### Table I Table I contains the inverse Fourier and wavelet transformation characteristics. In Column 1 we list the examined datasets. Column 2 gives the number of data in the various time series. In column 3 we indicate the processing techniques used, FP stands for Fourier Processing, whereas WP means Wavelet Processing. Column 4 provides the percentage of Fourier and wavelet coe cients above the threshold. In column 5, is the cross-correlation coe cient between the `models apos; and the original averaged data. data number of points signal nb coe cients

### Table 2 Performance of Wavelet Parameter (WP) Technique.

1998

"... In PAGE 13: ... The TSI moments (TSI-M) and TSI Legendre moments (TSI-LGM) provide a retrieval efficiency of 65% and 80%, respectively. The retrieval efficiency of the wavelet (WP) technique (shown in Table2 ) is approximately 97%. The joint Legendre moment and wavelet (TSI-LGM+WP) technique provides a retrieval efficiency of 99%.... In PAGE 13: ... This is expected since the order of similarity among different images obtained from the same original images in IDB2 is much lower compared to that of the IDB1. The WP technique has been found to provide a retrieval efficiency of approximately 55% (shown in Table2 ). The TSI-LGM+WP technique provides a 10% improvement in performance over TSI-LGM.... ..."

Cited by 6

### Table 2 Performance of Wavelet Parameter (WP) Technique.

1998

"... In PAGE 13: ... The TSI moments (TSI-M) and TSI Legendre moments (TSI-LGM) provide a retrieval efficiency of 65% and 80%, respectively. The retrieval efficiency of the wavelet (WP) technique (shown in Table2 ) is approximately 97%. The joint Legendre moment and wavelet (TSI-LGM+WP) technique provides a retrieval efficiency of 99%.... In PAGE 13: ... This is expected since the order of similarity among different images obtained from the same original images in IDB2 is much lower compared to that of the IDB1. The WP technique has been found to provide a retrieval efficiency of approximately 55% (shown in Table2 ). The TSI-LGM+WP technique provides a 10% improvement in performance over TSI-LGM.... ..."

Cited by 6

### Table 4: MSE for di erent TI single wavelets thresholding and TI multiwavelet bivariate thresholding

1998

"... In PAGE 10: ... Note that the TI multiwavelet bivariate de-noising works well when the noise level is high. The comparision between the TI multiwavelet bivariate de- noising and TI single wavelet de-noising is given in Table4 . The single wavelets that are used include Daubechies 4 (D4), Symmelet 8, Haar, and Coi et 4.... ..."

Cited by 5

### Table 5: Universal and Ideal thresholding. Standard frequencies were thresholded through- out. For consistency with the bivariate thresholds, the univariate thresholds are expressed relative to the standard deviation .

1998

"... In PAGE 9: ... Bivariate universal thresholding was used for GHM wavelets and classical universal thresholding for the Daubechies wavelets. Table5 .... ..."

Cited by 23

### Table 1: Techniques for threshold selection described in this paper

### Table 2. Numerical evaluation of a de-noised Stokes I spectrum. Fourier Wavelet Hierarchical Structure wavelet-packets shrinkage thresholding detection 3-level decomposition 2[ 10?5] 2[ 10?5] 2[ 10?5] 2[ 10?5] 2[ 10?5]

"... In PAGE 7: ... The analysis of a Stokes I spectrum shows the same basic results: see Fig. 4 and Table2 . This spectrum in- cludes the strong H line and allows us to test the various techniques in the presence of a strong, heavily oversampled spectral line.... ..."

### Table 1. Physiological responses and their features

2004

"... In PAGE 5: ... These signals have been selected because they can be measured non-invasively and are relatively resistant to movement artifact. Table1 shows the physiological responses that were selected and the various features that were derived from each response. Various signal processing techniques such as Fourier transform, wavelet transform, thresholding, and peak detection, were used to derive the relevant features from the physiological signals.... ..."

Cited by 3

### Table 2. Estimations using a hard threshold. Signal Algorithm Wavelet MSE

"... In PAGE 4: ... The thresholds of the Universal threshold algorithm and the Minimax algorithm increase when the sample size increases, while the threshold of the Rigorous SURE algorithm decreases and oscillate near a certain value. The best estimations using the soft thresholding rule are presented in Table 1, while the best estimations using the hard thresholding rule are shown in Table2 . The results in Table 1 show that the soft thresholding rule is better that the hard thresholding rule in terms of MSE (see also Table 2) for almost all synthetic signals.... In PAGE 4: ... The best estimations using the soft thresholding rule are presented in Table 1, while the best estimations using the hard thresholding rule are shown in Table 2. The results in Table 1 show that the soft thresholding rule is better that the hard thresholding rule in terms of MSE (see also Table2 ) for almost all synthetic signals. Table 2 shows that the best performance is achieved with the Coiflet wavelet when applied to different signals.... ..."