Wearable metamaterial dual-polarized high isolation UWB MIMO Vivaldi antenna for 5G and satellite communications

A low-profile Multiple Input Multiple Output (MIMO) antenna showing dual polarization, low mutual coupling, and acceptable diversity gain is presented by this paper. The antenna introduces the requirements of fifth generation (5G) and the satellite communications. A horizontally (4.8–31 GHz) and vertically polarized (7.6–37 GHz) modified antipodal Vivaldi antennas are simulated, fabricated, and integrated, and then their characteristics are examined. An ultra-wideband (UWB) at working bandwidths of 3.7–3.85 GHz and 5–40 GHz are achieved. Low mutual coupling of less than −22 dB is achieved after loading the antenna with cross-curves, staircase meander line, and integration of the metamaterial elements. The antennas are designed on a denim textile substrate with = 1.4 and h= 0.5 mm. A conductive textile called ShieldIt is utilized as conductor with conductivity of 1.8 × 10⁴. After optimizing the proposed UWB-MIMO antenna’s characteristics, it is increased to four elements positioned at the four corners of a denim textile substrate to be employed as a UWB-MIMO antenna for handset communications, 5G, Ka and Ku band, and satellite communications (X-band). The proposed eight port UWB-MIMO antenna has a maximum gain of 10.7 dBi, 98% radiation efficiency, less than 0.01 ECC, and acceptable diversity gain. Afterwards, the eight-ports antenna performance is examined on a simulated real voxel hand and chest. Then, it is evaluated and compared on physical hand and chest of body. Evidently, the simulated and measured results show good agreement between them. The proposed UWB-MIMO antenna offers a compact and flexible design, which is suitably wearable for 5G and satellite communications applications

Trojan Horse Infection Detection in Cloud Based Environment Using Machine Learning

Cloud computing technology is known as a distributed computing network, which consists of a large number of servers connected via the internet. This technology involves many worthwhile resources, such as applications, services, and large database storage. Users have the ability to access cloud services and resources through web services. Cloud computing provides a considerable number of benefits, such as effective virtualized resources, cost efficiency, self-service access, flexibility, and scalability. However, many security issues are present in cloud computing environment. One of the most common security challenges in the cloud computing environment is the trojan horses. Trojan horses can disrupt cloud computing services and damage the resources, applications, or virtual machines in the cloud structure. Trojan horse attacks are dangerous, complicated and very difficult to be detected. In this research, eight machine learning classifiers for trojan horse detection in a cloud-based environment have been investigated. The accuracy of the cloud trojan horses detection rate has been investigated using dynamic analysis, Cukoo sandbox, and the Weka data mining tool. Based on the conducted experiments, the SMO and Multilayer Perceptron have been found to be the best classifiers for trojan horse detection in a cloud-based environment. Although SMO and Multilayer Perceptron have achieved the highest accuracy rate of 95.86%, Multilayer Perceptron has outperformed SMO in term of Receiver Operating Characteristic (ROC) area