• Antenna and propagation considerations for amateur UAV monitoring

      Abolhasan M; Zhao, Nan; Yang, Xiaodong; Ren, Aifeng; Zhang, Zhiya; Zhao, Wei; Hu, Fangming; Ur-Rehman, Masood; Abbas, Haider; Xidian University; et al. (Institute of Electrical and Electronics Engineers Inc., 2018-05-18)
      The broad application spectrum of unmanned aerial vehicles is making them one of the most promising technologies of Internet of Things era. Proactive prevention for public safety threats is one of the key areas with vast potential of surveillance and monitoring drones. Antennas play a vital role in such applications to establish reliable communication in these scenarios. This paper considers line-of-sight and non-line-of-sight threat scenarios with the perspective of antennas and electromagnetic wave propagation.
    • Behavior-neutral smart charging of plugin electric vehicles: reinforcement learning approach

      Dyo, Vladimir; University of Bedfordshire (IEEE, 2022-06-16)
      High-powered electric vehicle (EV) charging can significantly increase charging costs due to peak-demand charges. This paper proposes a novel charging algorithm which exploits typically long plugin sessions for domestic chargers and reduces the overall charging power by boost charging the EV for a short duration, followed by low-power charging for the rest of the plugin session. The optimal parameters for boost and low-power charging phases are obtained using reinforcement learning by training on EV’s past charging sessions. Compared to some prior work, the proposed algorithm does not attempt to predict the plugin session duration, which can be difficult to accurately predict in practice due to the nature of human behavior, as shown in the analysis. Instead, the charging parameters are controlled directly and are adapted transparently to the user’s charging behavior over time. The performance evaluation on a UK dataset of 3.1 million charging sessions from 22,731 domestic charge stations, demonstrates that the proposed algorithm results in 31% of aggregate peak reduction. The experiments also demonstrate the impact of history size on learning behavior and conclude with a case study by applying the algorithm to a specific charge point.
    • Biometric behavior authentication exploiting propagation characteristics of wireless channel

      Zhao, Nan; Ren, Aifeng; Ur-Rehman, Masood; Zhang, Zhiya; Yang, Xiaodong; Hu, Fangming; Xidian University; University of Bedfordshire (Institute of Electrical and Electronics Engineers Inc., 2016-08-24)
      Massive expansion of wireless body area networks (WBANs) in the field of health monitoring applications has given rise to the generation of huge amount of biomedical data. Ensuring privacy and security of this very personal data serves as a major hurdle in the development of these systems. An effective and energy friendly authentication algorithm is, therefore, a necessary requirement for current WBANs. Conventional authentication algorithms are often implemented on higher levels of the Open System Interconnection model and require advanced software or major hardware upgradation. This paper investigates the implementation of a physical layer security algorithm as an alternative. The algorithm is based on the behavior fingerprint developed using the wireless channel characteristics. The usability of the algorithm is established through experimental results, which show that this authentication method is not only effective, but also very suitable for the energy-, resource-, and interface-limited WBAN medical applications.
    • IEEE Access special section: advances in interference mitigation techniques for device-to-device communications

      Ur-Rehman, Masood; Gao, Yue; Chaudhry, Mohammad Asad Rehman; Safdar, Ghazanfar Ali; Xu, Yanli; University of Essex; Queen Mary University of London; University of Toronto; University of Bedfordshire; Shanghai Maritime University (IEEE, 2019-12-17)
    • Microwave photonic downconversion with improved conversion efficiency and SFDR

      Paloi, Fadil; Haxha, Shyqyri; Mirza, Taimur; Alom, Mohammed Shah; University of Bedfordshire (Institute of Electrical and Electronics Engineers Inc., 2018-01-23)
      In this paper, we report a novel approach of microwave frequency downconversion with improved conversion efficiency and high dynamic range, using two different configuration schemes. The first proposed scheme is designed by using a dual-parallel dual-drive Mach-Zehnder modulator and the second one using dual-parallel dual-phase modulator. The radio frequency (RF) message signal and the local oscillator (LO) signal are feeding these two parallel connected modulators. By using a tight control of the system parameters, we have reported an effective optical carrier suppression, resulting in high conversion efficiency. We show that when the link is amplified, the relation between m{\mathrm {LO}} and m{\mathrm {RF}} plays a vital role and gives a high value of conversion efficiency, where key parameters lead to the LO and RF modulators modulation index. The conversion efficiency is improved by 5.72 dBm, compared with previously published work using DP-MZM, and 28.4 dBm, compared with the cascaded connected modulator. An experimental demonstration of a proof of concept is also carried out where the intermediate frequency to noise ratio of 69.5 dB is reported.
    • Nano-ferrite near-field microwave imaging for in-body applications

      Abbasi, Qammer Hussain; Ren, Aifeng; Qing, Maojie; Zhao, Nan; Wang, Mingming; Gao, Ge; Yang, Xiaodong; Zhang, Zhiya; Hu, Fangming; Ur-Rehman, Masood; et al. (Institute of Electrical and Electronics Engineers, 2018-06-04)
      In recent years, nanotechnology has become indispensable in our lives, especially in the medical field. The key to nanotechnology is the perfect combination of molecular imaging and nanoscale probes. In this paper, we used iron oxide nanoparticles as a nanoprobe because it is widely used in clinical MRI and other molecular imaging techniques. We built our own experimental environment and used absorbing materials during the whole experiment to avoid electromagnetic interference with the surroundings. Moreover, we repeated the experiment many times to exclude the influence of contingency. Hence, the experimental data we obtained were relatively precise and persuasive. Finally, the results demonstrated that the iron oxide nanoparticles were appropriate for use as contrast agents in biological imaging.
    • Unlink the link between COVID-19 and 5G Networks: an NLP and SNA based approach

      Bahja, Mohammed; Safdar, Ghazanfar Ali; University of Birmingham; University of Bedfordshire (Institute of Electrical and Electronics Engineers Inc., 2020-11-18)
      Social media facilitates rapid dissemination of information for both factual and fictional information. The spread of non-scientific information through social media platforms such as Twitter has potential to cause damaging consequences. Situations such as the COVID-19 pandemic provides a favourable environment for misinformation to thrive. The upcoming 5G technology is one of the recent victims of misinformation and fake news and has been plagued with misinformation about the effects of its radiation. During the COVID-19 pandemic, conspiracy theories linking the cause of the pandemic to 5G technology have resonated with a section of people leading to outcomes such as destructive attacks on 5G towers. The analysis of the social network data can help to understand the nature of the information being spread and identify the commonly occurring themes in the information. The natural language processing (NLP) and the statistical analysis of the social network data can empower policymakers to understand the misinformation being spread and develop targeted strategies to counter the misinformation. In this paper, NLP based analysis of tweets linking COVID-19 to 5G is presented. NLP models including Latent Dirichlet allocation (LDA), sentiment analysis (SA) and social network analysis (SNA) were applied for the analysis of the tweets and identification of topics. An understanding of the topic frequencies, the inter-relationships between topics and geographical occurrence of the tweets allows identifying agencies and patterns in the spread of misinformation and equips policymakers with knowledge to devise counter-strategies.