### Table 14: Correlation of subject-specific data with effectiveness and efficiency in repl. 1

1995

"... In PAGE 11: ...null 5.3), or efficiency of isolating faults (null 5.4). As shown in Table14... In PAGE 16: ...his analysis follows that of Section 7.1.5, question 5. We omit the table for brevity; see Table14 for comparison. Again the correlation values were poor; we could not predict either effectiveness or efficiency using the self-reported motivation and skill numbers.... ..."

Cited by 32

### Table 14: Correlation of subject-specific data with effectiveness and efficiency in repl. 1

"... In PAGE 11: ...null 5.3), or efficiency of isolating faults (null 5.4). As shown in Table14... In PAGE 16: ...his analysis follows that of Section 7.1.5, question 5. We omit the table for brevity; see Table14 for comparison. Again the correlation values were poor; we could not predict either effectiveness or efficiency using the self-reported motivation and skill numbers.... ..."

### Table 1: GROUPS OF SUBJECTS IN SPECIFIC BLOCKS

"... In PAGE 3: ... STEP 5: Output the results. Table1 and Table 2 can now be used as the final timetable. Subject block is determined from table 1 and the scheduled times from table 2.... ..."

### Table 1: PSNR values (dB) obtained by pre-processing a cardiac MR image using three denoising methods

"... In PAGE 2: ...1) where X and X denote the noise-free and the denoised images, respectively and is the total number of pixels. The results of applying our method to an MRI image are summarized in Table1 for three different levels of noise, while for visual assessment an image is shown in Figure 1 (b). 2 N Secondly, we compute the product of wavelet subbands at scales two and three.... ..."

### Table 1: RMS error of the cardiac respiratory parametric model describing the motion of the left coronary tree.

"... In PAGE 6: ... The procedure was imple- mented in MATLAB and required several hours to decompose each dataset. Table1 presents validation results for the CRPM. The results provide a measure of how well the CRPM can represent the coronary motion that was experimentally measured from the images.... ..."

### Table 7.25: Correlation of subject-specific data with effectiveness and efficiency in repl. 2

1995

Cited by 32

### Table 7.25: Correlation of subject-specific data with effectiveness and efficiency in repl. 2

1995

Cited by 32

### Table 7.25: Correlation of subject-specific data with effectiveness and efficiency in repl. 2

### Table 1. Cardiac Motion Reduction in IVUS images

2006

"... In PAGE 4: ... Table1 shows the percentage of cardiac frequency sup- pressed for the 10 patients. We report the percentages for each patient and the mean for each device.... ..."

Cited by 1

### Table 4 Comparison of Models with Weighting Based on PAYROLLa

2001

"... In PAGE 12: ... Thus, the logarithmic transformation did not remove the heteroscedasticity. Table4 shows the results of fitting several mod- els, using PAYROLL to weight subject-specific variances. Because the logarithmic transforma- tion did not capture all of the heteroscedasticity, we fit the models using both PP and LnPP as response variables.... In PAGE 12: ... Plots of standardized residu- als from these models versus PAYROLL, not dis- played here, indicate that this weighting captures the heteroscedasticity. Among the models with LnPP as the response, Table4 shows that the model with YEAR as an additional random effects component, but not fixed effects, provides the best fit to the data. This model can be written as LnPPit 5 a1i 1 a2iYEARt 1 b1 1 eit~PAYROLLit!1/2, where {eit} is an i.... In PAGE 12: ...here {eit} is an i.i.d. sequence of noise terms. Among the models with PP as the response vari- able, Table4 shows that the model with no addi- tional random or fixed components is the pre- ferred choice. This model is PPit 5 a1i 1 b1 1 eit~PAYROLLit!1/2.... ..."

Cited by 6