... using wavelet filters, is tested on various ECG signals recorded during three imaging sequences: Gradient Echo (GE), Fast Spin Echo (FSE) and Inversion Recovery with Spin Echo ...adapted wavelet functions ...
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ECG Signal Denoising by Discrete Wavelet Transform
... Abstract—The denoising of electrocardiogram (ECG) represents the entry point for the processing of this signal. The widely algorithms for ECG de- noising are based on discrete wavelet transform (DWT). In the other ...
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A Method of Symbol Rate Estimation Based on Wavelet Transform for Digital Modulation Signals
... In non-cooperative communication, symbol rate estimation is an important basis for blind estimation and blind demodulation of digital signals. The traditional symbol rate estimation methods, such as the cyclic ...
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A hybrid group method of data handling (GMDH) with the Wavelet Decomposition for Time Series Forecasting: A review
... one wavelet (Yuelong et ...in wavelet function selecting by threshold low and high frequency coefficients for the purpose of sequence ...with wavelet functions by training multi - scale ...
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Implementation of Sawtooth Wavelet Thresholding for Noise Cancellation in One Dimensional Signal
... scaling wavelet function with the signal to be analyzed yields the most cumulative energy concentrated in few number of wavelet approximation coefficients, leaving the details wavelet ...
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Performance Evaluation of Stereo Matching Algorithms in the Lack of Visual Features
... The multiresolution subregioning-based matching (MSM) consists of trying a range of disparities and using several correlation windows. For each disparity, a correlation window of a given size is used to compute a ...
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Decay properties of the discrete wavelet transform in n dimensions with independent dilation parameters
... In this section we will prove the converse of Lemma , which is our main result given in Theorem . That is, the convergence of the discrete wavelet transform determines the continuity of f at zero. So, in order ...
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Decimated and Undecimated Wavelet Transforms Based Estimation of Images
... and Wavelet transform etc, ...is wavelet transform. In this wavelet transform, the shrinkage method is popular method for estimation of images ...
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An Application of Wavelet Analysis to Meat Consumption Cycles
... that wavelet tools are good indicators of these ...the wavelet analysis un- dertaken here demonstrates not only changes of the meat consumption cycles in time, but also in ...
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Wavelets for Nonparametric Stochastic Regression with Pairwise Negative Quadrant Dependent Random Variables
... to a distribution admitting a density f . Similar to the set up in earlier literature [3,4,6], our estimator of r will be obtained by taking the ratio of wavelet estimators of g = r f and f. The estimator ...
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Comparative Analysis of Advanced Thresholding Methods for Speech Signal Denoising
... Ligang Du [9] presented key parameters in the wavelet packet denoising such as wavelet packet basis, decomposition layers, values of the threshold and threshold functions.The two methods evaluated the ...
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Two Activation Function Wavelet Network for the Identification of Functions with High Nonlinearity
... of wavelet into soft computing led to the development of wavelet- ...Activation Function Wavelet Network (TAFWN) is proposed for the identification of functions with high ...
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Topographical measures of functional connectivity as biomarkers for post-stroke motor recovery
... This graph analysis of task-state functional connectiv- ity illuminates several topological measures that corre- spond to chronic phase motor recovery after stroke. For subjects with lesions of the cerebral cortex, high ...
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Denoising of Computed Tomography Images using Wavelet Transform
... The de-noising process consists of decomposing the image, thresholding the detail coefficients, and reconstructing the image. The decomposition procedure of the de-noising example is accomplished by using the DWT. ...
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Damage Detection in Post-Tensioned Slab Using 2D Wavelet Transforms
... As illustrated in Fig.5 in the first case of damage identification studies, the cable damage in the post-tensioned slab has been characterized by applying the damage index in the second level of decomposition. The ...
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