Earticle

The stereo matching algorithms for binocular vision are very popular and widely applied. However, the algorithms may have lower matching quality or higher time complexity. To improve that, a new stereo matching algorithm based on square and gradient for binocular vision is proposed in the paper. It divides an image line into a serious of ranges with comparing the gradients of the points in left and right image lines. The best matching in each range is found based on the summery of squared differences. The algorithm inherits the high quality of gradient algorithm and high performance of SSD algorithm and meanwhile avoids the additive noise.

Discovering the AS paths between two ASes are invaluable for a wide area of network research and application activities. The traditional techniques for path discovery require direct access to the source node. Recently with more accurate AS relationship inferring algorithm and publicly available AS topology data, it is possible to infer AS paths without accessing the source. This paper proposes an efficient algorithm for inferring all pair shortest AS paths in a relationship annotated AS graph. The running time of the algorithm is O (NM), where N is the number of nodes and M is the number of edges in AS graph. The algorithm bases on the bread-first-search (BFS) algorithm, and experimental results show that it reduces running time dramatically compared with the existing algorithm whose running time is O (N3).

The identification of source camera is useful to improve the capability of evidence in the digital image such as distinguish the photographer taking illegal images and adopting digital images as evidence of crime. Lukášš, et al. showed the method for source camera identification based on the correlation of PNU (pixel non-uniformity) noise. However, the wavelet-based denoising filter for suppressing the random noise reduces the accuracy of camera identification. It is caused by the fact that the denoising filter diffuses the edge and makes the PNU noise less pronounced. Moreover, it is difficult to extract PNU noise from the images taken by cameras which are equipped with the image improvement functions such as motion blur correction, contrast enhancement, and noise reduction. In this paper, we propose a method for improving the camera identification accuracy by selecting pixels based on the texture complexity. We also propose a method for improving the identification accuracy by applying the image restoration method.

Wireless Sensor Network (WSN) is an emerging technology that is predicted to change the human life in future. This technology is composed of tiny sensing objects called sensors that are wirelessly scattered in the environment. Due to wireless nature and having limited lifetime (battery operated) there are many challenges for researchers to make this technology more useful. In this research work an energy efficient routing technique Energy Aware Intra Cluster Routing (EAICR) is presented that has increased energy efficiency up to 17% and increased the network lifetime up to 12% when compared with a well known routing algorithm MultiHop Router [1] .

The Multi-Agent/Multi-Goal distributed selection modeling system supports experts at different sites to judge the solutions with many relationships in the visualized modeling. The implicit relationships in judgment modeling templates are hard to find by directly surveying the templates. The paper deducts the implicit relationship based on the dimensions established by AHP. Firstly, search for all the relationship-chains between any two objects; secondly, calculate the weight ratio of every relationship-chain by means of the weight of every object and every relationship. The detailed searching algorithm and calculation are given. Any relationship-chain between any two objects in one/more templates can be discovered, including 1-1, n-1, 1-n, n-n deductions.