• Ultra wideband antenna diversity characterisation for off-body communications in an indoor environment

      Ur-Rehman, Masood; Abbasi, Qammer Hussain; Qaraqe, Khalid; Chattha, Hassan Tariq; Alomainy, Akram; Hao, Yang; Parini, Clive G.; Queen Mary College; University of Bedfordshire; University of Engineering and Technology, Pakistan (Institute of Electrical and Electronics Engineers Inc., 2014-11-13)
      In this paper radio channel characterisation and level system modeling for ultra wideband (UWB) in vivo communication is presented at different distances and angle between the the implant and the on-body node. Path loss is calculated for different scenarios and time delay analysis is performed. In addition, UWB-OFDM (orthogonal frequency division multiplexing) based system modeling is used to calculate the bit error rate (BER) performance. Result shows that BER remains less then 1e-3 for almost all cases up to 40 mm spacing between the implant and on-body node, when Eb/No is above 6 dB.
    • Understanding the cyber-victimisation of people with long term conditions and the need for collaborative forensics-enabled disease management programmes

      Alhaboby, Zhraa Azhr; Alhaboby, Doaa; al-Khateeb, Haider; Epiphaniou, Gregory; Ben Ismail, Dhouha Kbair; Jahankhani, Hamid; Pillai, Prashant; Jahankhani, Hamid; University of Bedfordshire; University of Duisburg-Essen; et al. (Springer, 2018-01-01)
      Research shows that people with long term conditions and disabilities are frequently labelled as vulnerable, and commonly victimised online. They require instrumental support to understand their conditions and empower them to manage their own treatment in everyday life. However, additional short and long term consequences related to cyber-victimisation could intensify existing psychological and health complications. For instance, ‘distress’ as a commonly reported impact of cyber-victimisation could theoretically lead to neurohormonal changes in the blood, increasing cortisol, catecholamine and insulin secretion resulting in increased blood glucose, heartbeat, blood pressure, urination and other changes. Therefore, in this study we demonstrate the need and explain the means towards extending support and risk assessment systems and procedures to cover the collection and preservation of incidents reported by individuals. This can be used to support third-party interventions such as taking a legal action in cases where the impact of cyber-victimisation is seen to escalate and worsen. As such, we first define vulnerable groups with long term conditions and provide a review of the impact of various types of cyber-victimisation on their health management. Then, we discuss how Disease Management Programmes (DMP) developed over time to include web-based applications as an example of existing cost-effective approaches to improve the quality of healthcare provided to people with long term conditions. We then demonstrate the added value of incorporating forensics readiness to enable Police intervention, support the victim’s eligibility for extended instrumental support from national health services. Finally, this level of documentation offers an opportunity to implement more accurate methods to assess risk associated with victimisation.
    • Unidirectional light propagation photonic crystal waveguide incorporating modified defects

      Soltani, A.; Ouerghi, F.; AbdelMalek, Fathi; Haxha, Shyqyri; Ademgil, Huseyin; Akowuah, Emmanuel K.; Université de Tunis El Manar; University of Bedfordshire; European University of Lefke; Kwame Nkrumah University of Science and Technology (Elsevier GmbH, 2016-11-29)
      In this paper, we have proposed a design of an Optical Diode-like in two-dimensional (2D) Photonic Crystal (PC) waveguide. The proposed device consists of 2D square-lattice PC structures, and it is based on two PC waveguides with different symmetric guiding modes, where various configurations of defects, including elliptic or/and semi-circular defects have been incorporated. The proposed one-way light propagation Optical Diode has been designed and optimized by employing in-house 2D Finite Difference Time Domain (FDTD) numerical method. We have reported that the unidirectional light propagation depends strongly on the coupling region between the introduced defects and the adjacent waveguides, and it also depends on the matching and mismatching between the defects and waveguide modes. It has been shown also that owing to its tunable features, the proposed Optical Diode can be potentially applied as a building block in future complex optical integrated circuits.
    • 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.
    • Using autoregressive modelling and machine learning for stock market prediction and trading

      Hushani, Phillip; University of Bedfordshire (Springer, 2018-09-29)
      Investors raise profit from stock market by maximising gains and minimising loses. The profit is difficult to raise because of the volatile nature of stock market prices. Predictive modelling allows investors to make informed decisions. In this paper, we compare four forecasting models: autoregressive integrated moving average (ARIMA), vector autoregression (VAR), long short-term memory (LSTM) and nonlinear autoregressive Exogenous (NARX). The results of predictive modelling are analysed and compared in terms of prediction accuracy. The research aims to develop a new profitable trading strategy. Our findings are: (i) the NARX model has provided accurate short-term predictions but failed long forecasts, and (ii) the VAR model can form a good trend line required for trading. Thus, the profitable trading strategy can combine the machine learning predictive modelling and technical analysis.
    • Utilising information foraging theory for user interaction with image query auto-completion

      Jaiswal, Amit Kumar; Liu, Haiming; Frommholz, Ingo; University of Bedfordshire (Springer, 2020-03-17)
      Query Auto-completion (QAC) is a prominently used feature in search engines, where user interaction with such explicit feature is facilitated by the possible automatic suggestion of queries based on a prefix typed by the user. Existing QAC models have pursued a little on user interaction and cannot capture a user’s information need (IN) context. In this work, we devise a new task of QAC applied on an image for estimating patch (one of the key components of Information Foraging Theory) probabilities for query suggestion. Our work supports query completion by extending a user query prefix (one or two characters) to a complete query utilising a foraging-based probabilistic patch selection model. We present iBERT, to fine-tune the BERT (Bidirectional Encoder Representations from Transformers) model, which leverages combined textual-image queries for a solution to image QAC by computing probabilities of a large set of image patches. The reflected patch probabilities are used for selection while being agnostic to changing information need or contextual mechanisms. Experimental results show that query auto-completion using both natural language queries and images is more effective than using only language-level queries. Also, our fine-tuned iBERT model allows to efficiently rank patches in the image.
    • Vehicular Ad Hoc Networks (VANETs): current state, challenges, potentials and way forward

      Eze, Elias Chinedum; Zhang, Sijing; Liu, Enjie; University of Bedfodshire (IEEE, 2014-10-27)
      Recent advances in wireless communication technologies and auto-mobile industry have triggered a significant research interest in the field of VANETs over the past few years. VANET consists of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications supported by wireless access technologies such as IEEE 802.11p. This innovation in wireless communication has been envisaged to improve road safety and motor traffic efficiency in near future through the development of Intelligent Transport Systems (ITS). Hence, government, auto-mobile industries and academia are heavily partnering through several ongoing research projects to establish standards for VANETs. The typical set of VANET application areas, such as vehicle collision warning and traffic information dissemination have made VANET an interested field of wireless communication. This paper provides an overview on current research state, challenges, potentials of VANETs as well the way forward to achieving the long awaited ITS.
    • Visual analytics for health monitoring and risk management in CARRE

      Zhao, Youbing; Parvinzamir, Farzad; Wei, Hui; Liu, Enjie; Deng, Zhikun; Dong, Feng; Third, Allan; Lukoševičius, Arūnas; Marozas, Vaidotas; Kaldoudi, Eleni; et al. (Springer Verlag, 2016-12-31)
      With the rise of wearable sensor technologies, an increasing number of wearable health and medical sensors are available on the market, which enables not only people but also doctors to utilise them to monitor people’s health in such a consistent way that the sensors may become people’s lifetime companion. The consistent measurements from a variety of wearable sensors implies that a huge amount of data needs to be processed, which cannot be achieved by traditional processing methods. Visual analytics is designed to promote knowledge discovery and utilisation of big data via mature visual paradigms with well-designed user interactions and has become indispensable in big data analysis. In this paper we introduce the role of visual analytics for health monitoring and risk management in the European Commission funded project CARRE which aims to provide innovative means for the management of cardiorenal diseases with the assistance of wearable sensors. The visual analytics components of timeline and parallel coordinates for health monitoring and of node-link diagrams, chord diagrams and sankey diagrams for risk analysis are presented to achieve ubiquitous and lifelong health and risk monitoring to promote people’s health.
    • Visualising Arabic sentiments and association rules in financial text

      AL-Rubaiee, Hamed Saad; Qiu, Renxi; Li, Dayou (SAI, 2017-02-28)
      Text mining methods involve various techniques, such as text categorization, summarisation, information retrieval, document clustering, topic detection, and concept extraction. In addition, because of the difficulties involved in text mining, visualisation techniques can play a paramount role in the analysis and pre-processing of textual data. This paper will present two novel frameworks for the classification and extraction of the association rules and the visualisation of financial Arabic text in order to realize both the general structure and the sentiment within an accumulated corpus. However, mining unstructured data with natural language processing (NLP) and machine learning techniques can be arduous, especially where the Arabic language is concerned, because of limited research in this area. The results show that our frameworks can readily classify Arabic tweets. Furthermore, they can handle many antecedent text association rules for the positive class and the negative class.
    • Wandering pattern sensing at S-band

      Yang, Xiaodong; Shah, Syed Aziz; Ren, Aifeng; Zhao, Nan; Fan, Dou; Hu, Fangming; Ur-Rehman, Masood; von Deneen, Karen M.; Tian, Jie; Xidian University; et al. (Institute of Electrical and Electronics Engineers Inc., 2017-12-27)
      Increasing prevalence of dementia has posed several challenges for care-givers. Patients suffering from dementia often display wandering behavior due to boredom or memory loss. It is considered to be one of the challenging conditions to manage and understand. Traits of dementia patients can compromise their safety causing serious injuries. This paper presents investigation into the design and evaluation of wandering scenarios with patients suffering from dementia using an S-band sensing technique. This frequency band is the wireless channel commonly used to monitor and characterize different scenarios including random, lapping, and pacing movements in an indoor environment. Wandering patterns are characterized depending on the received amplitude and phase information of that measures the disturbance caused in the ideal radio signal. A secondary analysis using support vector machine is used to classify the three patterns. The results show that the proposed technique carries high classification accuracy up to 90% and has good potential for healthcare applications.
    • A wearable antenna for mmWave IoT applications

      Ur-Rehman, Masood; Kalsoom, Tahera; Malik, Nabeel A.; Safdar, Ghazanfar Ali; Chatha, Hasan Tariq; Ramzan, Naeem; Abbasi, Qammer Hussain; University of Bedfordshire; University of West of Scotland; Islamic University; et al. (Institute of Electrical and Electronics Engineers Inc., 2019-01-14)
      A compact and flexible millimeter-wave (mmWave) antenna with central resonance frequency of 60 GHz has been designed. The performance of the antenna is evaluated numerically. The antenna exhibits a broad bandwidth of 9.8 GHz with a gain of 9.6 dBi. The antenna also provides good radiation coverage throughout the band of interest achieving a maximum efficiency of 70%. Simple structure, flexible geometry, ease of fabrication and excellent performance make it a well- suited option for body-centric IoT applications.
    • Web-based visual analytics of lifestyle data in MyHealthAvatar

      Zhao, Youbing; Parvinzamir, Farzad; Zhao, Xia; Deng, Zhikun; Ersotelos, Nikolaos; Dong, Feng; Clapworthy, Gordon J. (ICST, 2015-12-22)
      MyHealthAvatar is a project designed to collect lifestyle and health data to promote citizen's wellbeing. As a lifetime companion of citizens the amount of data to be collected is large. It is almost impossible for citizens, patients and doctors to view, utilise and understand these data without proper visual presentation and user interaction. Visual analytics of lifestyle data is one of the key features of MyHealthAvatar. This paper presents the visual analytics components in MyHealthAvatar to facilitate health and lifestyle data presentation and analysis, including 3D avatar, dashboard, diary, timeline, clock view and map. These components can be used cooperatively to achieve flexible visual analysis of spatial temporal lifestyle and health data.
    • What does the pedagogical agent say?

      Christopoulos, Athanasios; Conrad, Marc; Shukla, Mitul; University of Bedfordshire (Institute of Electrical and Electronics Engineers Inc., 2019-11-14)
      The successful employment of Virtual Reality Environments in distance education contexts led to the development of various frameworks and taxonomies related to the Virtual-Learning approach. However, when it comes to Blended or 'Hybrid' Virtual Learning (HVL) scenarios, where the learners are concurrently co-present both in the physical and in the virtual environment, the lines are hard to be drawn as this has been a relatively unexplored area. Considering the aforementioned change in the setup of the educational context, different implications, challenges and outcomes are expected to be observed. Motivated by this shortcoming, we conducted a series of experiments with Computer Science and Technology students and investigated the impact of interactions on learners' motivation to engage with the 3D virtual world and the educational activities by extension. In this paper, we discuss students' preconceptions towards the inclusion of 3D Virtual Learning Environments in the context of their studies and further elicit their thoughts related to the impact of the 'hybrid' interactions. In addition, we investigate the educational value of different Non-Player Characters (Pedagogical Agents) and their impact on the attractiveness of the virtual world and the educational tasks. The concluding remarks provide guidance to educators and instructional designers who work in such setups or consider to employ Pedagogical Agents. To this end, employing Pedagogical Agents requires careful consideration as they need to be meaningful and fully incorporated in the learner's task. Another take-away message concerns the elements that foster a situated learning experience as they are associated with immersive experiences.
    • μECM process investigation considering pulse signal features and EDL capacitance

      Mortazavi, Mina; Ivanov, Atanas (Springer, 2019-05-22)
      Micro-electrochemical machining (μECM) is a controlled anodic dissolution process between electrodes. The anodic dissolution, which follows Faraday’s laws of electrolysis, depends on characteristics of the electrodes materials, electrolyte properties, and pulse signal features. μECM is a challenging multidisciplinary task in which quality of the process and features of the finished products depend on a complex relation between different machining parameters including, electrical features of pulse signal, chemical features of electrolyte, physical features of tools, and thermodynamic features of the process. In this paper, influential machining parameters will be reviewed briefly, and pulse signal features will be investigated and analyzed considering the behavior of the electrode-electrolyte interface. The interface has capacitive feature and plays an important role in micromachining performance. The proposed simulation work presents the requirement for the pulse on-time in order to provide the maximum possible charging-discharging time for the capacitive behavior of the electrode-electrolyte interface.