Drug-target prediction method can effectively reduce costs and accelerate research process compared with traditional drug discovery. However， there are various challenges such as low balance of datasets and low precision of prediction in practical applications. Therefore， a drug-target interaction prediction method based on self-adaptive spherical evolution was proposed， namely ASE-KELM （self-Adaptive Spherical Evolution based on Kernel Extreme Learning Machine）. By the method， negative samples with high confidence were selected based on the principle that drugs with similar structures are likely to interact with targets. And to solve the problem that spherical evolution algorithm tends to fall into local optima， the feedback mechanism of historical memory of search factors and Linear Population Size Reduction （LPSR） were used to balance global and local search， which improved the optimization ability of the algorithm. Then the parameters of Kernel Extreme Learning Machine （KELM） were optimized by the self-adaptive spherical evolution algorithm. ASE-KELM was compared with algorithms such as NetLapRLS （Network Laplacian Regularized Least Square） and BLM-NII （Bipartite Local Model with Neighbor-based Interaction profile Inferring） on gold standard based datasets to verify the performance of the algorithms. Experimental results show that ASE-KELM outperforms comparison algorithms in AUC （Area Under the receiver operating Characteristic curve） and AUPR （Area Under the Precision-Recall curve） for the Enzyme （E）， G-Protein-Coupled Receptor （GPCR）， Ion Channel （IC）， and Nuclear Receptor （NR） datasets. And the effectiveness of ASE-KELM in predicting new drug-target pairs was validated on databases such as DrugBank.

The planning， deployment and optimization of mobile communication system networks all depend to varying degrees on the accuracy of the Reference Signal Receiving Power （RSRP） estimation. Traditionally， the RSRP of a signal receiver in a cell covered by a base station can be estimated by the corresponding wireless propagation model. In an urban environment， the wireless propagation models for different cells need to be calibrated using a large number of RSRP measurements. Due to the environment differences of different cells， the calibrated model is only applicable to the corresponding cell， and has low accuracy of RSRP estimation within the cell. To address these issues， the RSRP estimation problem was transformed into an image denoising problem and a cell-level wireless propagation model was obtained through image processing and deep learning techniques， which not only enabled RSRP estimation for the cell as a whole， but also was suitable to cells in similar environments. Firstly， the RSRP estimation map of the whole cell was obtained by predicting the RSRP of each receiver point by point through a random forest regressor. Then， the loss between the RSRP estimation map and the measured RSRP distribution map was regarded as the RSRP noise map， and a image denoising RSRP estimation method based on Conditional Generative Adversarial Network （CGAN） was proposed to reflect the environmental information of the cell through an electronic environmental map， which effectively reduced the RSRP of different cell. Experimental results show that the root mean square error of the proposed method is 6.77 dBm in predicting RSRP in a new cross-cell RSRP scenario without measured data， which is 2.55 dBm lower than that of the convolutional neural network-based RSRP estimation method EFsNet； in the same-cell RSRP prediction scenario， the number of model parameters is reduced by 80.3% compared with EFsNet.

In the process of driving， autonomous vehicles need to complete target detection， instance segmentation and target tracking for pedestrians and vehicles at the same time. An environment perception model was proposed based on deep learning for multi-task learning of these three tasks simultaneously. Firstly， spatio-temporal features were extracted from continuous frame images by Convolutional Neural Network （CNN）. Then， the spatio-temporal features were decoupled and refused by attention mechanism， and differential selection of spatio-temporal features was achieved by making full use of the correlation between tasks. Finally， in order to balance the learning rates between different tasks， the model was trained by dynamic weighted average method. The proposed model was validated on KITTI dataset， and the experimental results show that the F1 score is increased by 0.6 percentage points in target detection compared with CenterTrack model， the Multiple Object Tracking Accuracy （MOTA） is increased by 0.7 percentage points in target tracking compared with TraDeS（Track to Detect and Segment） model， and the \mathrm{A} {\mathrm{P}}_{\mathrm{50}} and \mathrm{A} {\mathrm{P}}_{\mathrm{75}} are increased by 7.4 and 3.9 percentage points respectively in instance segmentation compared with SOLOv2 （Segmenting Objects by LOcations version 2） model.

Aiming at the problem that pilot overhead required by downlink channel estimation of FDD (Frequency-Division Duplexing) massive MIMO (Multiple-Input Multiple-Output) was unaffordable, a pseudo-random pilot optimization scheme based on Compressive Sensing (CS) techniques with non-orthogonal pilot at the base station and the objective to minimize the cross correlation of the measurement matrix was proposed firstly. Then, a crossover and mutation judgment mechanism and an inner loop and outer loop mechanism were introduced to ensure the optimization of pilot sequence. Secondly, a Channel State Information (CSI) estimation algorithm based on CS techniques by utilizing the spatially common sparsity and temporal correlation in wireless MIMO channels was presented. Matrix estimation is performed by using LMMSE (Linear Minimum Mean Square Error) algorithm to accurately obtain CSI. Analysis and simulation results show that compared with random search pilot optimization scheme, location-based optimization scheme, local common support algorithm, Adaptive Structured Subspace Pursuit (ASSP) algorithm, Orthogonal Matching Pursuit (OMP) algorithm and Stepwise Orthogonal Matching Pursuit (StOMP) algorithm, the proposed algorithm can significantly achieve good channel estimation performance in the case of low pilot overhead ratio and low Signal-to-Noise Ratio (SNR).

Aiming at the tampering operation on digital video intra-frame objects, a tamper detection algorithm based on Principal Component Analysis (PCA) was proposed. Firstly, the difference frame obtained by subtracting the detected video frame from the reference frame was denoised by sparse representation method, which reduced the interference of the noise to subsequent feature extraction. Secondly, the denoised video frame was divided into non-overlapping blocks, the pixel features were extracted by PCA to construct eigenvector space. Then, k-means algorithm was used to classify the eigenvector space, and the classification result was expressed by a binary matrix. Finally, the binary morphological image was operated by image morphological operation to obtain the final detection result. The experimental results show that by using the proposed algorithm, the precision and recall are 91% and 100% respectively, and the F1 value is 95.3%, which are better than those the video forgery detection algorithm based on compression perception to some extent. Experimental results show that for the background still video, the proposed algorithm can not only detect the tampering operation to the moving objects in the frame, but also has good robustness to lossy compressed video.

Aiming at the universality of individual interaction and the heterogeneity of individual social influence in opinion spreading, an opinion formation model of social network was proposed on the basis of Hegselmann-Krause model. By introducing the concepts of intimacy between individuals, interpersonal similarity and interaction strength, the individual interactive set was extended, the influence weight was reasonably quantified, and more realistic view of interaction rule was built. Through a series of simulation experiments, the effects of main parameters in the model on opinion evolution were analyzed. The simulation results indicate that group views can converge to the same and form consensus under different confidence thresholds. And the larger the confidence threshold is, the shorter the convergence time is. When confidence threshold is 0.2, convergence time is only 10. Meanwhile, extending the interactive set and increasing the strength of interpersonal similarity will promote consensus formation. Besides, when the clustering coefficient and the average degree of scale-free network are higher, the group views are more likely to produce convergence effect. The results are helpful to understand the dynamic process of opinion formation, and can guide social managers to make decisions and analysis.

Concerning the demands of large capacity and high spectrum utilization in future mobile communication system, a method for optimizing the capacity of secondary user system in Multiple-Input Multiple-Output (MIMO) cognitive networks based on Non-Orthogonal Multiple Access (NOMA) was proposed. Firstly, the transmitted signals were pre-coded, and then the cognitive users were clustered according to channel gains. Secondly, the power allocation was performed for users after clustering. Finally, the Non-deterministic Polynomial-hard (NP-hard) multi-cluster objective function was transformed into solving the capacity of each sub-cluster. Meanwhile, taking into account Quality of Service (QoS) of cognitive users and requirement of Successive Interference Cancellation (SIC), the optimal power allocation coefficient, which is a constant between 0 and 1, was solved by using Lagrange function and Karush-Kuhn-Tucker (KKT) condition. The simulation results show that, the proposed method outperforms the average power allocation method. And when the channel quality is poor, compared with the MIMO cognitive network based on Orthogonal Multiple Access (OMA), the proposed method has improved the capacity of secondary user system significantly.

The error concealment is an important technique in the video transmission, and it can ensure the reconstruction video quality and efficiently recover the data loss and the data errors in the transmission process caused by severe transmission environments. In order to enhance the error resilience of AVS-P2, the paper proposed a new adaptive temporal-spatial error concealment method based on the redundancy motion vectors. To conceal a lost block, the paper used the spatial error concealment for the I-frame macroblocks, and used the temporal error concealment for the non-I-frame macroblocks. At the same time, according to the motion intensity of the macroblocks, it used the default error concealment of AVS-P2 and error concealment method based on redundancy motion vectors, respectively. Lastly, the proposed algorithm was realized based on the platform of the AVS-P2 RM52_20080721. The simulation results show that the proposed method is significantly better than the existing techniques in terms of both objective and subjective quality of reconstruction video.

In view of a large number of privacy disclosure issues when using k-anonymity method directly for multi-sensitive attribute data publishing, a joint privacy-sensitive properties preserving algorithm based on semantic similarity and multidimensional weighting was proposed. This algorithm realized security protection of the joint-sensitive property value and the semantic diversity of the privacy group with the help of the semantic similarity anti-clustering principle and counter-sensitive property value. According to different application needs, data privacy protection of different extent was provided. The experimental results show that this method can effectively protect data privacy and enhance data security and practicality.

As for the large differences between the visual and infrared images in gray value caused by different imaging mechanism, inconsistent contour, the low matching probability of traditional matching methods based on gray or feature, the gray information of visual and infrared images was introduced after researching a variety of Hausdorfff Distance (HD) algorithms. Image matching method based on the neighbor grayscale information Hausdorfff distance was proposed. Based on the calculation of the similarity of edge feature points, the calculation of image normalized grayscale variance was added into this method, which effectively solved the low probability problem caused by different edge of visual/infrared image in Hausdorff distance matching algorithms. The simulation results of visual and infrared images matching show that under various conditions, compared with the conventional Hausdorff distance method, the proposed algorithm effectively improves matching effect under different light conditions and anti-jamming of noise.