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Proposal of Millimeter-Wave Adaptive Glucose-Concentration Estimation System Using Complex-Valued Neural Networks

机译:基于复合性神经网络的毫米波自适应葡萄糖浓缩系统的提议

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This paper proposes an adaptive glucose-concentration estimation system. Diabetes prophylaxis and monitoring require long-term, frequent and accurate observation of blood sugar levels. Electromagnetic measurement is ideal in its non-invasiveness and the possibility of high accuracy. Debye relaxation model indicates that millimeter-wave is promising for this purpose. In this frequency band, however, both phase and magnitude changes have significant meaning. Then, we employ a complex-valued neural network that deals with phase and amplitude information adaptively in a consistent manner. Experiments demonstrate effective estimation of glucose concentration of a realistic range of blood sugar.
机译:本文提出了一种自适应葡萄糖浓度估计系统。糖尿病预防和监测需要长期,频繁和准确地观察血糖水平。电磁测量非常适用于其非侵入性和高精度的可能性。德拜德弛豫模式表明毫米波为此目的是有希望的。然而,在该频带中,相位和幅度变化都具有重要意义。然后,我们采用复方有价值的神经网络,其以一致的方式自适应地处理相位和幅度信息。实验表明了葡萄糖浓度的血糖现实范围的有效估计。

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