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In this paper, a dielectric resonator antenna (DRA) with high gain and wide impedance bandwidth for fifth-generation (5G) wireless communication applications is proposed. The dielectric resonator antenna is designed to operate at higher-order T E δ 15 x mode to achieve high antenna gain, while a hollow cylinder at the center of the DRA is introduced to improve bandwidth by reducing the quality factor. The DRA is excited by a 50   Ω microstrip line with a narrow aperture slot. The reflection coefficient, antenna gain, and radiation pattern of the proposed DRAs are analyzed using the commercially available full-wave electromagnetic simulation tool CST Microwave Studio (CST MWS). In order to verify the simulation results, the proposed antenna structures were fabricated and experimentally validated. Measured results of the fabricated prototypes show a 10-dB return loss impedance bandwidth of 10.7% (14.3–15.9GHz) and 16.1% (14.1–16.5 GHz) for DRA1 and DRA2, respectively, at the operating frequency of 15 GHz. The results show that the designed antenna structure can be used in the Internet of things (IoT) for device-to-device (D2D) communication in 5G systems.

Abstract-In this paper, we propose a new approach to generate quadrupling-frequency optical millimeter-wave (mm-wave) signal with carrier suppression by using two parallel Mach-Zehnder modulators (MZMs) in Radio-over-fiber (RoF) system. Among the numerous properties of this approach, the most important is that a filterless optical mm-wave at 60 GHz with an optical sideband suppression ratio (OSSR) as high as 40 dB can be obtained when the extinction ratio of the MZM is 25 dB. Simplicity and cost-effectiveness have made this approach a compelling candidate for future wave-division-multiplexing RoF systems.Theoretical analysis is conducted to suppress the undesired optical sidebands for the high-quality generation of frequency quadrupling mm-wave signal. The simulation results show that a 60 GHz mm-wave is generated from a 15 GHz radio frequency (RF) oscillator with an OSSR as high as 40 dB and an radio frequency spurious suppression ratio (RFSSR) exceeding 35 dB without any optical or electrical filter when the extinction ratio of the MZM is 25 dB. Furthermore, the effect of the non-ideal RF-driven voltage as well as the phase difference of RF-driven signals applied to the two MZMs on OSSR and RFSSR is discussed and analyzed. Finally, we establish a RoF system through simulation to verify the transmission performance of the proposed scheme. The Q-factor performance and eye patterns are given.

An electrically tunable, textile-based metamaterial (MTM) is presented in this work. The proposed MTM unit cell consists of a decagonal-shaped split-ring resonator and a slotted ground plane integrated with RF varactor diodes. The characteristics of the proposed MTM were first studied independently using a single unit cell, prior to different array combinations consisting of 1 × 2, 2 × 1, and 2 × 2 unit cells. Experimental validation was conducted for the fabricated 2 × 2 unit cell array format. The proposed tunable MTM array exhibits tunable left-handed characteristics for both simulation and measurement from 2.71 to 5.51 GHz and provides a tunable transmission coefficient of the MTM. Besides the left-handed properties within the frequency of interest (from 1 to 15 GHz), the proposed MTM also exhibits negative permittivity and permeability from 8.54 to 10.82 GHz and from 10.6 to 13.78 GHz, respectively. The proposed tunable MTM could operate in a dynamic mode using a feedback system for different microwave wearable applications.

Breast cancer is the most common cancer among women and it is one of the main causes of death for women worldwide. To attain an optimum medical treatment for breast cancer, an early breast cancer detection is crucial. This paper proposes a multi- stage feature selection method that extracts statistically significant features for breast cancer size detection using proposed data normalization techniques. Ultra-wideband (UWB) signals, controlled using microcontroller are transmitted via an antenna from one end of the breast phantom and are received on the other end. These ultra-wideband analogue signals are represented in both time and frequency domain. The preprocessed digital data is passed to the proposed multi- stage feature selection algorithm. This algorithm has four selection stages. It comprises of data normalization methods, feature extraction, data dimensional reduction and feature fusion. The output data is fused together to form the proposed datasets, namely, 8-HybridFeature, 9-HybridFeature and 10-HybridFeature datasets. The classification performance of these datasets is tested using the Support Vector Machine, Probabilistic Neural Network and Naïve Bayes classifiers for breast cancer size classification. The research findings indicate that the 8-HybridFeature dataset performs better in comparison to the other two datasets. For the 8-HybridFeature dataset, the Naïve Bayes classifier (91.98%) outperformed the Support Vector Machine (90.44%) and Probabilistic Neural Network (80.05%) classifiers in terms of classification accuracy. The finalized method is tested and visualized in the MATLAB based 2D and 3D environment.