As radio telescopes become more sensitive, radio frequency interference (RFI) is becoming more important for interesting signals of radio astronomy. There is a demand for developing an automatic, accurate and efficient RFI mitigation method. Therefore, we have investigated an RFI detection algorithm. First, we introduce an asymmetrically reweighted penalized least squares (ArPLS) method to estimate the baseline more accurately. After removing the estimated baseline, several novel strategies were proposed based on the SumThreshold algorithm for detecting different types of RFI. The threshold parameter in SumThreshold can be determined automatically and adaptively. The adaptiveness is essential for reducing human intervention and for the online RFI processing pipeline. Applications to data from the Five-hundred-meter Aperture Spherical Telescope (FAST) show that the proposed scheme based on ArPLS and SumThreshold is superior to some typically available methods for RFI detection with respect to efficiency and performance.
In the version of this article initially published, an equal-contributor footnote was missing for authors H. Xu, J. R. Niu and P. Chen. The Author contributions section has been amended to read "H.X., J.R.N. and P.C. contributed equally and led the data analysis". The changes have been made to the HTML and PDF versions of the article.
A novel image matching algorithm based on both Taguchi method and spatial clustering is proposed to optimize the Scale Invariant Feature Transform (SIFT) matching results. To improve the matching accuracy, adaptive spatial clustering is used. What is more, in order to get the fitting parameters to balance matching accuracy and quantity, Taguchi method is adopted to optimize the key parameter combination including the ratio threshold of Euclidean distance and the constrain parameters in the process of adaptive spatial clustering. Moreover, signal-to-noise ratio (SNR) results are analyzed by variance to get the effect factor which is taken as the basis for the selection of optimized parameters. The optimum parameters combination is obtained eventually. The final experimental results show that the matching quality based on SIFT feature are improved significantly.
With the rapid development of tourism, traditional simulated guide training system no longer meets the need of new age tourist guide resources training. It appears rather impendency and important to apply modern education technology and method to improve tourist guide practical teaching level to bring up tourist guide resources with high efficiency and quality. This article analyzed the maintaining problem and deficiency of traditional simulated guide training system, discussed how to apply Virtual Reality, multi-channel projecting and other technology to constitute tourist guide training system, and also introduced the developing technology and procedure of Virtual Tourist Guide Training System. This system would in large extend to break the limitation of practical teaching in time and space, improve teaching efficiency, reduce student managing risk, and save real scene teaching cost.
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