This study proposes a single-port dual-band independent microwave sensor for detecting solid materials. The sensor structure consists of a pair U-shaped resonators connected by a power divider and a microstrip feed line with an impedance of 50 Ohms with two different sensing hotspots. The $1^{\text {st}}$ and $2^{\text {nd}}$ resonators operate at a frequency of 1.200 GHz and 2.100 GHz, respectively. The type of material under test (MUT) is a standard material with a size of 10 mm $\times10$ mm (RO5880, RO4003C, and FR4). The proposed sensor had a very high accuracy compared with both of using permitivitty from datasheet and calibrated frequency from simulation for the $1^{\text {st}}$ and $2^{\text {nd}}$ resonator, respectively. Moreover, the pair of the U-shaped resonators had independent performances and they did not affect each other when the MUT was loaded. Furthermore, it can also operate with single-port measurement, which is a more efficient resource. Finally, the proposed microwave sensor can be a promising solution for detecting different permittivity of solid materials independently with high accuracy for applications such as the food industry, material quality control, and biomedical.

A compact and low-profile curve-feed complementary split-ring resonator (CSRR) microwave sensor for solid material detection is presented in this article. The curve-feed CSRR sensor was developed based on the CSRR configuration with triple rings (TRs) designed together, utilizing a high-frequency structure simulator (HFSS) microwave studio. The designed curve-feed CSRR sensor resonates at 2.5 GHz, performs in transmission mode, and senses shift in frequency. Four varieties of the sample under tests (SUTs) were simulated and measured. These SUTs are Air (without SUT), Roger 5880, Roger 4350, FR4, and detailed sensitivity analysis is being performed for the resonant band at 2.5 GHz. The finalized CSRR curve-feed sensor was integrated with defective ground structure (DGS) to deliver high-performance characteristics in microstrip circuits, which leads to a high Q-factor magnitude. The presented curve-feed sensor has a Q-factor of 520 at 2.5 GHz, with high sensitivity of about 1.072. The relationship between loss tangent, permittivity, and Q-factor at the resonant frequency has been compared and discussed. These disseminated outcomes make the suggested sensor ideal for characterizing solid materials.

When designing a microstrip antenna, the designers determined the desired parameters. However, the simulation software can only give the parameters result based on the given dimension. Therefore, optimization is required to meet the desired parameters. The designers usually do the optimization by the trial-error process. This research conducts machine learning implementation to predict the microstrip antenna dimension. The study focused on rectangular patch microstrip antenna with resonant frequency ranged from 1-8 GHz. The dataset used to make the prediction is obtained from simulation with antenna width ranged from 19-63 mm and length 10-54 mm. There are four algorithms employed: decision tree, random forest, Support Vector Regression (SVR), and Artificial Neural Network (ANN). Among all algorithms, random forest with estimator 15 gives the best result with Mean Square Error (MSE) value is 3.45. From the obtained result, the researchers can estimate the rectangular patch microstrip antenna dimension based on the desired parameters, which can’t be done by the antenna simulation software before.

This paper proposes the integration of microwave sensors (MS) and antenna sensors (AS) for contact and non-contact characterization of solid materials. Integration between MS and AS were determined based on the reflection coefficient (S11) where S11 of ≥ -10 dB was proposed as MS and S ₁₁ of ≤ -10 dB was proposed as AS. The proposed sensor consists of a dual T-Shaped resonator with single port operating at a resonant frequency of f _r1 = 1.81 GHz for MS and f _r2 = 2.34 GHz for AS using FR-4 substrate with ε _r of 4.3, tan δ of 0.0265 and thickness of 1.6 mm. Furthermore, the two resonant frequencies had independent characteristics with different sensing hotspots. MS are proposed for permittivity detection of the Material Under Test (MUT) for contact detection while non-contact detection is proposed by utilizing AS. The procedure for contact detection was carried out by placing the material under test on the surface of the sensor while non-contact detection was carried out by placing the MUT with an air gap between the MUT (d) and the AS was 0.5 - 1.5 mm. The measurement results showed that the average accuracy was 99.30% and 99.31% for the MS and AS for the permittivity range of 1 - 6.15 while the normalized sensitivities of the proposed sensors were 1.15 % and 0.16%, respectively. The results of this research are novel and can be recommended for several purposes related to biomedical and pharmaceutical industries requiring contact and non-contact detection of material characterization.

This paper outlines the design and the implementation of a planar microwave resonator sensor for sensing application using the perturbation concept in which the dielectric characteristics of the resonator influence the quality factor (QF) and the resonance frequency. The designed sensor is fabricated using Roger 5880, and it is operating at 2.27 GHz in ranges of 1-3 GHz for testing solid materials. In addition, applying a specific experimental methodology, practical material is used as material samples such as those in Roger 5880, Roger 4350, and FR4. To investigate the microwave resonator sensor performance, an equivalent circuit model (ECM) is introduced. The proposed sensor has achieved a narrow bandwidth and high QF value of 240 at an operating frequency of 2.27GHz. Besides, the sensitivity and accuracy of the sensor is more than 80%, which makes this sensor an excellent solution to characterize the material, especially in discovering the material characteristics and quality.