This article presents a four-element multiple-input multiple-output (MIMO) antenna design for next-generation millimeter-wave (mmWave) communication systems. The single antenna element of the MIMO systems consists of a T-shaped and plow-shaped patch radiator designed on an ultra-thin Rogers RT/Duroid 5880 substrate. The dimensions of the single antenna are 10 × 12 mm${}^2$. The MIMO system is designed by placing four elements in a polarization diversity configuration whose overall dimensions are 24 × 24 mm${}^2$. From the measured results, it is observed that the MIMO antenna provides 9.23 GHz impedance bandwidth ranging from 22.43 to 31.66 GHz. In addition, without the utilization of any decoupling network, a minimum isolation of 25 dB is achieved between adjacent MIMO elements. Furthermore, the proposed MIMO antenna system is fabricated, and it is noted that the simulated results are in good agreement with the measured results. Through the achieved results, it can be said that the proposed MIMO antenna system can be used in 5G mmWave radio frequency (RF) front-ends.

This paper describes a singly-fed circularly polarized rectangular dielectric resonator antenna (RDRA) for MIMO and 5G Sub 6 GHz applications. Circular polarization was achieved for both ports using a novel-shaped conformal metal strip. To improve the isolation between the radiators, a “S” shaped defective ground plane structure (DGPS) was used. In order to authenticate the estimated findings, a prototype of the suggested radiator was built and tested experimentally. Over the desired band, i.e., 3.57–4.48 GHz, a fractional impedance bandwidth of roughly 36.63 percent (−10 dB as reference) was reached. Parallel axial ratio bandwidth of 28.33 percent is achieved, which is in conjunction with impedance matching bandwidth. Between the ports, isolation of −28 dB is achieved Gain and other far-field parameters are also calculated and found to be within their optimum limits

Internationalized Domain Names (IDNs) enhance global accessibility by supporting Unicode characters in web addresses. However, this capability introduces new security vulnerabilities, particularly through homoglyphs—visually similar characters such as the Cyrillic ‘’ (U+0435) and the Latin ‘e’ (U+0065)—which enable attackers to craft deceptive domain names. These IDN homograph exploits are increasingly used in phishing attacks, creating URLs that are visually indistinguishable from legitimate sites. As the adoption of IDNs continues across browsers and domain registries, the risk of such exploits grows. According to the IDC 2023 Global DNS Threat Report, 90% of organizations faced domain-based attacks, with average losses of $1.1 million per incident. In this study, we propose a web protection model for the real-time detection and mitigation of IDN homograph threats. Central to our model is the construction of Unisimchar, a dynamic homoglyph database that extends prior work by incorporating Unicode block-level structures and performing pixel-level similarity analysis using 32×32 glyph images. Unlike static or manually maintained lists, Unisimchar enables automated and scalable identification of confusable character pairs through a threshold-based visual matching algorithm. The model processes over 1 million domain names sourced from Majestic Million, Zone Files, and Daily Domains, revealing that 80% of homoglyph pairs occur within high-traffic domains worldwide, with regional concentrations observed in Pakistan and Europe. With a multithreaded architecture, the model achieves domain-level detection within 0.07 seconds, making it suitable for real-time threat prevention. The comprehensive evaluation includes a human perception study to validate visual similarity thresholds and quantitative experiments using standard metrics such as precision, recall, and F1 score. The results demonstrate that our model significantly improves the accuracy of homoglyph detection compared to existing methods, offering a practical and extensible defense mechanism against modern IDN homograph exploits in the web ecosystem.

The study examines the application of machine learning algorithms (MLAs) to enhance free space optical (FSO) communication performance by predicting quality-factor (QF) from key system parameters. FSO technology has developed as a promising solution for front-haul links in 5G, beyond the 5G (B5G), and 6G transmission networks. Nevertheless, performance of an FSO communication link is limited by environmental challenges like weather conditions, attenuation and turbulence that can degrade signal quality and affect QF at the receiving end. Using the data collected through simulation analysis in OptiSystem, we trained several MLAs in order to analyze performance of this system through accurate prediction of the QF. Analysis shows that received power serves as an important parameter in translating the overall QF of the received signal. Furthermore, it is shown that the prediction accuracy of the tree-based approach such as random forest and gradient boosting ranges above 97% are reliant on channel conditions and the predicted parameter type.