There is no clear space structure among images, so correlation information of images' space structure could not be utilized effectively. To tackle this problem, a new space relationship feature based image retrieval method was proposed. Firstly, feature vectors were extracted from all images which contain the queried image. And then, all of the every two feature vectors' similarities were calculated to form a similarity matrix. The set of similarity matrix's columns was taken as the new feature vector, namely the new space relationship feature vector, so the former feature vectors could be mapped into a Euclidean space. Finally, similarities were calculated on the new feature space. In this way, the problem of feature vectors' similarity was turned into the new space relationship feature vectors' similarity. The space structure among images was clearer than before on the new feature space, so the accuracy of image retrieval was improved. The experimental results on the Corel database show that the average retrieval precision, recall-precision and visual evaluation metric of the proposed method have advantage over the color histogram in the image retrieval task. The proposed image retrieval method based on the new space relationship feature sufficiently utilizes correlation information of images' space structure and it has better retrieval effect.

To solve the problems of traditional relaying forward protocol: amplifying both the signals and the noise, possibly forwarding incorrect decoded signals and low reliability of decision on relay node in the two-way relay system, two Mutual Information Forwarding (MIF) schemes based on network coding were suggested. Firstly, the relay node forwards the hard decisions from two source nodes, and the reliability of these hard decisions. Then the receiving terminal detects and makes a decision on the combined result, which are based on the forwarding signal from relay node and its reliability, also the signal from another source node. The expressions for the received Signal-to-Noise Ratio (SNR) were derived, and numerical simulations were done in Additive White Gaussian Noise (AWGN) channel. At the same time, Monte Carlo simulation was conducted in AWGN channel and Rayleigh channel. Results show that both two proposed schemes under two kinds of channels have 1 dB and 1-2 dB gain than the Estimate-and-Forward (EF) scheme in Bit Error Ratio (BER) performance. The simulation results show that, the network coding scheme based on MIF significantly improves BER performance in relay forward of two-way relay network.

In order to solve the problem of not using the product heterogeneity well in recommendation algorithm, a modified mass diffusion algorithm was presented by considering the effect of the object popularity information on the user preference prediction. By introducing a tunable parameter of product popularity and simulating the mass diffusion process on the user-product bipartite network, the effect of the product popularity was quantitatively characterized. The experimental results on three empirical data sets which named MovieLens, Netflix and Last.FM show that, compared with the traditional mass diffusion method, the proposed algorithm can enhance the average ranking score by 25.6%, 10.96% and 1.2% respectively, and increase the diversity of the recommendation lists by 59.30%, 53.07% and 8.59% respectively. The proposed non-equilibrium mass diffusion algorithm can get more practical results.