• Towards 5G: a reinforcement learning-based scheduling solution for data traffic management

      Comşa, Ioan-Sorin; Zhang, Sijing; Aydin, Mehmet Emin; Kuonen, Pierre; Lu, Yao; Trestian, Ramona; Ghinea, Gheorghiţă; Brunel University; University of Bedfordshire; University of the West of England; et al. (IEEE, 2018-08-06)
      Dominated by delay-sensitive and massive data applications, radio resource management in 5G access networks is expected to satisfy very stringent delay and packet loss requirements. In this context, the packet scheduler plays a central role by allocating user data packets in the frequency domain at each predefined time interval. Standard scheduling rules are known limited in satisfying higher Quality of Service (QoS) demands when facing unpredictable network conditions and dynamic traffic circumstances. This paper proposes an innovative scheduling framework able to select different scheduling rules according to instantaneous scheduler states in order to minimize the packet delays and packet drop rates for strict QoS requirements applications. To deal with real-time scheduling, the Reinforcement Learning (RL) principles are used to map the scheduling rules to each state and to learn when to apply each. Additionally, neural networks are used as function approximation to cope with the RL complexity and very large representations of the scheduler state space. Simulation results demonstrate that the proposed framework outperforms the conventional scheduling strategies in terms of delay and packet drop rate requirements.
    • Towards DR inventor: a tool for promoting scientific creativity

      O’Donoghue, D.P.; Saggion, H.; Dong, Feng; Hurley, D.; Abgaz, Y.; Zheng, X.; Corcho, O,; Zhang, J.J.; Careil, J.M.; Mahdian, B.; et al. (Jozef Stefan Institute, 2014-12-31)
      We propose an analogy-based model to promote creative scientific reasoning among its users. Dr Inventor aims to find novel and potentially useful creative analogies between academic documents, presenting them to users as potential research questions to be explored and investigated. These novel comparisons will thereby drive its users’ creative reasoning. Dr Inventor is aimed at promoting Big-C Creativity and the H-creativity associated with true scientific creativity.
    • Towards sparse characterisation of on-body ultra-wideband wireless channels

      Yang, Xiaodong; Ren, Aifeng; Zhang, Zhiya; Ur-Rehman, Masood; Abbasi, Qammer Hussain; Alomainy, Akram; Xidian University; University of Bedfordshire; Texas A&M University at Qatar (IET, 2015-07-01)
      With the aim of reducing cost and power consumption of the receiving terminal, compressive sensing (CS) framework is applied to on-body ultra-wideband (UWB) channel estimation. It is demonstrated in this Letter that the sparse on-body UWB channel impulse response recovered by the CS framework fits the original sparse channel well; thus, on-body channel estimation can be achieved using low-speed sampling devices.
    • Tracking human motion direction with commodity wireless networks

      Rahaman, Habibur; Dyo, Vladimir; University of Bedfordshire (IEEE, 2021-09-07)
      Detecting when a person leaves a room, or a house is essential to create a safe living environment for people suffering from dementia or other mental disorders. The approaches based on wearable devices, e.g. GPS bracelets may detect such events require periodic maintenance to recharge or replace batteries, and therefore may not be suitable for certain types of users. On the other hand, camera-based systems require illumination and raise potential privacy concerns. In this paper, we propose a device-free walking direction detection approach based on RF-sensing, which does not require a person to wear any equipment. The proposed approach monitors the signal strength fluctuations caused by the human body on ambient wireless links and analyses its spatial patterns using a convolutional neural network to identify the walking direction. The approach has been evaluated experimentally to achieve up to 98% classification accuracy depending on the environment.
    • Tracking objects robot for healthcare environments

      Kanjaruek, Saranya; Li, Dayou; Khon Kaen University, Thailand; University of Bedfordshire (Institute of Electrical and Electronics Engineers Inc., 2018-02-01)
      There are many elder who are affected from Alzheimer's disease. Memory problems, confusion and forgetting objects, recent conversations or places are example of Alzheimer symptoms. Nursing home needs medical devices or robot to provide service for patients. Tracking Objects Robot traces and locates objects for the Alzheimer's patients to find objects in environment. This paper uses frequency and recency technique in order to locate the location of objects in dynamic environment by associating semantic knowledge of object with instance in ontology.
    • Transferring porous layer from InP wafer based on the disturbance

      Zhang, Yang; Cao, Liang; Chai, Xiangyu; Liang, Kaihua; Han, Yong-Lu; Wang, Yanqi; Wang, Zuobin; Wang, Shuting; Weng, Zhankun; Changchun University of Science and Technology; et al. (Institute of Electrical and Electronics Engineers Inc., 2017-01-16)
      We present a new method to transfer the three dimensional (3D) porous layer from InP wafer based on the disturbance, during electrochemical etching of n-InP (100) with chronopotentiometry with current ramp in 3 mol-L-1 NaCl solution. The potential bursting phenomenon was observed due to the disturbing instantaneously. In addition, the correlation between the amplitude of the potential and the porous layers separated from the InP wafer was discussed.
    • Tri-band millimetre-wave antenna for body-centric networks

      Ur-Rehman, Masood; Adekanye, Michael; Chattha, Hassan Tariq; University of Bedfordshire; Islamic University Madinah (Elsevier, 2018-04-03)
      This paper presents design of a tri-band slotted patch antenna operating at millimetre-wave frequencies of 28 GHz, 38 GHz and 61 GHz. The proposed antenna carries an overall size of 5.1mm×5mm×0.254mm employing a single layer, slotted patch structure combining L- and F-shaped slots. It is excited by a single-feed microstrip line. The antenna is tested in free space as well as in wearable configurations and results show that it offers a good impedance matching, sufficient -10 dB bandwidth and wide radiation coverage at the three bands of interest effectively countering the effects of human body presence. It achieves a peak gain of 7.2 dBi in off-body and 8.3 dBi in on-body configuration. Minimum efficiency values are observed to be 85% in off-body while 54% in on-body scenarios. A comparative analysis with published relevant work shows that the proposed antenna is inexpensive, easy to integrate and works efficiently in tri-band wearable and implantable arrangements. These features make it a good candidate for current and future applications of Body-centric Networks operating at millimetre-wave ranges.
    • Trustworthiness in the patient centred health care system

      Liu, Enjie; Feng, Xiaohua; University of Bedfordshire (Springer Verlag, 2014-06-27)
      The trend of the future health care system is patient centred, and patients' involvement is a key to success. ICT will play an important role in enabling and helping patients or citizens to manage and communicate on the individual's health related issues. This includes private and confidential information. Trustworthiness is therefore one of the most vital aspects in such systems. This paper first presents the prototype structure of the health care system, and then discusses questions regarding the trustworthiness of the system. © Springer-Verlag Berlin Heidelberg 2014.
    • Tunable electrochemical oscillation and regular 3D nanopore arrays of InP

      Chai, Xiangyu; Weng, Zhankun; Xu, Liping; Wang, Zuobin; Changchun University of Science and Technology; University of Bedfordshire (Electrochemical Society Inc., 2015-06-16)
      Tunable potential oscillations are obtained by electrochemical etching of n-InP (100) in the 3MNaCl solution using the chronoptentiometry with current ramp. The regular 3D nanopore arrays are formed with the change of the current density from 320 to 260 mA · cm-2 at the scan rate 0.72 - 0.80 mA · cm-2 · s-1. The results showed that the current density ranges and scan rate have the effect on the E-t curves and the pore's morphology. The scan rate can regulate not only on the charge consumed per period but also on the amplitude of potential oscillation, and shown that the charge per period and the amplitude can be tuned when proper electrochemical parameters are selected. Furthermore, the pore's morphology will change from the regular structure to irregular with the increasing of the scan rate. In addition, the relation has also been discussed between the E-t curves and the pore's morphology.
    • Tunable phantoms and their verification

      Zhang, Qing; Ur-Rehman, Masood; Yang, Xiaodong (American Scientific Publishers, 2020-01-01)
      Digital phantoms are very important for body area networks and other biomedical applications. However, it is important to note that most existing phantoms are static, including 3D scanned and voxel models. Recent research has revealed that tunable phantoms are still very necessary for body area networks since various postures should be considered. In this paper, parameterized digital phantoms are generated from 2D images. The train of thought and results presented in the paper are worth reference for phantom researchers.
    • Tuning of Customer Relationship Management (CRM) via Customer Experience Management (CEM) using sentiment analysis on aspects level

      AL-Rubaiee, Hamed Saad; Alomar, Khalid; Qiu, Renxi; Li, Dayou; University of Bedfordshire; King Abdulaziz University (Science and Information Organization, 2018-12-31)
      This study proposes a framework that combines a supervised machine learning and a semantic orientation approach to tune Customer Relationship Management (CRM) via Customer Experience Management (CEM). The framework extracts data from social media first and then integrates CRM and CEM by tuning and optimising CRM to reflect the needs and expectations of users on social media. In other words, in order to reduce the gap between the users' predicted opinions in CRM and their opinions on social media, the existing data from CEM will be applied to determine the similar behavioural patterns of customers towards similar outcomes within CRM. CRM data and extracted data from social media will be consolidated by the unsupervised data mining method (association). The framework will lead to a quantitative approach to uncover relationships between the extracted data from social media and the CRM data. The results show that changing some aspects of the e-learning criteria that were required by students in their social media posts can help to enhance the classification accuracy in the learning management system (LMS) data and to understand more students' studying statuses. Furthermore, the results show matching between students' opinions in CRM and CEM, especially in the negative and neutral classes.
    • 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.