It is well-known that the traditional functional regression model mainly based on least square or likelihood method. These methods usually rely some strong assumptions, such as error independence and normality, are not always satisfied. For example, response variable may contain outliers, term serially correlated. Violation of assumptions can result in unfavorable influences estimation. Therefo...

In this work, we propose a surface fitting strategy based on a two-step model to remove noise from digital images. In the first step, we minimize the total variation energy functional of an image by using the projection gradient method in order to obtain the dual variable as the smoothed normal vector. In the second step, we try to find a surface as the recovered image to fit the smoothed norma...

Many optimization problems in electrical engineering consider a large number of design parameters. A sensitivity analysis identifies the parameters with strongest influence on problem interest. This paper introduces adjoint variable method as an efficient approach to study sensitivities nonlinear electroquasistatic time domain. In contrast more common direct method, has computational cost nearl...