In order to improve the error code performance of Low Density Parity Check (LDPC) code Offset Min-Sum (OMS) algorithm, a low complexity OMS algorithm for 5G LDPC codes was proposed based on 5G NR standard. Aiming at the problem that the offset factor value calculation in the traditional algorithm is not accurate enough, the density evolution was used to obtain a more accurate offset factor value, which was used to the check node updating in order to enhance the performance of OMS algorithm. And the obtained offset factor value was approximated by using the linear approximation method, so as to reduce the complexity of the algorithm while ensuring decoding performance. For the influence of the variable node oscillation phenomenon on the decoding, the Log-Likelihood Ratio (LLR) message values before and after node updating were weighted, so the oscillation of the variable node was reduced, and the convergence speed of the decoder was improved. The simulation results show that compared with Normalized-Min-Sum (NMS) algorithm and OMS algorithm, the proposed algorithm improves the decoding performance by 0.3-0.5 dB when the Bit-Error Rate (BER) is 10 ^-5, and the average iteration times reduced by 48.1% and 24.3% respectively. At the same time, the difference between the performance of the proposed algorithm and LLR-BP (Log-Likelihood Ratio-Belief Propagation) algorithm performance is only nearly 0.1 dB.

According to the new application scenario with both historical data and stream features, an online feature selection based on group feature selection algorithm and streaming features was proposed. To compensate for the shortcomings of single clustering algorithm, the idea of clustering ensemble was introduced in the group feature selection of historical data. Firstly, a cluster set was obtained by multiple clustering using k-means method, and the final result was obtained by integrating hierarchical clustering algorithm in the integration stage. In the online feature selection phase of the stream feature data, the feature group generated by the group structure was updated by exploring the correlation among the features, and finally the feature subset was obtained by group transformation. The experimental results show that the proposed algorithm can effectively deal with the online feature selection problem in the new scenario, and has good classification performance.

The traditional blind detection methods of image copy-paste forgery are time consuming, of high computation cost and low detection precision. A blind detection algorithm of copy-paste image forgery based on Mean Shift (MS) was proposed in this paper, which extracted Speed Up Robust Feature (SURF) points and then performed feature matching utilizing the method of best bin first in order to filter redundant points and locate the copy-paste forgery regions preliminarily. Pixels with the same or similar attributes would be segmented in the same region after implementing MS. The copy-paste regions could be detected according to the position dependency between matched feature point with its segmented region of MS and the detection result would be further refined by comparing the similarity of edge histogram and HSV (Hue-Saturation-Value) color histogram among the segmented regions of matched SURF and its neighborhood, and those regions with large similarity were included in the forged region. The experimental results show that the copy-paste forgery regions are detected accurately in the image with clear outline and rich details, and the proposed algorithm can robustly and efficiently detect the copy-paste forgery regions of image.