Watermark extraction by magnifying noise and applying global minimum decoder

Author name used in this publication: David ZhangCenter for Multimedia Signal Processing and Department of ComputingVersion of RecordPublishe

Work Function of Single-wall Silicon Carbide Nanotube

Using first-principles calculations, we study the work function of single wall silicon carbide nanotube (SiCNT). The work function is found to be highly dependent on the tube chirality and diameter. It increases with decreasing the tube diameter. The work function of zigzag SiCNT is always larger than that of armchair SiCNT. We reveal that the difference between the work function of zigzag and armchair SiCNT comes from their different intrinsic electronic structures, for which the singly degenerate energy band above the Fermi level of zigzag SiCNT is specifically responsible. Our finding offers potential usages of SiCNT in field-emission devices.Comment: 3 pages, 3 figure

Comparison of ν-support vector regression and logistic equation for descriptive modeling of Lactobacillus plantarum growth

Due to the complexity and high non-linearity of bioprocess, most simple mathematical models fail to describe the exact behavior of biochemistry systems. As a novel type of learning method, support vector regression (SVR) owns the powerful capability to characterize problems via small sample, nonlinearity, high dimension and local minima. In this paper, we developed a ν-SVR model with genetic algorithms (GA) in the pre-estimate in Lactobacillus plantarum fermentation by comparing the predicting capability of logistic model and SVR model. 5-fold cross validation technique was applied in the SVR train to avoid over-fitting. The information of SVR parameters were obtained in the generation of 150 and the optimal parameters were C= 235.8935, σ= 8.3608, ν=0.7587. Correspondingly, the logistic model parameters μmax and xmax were estimated as 0.4791 and 0.3498, respectively. The experimental results demonstrated that, SVR model excelled the logistic model based on the normalized mean square error (NMSE), mean absolute percentage error (MAPE) and the Pearson correlation coefficient R. We found that the ν-SVR model optimized by genetic algorithms could be a potential monitoring method for prediction of biomass.Key words: Support vector regression, genetic algorithm, logistic model, prediction of biomass

High incidence of plasmids in marine Vibrio species isolated from Mai Po Nature Reserve of Hong Kong

Mai Po Nature Reserve is the largest mangrove ecosystem and the most polluted coastal water body in Hong Kong. Plasmids screening of 100 Vibrio isolates randomly showed 45 % of them contained 1-3 plasmids. These plasmid(s)-bearing isolates could be divided into 12 groups based on their plasmid profiles. Phylogenetic analysis of the partial 16S rRNA gene sequences confirmed that all plasmid(s)-bearing isolates belonged to Vibrio cholerae. Full DNA sequences of the plasmids in Groups I (pVCG1.1 and pVCG1.2), II (pVCG2.1), III (pVCG3.2) and IV (pVCG4.1) have been determined and the results showed that pVCG1.1, pVCG2.1 and pVCG3.2 were almost identical. Plasmids pVCG1.1, pVCG1.2 and pVCG4.1 are comprised of 4,439, 2,357 and 2,163 bp with the overall G+C content of 45.57, 53.54 and 43.09 %, respectively. pVCG1.1 is a novel plasmid, and plasmids pVCG1.2 and pVCG4.1 showed homology of replication initiation proteins to that of the theta type replicons. Attempts to cure the plasmids from their hosts were unsuccessful. These data suggest that plasmids of Vibrio spp. are a significant gene reservoir in the marine ecosystem.published_or_final_versio

Robust and Accurate Point Set Registration with Generalized Bayesian Coherent Point Drift

Point set registration (PSR) is an essential problem in surgical navigation and image-guided surgery (IGS). It can help align the pre-operative volumetric images with the intra-operative surgical space. The performances of PSR are susceptible to noise and outliers, which are the cases in real-world surgical scenarios. In this paper, we provide a novel point set registration method that utilizes the features extracted from the PSs and can guarantee the convergence of the algorithm simultaneously. More specifically, we formulate the PSR with normal vectors by generalizing the bayesian coherent point drift (BCPD) into the six-dimension scenario. Our contributions can be summarized as follows. (1) The PSR problem with normal vectors is formulated by generalizing the Bayesian coherent point drift (BCPD) approach; (2) The updated parameters during the algorithm's iterations are given in closed-forms; (3) Extensive experiments have been done to verify the proposed approach and its significant improvements over the BCPD has been validated. We have validated our proposed registration approach on both the human femur model. Results demonstrate that our proposed method outperforms the state-of-the-art registration methods and the convergence is guaranteed at the same time