Add to ORKGNew AE signal parameters (wavelet counts) are introduced using atwo-level threshold counting of wavelet coefficients. The application of wavelet counts is illustrated in three examples of both real and simulated AE data. The significance of various classicaland newly introduced AE signal parameters used to AE source identification is tested using theneural network sensitivity and factor analyses
<p>The principle is that the wavelet coefficients of the original signal are processed by using the ...
We introduce so-called analytic stationary wavelet transform thresholding where, using the discrete ...
De-noising algorithms based on wavelet thresholding replace small wavelet coefficients by zero and k...
This paper reports a new signal classification tool, a modified wavelet network called Thresholding ...
This paper reports a new signal classification tool, a modified wavelet network called Thresholding ...
Due to simple calculation and good denoising effect, wavelet threshold denoising method has been wid...
Wavelets are applied to identify the time delay and thresholds in open-loop threshold autoregressive...
Wavelet threshold algorithms replace small magnitude wavelet coefficients with zero and keep or shri...
Wavelet threshold estimators for data with stationary correlated noise are constructed by applying a...
Usually, methods for thresholding wavelet estimators are implemented term by term, with empirical co...
We consider the testing and estimation of thresholds in heteroscedastic threshold autoregressive mod...
In this paper, we evaluate thresholding algorithms applied in wavelet filter bank (FB) based passive...
Automatic identification of the digital modulation type of a signal has found applications in many a...
By studying the traditional threshold function , it is found that the hard threshold function is dis...
Abstract—The paper provides a formal description of the sparsity of a representation via the detecti...
<p>The principle is that the wavelet coefficients of the original signal are processed by using the ...
We introduce so-called analytic stationary wavelet transform thresholding where, using the discrete ...
De-noising algorithms based on wavelet thresholding replace small wavelet coefficients by zero and k...
This paper reports a new signal classification tool, a modified wavelet network called Thresholding ...
This paper reports a new signal classification tool, a modified wavelet network called Thresholding ...
Due to simple calculation and good denoising effect, wavelet threshold denoising method has been wid...
Wavelets are applied to identify the time delay and thresholds in open-loop threshold autoregressive...
Wavelet threshold algorithms replace small magnitude wavelet coefficients with zero and keep or shri...
Wavelet threshold estimators for data with stationary correlated noise are constructed by applying a...
Usually, methods for thresholding wavelet estimators are implemented term by term, with empirical co...
We consider the testing and estimation of thresholds in heteroscedastic threshold autoregressive mod...
In this paper, we evaluate thresholding algorithms applied in wavelet filter bank (FB) based passive...
Automatic identification of the digital modulation type of a signal has found applications in many a...
By studying the traditional threshold function , it is found that the hard threshold function is dis...
Abstract—The paper provides a formal description of the sparsity of a representation via the detecti...
<p>The principle is that the wavelet coefficients of the original signal are processed by using the ...
We introduce so-called analytic stationary wavelet transform thresholding where, using the discrete ...
De-noising algorithms based on wavelet thresholding replace small wavelet coefficients by zero and k...