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Microwave Sensors With Artificial Neural Network Implementation for Permittivity Measurement
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Author(s): Man Seng Sim (Universiti Teknologi Malaysia, Malaysia), Kok Yeow You (Universiti Teknologi Malaysia, Malaysia), Fahmiruddin Esa (Universiti Tun Hussein Onn Malaysia, Malaysia), Raimi Dewan (Universiti Teknologi Malaysia, Malaysia), DiviyaDevi Paramasivam (Universiti Teknologi Malaysia, Malaysia)and Rozeha A. Rashid (Universiti Teknologi Malaysia, Malaysia)
Copyright: 2025
Pages: 38
Source title:
Expert Artificial Neural Network Applications for Science and Engineering
Source Author(s)/Editor(s): Lingala Syam Sundar (Prince Mohamamd Bin Fahd University, Saudia Arabia), Deepanraj Balakrishnan (Prince Mohammad Bin Fahd University, Saudi Arabia)and Antonio C.M. Sousa (University of Aveiro, Portugal)
DOI: 10.4018/979-8-3693-7250-0.ch004
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Abstract
This book chapter reviews the applications of artificial neural networks (ANNs) in microwave sensors to extract the properties of samples under test (SUTs). ANNs enable the determination of complex and nonlinear relationships between sensing data and desired output data. An overview of ANN architecture, working principle, and state-of-the-art advances of ANN-assisted microwave sensors is provided. Subsequently, a planar microwave sensor based on microstrip transmission line and complementary split ring resonators (CSRRs) is designed and simulated using Computer Simulation Technology (CST). The use of MATLAB software to implement the ANN for the prediction of permittivity of SUTs is demonstrated. Additionally, the challenges and future prospects of ANN-assisted microwave sensors are examined.
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