LEE- Elektronik Mühendisliği Lisansüstü Programı
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Konu "Dielectric properties" ile LEE- Elektronik Mühendisliği Lisansüstü Programı'a göz atma
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ÖgeMicrowave dielectric property characterization with open-ended coaxial probe and sensing depth analysis of the probes for biological tissues(Lisansüstü Eğitim Enstitüsü, 2022) Aydınalp, Cemanur ; Abdolsaheb, Tuba Yılmaz ; 725335 ; Elektronik ve Haberleşme MühendisliğiInherent dielectric property discrepancy at microwave frequencies between the healthy and malignant tissues enabled many different microwave diagnostic technologies among these microwave breast cancer imaging, microwave hyperthermia, and microwave ablation are popular research topics. To develop and test such technologies the dielectric properties of the biological tissues must be quantified. This is mostly done with the open-ended coaxial probes in the laboratory environment due to advantages of the technique including but not limited to minimal sample preparation requirements, commercial availability and broadband measurement capabilities. Despite being commercially available, the technique suffers from high error rates and remains overlooked as a potential diagnostic technology. The error sources can be categorized as the sample and equipment related complications. The sample related error sources can be mitigated via the selection of an appropriate probe for dielectric property characterization. Particularly, biological tissues are known to be heterogeneous contributing to the high measurement error due to sample. Hence, it is important to analyze the sensing depth of the probes under different conditions including using samples with varying dielectric properties and probes with different aperture diameters. Next, the equipment related errors mostly due to the mathematical approach which can potentially be diminished via the introduction of new retrieval methodologies. Towards this end, in an attempt to enable diagnostics applications of the open-ended coaxial probe technique, this thesis focuses on the improvement of the two shortcomings by sensing depth characterization and introducing a deep learning based model for dielectric property retrieval. In the first part of the thesis, sensing depth analysis of the 2.2 mm diameter open-ended coaxial probe was performed using two different double-layered configurations to mimic the tissue heterogeneity. The double-layered configurations are used to mimic the heterogeneous skin tissue in order to establish the potential use of the open-ended coaxial probe method for skin cancer diagnosis. To this end, the sensing depth analysis was performed via simulations and measurements. The double-layered sample configurations are composed using skin-mimicking phantom and olive oil or triton X-100 liquids. In addition, the experiments were carried out by following a newly proposed measurement protocol, which can be easily applied to any tissue type. The results show that the sensing depth was independent of the frequency of operation (0.5-6 GHz) and was affected by the following conditions: by the material located immediately at the probe tip, and by the dielectric property contrast between the two layers. Thus, in order to accurately obtain dielectric property measurement results using the open-ended coaxial probe method, there is a need to establish a pre-measurement protocol to minimize the error due to the skin tissue diversity. The second part of thesis reports the sensing depth analysis of the open-ended coaxial probe for ex vivo experiments on real heterogeneous tissue. The knowledge on the sensing depth of the probe can help eliminate the errors due to tissue heterogeneity. Accurate classification of tissues with similar dielectric properties can be obtained by minimizing the measurement errors. Therefore, this method can be applied in practical applications, such as microwave biopsy. In this work, double-layered sample configuration consisting of an ex vivo rat's breast or wet skin as first layer and pure liquids olive oil or triton X-100 as second layer was utilized to perform the sensing depth analysis of the probe from 0.5 to 6 GHz frequency range. A straight forward, adoptable experimental protocol was established and employed in this study. The analysis was performed by determining five different the percent change in measured dielectric property values. The results indicate a discrepancy of 52%-84% of the measured dielectric property when a membrane layer (between 0.4-0.8 mm thickness) was present on the wet skin tissue and breast tissue. The aim of the third part of this thesis is to analyze and to specify the sensing depth of the open-ended coaxial probe in order to employ the appropriate probe aperture dimension for any given measurement set-up. The proposed method has the potential to reduce the errors due to tissue heterogeneity for skin cancer diagnosis. This work presents the sensing depth comparison of three different probes with different aperture sizes. Simulations of the probes with 0.5, 0.9 and 2.2 mm-diameters terminated with a double-layered skin tissue and olive oil sample configuration were performed. It should be noted that probes with different aperture diameters were investigated in the literature but no information was reported on probes with small aperture sizes. An experimental validation of the simulated scenario was performed with the 2.2 mm-diameter probe and the fully developed double-layered configuration. The acquired simulations and experimental results indicate a proportional relation between the sensing depth and the aperture of the probe. From this relation, it can be concluded that probes with smaller aperture size can possibly help to obtain more precise results from the heterogeneous tissues which can lead to the accurate characterization of thin skin tissue layers. In order to obtained more accurate results especially for tissues with multi-layered structures or membrane-like layers, it is recommended to a establish measurement protocols to prepare the surface of the tissue. In the fourth and last part of the thesis, a novel approach for the determination of material dielectric properties from the reflection coefficient response of the open-ended coaxial probe is proposed. This technique retrieves the Debye parameters of the material under test using a deep learning model which is trained with numerically generated data. The ability to train the deep learning model with synthetic data provide the advantage of rapid generation of a large variety of materials as a dataset. Additionally, the presented method can be easily adapted to any type of probe with desired dimensions and materials. An experimental verification of the trained deep learning model was performed by testing the network with measured reflection coefficients obtained from five different standard liquids, four mixtures, and a gel-like material. A comparison of the acquired results from the deep learning model with literature values is also performed. Finally, a large-scale statistical verification of the retrieved dielectric property from the proposed technique is presented.