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摘要：当前正处于数字信息化时代，数字信号处理技术受到人们的广泛关注，其理论及算法随计算机技术和微电子技术的发展得到了飞速的发展，被广泛应用语音图像处理、数字通讯、谱分析、模式识别、自动控制等领域。数字滤波器是数字信号中最重要的组成部分之一，几乎出现在所有的数字信号处理系统中。数字滤波器是指完成信号滤波处理的功能，用有限精度算法实现的离散时间线性非时变系统，其输入是一组（由模拟信号取样和量化）数字量，其输出是经过变换的另一组数字量。数据平滑是统计语言建模的关键技术，它不仅可以改进语言模型的性能，还可以提高语音识别、文字识别等应用领域的系统识别率，不同的数据平滑方法之间的对应在各种不同规模的训练集上操作。各种平滑算法中，以Good—Turing估计、线性插值平滑、Katz’s回退式平滑最为典型和常用。由于射线和探测器中固有的统计涨落、电子学系统的噪声影响，谱数据有很大的统计涨落。谱数据的涨落使谱数据处理产生误差。在γ 能谱的分析中，如果被分析的核素活度很低，或被分析的是发射多支γ 射线核素所辐射的弱分支，或测量时间太短，那么，由于计数的统计涨落，可能使谱中相邻道计数的分散度较大，致使谱峰模糊。为了减少能谱测量数据的统计涨落，又保留谱峰的全部重要的特征，以便谱的分析，必须对实测γ 能谱原始数据进行光滑。关键词：数字滤波器；数据平滑；语料库；线性插值平滑；统计涨落8366
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Research and implementation of spectral data smoothing algorithm based on the digital filtering
Abstract:Current is in the digital information age, digital signal processing technology is widespread attention, its theory and algorithm along with the development of the computer technology and microelectronic technology obtained the rapid development and be widely applied in voice and image processing, digital communications, spectrum analysis, pattern recognition, automatic control and other fields. Digital filter is one of the most important part of digital signal, almost appeared in all digital signal processing systems. Filtering processing of digital filter is refers to the complete function, with limited accuracy algorithm of discrete time linear time-invariant system, its input is a set of (by the analog signal sampling and quantization) digital quantity, its output is another digital quantity after transforming. Data smoothing is the key technology of statistical language modeling, It not only can improve the performance of language modeling, it Can also improve speech recognition and Application areas such as language identification system recognition rate. Different data smoothing method should be at the contrast between the different scale of operation on the training set. A variety of smoothing algorithms, To Good-Turing estimate, linear interpolation smoothing, Katz’s back-off-type is most typical and commonly used smoothing. In this paper, various methods of data smoothing empirical comparison, and discussed the impact of these data smoothing method performance of relevant factors. Due to inherent statistical fluctuation and the electronics system of noise influence in the rays and the probe, Spectral data has a lot of fluctuations. Spectral data fluctuation spectrum data processing error is produced. In gamma energy spectrum analysis, if the analysis of nuclide activity is very low, or is the analysis of the emission of radiation by gamma rays nuclide more weak branches, or the measuring time is too short, so, because of the statistical fluctuation count, may make the adjacent word count in the spectral dispersion larger and lead to the peak fuzzy. In order to reduce the spectrum measurement data of statistical fluctuation, and keep all the important feature of spectral peak to facilitate analysis of the spectral , must be smooth to the measured gamma spectrometry original data.
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