### Table 6.3: Filter properties for convolution and lifting based implementations.

### Table 1: Discrete Wavelet Transform

### Table 6.2: PSNR and hardware performance of convolution and lifting based implementa- tions.

### Table 2. Scales of the 4-point Daubechies Discrete Wavelet Transform

1993

"... In PAGE 6: ... The DWT coefficients for each running-mean ERP were squared, summed, averaged and plotted as a function of time relative to the stimulus. Each row of graphs represents one scale of the transform beginning with the smallest scales at the top (see Table2 ) and proceeding to the largest scale at the bottom. Each column of graphs corresponds to one electrode site in the order Fz, Cz, Pz, from left to right.... In PAGE 12: ...transform was based on the 4-point Daubechies filters which appeared to be superior to the 20-point filters used in the initial linear regression models. Second, since low frequency information seemed valuable in the linear regression models, the range of the transform was extended, adding a fifth scale ( Table2 ). Third, selection of the coefficients was not performed by the decimation approach taken for the linear regression models.... ..."

Cited by 4

### Table 6: Discrete Fourier transform vs. wavelet-packet transform for spare representation example

"... In PAGE 16: ... To motivate the choice of wavelet over Fourier consider the function f(x1; x2) = b0 + b1x1 + b2x2 + b3x1x2, and the associated samples show in Table 5. If we perform a discrete (trigonomic) Fourier and a wavelet-packet transform on the data, we obtain the results presented in Table6 . The wavelet transform is seen to provide a sparser representation of the feature variables, re ecting the orthogonal basis in the feature space.... ..."

### Table 3.2. Discrete Wavelet Transform

### Table 1: Results for fast discrete cosine transform

1995

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### Table 5: Results for fast discrete cosine transform

1995

Cited by 15

### Table 5: Results for fast discrete cosine transform

1995

Cited by 15