Exact compressed sensing (CS) recovery theoretically depends on a large number of random measurements. In this study, the authors present a novel CS measurement technique based on the cellular automata chaos (CAC) model. The proposed method selects original signal thresholding (OST) as its initial seed to realise CS signal coding. The benefits of CS coding with CAC-OST are that: (i) the signal compression ratio of this coding method can be far below the signal sparsity level and (ii) the signal can be recovered perfectly, even with slow CS measurements. This study reports some experiments that demonstrate the excellent performance of CAC-OST in CS coding.
Collaborative filtering systems have achieved great success in both research and business applications. One of the key technologies in collaborative filtering is similarity measure. Cosine-based and Pearson correlation-based methods are popular ways for similarity measure, but have low accuracy. In this paper, we propose a novel method for similarity measure, referred as hierarchical pair-wise sequence (HPWS). In HPWS, we take into account both the sequence property of user behaviors and the hierarchical property of item categories. We design a collaborative filtering recommendation system to evaluate the performance of HPWS based on the empirical data collected from a real P2P application, i.e. "byrBT" in CERNET. Experiment results show that HPWS outperforms traditional Cosine similarity and Pearson similarity measures under all scenarios.
With the rapid development of the remote sensing satellite, the size and resolution of remote sensing image grow increasingly. The evaluation of image quality requires precise information of ground control points extracted from remote sensing image and reference image. Therefore, we propose an adaptive Wallis enhancement based on radiation-parameters to increase the number of ground control points and to improve the matching precision. First, feature vectors of sub-region are constructed by computing image radiation-parameters, and then the sub-region terrain in the remote sensing image can be recognized using nearest neighbor classifier. Second, according to specific type of sub-region terrain, we enhance images using adaptive Wallis filter with local parameters. Finally, two-level matching method is used to extract and match the control points. The experiments show that compared with existing Wallis filter which are based on global parameters, our method gets better results in the detail enhancement on ZY-3 image so that more and higher accurate ground control points can be effectively extracted to achieve the evaluation of geometric precision automatically and accurately.
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