Wang, Lulu

In this paper, we present the design and equivalent circuit model (ECM) of a fractal slot-loaded super wideband (SWB) antenna for compact and high-performance applications operating in the 3–40 GHz range. The proposed antenna features a compact dimension of 40 × 35 × 1.57 mm³, a measured bandwidth ratio of 13:1, a peak gain of 9.7 dBi, an average radiation efficiency of 94%, and a low cross-polarization level across the entire bandwidth. The presented ECM is derived using transmission line theory and incorporates the individual behavior of each constituting element of the antenna. A dual sequential optimization approach is employed to determine the optimal element values. The ECM results show good agreement with both simulated and measured results in terms of the magnitude of reflection coefficient |S11| and both real and imaginary impedances with low mean absolute percentage errors of 4.9%, 7.5%, and 7.7%, respectively, demonstrating the model’s ability to accurately predict the antenna’s performance.

Abstract Globally, breast cancer (BC) remains a significant cause to female mortality. Early detection of BC plays an important role in reducing premature deaths. Various imaging techniques including ultrasound, mammogram, magnetic resonance imaging, histopathology, thermography, positron emission tomography, and microwave imaging have been employed for obtaining breast images (BIs). This review provides comprehensive information of different breast imaging modalities and publicly accessible BI sources. The advanced machine learning (ML) techniques offer a promising avenue to replace human involvement in detecting cancerous cells from BIs. The article outlines various ML algorithms (MLAs) which have been extensively used for identifying cancerous cells in BIs at the early stages, categorizing them based on the presence or absence of malignancy. Additionally, the review addresses current challenges associated with the application of MLAs in BC identification and proposes potential solutions.