For the problems that the performance of the nearest neighbor classification algorithm is greatly affected by the adopted similarity or distance measuring method， and it is difficult to select the optimal similarity or distance measuring method， with multi-similarity method adopted， a K-Nearest Neighbor algorithm with Ordered Pairs of Normalized real numbers （OPNs-KNN） was proposed. Firstly， the new mathematical theory of Ordered Pair of Normalized real numbers （OPN） was introduced in machine learning. And all the samples in the training and test sets were converted into OPNs by multiple similarity or distance measuring methods， so that different similarity information was included in each OPN. Then， the improved nearest neighbor algorithm was used to classify the OPNs， so that different similarity or distance measuring methods were able to be mixed and complemented to improve the classification performance. Experimental results show that compared with 6 improved nearest neighbor classification algorithms， such as distance-Weighted K-Nearest-Neighbor rule （WKNN） rule on Iris， seeds， and other datasets， OPNs-KNN has the classification accuracy improved by 0.29 to 15.28 percentage points， which proves that the performance of classification can be improved greatly by the proposed algorithm.

Most existing cloud storage systems are based on the model, which leads to a full dataset scan for multi-dimensional queries and low query efficiency. A KD-tree and R-tree based multi-dimensional cloud data index named KD-R index was proposed. KD-R index adopted two-layer architecture: a KD-tree based global index was built in the global server and R-tree based local indexes were built in local server. A cost model was used to adaptively select appropriate R-tree nodes to publish into global KD-tree index. The experimental results show that, compared with R-tree based global index, KD-R index is efficient for multi-dimensional range queries, and it has high availability in the case of server failure.

One camera is used in the autonomous navigation system of intelligent vehicle, and camera calibration is the key and precondition of the intelligent vehicles correct and safe navigation. Through the study of ideal model and actual model of camera, an improved calibration method was proposed. In the process of obtaining internal and external parameters of the camera, this algorithm combined the advantages of both linear and nonlinear calibration, which calibrated parts of the parameters of camera firstly and then considered simplifying the distortion model. The nonlinear equation systems can be changed into linear equation systems. Additionally, all the parameters of camera were acquired through many times iteration. On the one hand, it can ensure the calibration accuracy. On the other hand, it reduces the complexity of the cameras actual model. The experimental results show that the method can meet the requirements of vision navigation.