Motion compensation is widely used in many video coding standards. Due to its bandwidth requirement and complexity, motion compensation is one of the most challenging parts in the design of high definition video decoder. In this paper, we propose a high performance and low bandwidth motion compensation design, which supports H.264/AVC, MPEG-1/2 and Chinese AVS standards. We introduce a 2-Dimensional cache that can greatly reduce the external bandwidth requirement. Similarities among the 3 standards are also explored to reduce hardware cost. We also propose a block-pipelining strategy to conceal the long latency of external memory access. Experimental results show that our motion compensation design can reduce the bandwidth by 74% in average and it can real-time decode 1920x1088@30 fps video stream at 80 MHz.

Compressive spectral imaging (CSI) systems capture the 3D spatiospectral data by measuring the 2D compressed focal plane array (FPA) coded projection with the help of reconstruction algorithms exploiting the sparsity of signals. However, the contradiction between the multi-dimension of the scenes and the limited dimension of the sensors has limited improvement of recovery performance. In order to solve the problem, a novel CSI system based on a coded aperture snapshot spectral imager, RGB-CASSI, is proposed, which has two branches, one for CASSI, another for RGB images. In addition, considering that conventional reconstruction algorithms lead to oversmoothing, a RGB-guided low-rank (RGBLR) method for compressive hyperspectral image reconstruction based on compressed sensing and coded aperture spectral imaging system is presented, in which the available additional RGB information is used to guide the reconstruction and a low-rank regularization for compressive sensing and a non-convex surrogate of the rank is also used instead of nuclear norm for seeking a preferable solution. Experiments show that the proposed algorithm performs better in both PSNR and subjective effects compared with other state-of-art methods.

An transfer learning method, called Kernel Canonical Correlation Analysis plus (KCCA+), is proposed for heterogeneous Cross-company defect prediction. Combining the kernel method and transfer learning techniques, this method improves the performance of the predictor with more adaptive ability in nonlinearly separable scenarios. Experiments validate its effectiveness.

Let G be a graph and K be a set of target vertices of G. Assume that all vertices of G, except the vertices in K, may fail with given probabilities. The K-terminal reliability of G is the probability that all vertices in K are mutually connected. This reliability problem is known to be #P-complete for general graphs. This work develops the first polynomial-time algorithm for computing the K-terminal reliability of circular-arc graphs.

This paper proposes a new admission control strategy for ATM networks, which is based on the simulation approach and regression results. Instead of using many traffic descriptors, in our strategy only numbers of connections of different types are needed in performing admission control. The strategy is evaluated from different points of view, real-time, safety, policing and its efficiency which is referred as allowed utilized bandwidth. Since the admission criteria is developed in a form of regression model, the computation of performance for accepting a new connection is quick and easy. Using the confidence region in statistics to represent the admission criteria, a conservative estimation of performance can be achieved. Besides, this strategy is quite independent, thus can be compatible with most policing functions. Finally, its bandwidth utilization is found to be above 0.54. However, the success of this strategy still depends on the reality of input traffic model. Whenever the traffic can be clearly described, the proposed strategy can be easily and precisely applied. Therefore, we also build a traffic model for different type of traffic including constant-bit-rate (CBR), variable-bit-rate (VBR) and bursty traffic. The application of the proposed strategy to different multiplexing schemes, like priority queues and polling system, etc., should be further studied. Considering different level of performance requirement for different type of traffic, which should aid the bandwidth utilization of this strategy, is also an interesting research issue.

Let G be a probabilistic graph, in which the vertices fail independently with known probabilities. Let K represent a specified subset of vertices. The K-terminal reliability of G is defined as the probability that all vertices in K are connected. When |K|=2, the K-terminal reliability is called the 2-terminal reliability, which is the probability that the source vertex is connected to the destination vertex. The problems of computing K-terminal reliability and 2-terminal reliability have been proven to be #P-complete in general. This work demonstrates that on multi-tolerance graphs, the 2-terminal reliability problem can be solved in polynomial-time and the results can be extended to the K-terminal reliability problem on bounded multi-tolerance graphs.

Since camera networks for surveillance are becoming extremely dense, finding the most informative and desirable views from different cameras are of increasing importance. In this paper, we propose a camera selection method to achieve the goal of providing the clearest visibility possible and selecting the cameras which exactly capture targets for the far-field surveillance. We design a benefit function that takes into account image visibility and the degree of target matching between different cameras. Here, visibility is defined using the entropy of intensity histogram distribution, and the target correspondence is based on activity features rather than photometric features. The proposed solution is tested in both artificial and real environments. A performance evaluation shows that our target correspondence method well suits far-field surveillance, and our proposed selection method is more effective at identifying the cameras that exactly capture the surveillance target than existing methods.