首页> 外文会议>International Symposium on Intelligent Signal Processing and Communications Systems >Application of intensity estimation method of the FID signal based on the high-order Prony estimation Method and selective evaluation criterion to Purity Estimation in quantitative NMR, dependence of the 13C decoupling and the sample spinning
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Application of intensity estimation method of the FID signal based on the high-order Prony estimation Method and selective evaluation criterion to Purity Estimation in quantitative NMR, dependence of the 13C decoupling and the sample spinning

机译:基于高阶Prony估计方法和选择性评估准则的FID信号强度估计方法在定量NMR纯度估计, 13 C解耦和样品旋转中的应用

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The methods based on Prony Method or high-order Prony method have been proposed with regard to the intensity estimation method of the FID signal. These are methods to estimate the intensity of the FID signal from the parameter which may decide the characteristics of the signal generation model. On the other hand, these have faults to improve, though there are few subjective factors compared with the traditional “Integral Method” in estimating the intensity. This paper have point out that the estimation accuracy of traditional method based on the signal generation model depends on the model order of the signal generation model and show that it will get worse according to the choice of the model order. We will try to improve the estimation accuracy by introducing the Selective reconstitution Error method (S. E.) to the traditional method. This S. E. use, both the number of the signal peak and a priori the frequency information are remarkable factors, we can improve the estimation accuracy of the signal intensity with the method including S. E.. In the experiment with estimation of the intramolecular integral ratio of Acetaminophen, it is shown that the purity can be estimated to with the error of about 2% of the true value in the case of the “sample spinnig” is on.
机译:关于FID信号的强度估计方法,已经提出了基于Prony方法或高阶Prony方法的方法。这些是根据可以确定信号生成模型的特征的参数来估计FID信号强度的方法。另一方面,尽管在估算强度方面与传统的“积分方法”相比,主观因素很少,但这些问题仍有待改进。本文指出,基于信号生成模型的传统方法的估计精度取决于信号生成模型的模型阶数,并且表明,随着模型阶数的选择,该方法的估计精度会变差。我们将通过在传统方法中引入选择性重构误差方法(S. E.)来提高估计的准确性。这种SE的使用,无论是信号峰值的数量还是先验的频率信息都是显着的因素,我们可以采用SE的方法来提高信号强度的估计精度。结果表明,在“样品旋转”打开的情况下,可以估计纯度为真值的2%左右。

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