In the high-speed mobile communications, transceivers always face large Doppler shift and limited pilot overhead, which severely influence the overall performance of the Traditional Carrier Synchronization Pattern (TCSP). Thus, an autocorrelation increment based Carrier Parameter Estimation Decoupling Technique (CPEDT) was proposed and was applied to the TCSP (CPEDT-TCSP). Firstly, a pilot signal with certain length was selected at the receiving end to perform the operation of modulation removal, and then the correlation operation with an effective delay length α was performed on the signal with modulation removal. The frequency offset was estimated by the result of the correlation operation, and the conjugate form of the correlation operation result with α as half of the pilot length was used to make the maximum likelihood phase offset estimation with the signal with modulation removal. Theoretical analysis and simulation results show that with pilot starting location of zero, the CPEDT-TCSP can implement the decoupling between the frequency offset estimation and the phase offset estimation in the TCSP, and can reduce the computational complexity of complex multiplication from L to 1 in the maximum likelihood phase offset estimation, therefore is more suitable for high-speed mobile communications.

Focused on the issue that the attribute reduction algorithm based on neighborhood rough set only considers the influence of a single attribute on the decision attribute, and fails to consider the correlation among different attributes, a Neighborhood Rough Set attribute reduction algorithm based on Chi-square test (ChiS-NRS) was proposed. Firstly, the Chi-square test was used to calculate the correlation, and the influence between the related attributes was considered when selecting the important attributes, making the time complexity reduced and the classification accuracy improved. Then, the improved algorithm and the Gradient Boosting Decision Tree (GBDT) algorithm were combined to establish a classification model and the model was verified on UCI datasets. Finally, the proposed model was applied to predict the occurrence of microvascular invasion in hepatocellular carcinoma. The experimental results show that the proposed algorithm has the highest classification accuracy on some UCI datasets compared with the reduction algorithm without reduction and neighborhood rough set reduction algorithm. In the prediction of microvascular invasion in hepatocellular carcinoma, compared with Convolution Neural Network (CNN), Support Vector Machine (SVM) and Random Forest (RF) prediction models, the proposed model has the prediction accuracy of 88.13% in test set, the sensitivity, specificity and the Area Under Curve (AUC) of Receiver Operating Curve (ROC) of 88.89%, 87.5% and 0.90 respectively are the best. Therefore, the prediction model proposed can better predict the occurrence of microvascular invasion in hepatocellular carcinoma and assist doctors to make more accurate diagnosis.

In recent years, the tendency of depression tends to occur at a younger age and affects more people. Although research on the topic has achieved some results, it still lacks a more objective and accurate method for identifying depressive tendencies, and research on depressive tendencies from multiple perspectives is lacking. Therefore, the combination of mental health self-check table and eye-tracking was proposed as a method for identifying depressive tendencies and was studied from multiple perspectives. The innovative features of eye movement, memory, cognitive style, and network behaviors were incorporated. In order to address complex feature relationship and extract more useful information, a scanning process with combining a stacking method was proposed to form a proposed recognition model for depressive tendencies called scanning stacking model. To comprehensively and objectively evaluate the performance of scanning and stacking model, the independent contributions of both scanning process and stacking method were evaluated in the experiment. The experimental results show that the independent contribution of scanning process is 0.03, and the independent contribution of stacking method is 0.02. In addition, the scanning stacking model was compared with several models from parameter R-squared, Mean Square Error (MSE) and average absolute error, and the results show that the scanning stacking model has better prediction effect.

Although the relevant research on Web advertisement effect has achieved good results, there are still a lack of thorough research on the interaction between advertisement and each blue link in a Web page, as well as a lack of thorough analysis of the impact of user characteristics and advertising features, and advertising metrics are also inappropriate. Therefore, a method based on multi-modal feature fusion was proposed to study the effectiveness of Internet advertising and user behavior patterns under the influence of multiple factors. Through the quantitative analysis of multi-modal features, the attractiveness of advertising was verified, and the attention effects under different conditions were summarized. By mining frequent patterns of user behavior information and combining with the characteristics of the data, the Directional Frequent Browsing Patterns (DFBP) algorithm was proposed to directionally mine the most common browsing patterns of users with fixed-length. Memory was used as a new index to measure the quality of advertising, and the random forest algorithm was improved by frequent pattern, then a new advertising memory model was built by fusing multimodal features. Experimental results show that the memory model has an accuracy of 91.64%, and it has good robustness.

Bilinear pairings computation is one of the basic operations of public key cryptography algorithm, which is widely used in the identity-based encryption and attributed-based encryption schemes. However, all of the efficient outsourcing algorithms of bilinear pairings are based on two untrusted servers, which is difficult to be realized in practical applications. In order to solve the problem, a secure outsourcing algorithm of bilinear pairings with single server was proposed. The input of users' device was took for blind treatment, which could protect the input and output confidentiality and verify the correctness of the server output by a small amount of pre-computations. The experimental results show that the proposed algorithm reduces the computation of the users' device just by several point additions and multiplications, and its verifiability probability is 2/5. Compared with the previous schemes, the proposed scheme is based on one single untrusted server and easier to be realized in reality.

Focusing on the issue that Advanced Audio Coding (AAC) is being transmitted unsafely and the steganalysis scheme's development has fallen behind relatively, a universal steganalysis scheme of AAC compressed domain was proposed. Based on the embedding influence caused by several known AAC Modefied Discrete Cosine Transform (MDCT) stegonagraphy methods, several steganalysis sub-featrues were constructed from multi-order differential correlations of AAC's inter-frame and intra-frame MDCT coefficients, then the sub-features were fused by different weights according to AAC's own coding principle, and the random forest classifier was used to distinguish embedded audio and normal audio. The experimental results show that the proposed steganalysis scheme has good performance in general detection compared to the existing algorithm; especially, the detecting rate of each stegonagraphy method is higher than 80% when the realative embedding rate is 50%.

For the nonlinear, non-Gaussian and high dynamic model in Strapdown Inertial Navigation System/Global Navigation Satellite System (SINS/GNSS) tightly integrated navigation system, the general K-means Particle Swarm Optimization (PSO) algorithm was ineffective, and the particle impoverishses and diverges greatly. A novel K-means PSO algorithm was proposed. According to the Geometric Dilution Of Precision (GDOP) of the SINS/GNSS tightly integrated navigation system, the weight of particle was updated, and the weight of each K-means was updated. The novel algorithm was applied in SNS/GNSS tightly integrated navigation system. The simulation result shows that the novel algorithm can restrain the divergence effectively and it improves precision.

The recognition results of the same target by different sensors are often contradictory in wireless sensor networks. The use of data fusion based on DempsteShafer (D-S) evidence theory could solve this problem. However, when using D-S evidence combination formula to compute，with the increase of the target identity, the computation will be growing rapidly. The processing ability of sensor nodes is limited and the data of decision in sensor networks are redundant, thus，a way was proposed to reduce the number of target identity by preprocessing and to reduce the computation; and it could rule out the data with errors through greater consistency test; therefore，it makes decision results more accurate.