With the widely deployment of wireless sensor networks in smart industrial systems, lots of unauthorized attacking from the adversary is greatly threating the security and privacy of the entire industrial systems, of which node replication attacks can hardly be defended since it is conducted in the physical layer. To solve this problem, we propose a secure random key distribution scheme, called SRKD, which provides a new method for the defense against the attack. Specifically, we combine a localized algorithm with a voting mechanism to support the detection and revocation of malicious nodes. We further change the meaning of the parameter s to help prevent the replication attack. Furthermore, the experimental results show that the detection ratio of replicate nodes exceeds 90% when number of network nodes reaches 200, which demonstrates the security and effectiveness of our scheme. Compared with existing state-of-the-art schemes, SRKD also has good storage and communication efficiency.

Multichannel physiological datasets are usually nonlinear and separable in the field of emotion recognition. Many researchers have applied linear or partial nonlinear processing in feature reduction and classification, but these applications did not work well. Therefore, this paper proposed a comprehensive nonlinear method to solve this problem. On the one hand, as traditional feature reduction may cause the loss of significant amounts of feature information, Kernel Principal Component Analysis (KPCA) based on radial basis function (RBF) was introduced to map the data into a high-dimensional space, extract the nonlinear information of the features, and then reduce the dimension. This method can provide many features carrying information about the structure in the physiological dataset. On the other hand, considering its advantages of predictive power and feature selection from a large number of features, Gradient Boosting Decision Tree (GBDT) was used as a nonlinear ensemble classifier to improve the recognition accuracy. The comprehensive nonlinear processing method had a great performance on our physiological dataset. Classification accuracy of four emotions in 29 participants achieved 93.42%.

Passive reconstruction methods such as traditional multi-view stereo are capable to accurate reconstruction results. However, the depth calculation of multi-view stereo encounters significant difficulties, especially when the corresponding points have some degree of inaccuracy or unreliability. In this paper, we make use of geometric and shading cues information in the multi-view stereo approach to construct a robust energy function, which is also effective to optimize the depth information provided by multi-view stereo. This work improves the accuracy of point cloud depth. we evaluated our algorithm in the famous DTU datasets, and established our own dinosaur datasets. All the data is collected by mobile devices under natural light condition, while the reconstruction results are completed and accurate.