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Implementation of nonlinear blind source separation for CHEMFET sensor arrays

机译:ChemFET传感器阵列的非线性盲源分离的实现

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In this study, a method to improve selectivity of chemically field-effect transistor (CHEMFET) sensor towards the main ion concentration in mixed solution is discussed. The approach is based on artificial neural network (ANN) as a post processing stage that performs the estimation of ion concentration in a mixed solution. CHEMFET sensor is viewed as non-linear model producing signal fed to blind-source separation algorithm. To describe how the ions interfere with main ion, the source signal of CHEMFET sensor is generated based on CHEMFET model. The sensor response is converted to frequency by using voltage to frequency converter (VFC). Simulation results confirm that the algorithm is able to separate the mixing signal.
机译:在该研究中,讨论了提高化学场效应晶体管(ChemFET)传感器朝向混合溶液中的主离子浓度的选择性的方法。该方法基于人工神经网络(ANN)作为后处理阶段,其在混合溶液中执行离子浓度的估计。 ChemFET传感器被视为馈送到盲源分离算法的非线性模型产生信号。为了描述离子如何干扰主离子,基于ChemFET模型产生ChemFET传感器的源信号。传感器响应通过使用电压转换为频率转换器(VFC)。仿真结果证实该算法能够分离混合信号。

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