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
A novel scalable model of substrate components for deep n-well (DNW) RF MOSFETs with different number of fingers is presented for the first time. The test structure developed in [1] is employed to directly access the ...A novel scalable model of substrate components for deep n-well (DNW) RF MOSFETs with different number of fingers is presented for the first time. The test structure developed in [1] is employed to directly access the characteristics of the substrate to extract the different substrate components. A methodology is developed to directly extract the parameters for the substrate network from the measured data. By using the measured two-port data of a set of nMOSFETs with different number of fingers, with the DNW in grounded and float configuration, respectively, the parameters of the scalable substrate model are obtained. The method and the substrate model are further verified and validated by matching the measured and simulated output admittances. Excellent agreement up to 40 GHz for configurations in common-source has been achieved.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A novel scalable model of substrate components for deep n-well (DNW) RF MOSFETs with different number of fingers is presented for the first time. The test structure developed in [1] is employed to directly access the characteristics of the substrate to extract the different substrate components. A methodology is developed to directly extract the parameters for the substrate network from the measured data. By using the measured two-port data of a set of nMOSFETs with different number of fingers, with the DNW in grounded and float configuration, respectively, the parameters of the scalable substrate model are obtained. The method and the substrate model are further verified and validated by matching the measured and simulated output admittances. Excellent agreement up to 40 GHz for configurations in common-source has been achieved.
