Focusing on the problem that open and insecure wireless channel between reader and tag in Radio Frequency IDentification (RFID) system is vulnerable to multiple malicious attacks, a new ultra-lightweight RFID Mutual Authentication Protocol based on Regeneration (RRMAP) was proposed. Firstly, the regeneration transformation of the first-stage reverse sequence self-combination transformation on two binary arrays was performed to achieve its own bit confusion effect. Secondly, the result of first-stage was used for the second-stage parity adjacent crossover-XOR operation, thus whole regeneration transformation was completed. Finally, through new definition of regeneration transformation, the left circular shift operation and modular 2^ m addition operation were combined to construct secret communication messages during authentication process, which could effectively solve security problems existing in RFID system currently. The BAN (Burrows-Abadi-Needham) logic formal proof was given to show the availability of protocol. The security analysis and performance comparison show that RRMAP has strong security and privacy protection attributes which can resist some common malicious attacks.

As a kind of pure location routing algorithm in Wireless Multimedia Sensor Network (WMSN), Two Phase Geographic Greedy Forwarding (TPGF) helps to select the next-hop node which is of nearest distance to the destination from neighbor ones. In some cases, the distance between the next-hop node and the destination node could be farther than that of the current node and the destination node; At the same time, by numbering the nodes, TPGF solves the problem of hole and satisfies the Quality of Service (QoS) requirements. In line with the strategy of selecting the next-hop node farther than the current node, action-angle variables and DATF (Direction-Angle Greedy Forwarding) algorithm were introduced to guarantee and optimize the process of selecting the bound nodes. The simulation result indicates that DATF algorithm shows better performance than TPGF in both energy consumption and end-to-end transmission delay and also has a significant effect on solving the problem of hole.

The deficiency of Gaussian Mixture Model (GMM) is the high computation cost and cannot deal with the shadow and ghosting. An improved foreground detection algorithm based on GMM is proposed in this paper. By analyzing the stability of the background, intermittent or continuous frame updating is chose to update the parameters of the GMM.It can efficiently reduce the runtime of the algorithm. In the background updating,the updating rate is associated with the weight and this makes it change with the weight.The background pixels which appear after the objects moving set a larger updating rate.It can improve the stability of the background and solve the problem of ghosting phenomenon and the transformation of background and foreground.After objects detection,the algorithm eliminates the shadow based on the RGB color space distortion model and treats the result by Gauss Pyramid filtering and morphological filtering.Through the whole process,a better contour is obtained. The experimental results show that this algorithm has improved the calculation efficiency and accurately segmented the foreground object.

When Marginal Fisher Analysis (MFA) is applied to face recognition, it suffers the small size sample problem. If principal component analysis is used to deal with the problem, some useful components will get lost for classification. If replacing the objective function of MFA with maximum margin criterion, it would be difficult to find the optimal parameter. Therefore, in this paper, the regularized MFA method was proposed. It constructed a regularized item by a small number multiplying the identity matrix, and the regularized item was added to within-class scatter matrix so that the resulting matrix was not singular. This method does not lose any useful component for classification and is easy to determine the parameter. Because a sample usually can be linearly represented by few neighbors in the same class, the sparse representation classification was used to further improve the recognition accuracy after regularizing MFA. Experiments were carried out FERET and AR database, and results show that the proposed method can significantly improve the recognition accuracy compared with some classic dimensionality reduction methods.

In order to obtain better compression on image edge, an improved Set Partitioning In Hierarchical Trees (SPIHT) algorithm based on prior scanning the coefficients around which there were more significant coefficients was proposed. The coefficients or sets were sorted according to the number of surrounding significant coefficients before being coded, and the previous significant coefficients were refined as soon as the sets around which there existed any significant coefficients had been scanned. The scanning order was confirmed adaptively and did not need any extra storage. It can code more significant coefficients at a specified compression ratio. The experimental results show that the method can improve PSNR and the subjective visual experience compared with SPIHT.