In visible-infrared cross-modal person re-identification， the modal differences will lead to low identification accuracy. Therefore， a dual-stream structure based cross-modal person re-identification relation network， named IVRNBDS （Infrared and Visible Relation Network Based on Dual-stream Structure）， was proposed. Firstly， the dual-stream structure was used to extract the features of the visible light modal and the infrared modal person images respectively. Then， the feature map of the person image was divided into six segments horizontally to extract relationships between the local features of each segment and the features of other segments of the person and the relationship between the core features and average features of the person. Finally， when designing loss function， the Hetero-Center triplet Loss （HC Loss） function was introduced to relax the strict constraints of the ordinary triplet loss function， so that image features of different modals were able to be better mapped into the same feature space. Experimental results on public datasets SYSU-MM01 （SunYat-Sen University MultiModal re-identification） and RegDB （Dongguk Body-based person Recognition） show that the computational cost of IVRNBDS is slightly higher than those of the mainstream cross-modal person re-identification algorithms， but the proposed network has the Rank-1 （similarity Rank 1） and mAP （mean Average Precision） improved compared to the mainstream algorithms， increasing the recognition accuracy of the cross-modal people re-identification algorithm.

To solve the problem of insufficient sample resources and poor noise immunity for single image Super Resolution (SR) restoration, a single image super-resolution algorithm based on structural self-similarity and deformation block feature was proposed. Firstly, a scale model was constructed to expand search space as much as possible and overcome the shortcomings of lack of a single image super-resolution training sample. Secondly, the limited internal dictionary size was increased by geometric deformation of sample block. Finally, in order to improve anti-noise performance of reconstructed picture, the group sparse learning dictionary was used to reconstruct image. The experimental results show that compared with the excellent algorithms such as Bicubic, Sparse coding Super Resolution (ScSR) algorithm and Super-Resolution Convolutional Neural Network (SRCNN) algorithm, the super-resolution images with more subjective visual effects and higher objective evaluation can be obtained, the Peak Signal-To-Noise Ratio (PSNR) of the proposed algorithm is increased by about 0.35 dB on average. In addition, the scale of dictionary is expanded and the accuracy of search is increased by means of geometric deformation, and the time consumption of algorithm is averagely reduced by about 80 s.

Aiming at the ill-posed inverse problem of single-image Super Resolution (SR) restoration, a single image super resolution algorithm combining with structural self-similarity and convolution networks was proposed. Firstly, the self-structure similarity of samples to be reconstructed was obtained by scaling decomposition. Combined with external database samples as training samples, the problem of over-dispersion of samples could be solved. Secondly, the sample was input into a Convolution Neural Network (CNN) for training and learning, and the prior knowledge of the super resolution of the single image was obtained. Then, the optimal dictionary was used to reconstruct the image by using a nonlocal constraint. Finally, an iterative backprojection algorithm was used to further improve the image super resolution effect. The experimental results show that compared with the excellent algorithms such as Bicubic, K-SVD (Singular Value Decomposition of k iterations) algorithm and Super-Resolution Convolution Neural Network (SRCNN) algorithm, the proposed algorithm can get super-resolution reconstruction with clearer edges.

To solve the problem that current signcryption schemes based on lattice cannot achieve forward security, a new identity-based signcryption scheme with forward security was proposed. Firstly, lattice basis delegation algorithm was used to update the users' public keys and private keys. Then, the preimage sampleable functions based on Learning With Errors (LWE) over lattice was used to sign the message,and the signature was also used to encrypt the message. The scheme was proved to be adaptive INDistinguishiability selective IDentity and Chosen-Ciphertext Attack (IND-sID-CCA2) secure, strong UnForgeable Chosen-Message Attack (sUF-CMA) secure and forward secure. Compared with the signcryption schemes based on pairings, the proposed scheme has more advantages in computational efficiency and ciphertext extension rate.

The present Identity Based Encryption (IBE) scheme cannot meet user revocation and fuzzy identity extraction at the same time, a Revocable Fuzzy IBE (RFIBE) scheme based on hardness of Learning With Errors (LWE) problem over ideal lattice was proposed to resolve the above problems by using revocable binary trees and threshold secret sharing algorithm. Firstly, the trapdoor generating function over ideal lattice and threshold secret sharing algorithm were used to generate user' private key. Then an RFIBE scheme was put forward by using revocable binary trees. Finally, the scheme was proved to be INDistinguishabity against selective IDentity and Chosen Plaintext Attack (IND-sID-CPA) secure. Compared with previous IBE scheme, RFIBE has stronger practicability with the function of revocation and efficient fuzzy identity extraction.

For the requirements of building Project Information Portal (PIP) data center based on a unified data model, a manageable entity object-oriented data model was proposed. The project data were treated as a series of managerial entity based on management workflows which were decomposed according to the whole life cycle. The conceptual layer data model was designed. The project data could be naturally represented and recorded by using this model. The data organization method was presented based on MongoDB (document-oriented database technology). The cluster storage architecture for PIP was also addressed. The experiments show that it has efficient performance in data writing and querying. It also has high availability and storage capacity scalability.

The major procedures of text classification such as uniform text format expression, training, testing and classifying based on Naive Bayesian text classification algorithm were implemented using MapReduce programming mode. The experiments were given in Hadoop cloud computing environment. The experimental results indicate basically linear speedup with an increasing number of node computers. A recall rate of 86% was achieved when classifying Chinese Web pages.

To the current situation of streaming media DRM,a streaming media DRM system based on the three-layer architecture of key was presented.The whole system model was introduced and its security was discussed,and then implementation of the system was described in detail.