Aiming at insufficient robustness of depth map in multi-view 3D video with depth format, a 3D robust video watermarking algorithm based on depth map was proposed. Firstly, a depth map was divided into 4×4 nonoverlapped blocks, the mean square error of each block of pixels was calculated, and a threshold was set to distinguish the texture block and the flat block. Secondly, the block energy value of the texture block was calculated, and a threshold was set according to the calculated result to selectively embed the watermark bits. Finally, the transformed and quantized DC coefficients of each block were obtained and used to construct a 3×3 invertible matrix, then QR decomposition was performed on the invertible matrix and the watermark was embedded in the decomposed Q matrix. The proposed algorithm guarantees that the average Peak Signal to Noise Ratio (PSNR) is a constant, and the average Bit Error Rate (BER) under re-encoding attack with different Quantization Parameter (QP) values (25, 30, 35, 40) is 14.9%. Experimental results show that the algorithm has good robustness and embedding capacity, and has little impact on the quality of video.

The battery capacity of a terminal device is limited and the data transmission energy consumption is too large between devices in the Device to Device (D2D) cache communication, which lead to the decline of the file unloading rate. In order to solve the problem, a Caching communication content Sharing Algorithm for minimizing inner-Cluster node energy consumption (CCSA)was proposed. Firstly, the user nodes in the network were modeled as Poisson cluster process in view of the random distribution characteristics of user terminals. The unloading model was established based on the energy and communication distance of the node devices, and an adaptive cluster head selection weighting formula was designed. Secondly, the energy and distance weighted sum of nodes were traversed, and the local optimal principle of greedy algorithm was used to select cluster head node. Thus, the user node communication distance was optimized to ensure that users' energy consumption was the lowest to prolong their survival cycles, and the unloading rate of the system was improved. The experimental results show that, compared with the clustered Random cluster head (Random) and the non-clustered Energy Cost optimal (EC) energy consumption optimization algorithms, when the network energy consumption is optimal, the proposed algorithm prolongs the system survival cycle by about 60 percentage points and 72 percentage points. The proposed CCSA can improve the unloading rate and reduce the unloading energy consumption of the system.

Considering the low robustness of existing watermarking algorithms based on High Efficiency Video Coding (HEVC) standard, a robust video watermarking algorithm for HEVC based on intra-frame prediction modes of multi-partitioning was proposed. Firstly, in order to eliminate the intra-frame error propagation after embedding the watermark, embeddable regions were selected and the texture direction was calculated for 4×4 luminance blocks. Secondly, a scheme was proposed that the 33 angular prediction modes were divided into four pattern sets, which were recorded as follows:upper horizontal, lower horizontal, upper vertical, lower vertical. Finally, the four pattern sets were mapped to the values of the current and next to be embedded watermarks. Once the current 4×4 luminance blocks met the pattern sets, the current 33 angular prediction modes were truncated into the current pattern sets and the watermark was embedded. The watermark was extracted by the texture direction and four prediction pattern sets at decoding side. The experimental results show that, the average Peak Signal to Noise Ratio (PSNR) of the proposed algorithm is almost unchanged. In addition, the proposed algorithm achieves the Bit Error Rate (BER) of 14.1% under re-encoded attacks. Therefore, the proposed algorithm has low video distortion and can well resist the re-encoded attacks in robustness.

Concerning the low accuracy and convergence of standard Particle Swarm Optimization (PSO) algorithm, an improved particle swarm optimization based on particle filter re-sampling and mutation named RSPSO was proposed. By using the resampling characteristic of abandoning particles with low weights and duplicating and retaining particles with high weights, an existing method for mutation was adopted to overcome the disadvantage of particle degeneracy, which greatly enhanced the local search capability in the later searching stage of PSO algorithm. RSPSO algorithm was compared with the standard algorithm and some other improved algorithms in the literature under different benchmark functions. The experimental results show that RSPSO has faster convergence, higher accuracy and better stability, and it is able to solve multi-modal problems globally.

Time series are often produced in complex systems which are controlled both by macroscopic level and microscopic level laws, with long memory effect and short-term irregularly fluctuations coexisting in the same series. Traditional analysis and forecasting methods didn't distinguish these multi-level influences and always made a single model for predication, which had to introduce a lot of parameters to describe the characteristics of complex systems and result in the loss of efficiency and accuracy. However,we decomposed time series into several ones with different smoothness, all the sub-time series were respectively modeled and predicated with multi-scale sampling. Then the forecasting results of sub-time series were composed to get the result of the original time series. The experiment results on the stock forecasting show that the method is efficient, even for the time series with large fluctuations.