A CART decision tree algorithm based on attribute weight is proposed in this paper because of the present problems of complex classification, poor accuracy, low efficiency, and severe memory consumption of CART decision. What is more, the algorithm is combined with the parallel computing model of MapReduce. Theory of attribute weights is used in the algorithm. A decision tree is built through the sum of weights, which is decided by the degree that the attributes affect a decision. Thus the accuracy of classification through decision tree is improved. Parallel sorting algorithms of CART decision tree for massive data is implemented through the MapReduce programming technology of cloud computing. All the results of theoretical analysis and experimental comparison show that it is very important to mark attributes by weights through MapReduce. Furthermore, the accuracy of the classification of large sample data sets is improved significantly, classification efficiency of decision tree is improved and the trained time is also significantly reduced.

Network of wireless micro-sensor for monitoring physical environments has emerged as an important new application area for wireless technology. The strength of security protocols and encryption algorithms for ad hoc sensor network has been focused. Random numbers play a crucial role in them, so the quality of random number becomes one of important indexes. Because key attributes of these new types of network system are the severely constrained computation and energy resources, and an ad hoc operational environment, the design of random number generator must consider them. This paper presents new and security random number generator architecture for this type network. The philosophy architecture is based on single electron circuit which ensures the unpredictability of the produced random numbers. The proposed architecture is a flexible solution taking into account the performance, power consumption, flexibility, cost and area.

Measurement of image similarity is a fundamental issue in both image

In the field of computer vision and photogrammetry, it is constantly necessary to online or real-time acquire the camera parameters through self-calibration. This paper presents a method about self-calibration of camera with rotary motion based on SIFT feature matching. The proposed approach first shoots more than three images of the same scene by rotating the camera and keeping its position and internal parameters unchanging. After SIFT feature extraction and sequentially cycled matching for all images, the optimal reference image and effective images are determined by virtue of the algorithm on pose estimation. According to the coordinates of matched SIFT features, all 2D projection transformation matrices which transforms the reference into other effective images are calculated. With these matrices, relevant linear equations are established and the internal matrix of camera is solved. The proposed method can be online applied to quickly, accurately and stably obtain internal parameters of camera. Real data has been used to test the proposed approach, and very good results have been achieved.