To solve the problem that contrast enhancement effect is affected by the embedding rate in most existing Reversible Data Hiding (RDH) algorithms, a new RDH method based on texture partition for medical images was proposed. Firstly, the contrast of an image was stretched to enhance image contrast, and then according to the characteristics of medical image texture, the medical image was divided into high and low texture levels. The key partion of the medical image mainly had high texture level. To enhance the contrast of high texture level further and guarantee the infomation embedding capacity, different embedding processes were adopted for high and low texture levels. In order to compare the effect of contrast enhancement between the proposed method and other RDH algorithms for medical images, No-Reference Contrast-Distorted Images Quality Assessment (NR-CDIQA) was adopted as the evaluation standards. The experimental results show that the marked images processed by the proposed method can get better NR-CDIQA and contrst enhancement in different embedding rate.

The existing uneven clustering routing algorithms do not consider the optimal path selection between cluster heads and base station, which leads to unbalanced energy consumption. In order to balance energy consumption of transmission paths, this paper proposed an uneven clustering routing algorithm based on minimum spanning tree. The algorithm utilized residual energy of nodes and the distance between nodes and base station to select cluster heads, and then generated minimum spanning tree to search the optimal transmission paths, which reduced energy consumption on the transmission paths and effectively solved unbalanced energy consumption. The theoretical analysis and experimental results show that the algorithm is better than the existing Energy Efficient Uneven Clustering (EEUC) and Energy Balancing Clustering Algorithm (EBCA) in terms of the number of live nodes and energy consumption.

The Bayesian anti-spam filter has strong classification capacity and high accuracy, but the mail training and learning at early stage consume mass system and network resources and affect system efficiency. A MapReduce-based Bayesian anti-spam filtering mechanism was proposed, which first improved the traditional Bayesian filtering technique, and then optimized the mail training and learning by taking advantage of mass data processing of MapReduce. The experimental results show that, compared with the traditional Bayesian filtering technique, K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) algorithms, the MapReduce-based Bayesian anti-spam filtering mechanism performs better in recall, precision and accuracy, reduces the cost of mail learning and classifying and improves the system efficiency.