The copy-move forgery detection (CMFD) begins with the preprocessing until the image is ready to process. Then, the image features are extracted using a feature-transform-based extraction called the scale-invariant feature transform (SIFT). The last step is features matching using Generalized 2 Nearest- Neighbor (G2NN) method with threshold values variation. The problem is what is the optimal threshold value and number of keypoints so that copy-move detection has the highest accuracy. The optimal threshold value and number of keypoints had determined so that the detection has the highest accuracy. The research was carried out on images without noise and with Gaussian noise.

The BeiDou navigation satellite system (BDS) is one of the four global navigation satellite systems. More attention has been paid to the positioning algorithm of the BDS. Based on the study on the Kalman filter (KF) algorithm, this paper proposed a novel algorithm for the BDS, named as the minimum dispersion coefficient criteria Kalman filter (MDCCKF) positioning algorithm. The MDCCKF algorithm adopts minimum dispersion coefficient criteria (MDCC) to remove the influence of noise with an alpha-stable distribution (ASD) model which can describe non-Gaussian noise effectively, especially for the pulse noise in positioning. By minimizing the dispersion coefficient of the positioning error, the MDCCKF assures positioning accuracy under both Gaussian and non-Gaussian environment. Compared with the original KF algorithm, it is shown that the MDCCKF algorithm has higher positioning accuracy and robustness. The MDCCKF algorithm provides insightful results for potential future research.