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One of the challenges in brain-computer interface systems is obtaining motor imagery recognition from brain activities. Brain-signal decoding robustness and system performance improvement during the motor imagery process are two of the essential issues in brain-computer interface research. In conventional approaches, ineffective decoding of features and high complexity of algorithms often lead to unsatisfactory performance. A novel method for the recognition of motor imagery tasks is developed based on employing a modified S-transforms for spectro-temporal representation to characterize the behavior of electrocorticogram activities. A classifier is trained by using a support vector machine, and an optimized wrapper approach is applied to guide selection to implement the representation selection obtained. A channel selection algorithm optimizes the wrapper approach by adding a cross-validation step, which effectively improves the classification performance. The modified S-transform can accurately capture event-related desynchronization/event-related synchronization phenomena and can effectively locate sensorimotor rhythm information. The optimized wrapper approach used in this scheme can effectively reduce the feature dimension and improve algorithm efficiency. The method is evaluated on a public electrocorticogram dataset with a recognition accuracy of 98% and an information transfer rate of 0.8586 bit/trial. To verify the effect of the channel selection, both electrocorticogram and electroencephalogram data are experimentally analyzed. Furthermore, the computational efficiency of this scheme demonstrates its potential for online brain-computer interface systems in future cognitive tasks.

With rapid increase of waste paper recycling, more and more researchers focus on the advanced technologies of sorting impurities form waste paper. The principals of main identification and sorting technologies are introduced in this paper, whose advantages and disadvantages are discussed. Because of importance of sorting technologies based on near-infrared spectroscopy, we emphasis its principle, application and research status etc.. At last, research and development tendency of identification and sorting technology are prospected.

In this paper a new method is proposed for intelligent sort of battery from waste paper based on Invariant Moments and Fractal Dimension (IMFD). In IMFD, a new distinctive feature that based on the edge contours is extracted to describe the target picture. At first invariant moments technique is used to extract Hus seven moments as the global descriptors from the target image. Then the fractal dimensions are extracted as local invariant descriptors by using Fractal Dimension. At last, a combing descriptor is built according the distinctive feature, which combines the global descriptor and the local descriptor together. The features are highly distinctive, and can be matched with high probability against a large database of features. The practical tests performed in this article show that the proposed method has a significant effect on increasing the stability, the accuracy of classification, and also can effectively against the huge-information during the image processing.

The problem of surface reconstruction from unorganized points has been, and continues to be, an important topic of research. Surface reconstruction can be widely used in reverse engineering and visualization of scientific data, etc. In this paper, a new algorithm for surface reconstruction from unorganized points in R is proposed. The algorithm is based on the theorem that the tangent plane of any point on a manifold surface local linear approximates the surface, which means that any point in one scattered point's neighborhood can be found one and only one projection in its tangent plane. In order to reconstruct a surface interpolating the scattered points, we first project the neighbor points of one sample point p to its tangent plane and find its 2D starlike Delaunay neighbors. After that, we define the points whose projection is 2D Delaunay neighbors of p as its 3D Delaunay neighbors. At last, the triangular mesh can be obtained based on the principle that three points consisted one triangular plane patch ifthey are 3D Delaunay neighbors each other. Experimental results show that this algorithm is effective, robust and the output mesh accords with Delaunay character.