Jing Hu

In this study, different data types are employed according to the objectives of each experiment. Simulated orbit data are used for analyzing the effects of atmospheric drag modeling in order to ensure controlled and consistent comparisons. In contrast, real on-orbit data from the Gaofen-7 satellite are used for evaluating gravity field models with the aim of validating the accuracy and adaptability of the proposed method in real orbital environments. By examining the effects of various gravity field orders on orbit determination accuracy, computation time, and other factors using spaceborne GNSS navigation receivers, the study concludes that setting the gravity field model order to 60 is the best option for LEO satellites at 500 km altitude. With this setup, the orbit determination accuracies in the radial, tangential, and normal directions are 0.420 m, 0.191 m, and 0.225 m, respectively, for a three-dimensional position accuracy of 0.522 m. The computation time is 286 ms.

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.

To advance the study of lip-reading recognition in accordance with Chinese pronunciation norms, we carefully investigated Mandarin tone recognition based on visual information, in contrast to that of the previous character-based Chinese lip reading technique. In this paper, we mainly studied the vowel tonal transformation in Chinese pronunciation and designed a lightweight skipping convolution network framework (SCNet). And, the experimental results showed that the SCNet was sensitive to the more detailed description of the pitch change than that of the traditional model and achieved a better tone recognition effect and outstanding antiinterference performance. In addition, we conducted a more detailed study on the assistance of the deep texture information in lip-reading recognition. We found that the deep texture information has a significant effect on tone recognition, and the possibility of multimodal lip reading in Chinese tone recognition was confirmed. Similarly, we verified the role of the SCNet syllable tone recognition and found that the vowel and syllable tone recognition accuracy of our model was as high as 97.3%, which also showed the robustness of our proposed method for Chinese tone recognition and it can be widely used for tone recognition.

At present, most of the full reference laser disturbing image quality assessment methods need to know the position information of the disturbing spot and the target in advance, so that the assessment process is restricted by the prior knowledge and the preprocessing method. Aiming at this problem, this paper proposes a laser disturbing image quality assessment method based on convolution feature similarity (CNNSIM), which analyzes the output features of the image before and after laser disturbing in the convolution network. The occlusion degree of key information in the disturbing image is assessed by using the hierarchy and the sensitivity to occlusion of features, thus avoiding the input requirement of target/spot location information. The simulation experiment verifies the effectiveness of the new assessment method in different scenarios.

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%.