Microwave hyperthermia (MH) requires the selective focusing of microwave energy on the targeted region while minimally affecting the healthy tissue. Emerging from the simple nature of the linear antenna arrays, this work demonstrates focusing maps as an application guide for MH focusing by adjusting the antenna phase values. The focusing of the heating potential (HP) on different density breast models is performed via the proposed method using Vivaldi antennas. The effect of the tumor conductivity on the focusing is discussed. As a straightforward approach and utilizing the Vivaldi antennas, the system can be further combined with MH monitoring application.

Microwave hyperthermia (MH) requires the effective calibration of antenna excitations for the selective focusing of the microwave energy on the target region, with a nominal effect on the surrounding tissue. To this end, many different antenna calibration methods, such as optimization techniques and look-up tables, have been proposed in the literature. These optimization procedures, however, do not consider the whole nature of the electric field, which is a complex vector field; instead, it is simplified to a real and scalar field component. Furthermore, most of the approaches in the literature are system-specific, limiting the applicability of the proposed methods to specific configurations. In this paper, we propose an antenna excitation optimization scheme applicable to a variety of configurations and present the results of a convolutional neural network (CNN)-based approach for two different configurations. The data set for CNN training is collected by superposing the information obtained from individual antenna elements. The results of the CNN models outperform the look-up table results. The proposed approach is promising, as the phase-only optimization and phase–power-combined optimization show a 27% and 4% lower hotspot-to-target energy ratio, respectively, than the look-up table results for the linear MH applicator. The proposed deep-learning-based optimization technique can be utilized as a protocol to be applied on any MH applicator for the optimization of the antenna excitations, as well as for a comparison of MH applicators.

In microwave hyperthermia tumor therapy, electromagnetic waves focus energy on the tumor to elevate the temperature above its normal levels with minimal injury to the surrounding healthy tissue. Microwave hyperthermia applicator design is important for the effectiveness of the therapy and the feasibility of real-time application. In this study, the potential of using fractal octagonal ring antenna elements as a dipole antenna array and as a connected array at 2.45 GHz for breast tumor hyperthermia application was investigated. Microwave hyperthermia treatment models consisting of different fractal octagonal ring antenna array designs and a breast phantom are simulated in COMSOL Multiphysics to obtain the field distributions. The antenna excitation phases and magnitudes are optimized using the global particle swarm algorithm to selectively increase the specific absorption rate at the target region while minimizing hot spots in other regions within the breast. Specific absorption rate distributions, obtained inside the phantom, are analyzed for each proposed microwave hyperthermia applicator design. The dipole fractal octagonal ring antenna arrays are comparatively assessed for three different designs: circular, linear, and Cross—array. The 16-antenna dipole array performance was superior for all three 1-layer applicator designs, and no distinct difference was found between 16-antenna circular, linear, or cross arrays. Two-layer dipole arrays have better performance in the deep-tissue targets than one-layer arrays. The performance of the connected array with a higher number of layers exceeds the performance of the dipole arrays in the superficial regions, while they are comparable for deep regions of the breast. The 1-layer 12-antenna circular FORA dipole array feasibility as a microwave hyperthermia applicator was experimentally shown.

Microwave breast hyperthermia (MH) aims to increase the temperature at the tumor location with minimal change in the healthy tissue. To this end, the specific absorption rate ($SAR$) inside the breast is optimized. The choice of the MH applicator design is important for a superior energy focus on the target. Although hyperthermia treatment planning (HTP) changes for every patient, the MH applicator is required to be effective for different breast models and tumor types. The linear applicator ($LA$) is one of the previously proposed applicator designs with linearly arranged antennas; however, it suffers from low focusing ability in certain breast regions due to its unsymmetrical geometrical features. In this paper, we propose to radially adjust the $LA$ to obtain alternative excitation schemes without actually changing the applicator. Antipodal Vivaldi antennas were utilized, and the antenna excitations were optimized with particle swarm optimization (PSO). The comparison of the rotated and the fixed linear applicator, between 12-antenna circular and linear applicators, and finally, between a 24-antenna circular applicator are provided. Within the 12 rotation angles and two target locations that were analyzed, the 135° axially rotated linear applicator gave a 35% to 84% higher target-to-breast $SAR$ ratio ($TB{R}_S$) and a 21% to 28% higher target-to-breast temperature ratio ($TB{R}_T$) than the fixed linear applicator. For the deep-seated target, the 135° rotated linear applicator had an 80% higher $TB{R}_S$ and a 59% higher $TB{R}_T$ than the 12-antenna circular applicator, while the results were comparable to the 24-antenna circular applicator.

Salinity is a key indicator in environmental, agricultural, and marine systems, requiring accurate, real-time monitoring to support sustainable resource management. While traditional measurement methods offer many advantages, they are impractical due to preparation requirements, contact-based operation, and sensitivity to environmental factors. In contrast, microwave sensing has emerged as a promising alternative for salinity detection,providing contactless operation, robustness, and sensitivity across wide frequency bands. In this study, we present a non-contact microwave sensing system integrated with a deep neural network (DNN) model for high-precision salinity prediction in water samples. The system, based on two Vivaldi antennas and PA6 plates matching layer, collects reflection and transmission coefficients (RCs and TCs) from water samples with salinity ranging from 0 to 30,000 ppm in increments of 1,000 ppm. A dataset of 620 measurements was acquired at 751 frequency points (6759 features) and was used to train and test the DNN model. Furthermore, four different feature selection methods, T-test, Lasso-L1, mutual information(MI), and correlation analysis (CA), were employed to improve predictive accuracy. When the CA method was applied to the full frequency band (0.5–8 GHz), the Mean Absolute Error (MAE) decreased from 299.91 ppm to 119.57 ppm, while the coefficient of determination (R²) increased from 0.998632 to 0.999674, as the feature size was reduced by half. In addition, multiple narrow frequency bands were analysed to identify optimal bands, and the CA method selected 680 features in the 0.5-2 GHz range, resulting in an MAE of 106.27 ppm and an R² of 0.999702. In addition, when the MI method was applied only to the TC, the best performance was achieved in the 0.5–2.5 GHz band, yielding an MAE of 126.37 ppm and an R²: 0.999695, while utilizing only 302 features.