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This method introduces dummy variables u and d.
The method introduces a tradeoff parameter between accuracy and speed.
However, the proposed method introduces no ghost artifacts.
However, this method introduces stochastic effects to parameter estimates.
The method introduces favorable, fatigue strength increasing compressive residual stresses.
This method introduces a regularization parameter that takes into account the noise effect.
Our method introduces several new contributions with respect to the state of the art.
But, this method introduces perceptible artifacts into original image and degrades the perceived quality of image.
The proposed RVSIM method introduces the GM similarity thus improves the assessment of performance.
This method introduces upper approximation and lower approximation into k-means clustering algorithm.
The proposed method introduces redundancies into the signal, analogous to an error correcting code (ECC).
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