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NMR Data Analysis: A Time-Domain Parametric Approach Using Adaptive Subband Decomposition

机译:NMR数据分析:使用自适应子带分解的时域参数方法

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This paper presents a fast time-domain data analysis method for one- and two-dimensional Nuclear Magnetic Resonance (NMR) spectroscopy, assuming Lorentzian lineshapes, based on an adaptive spectral decomposition. The latter is achieved through successive filtering and decimation steps ending up in a decomposition tree. At each node of the tree, the parameters of the corresponding subband signal are estimated using some high-resolution method. The resulting estimation error is then processed through a stopping criterion which allows one to decide whether the decimation should be carried on or not. Thus the method leads to an automated selection of the decimation level and consequently to a signal-adaptive decomposition. Moreover, it enables one to reduce the processing time and makes the choice of usual free parameters easier, comparatively to the case where the whole signal is processed at once. The efficiency of the method is demonstrated using 1-D and 2-D 13C NMR signals.
机译:本文基于自适应谱分解,假设洛伦兹线形,提出了一种用于一维和二维核磁共振(NMR)光谱的快速时域数据分析方法。后者是通过以分解树结束的连续滤波和抽取步骤实现的。在树的每个节点上,使用某种高分辨率方法估计相应子带信号的参数。然后,通过停止准则处理所产生的估计误差,该准则允许人们决定是否应进行抽取。因此,该方法导致抽取水平的自动选择,并因此导致信号自适应分解。而且,与一次处理整个信号的情况相比,它可以减少处理时间,并使通常的自由参数的选择更加容易。使用1-D和2-D 13C NMR信号证明了该方法的效率。

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