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With the help of CsPbBr3 perovskite quantum material, we propose a wearable antenna that operates in the terahertz frequency range. CsPbBr3 was specifically employed to improve the performance of the antenna, and the findings reveal that the reflection coefficient and radiation efficiency of the antenna are improved. The performance of the antenna is assessed both on the skin and in free space. Based on the simulation findings, the suggested antenna has a bandwidth of 29 GHz and provides radiation efficiency of 90% in free-space and 45% on the human body. Moreover, gains of 4.5dBi and 6.2dBi, respectively, in the free space and human body scenarios are achieved. As a result of the antenna’s tiny and flexible construction, as well as its outstanding impedance matching and high efficiency, it is a great option for short-distance wireless communication in close proximity to the human body.

In anticipation of a rapid increase in wireless communication, MIMO is one key technology to be explored for 5G. Conventional approaches are unable to predict many of the key characteristics for MIMO channels, and more detailed methods suffer from significant computational complexity due to the number of antennas in a MIMO array. In this work, the beamforming performance for moving users in a large cell with effective channel throughput has been explored. The Glasgow University campus model is used to estimate channel properties when various beamforming techniques are implemented. The techniques explored are Maximum Ratio Transmission (MRT) (for transmitter), Equal Gain Combining (EGC), Selection Combining (SC), and Max Ratio Combining (MRC) (for receiver) beamforming in 3GPP Long Term Evolution (LTE). Throughput, received signal strength, and signal to noise interference ratio (SINR) are determined. By implementing the beamforming techniques, on average, we are able to improve the throughput from 9 Mbps to 14 Mbps. The best throughput/SINR has been observed with MRT-MRC in comparison to No-beamforming.

This paper presents a platform tolerant ultra-high radio frequency identification (UHF RFID) tag design for hard to tag supply chain items such as large metal and wooden containers. The proposed design consists is backed by multi-resonant surface carefully designed using characteristics mode analysis. As a result, this tag design features a long read range and covers most of UHF RFID frequency bands. The evaluation of the read range associated with fabricated prototype of proposed tag configuration is done through turn on power measurements using Tagformance Pro setup. A variety of subjects with different physical constitutions such as metal, wood and glass are considered in order to demonstrate the robustness of our solution. The proposed tag offers a relatively longer reader range of 12.5 m after mounting on piece of metal, wood and glass materials. Therefore, this tag design is suitable candidate for supply chain visibility and cargo monitoring.

Beamforming is a cellular base station traffic-signaling system that determines the most effective data-delivery path for a specific user while reducing interference for neighboring users, and it has now become an integral technology in modern wireless communication systems. Contextual beamforming has evolved as an intelligent method in which the location information of a mobile user is predicted based on prior knowledge, and subsequently reconfigure the antenna arrays. Such a system relies heavily on a machine and deep learning applied on the data gathered to infer the context. This paper presents an antenna beamforming approach that exploits the location information of a moving mobile user in a mobile network. Our results show that network parameters such as the received power and propagation path information can assist to construct a machine learning-based adaptive beamforming framework.

Diabetes is a large healthcare burden worldwide. There is substantial evidence that lifestyle modifications and drug intervention can prevent diabetes, therefore, an early identification of high risk individuals is important to design targeted prevention strategies. In this paper, we present an automatic tool that uses machine learning techniques to predict the development of type 2 diabetes mellitus (T2DM). Data generated from an oral glucose tolerance test (OGTT) was used to develop a predictive model based on the support vector machine (SVM). We trained and validated the models using the OGTT and demographic data of 1,492 healthy individuals collected during the San Antonio Heart Study. This study collected plasma glucose and insulin concentrations before glucose intake and at three time-points thereafter (30, 60 and 120 min). Furthermore, personal information such as age, ethnicity and body-mass index was also a part of the data-set. Using 11 OGTT measurements, we have deduced 61 features, which are then assigned a rank and the top ten features are shortlisted using minimum redundancy maximum relevance feature selection algorithm. All possible combinations of the 10 best ranked features were used to generate SVM based prediction models. This research shows that an individual's plasma glucose levels, and the information derived therefrom have the strongest predictive performance for the future development of T2DM. Significantly, insulin and demographic features do not provide additional performance improvement for diabetes prediction. The results of this work identify the parsimonious clinical data needed to be collected for an efficient prediction of T2DM. Our approach shows an average accuracy of 96.80% and a sensitivity of 80.09% obtained on a holdout set.