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This research focuses on the design and optimization of a graphene-based microwave ablation applicator (MWA) for tumor treatment. The unique properties of graphene, such as high impedance at microwave frequency and tunable Fermi energy, make it an ideal candidate for inducing charge carriers through an electric field or chemical doping. The study further employs machine learning techniques, including support vector regression (SVR) and artificial neural networks (ANN) to predict the ablation zone. The applicator design incorporates a helix antenna element connected to a coaxial cable working at 2.45 GHz with a graphene sheet and T-ring attached to the outer conductor. The challenge of achieving a zero-Fermi energy is addressed by introducing defects to increase surface resistivity, resulting in an impedance close to the required value. The results show that the graphene-based applicator enhances the ablation zone, leading to efficient and controlled tumor treatment. To predict the ablation zone accurately, the study employs machine learning techniques, including SVR and ANN utilizing Taguchi method to reduce computational complexity. Large and round ablation zone is achieved using novel applicator. Further, the performance metrics, including root-mean-squared error and coefficient of determination (R2), are utilized to evaluate the predictive capabilities of the model and found to be optimum. The research demonstrates the potential of graphene in improving MWA treatment and highlights the importance of machine learning in optimizing MWA and predicting treatment outcomes. [ABSTRACT FROM AUTHOR]

In this communication, a planar dual port multiple input multiple output antenna of size 1.2λ0 × 0.6λ0 × 0.008λ0 with LHCP/RHCP features is reported for the fifth-generation new radio n77/n78 sub-6 GHz wireless applications band. The single unit of the proposed design consists of a modified L-shape rectangular radiator with Z-shape slot loaded DGS. The defected ground structure is optimized through machine learning algorithms to achieve the maximum ARBW (output) by Right Shifting (RS) and left shifting (LS) the DGS and obtaining input features. The performance metric for ANN with ADAM optimizer was found to be optimal with MSE and R2 of 0.99 and 0.82, respectively. ANNs can leverage gradient information to guide the optimization process. This enables faster convergence towards optimal solutions compared to popular GAs and PSO, which are often gradient-free optimization methods. The MIMO configuration is achieved by creating a mirror image of the single unit about the x-axis. The salient features of the proposed design are (a) Impedance bandwidth (IBW) of 3.0–4.2 GHz covering the n77/n78 band, (b) 3-dB axial ratio bandwidth (ARBW) of the 2.6–3.9 GHz (c) Port-1 is generating RHCP while Port-2 is generating LHCP, results in polarization diversity. Different diversity performance parameters (ECC < 0.005, DG ~ 9.99 dB, and MEG < 3 dB) are in the optimum range confirming the proposed configuration as a suitable design for a MIMO radiator. [ABSTRACT FROM AUTHOR]

This work proposes a novel dielectric resonator antenna design for 5G-based IoT applications that operates in the sub-6 GHz frequency range. The DR antenna is built on a 1.6-mm-thick FR-4 substrate with dimensions of 30*50 mm2. The proposed dielectric resonator antenna is made of alumina and is excited using a microstrip feedline of 50 Ω. Because of the hybrid arrangement of cylindrical and rectangular dielectric resonator elements on the FR4 substrate, this proposed structure has improved the radiation mechanism. The antenna design process begins with creating the antenna in the Ansys HFSS EM simulator, which is then optimized using machine learning based on the antenna geometry's target factors. A data set of 2625 sample data values is generated in HFSS and fed to various machine learning algorithms for further optimization based on data trends. Post-optimization, the antenna design is fabricated and tested. The proposed antenna offers a wide bandwidth of 1.1 GHz between 3.5 and 4.6 GHz and resonates at 3.9 GHz making it suitable for 5G sub-6 GHz IoT applications. [ABSTRACT FROM AUTHOR]