Nuclear Techniques ›› 2014, Vol. 37 ›› Issue (12): 120203-120203.doi: 10.11889/j.0253-3219.2014.hjs.37.120203

• LOW ENERGY ACCELERATOR, RAY AND APPLICATIONS • Previous Articles     Next Articles

Smoothing and evaluation of spectrum based on TI-DWT-MSSNF

LI Lei1,2 TUO Xianguo1,3 LIU Mingzhe1,2 SHI Rui1,2 WANG Jun1,2   

  1. 1(State Key Laboratory of Geohazard Prevention & Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China) 2(Provincial Key Laboratory of Applied Nuclear Techniques in Geosciences, Chengdu University of Technology, Chengdu 610059, China) 3(Laboratory of National Defense for Radioactive Waste and Environmental Security, Southwest University of Science and Technology, Mianyang 621010, China)
  • Received:2014-01-26 Revised:2014-09-05 Online:2014-12-10 Published:2014-12-04
  • Supported by:

    ;National Natural Science Foundation of China

Abstract: Background: Nuclear decay, electronic noise, statistic fluctuations, etc., exist inherently. Therefore, the measured spectrum always has statistic fluctuation. Purpose: In order to reduce the statistical fluctuation and electronics noise in detector, translation invariant discrete wavelet transform (TI-DWT) wavelet modulus maxima spatial selectivity filter (TI-DWT-MSSNF) smoothing algorithm was put forward to preprocess data for the de-convolution of spectrum. Methods: The ?-spectrum was acquired by using ORTEC-8 channel ? spectrometer to measure the source numbered AMPU1103 (239Pu and 241Am) under vacuum conditions of ?0.03MPa. The ?-spectrum was obtained by using ? spectrometer and 137Cs source. Cubical smoothing algorithm with five-point approximation (“5-3”), the traditional method of wavelet modulus maxima (WTMM) and TI-DWT-MSSNF were applied to smooth ? and ? spectra. Results: The study showed that TI-DWT-MSSNF method could eliminate statistical fluctuation more thoroughly, retain feature information better compared with “5-3” and WTMM. The D(r) values of TI-DWT-MSSNF were greater and ?2 values of TI-DWT-MSSNF were more close to 1 compared with those of “5-3” and WTMM. Conclusion: Comprehensive research indicates that it is feasible to reduce the statistical fluctuations of spectrum using TI-DWT-MSSNF. And TI-DWT-MSSNF outperforms both the “5-3” and WTMM.

Key words: Translation invariant discrete wavelet transform (TI-DWT), Modulus maxima, Smoothing evaluation