In this communication, a novel corrugated Vivaldi antenna (CVA) is developed for microwave imaging (MWI) applications. In particular, different from the previous works, the lengths of the corrugations are independently optimized to reach the final design. It is shown that by letting more parameters to be optimized in the design process, we can obtain a more effective CVA, which has more suitable characteristics for MWI applications (i.e., higher gain, broader bandwidth) compared with the previous designs. Apart from that, the imaging performance of the proposed design is compared with a generic VA having the same size of the proposed antenna. As imaging algorithm, a recently introduced qualitative MWI technique, the scattering parameter-based linear sampling method (S-LSM), is employed. The similarity between exact shapes of the targets and the obtained qualitative results are compared with the help of the well-known Jaccard index. Experimental results show that the proposed CVA performs better than a generic VA in such real-world MWI problems.

This work presents a new method to measure the complex dielectric permittivity (CDP) variation of materials under test (MUTs) with open-ended coaxial probes. The CDP variation of an MUT is a continuous function of frequency as modeled with various dielectric relaxation models. Hence, the accuracy and the repeatability of CDP measurements can be improved by enforcing such spectral continuity. Here, we derive a new formulation that explicitly enforces the spectral continuity via the Debye relaxation model. In particular, we construct a cost functional based on an admittance integral at the tip of an open-ended coaxial probe. The Debye model parameters are substituted into the admittance integral to form a set of nonlinear equations. Later, these equations are iteratively minimized by a Gauss–Newton-based minimization scheme, for measured reflection coefficients when the tip of the probe is in contact with the MUT. The presented method differs from the conventional CDP measurement techniques in two aspects. First, the method looks for a global solution across the investigated frequency spectrum instead of solving CDP values individually at each frequency step. Second, the Debye parameters are directly retrieved without any data-fitting. The presented method is experimentally verified with various liquid samples, and the accuracy of obtained results is compared against the literature.

The orthogonality sampling method is a recently introduced qualitative inverse scattering approach for the estimation of the morphological properties of unknown targets. Both the simplicity of implementation and the applicability to various measurement configurations make this method very effective. In this paper, a general physical understanding of this otherwise ‘pure mathematical’ method is given for the first time. Such an interpretation is derived from the relationship between the currents induced in the investigated scenario and the so-called reduced scattered field, which is the core of the orthogonality sampling indicator function. Interestingly, the nature of such a relationship implies that the reduced scattered field can be related to the radiating component of the induced currents. A direct consequence of such a result is that the orthogonality sampling method is capable of imaging discontinuities within the unknown targets and hence identify regions with different electromagnetic properties. This possibility represents a unique feature among qualitative inverse scattering methods, as these latter have been introduced as tools to image just the external shape of the unknown targets. The new interpretation as well as the above distinctive capability of the orthogonality sampling method are proved with examples on both simulated and experimental data.

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 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 openended 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 fourpole 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 realtime 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 $\pm$ 0.88% is obtained for CPs’ prediction at the test stage. Furthermore, DLM is tested with real RCs’ data measured from four different tissuemimicking 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) $\pm$ (std.dev) percent relative error ranging from 2.08 $\pm$ 0.4 to 10.84 $\pm$ 2.43 within the frequency band of interest was obtained after comparison.