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The effectiveness of a quantization method was quantified by the commonly used mean absolute error (MAE) and mean squared error (MSE) measures: MAE I, I ^ = 1 H W ∑ h = 1 H ∑ w = 1 W I ( h, w ) - I ^ ( h, w ) 1 MSE I, I ^ = 1 H W ∑ h = 1 H ∑ w = 1 W I ( h, w ) - I ^ ( h, w ) 2 2 (6).
However, cell migration using this method was quantified by manually counting cells (which is a tedious and time consuming process).
The accuracy of the method was quantified by measuring of the vertical ΔZ and radial ΔR displacements between the stimulation site and the detected center of excitation (see Fig. 8a). Figure 8b and 8c show plots of the Z coordinate of the stimulating lead versus detected Z for periodic and single source respectively.
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Uncertainty in applying the method is quantified.
The performance of our method is quantified using quality assessment metrics.
This is the first time that the amount of bias associated with each method is quantified.
The performance of each method is quantified and compared based on three norms and the best methods are identified.
The effectiveness of the method is quantified by how well our quality metric could predict system performance among the defined quality sets.
The performance of the method is quantified and it is demonstrated that the obtained results are substantially better when dealing with multi-dimensional data.
The computational efficiency of the new method is quantified by comparing its results with other PSO variants found in the literature.
The impact of the explicit interface treatment on the stability and accuracy of the multi-block method is quantified for the advection and diffusion equations.
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