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The domain decomposition method is a superior method in solving big and complicated problem. This article puts forward a domain decomposition method based on the weighted average, this method decomposes the grid based on the land cover, and discrete parameters using time dimension finite difference method, and then the patches of each grid are weighted aggregated to obtain the final output. This paper...
Directed Information (DI) has recently been introduced to quantify the causality between two signals. However, one major remaining issue with DI is the computational complexity which increases dramatically with the length of the signal. Current simplified DI computation methods are either model dependent or focus on short-time intervals losing most of the causal dependencies. In this paper, we introduce...
Monte Carlo Method has been proven very successful for non-linear and non-Gaussian estimation problems. However, a Monte Carlo filter usually has a lot of samples, which increase the computational load greatly. This paper presents threshold constraint into Monte Carlo. The target model is defined by the color information of the tracked object. Use the Bhattacharyya coefficient to get the similarity...
Directed Information (DI) is used to quantify the causal and dynamic relations between two signals. The main advantage of using DI compared to other measures of causality is that it does not assume an underlying signal model and thus can capture both linear and nonlinear interactions between signals. However, one major problem in computing the DI from data is the high computational cost and the unreliability...
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