The gross calorific value (GCV) or heating value of a sample of fuel is one of the important properties which defines the energy of the fuel. Many researchers have proposed empirical formulas for estimating GCV value of coal. There are some known methods like Bomb Calorimeter for determining the GCV in the laboratory. But these methods are cumbersome, costly and time consuming. In this paper, m...

the gross calorific value (gcv) or heating value of a sample of fuel is one of the important properties which defines the energy of the fuel. many researchers have proposed empirical formulas for estimating gcv value of coal. there are some known methods like bomb calorimeter for determining the gcv in the laboratory. but these methods are cumbersome, costly and time consuming. in this paper, m...

Generalized cross validation (GCV) is one of the most important approaches used to estimate parameters in the context of inverse problems and regularization techniques. A notable example is the determination of the smoothness parameter in splines. When the data are generated by a state space model, like in the spline case, efficient algorithms are available to evaluate the GCV score with comple...

The study of human cytomegalovirus (HCMV) antiviral drug resistance has enhanced knowledge of the virological targets and the mechanisms of antiviral activity. The currently approved drugs, ganciclovir (GCV), foscarnet (FOS), and cidofovir (CDV), target the viral DNA polymerase. The widespread use of ganciclovir (GCV) to treat cytomegalovirus (CMV) infections in immuno-suppressed patients has l...

To apply the Generalized Cross-Validation (GCV) as a stopping rule for an iterative method, we must estimate the trace of the so-called influence matrix which appears in the denominator of the GCV function. In the case of conjugate gradient, unlike what happens with stationary iterative methods, the regularized solution has a nonlinear dependence on the noise which affects the data of the probl...

| Superresolution reconstruction produces a high resolution image from a set of aliased low resolution images. We model the low resolution frames as blurred and down-sampled, shifted versions of the high resolution image. In many applications, the blurring process, i.e., point spread function (PSF) parameters of the imaging system, is not known. In our blind superresolution algorithm, we rst es...

Inverse problems are usually ill-posed in such a way that it is necessary to use some method to reduce their deficiencies. The method that we choose is the regularization by derivative matrices. There is a crucial problem in regularization, which is the selection of the regularization parameter λ. In this work we use generalized cross validation (GCV) as a tool for the selection of λ. GCV was p...

In many image restoration/resolution enhancement applications, the blurring process, i.e., point spread function (PSF) of the imaging system, is not known or is known only to within a set of parameters. We estimate these PSF parameters for this ill-posed class of inverse problem from raw data, along with the regularization parameters required to stabilize the solution, using the generalized cro...

Linear parametric regression models of fMRI time series have correlated residuals. One approach to address this problem is to condition the autocorrelation structure by temporal smoothing. Smoothing splines with the degree of smoothing selected by generalized cross-validation (GCV-spline) provide a method to find an optimal smoother for an fMRI time series. The purpose of this study was to dete...