In order to improve the robustness of the differential number watermarking (DNW) algorithm proposed by us before, we proposed turbo-based DNW (T-DNW) in which the turbo code was employed in the DNW algorithm. The turb...In order to improve the robustness of the differential number watermarking (DNW) algorithm proposed by us before, we proposed turbo-based DNW (T-DNW) in which the turbo code was employed in the DNW algorithm. The turbo code was used to encode the message prior to watermark embedding and decode the watermark posterior to watermark detection. From the analysis and experiments, the following conclusion could be drawn. The T-DNW algorithm has little higher computational complexity than DNW. And both algorithms have the same performance in terms of watermark visual quality impact. Furthermore, the T-DNW algorithm is much more robust against some common attack than DNW. Although the T-DNW algorithm sacrifices a half payload, we think the achievements are encouraging.展开更多
This study firstly improved the Generalized Autoregressive Conditional Heteroskedast model for the issue that financial product sales data have singular information when applying this model, and the improved outlier d...This study firstly improved the Generalized Autoregressive Conditional Heteroskedast model for the issue that financial product sales data have singular information when applying this model, and the improved outlier detection method was used to detect the location of outliers, which were processed by the iterative method. Secondly, in order to describe the peak and fat tail of the financial time series, as well as the leverage effect, this work used the skewed-t Asymmetric Power Autoregressive Conditional Heteroskedasticity model based on the Autoregressive Integrated Moving Average Model to analyze the sales data. Empirical analysis showed that the model considering the skewed distribution is effective.展开更多
文摘In order to improve the robustness of the differential number watermarking (DNW) algorithm proposed by us before, we proposed turbo-based DNW (T-DNW) in which the turbo code was employed in the DNW algorithm. The turbo code was used to encode the message prior to watermark embedding and decode the watermark posterior to watermark detection. From the analysis and experiments, the following conclusion could be drawn. The T-DNW algorithm has little higher computational complexity than DNW. And both algorithms have the same performance in terms of watermark visual quality impact. Furthermore, the T-DNW algorithm is much more robust against some common attack than DNW. Although the T-DNW algorithm sacrifices a half payload, we think the achievements are encouraging.
文摘This study firstly improved the Generalized Autoregressive Conditional Heteroskedast model for the issue that financial product sales data have singular information when applying this model, and the improved outlier detection method was used to detect the location of outliers, which were processed by the iterative method. Secondly, in order to describe the peak and fat tail of the financial time series, as well as the leverage effect, this work used the skewed-t Asymmetric Power Autoregressive Conditional Heteroskedasticity model based on the Autoregressive Integrated Moving Average Model to analyze the sales data. Empirical analysis showed that the model considering the skewed distribution is effective.
