谐振器
灵敏度(控制系统)
基质(水族馆)
折射率
材料科学
生物传感器
吸收(声学)
石墨烯
公制(单位)
多项式回归
多项式的
腔衰荡光谱
线性回归
光学
生物系统
光电子学
数学
计算机科学
电子工程
吸收光谱法
物理
机器学习
纳米技术
数学分析
复合材料
工程类
海洋学
运营管理
地质学
生物
作者
Shobhit K. Patel,Jaymit Surve,Juveriya Parmar,N. Ayyanar,Vijay Katkar
出处
期刊:IEEE Transactions on Nanobioscience
[Institute of Electrical and Electronics Engineers]
日期:2023-04-01
卷期号:22 (2): 430-437
被引量:15
标识
DOI:10.1109/tnb.2022.3201237
摘要
Machine learning is the latest approach to optimize the performance of absorbers, sensors, etc. A sensor with behavior prediction using polynomial regression is presented. Three different variations of metasurfaces namely double split-ring resonator, single split ring resonator, split ring resonator with thin wire are analyzed. The proposed design aims to achieve the highest sensitivity by observing different designs and different parameter variation. The highest sensitivity is achieved for double split-ring resonator and single split ring resonator designs. The change in thickness of different parameter affect the absorption and the highest sensitivity is calculated based on these variations. The polynomial regression (PR) model is employed to predict the absorption values for assorted combinations of intermediate wavelength values with angle variation, substrate thickness, substrate length, substrate width, graphene potential, and resonator thickness values. Test Cases R-30 and R-50 are evaluated using R2 score metric to assess the effectiveness of PR model for predicting the values of absorption. R2 score close to 1.0 is achieved for all the experiments at a higher (more than 5) polynomial degree, which proves the prediction efficiency of a regression model. The proposed biosensor designed with a PR model can be applied in biomedical applications for hemoglobin detection.
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