Spatial interpolation methods usually differ in their underlying mathematical concepts. Each has inherent advantages and disadvantages, choosing a method should be based on the type of data to analyzed. This paper, therefore, compares evaluates performances well-established techniques that can used estimate monthly rainfall Thailand. The approaches analyzed include inverse distance weighting (I...

Spatial interpolation of rain gauge data is important in forcing of hydrological simulations or evaluation of weather predictions, for example. This paper investigates the application of statistical distance, like one minus common variance of observation time series, between data sites instead of geographical distance in interpolation. Here, as a typical representative of interpolation methods ...

Interpolation technique in color image demosaicing is a big challenge to obtain RGB channels as perceived by humans. The existing demosaicing methods adopt i terative residual interpolation (IRI) technique, which performs iterations on the residuals. These methods are unable to reconstruct G channel efficiently with less iterations. Hence a new demosaicing method is proposed, which performs les...

The need to approximate functions is ubiquitous in science, either due empirical constraints or high computational cost of accessing the function. In high-energy physics, precise computation scattering cross-section a process requires evaluation computationally intensive integrals. A wide variety methods machine learning have been used tackle this problem, but often motivation using one method ...