导纳
计算机科学
同轴
介电常数
试验数据
生物医学工程
人工智能
材料科学
声学
生物系统
电介质
物理
工程类
电阻抗
光电子学
电气工程
生物
电信
程序设计语言
作者
Sulayman Joof,Cemanur Aydınalp,İsmail Dilman,Mehmet Nuri Akıncı,Tuba Yilmaz,Mehmet Çayören,Íbrahim Akduman
出处
期刊:IEEE Transactions on Microwave Theory and Techniques
日期:2022-11-01
卷期号:70 (11): 5105-5115
被引量:8
标识
DOI:10.1109/tmtt.2022.3209701
摘要
In this work, we proposed a general guideline and a technique for retrieving the complex permittivities (CPs) of material under test (MUT) from the measured reflection coefficients (RCs) obtained with the open-ended coaxial probe (OECP) through a deep learning model (DLM). Particularly, lossy materials such as phantoms mimicking the biological tissues are considered as MUT in this study. The dataset used to design (train, validate, and test) the DLM is synthetically generated using the relationship between the CPs of the MUT, namely, admittance model, and the four-pole Cole–Cole relaxation model. Moreover, the technique is implemented to accurately predict the CPs of biological tissues with real measured RCs’ data from 0.5 to 20 GHz with a 50-MHz resolution. This technique eliminates the need to physically perform measurements required to create the dataset for DLM and it can be easily implemented for real-time CPs’ measurement of biological tissues. The designed DLM is initially trained, validated, and tested with 80%, 10%, and 10% of the total generated synthetic dataset, respectively. A percent relative error of 1.5 ± 0.88% is obtained for CPs’ prediction at the test stage. Furthermore, DLM is tested with real RCs’ data measured from four different tissue-mimicking phantoms: skin, muscle, blood, and fat. Predicted CPs from DLM are compared with the CPs’ results obtained from a commercially available OECP measurement kit. A (mean) ± (std.dev) percent relative error ranging from 2.08 ± 0.4 to 10.84 ± 2.43 within the frequency band of interest was obtained after comparison.
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