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Most wavelet-based denoising algorithms use the standard wavelet transform. Recently, the dual-tree complex wavelet transform (DTCWT) has been proposed as a novel analysis tool featuring near shift-invariance which is important to improve the denoising performance. In this paper, a spectra denoising algorithm based on the DTCWT is proposed. Experimental(More)
Sequence alignment reveals the relations between the characters in different sequences, and there are the reverse complement relations between the characters in DNA double strand. We introduce sequence alignment to the field of DNA computing, propose the definitions of complement alignment and reverse complement alignment, give a method of computing the(More)
The aim of astronomical spectra denoising is to reduce the noise level, while preserving astrophysical information hidden in spectra. Many astronomical spectra denoising approaches exist, in which wavelet thresholding approaches are widely adopted in many literatures. For spectra denoising using wavelet thresholding, the thresholding function greatly(More)
—In astronomy, the Charge Coupled Device (CCD) images obtained from celestial observations are usually stored in Flexible Image Transport System (FITS) format and the sizes of such images are usually very large, 8 Megabytes or more being no unusual. Moreover, the amount of such images is very great due to continuous celestial observations. So the demand of(More)
Large Sky Area Multi-Object Fiber Spectroscopy Telescope (LAMOST) is a meridian reflecting Schmidt telescope. For each observation, it will produce tens of thousands of spectra. The spectra obtained from LAMOST pilot survey and the first two years of its regular survey, LMOST data release 2 (DR2) was released online in December 2014. This data set contains(More)
Classifying stellar spectra is an important work in astronomy. Numerous automated classification techniques have been explored for spectra data classification. But achieving high accuracy of spectral classification is still a goal of study. Random Forest is a recently available ensemble learning algorithm. Existing literatures have shown the superior(More)
describes in detail the method to process the characteristics of the spectral lines using the Gaussian fitting based on the least-squares method. By separately truncating the peak area data and statistically analyzing and setting fitting parameters, multiple peaks overlap interference can be better avoided, and more accurate fitting of spectral(More)