Serial RapidIO (SRIO) is a high-performance interconnection standard for embedded systems. Cyclic Redundancy Check (CRC) provides protection for packet transmissions and impacts the device performances. In this paper, two CRC calculation strategies, based on adjustable slicing parallelization and simplified calculators, are proposed. In the first scheme, the temporary CRC result of the previous cycle (CPre) is considered as a dependent input for the new cycle and is combined with a specific segment of packet data before slicing parallelization. In the second scheme, which can reach a higher maximum working frequency, CPre is considered as an independent input and is separated from the calculation of packet data for further parallelization. Performance comparisons based on ASIC and FPGA implementations are demonstrated to show their effectiveness. Compared with the reference designs, more than 34.8% and 13.9% of average power can be improved by the two proposed schemes at 156.25MHz in 130nm technology, respectively.

This paper proposes a novel object tracking method via online boosting. The on-line boosting technique is combined with local features to treat tracking as a keypoint matching problem. First, We improve matching reliability by exploiting the statistical repeatability of local features. In addition, we propose 2D scale-rotation invariant quasi-keypoint matching to further improve matching efficiency. Benefiting from SURF feature's statistical repeatability and the complementary quasi-keypoint matching technique, we can easily find reliable matching pairs and thus perform accurate and stable tracking. Experimental results show that the proposed method achieves better performance compared with previously reported trackers.

This paper proposes an on-line rigid object tracking framework via discriminative object appearance modeling and learning. Strong classifiers are combined with 2D scale-rotation invariant local features to treat tracking as a keypoint matching problem. For on-line boosting, we correspond a Gaussian mixture model (GMM) to each weak classifier and propose a GMM-based classifying mechanism. Meanwhile, self-organizing theory is applied to perform automatic clustering for sequential updating. Benefiting from the invariance of the SURF feature and the proposed on-line classifying technique, we can easily find reliable matching pairs and thus perform accurate and stable tracking. Experiments show that the proposed method achieves better performance than previously reported trackers.