Lei Sun

National University of Defense Technology, Ch’ang-sha-shih, Hunan, China

Are you Lei Sun?

Claim your profile

Publications (5)1.13 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: Purpose: The purpose of this paper is to propose a Chebyshev collocation method (CCM) for Hallén's equation of thin wire antennas. Design/methodology/approach: Since the current induced on the thin wire antennas behaves like the square root of the distance from the end, a smoothed current is used to annihilate this end effect. Then the CCM adopts Chebyshev polynomials to approximate the smoothed current from which the actual current can be quickly recovered. To handle the difficulty of the kernel singularity and to realize fast computation, a decomposition is adopted by separating the singularity from the exact kernel. The integrals including the singularity in the linear system can be given in an explicit formula while the others can be evaluated efficiently by the fast cosine transform or the fast Fourier transform. Findings: The CCM convergence rate is fast and this method is more efficient than the other existing methods. Specially, it can attain less than 1 percent relative errors by using 32 basis functions when a/h is bigger than 2× 10-5 where h is the half length of wire antenna and a is the radius of antenna. Besides, a new efficient scheme to evaluate the exact kernel has been proposed by comparing with most of the literature methods. Originality/value: Since the kernel evaluation is vital to the solution of Hallén's and Pocklington's equations, the proposed scheme to evaluate the exact kernel may be helpful in improving the efficiency of existing methods in the study of wire antennas. Due to the good convergence and efficiency, the CCM may be a competitive method in the analysis of radiation properties of thin wire antennas. Several numerical experiments are presented to validate the proposed method.
    No preview · Article · Jul 2015 · COMPEL International Journal of Computations and Mathematics in Electrical
  • Source
    Dong Xu · Lei Sun · Jianshu Luo
    [Show abstract] [Hide abstract]
    ABSTRACT: Noise estimation of hyperspectral remote sensing image is In this paper, not only the spectral correlation removing is considered, but the spatial correlation removing by wavelet transform is considered as well. Therefore, a new method based on multiple linear regression (MLR) and wavelet transform is proposed to estimate the noise of hyperspectral remote sensing image. Numerical simulation of AVIRIS data is carried out and the real data Hyperion is also used to validate the proposed algorithm. Experimental results show that the method is more adaptive and accurate than the general MLR and the other classified methods.
    Full-text · Article · Dec 2013
  • Source
    Dong Xu · Lei Sun · Jianshu Luo · Zhihui Liu
    [Show abstract] [Hide abstract]
    ABSTRACT: A new denoising algorithm is proposed according to the characteristics of hyperspectral remote sensing image (HRSI) in the curvelet domain. Firstly, each band of HRSI is transformed into the curvelet domain, and the sets of subband images are obtained from different wavelength of HRSI. And then the detail subband images in the same scale and same direction from different wavelengths of HRSI are stacked to obtain new 3-D datacubes of the curvelet domain. Again, the characteristics analysis of these 3-D datacubes is performed. The analysis result shows that each new 3-D datacube has the strong spectral correlation. At last, due to the strong spectral correlation of new 3-D datacubes, the multiple linear regression is introduced to deal with these new 3-D datacubes in the curvelet domain. The simulated and the real data experiments are performed. The simulated data experimental results show that the proposed algorithm is superior to the compared algorithms in the references in terms of SNR. Furthermore, MSE and MSSIM in each band are utilized to show that the proposed algorithm is superior. The real data experimental results show that the proposed algorithm effectively removes the common spotty noise and the strip noise and simultaneously maintains more fine features during the denoising process.
    Preview · Article · Jul 2013 · Mathematical Problems in Engineering
  • Source
    Dong Xu · Lei Sun · Jianshu Luo
    [Show abstract] [Hide abstract]
    ABSTRACT: Hyperspectral remote sensing image is easily contaminated by noise, which will affect the application of hyperspectral image, such as target detection, classification and segmentation, etc. Therefore, a denoising method of hyperspectral remote sensing image based on multiple linear regression (MLR) and wavelet shrinkage (WS) is proposed. Firstly, the residual image and the predicted image are obtained via MLR. Secondly, WS is performed on the residual image to remove the noise in the spatial domain. Lastly, a final denoised image is obtained by the predicted image and the corrected residual image. The experimental results show that the proposed method can improve signal-to-noise ratio (SNR) of the hyperspectral image efficiently.
    Full-text · Conference Paper · Mar 2013
  • Lei Sun · Jianshu Luo

    No preview · Conference Paper · Jan 2009