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China Aviation Oil had to ask for help from the group for the first on October 10, 2004 due to it was unable to operate the subsidiary with huge losses. On December 1st, 2004, after a massive loss of $550 million U.S. dollars, CAO announced an application for bankruptcy protection in court.

The moving target detection and tracking is always an important but challenging task with applications in radar and sonar, video surveillance, web search, bioinformatics, etc. An effective method for the moving target detection and tracking in reverberation environments is presented by utilizing spare and low-rank matrix decomposition. Due to the strong coherence between the beamspace data with the almost same background (reverberation structure), the matrix with beamspace data has potentially low-rank structure. Accordingly, the moving targets can be modeled as the sparse component, and separated from the reverberation.

Passive sources localization in the presence of strong interferences is generally a difficult problem. A sparse-representation-based adaptive interference suppression (SRAIS) method is proposed in this paper for interference suppression and bearing estimation, which can reduce the power loss of the TOI signal and have more accurate direction-of-arrival (DOA) estimation, especially when the input powers of the TOI signal and the interferences are at the almost same level. Simulation and experimental results are also given.

Sparse representation based classification (SRC) is an efficient method with high recognition rate in many pattern recognition applications. Unfortunately, the original SRC method generally requires rigid alignment. In this paper, the feature-based SRC method is addressed by using PCA-SIFT descriptors. The presented method is not only efficient for alignment-free, face recognition, but also robust for the image illumination and affine, where the image processing is moved from pixel-domain into the feature-domain, i.e. PCA-SIFT descriptors. Experimental results show the presented method in this paper has higher recognition rate, more robustness, and lower computational complexity than MKD-SRC and SRC in the above scenarios.