• Gender roles and the expression of driving anger among Ukrainian drivers

      Sullman, M.J.M.; Stephens, A.N.; Hill, Tetiana; (Wiley, 2016-03-10)
      The current study investigated the validity of the revised (25‐item) version of the driving anger expression inventory (DAX) on a novel sample of 385 drivers from Ukraine. The roles of sex and gender in relation to self‐reported aggressive tendencies were also examined. Confirmatory factor analysis supported the four‐factor structure of the DAX (adaptive/constructive expression; use of the vehicle to express anger; verbal aggressive expression; and personal physical aggressive expression), and the three aggressive factors were found to have positive relationships with trait anger and driving anger, while adaptive/constructive expression was negatively related to trait and driving anger. Drivers who reported recent near‐misses or loss of concentration scored higher on verbal aggressive expression. Those who had recently received a traffic ticket also reported higher levels of all three types of aggressive anger expression. Further, the presence of feminine traits, but not sex, predicted more adaptive/constructive behaviors and lower scores for verbal aggressive expression, personal physical aggressive expression, and total aggressive expression. However, masculine traits did not predict any of these factors. This research concludes that the revised DAX is a valid tool to measure the expression of driving anger and that the endorsement of feminine traits is related to less aggressive expression of driving anger.
    • Green communications: techniques and challenges

      Malik, Nabeel A.; Ur-Rehman, Masood; ; University of Bedfordshire (European Alliance for Innovation, 2017-10-04)
      Green technology has drawn a huge amount of attention with the development of the modern world. Similarly with the development in communication technology the industries and researchers are focusing to make this communication as green as possible. In cellular technology the evolution of 5G is the next step to fulfil the user demands and it will be available to the users in 2020. This will increase the energy consumption by which will result in excess emission of co2. In this paper different techniques for the green communication technology and some challenges are discussed. These techniques include device-to-device communication (D2D), massive Multiple-Input Multiple-Output (MIMO) systems, heterogeneous networks (HetNets) and Green Internet of Things (IoT).
    • Growing green with improved profit through reduced power consumption in LTE networks

      Kanwal, Kapil; Safdar, Ghazanfar Ali; University of Bedfordshire (Association for Computing Machinery, 2017-03-31)
      Long Term Evolution (LTE) is well known 4G technology which promises higher data rates. Due to advancements in smart phones and new applications, the user's data requirements have significantly increased. The data hungry users engage radio resources over long periods of time thus resulting into higher energy consumption by Base stations (BSs). Increased energy consumption due to higher data rates directly increases Operational Expenditure (OPEX) thereby ensuing economic and environmental benefits, i.e. profitability and Global Warming. This paper presents detailed performance analysis of our novel joint resources block switching off and bandwidth expansion based energy saving scheme. Our proposed scheme offers 29% energy saving thus results in to decreased CO2 emissions (approximately 1.12 tonnes/ BS) and reduced OPEX thereby enabling mobile vendors to have high profile in Growing Green and help them to improve both environmental and economic aspects. Vendors could enjoy increased profit and stay Green through usage of our energy saving scheme.
    • Growth of nerve cells induced by diverse nanopillar arrays

      Liu, Mengnan; Dong, Litong; Yang, Xueying; Guo, Xuan; Wang, Xuan; Xie, Chenchen; Song, Zhengxun; Wang, Zuobin; Li, Dayou; Changchun University of Science and Technology; et al. (IEEE, 2020-01-02)
      The nanotopographies can induce the growth of nerve cells and the growth of their synapses. Studying the anisotropic structures for the guidance of neuronal synapses is beneficial to the in vitro repair of neurons and the development of regenerative medicine. Thus, studying how diverse nanopillar arrays affect the growth of nerve cells is essential. This paper employed the technology of laser interference lithography (LIL) to fabricate different nanopillar arrays with the same and different size gaps between the X and Y directions, and observe how the structures induce the growth of nerve cells and their synapses.
    • GSWO: A programming model for GPU-enabled parallelization of sliding window operations in image processing

      Yang, Po; Clapworthy, Gordon J.; Dong, Feng; Codreanu, Valeriu; Williams, David; Liu, Baoquan; Roerdink, Jos B.T.M.; Deng, Zhikun; University of Bedfordshire; Liverpool John Moores University; et al. (Elsevier, 2016-07-02)
      Sliding Window Operations (SWOs) are widely used in image processing applications. They often have to be performed repeatedly across the target image, which can demand significant computing resources when processing large images with large windows. In applications in which real-time performance is essential, running these filters on a CPU often fails to deliver results within an acceptable timeframe. The emergence of sophisticated graphic processing units (GPUs) presents an opportunity to address this challenge. However, GPU programming requires a steep learning curve and is error-prone for novices, so the availability of a tool that can produce a GPU implementation automatically from the original CPU source code can provide an attractive means by which the GPU power can be harnessed effectively. This paper presents a GPU-enabled programming model, called GSWO, which can assist GPU novices by converting their SWO-based image processing applications from the original C/C++ source code to CUDA code in a highly automated manner. This model includes a new set of simple SWO pragmas to generate GPU kernels and to support effective GPU memory management. We have implemented this programming model based on a CPU-to-GPU translator (C2GPU). Evaluations have been performed on a number of typical SWO image filters and applications. The experimental results show that the GSWO model is capable of efficiently accelerating these applications, with improved applicability and a speed-up of performance compared to several leading CPU-to-GPU source-to-source translators 
    • Guaranteed timely delivery of control packets for reliable industrial wireless networks in industry 4.0 Era

      Karimireddy, Thanmayee; Zhang, Sijing; University of Bedfordshire (IEEE Computer Society, 2017-07-27)
      Industry 4.0 initiated the need of networking the factory automation and manufacturing facilities by forming industrial wireless networks. The industrial wireless networks transmit control packets having hard real time traffic with stringent deadline and reliability requirements. This paper presents a novel control panel approach that guarantees timely delivery of the control packets in a reliable industrial wireless network. Considering the wireless channel contention in these networks, the control panel aims at offering deterministic channel access time to each node in the network based on their relative message deadlines. A deterministic channel access algorithm is proposed in this paper to offer time guaranteed control packet delivery and a parameter, Pguarantee is developed to determine the probability of timely delivery of control packets before the deadline. The use of this proposed deterministic channel access algorithm in Industry 4.0 applications along with adaptive error control approach helps in supporting the timely and reliable delivery of the control packets. The performance of the proposed method is shown through detailed analytical and simulation experiment results in this paper.
    • Healthcare-related data integration framework and knowledge reasoning process

      Yu, Hong Qing; Zhao, Xia; Deng, Zhikun; Dong, Feng; University of Bedfordshire; Birmingham City University (Springer Verlag, 2017-07-12)
      In this paper, we illustrate sensor data based healthcare information integration framework with semantic knowledge reasoning power. Nowadays, more and more people start to use mobile applications that can collects data from a variety of health and wellbeing sensors and presents significant correlations across sensors systems. However, it is difficult to correlate and integrate data from these varieties provided users with overall wellbeing picture and hidden insights about systematic health trends. The paper presents a data semantic integration solution using semantic web technologies. The process includes knowledge lifting and reasoning process that could feedback many hidden health factors and personal lifestyle analysis using semantic rule language (SWRL).
    • History-assisted energy-efficient spectrum sensing for infrastructure-based cognitive radio networks

      Syed, Tazeen Shabana; Safdar, Ghazanfar Ali; University of Bedfordshire (IEEE, 2016-06-28)
      Spectrum sensing is a prominent functionality to enable dynamic spectrum access (DSA) in cognitive radio (CR) networks. It provides protection to primary users (PUs) from interference and creates opportunities of spectrum access for secondary users (SUs). It should be performed efficiently to reduce the number of false alarms and missed detections. Continuous sensing for a long time incurs cost in terms of increased energy consumption; thus, spectrum sensing ought to be energy efficient to ensure the prolonged existence of CR devices. This paper focuses on using of history to help achieve energy-efficient spectrum sensing in infrastructure-based CR networks. The scheme employs an iteratively developed history processing database that is used by CRs to make decisions about spectrum sensing, subsequently resulting in reduced spectrum scanning and improved energy efficiency. Two conventional spectrum sensing schemes, i.e., energy detection (ED) and cyclostationary feature detection (CFD), are enriched by history to demonstrate the effectiveness of the proposed scheme. System-level simulations are performed to investigate the sensitivity of the proposed history-based scheme by performing detailed energy consumption analysis for the aforementioned schemes. Results demonstrate that the employment of history ensued in improved energy efficiency due to reduced spectrum scanning. This paper also suggests which spectrum sensing scheme can be the best candidate in a particular scenario by looking into computational complexity before comparative analysis is presented with other states of the art.
    • How do students 'really' interact with virtual worlds? the influence of proper induction for virtual interactions

      Christopoulos, Athanasios; Conrad, Marc; Shukla, Mitul; University of Bedfordshire (SciTePress, 2016-04-24)
      Our ongoing research focuses on the ways that interactions affect learner engagement with a virtual world and, consequently, the educational activities that take place within it when a hybrid learning approach is used. It aims to form a complete taxonomy of the types of interactions that can lead to the development of engaging and interactive learning experiences. In this paper, we examine the impact that the orientation (induction) process has on learner engagement by observing a cohort of postgraduate students while using an OpenSim-based institutionally hosted virtual world. The results of our study highlight that educators and instructors need to plan their in-world learning activities very carefully and with a focus on interactions if engaging activities are what they want to offer their students. Additionally, it seems that student interactions with the content of the virtual world and the in-class student-to-student interactions have stronger impact on student engagement when hybrid methods are used. We confirm and further enhance our hypothesis investigating student feelings and thoughts about the interaction taking place within a virtual world when that is used in higher education.
    • How interactive is your virtual world?: examining student engagement on virtual learning activities

      Christopoulos, Athanasios; Conrad, Marc; Shukla, Mitul; University of Bedfordshire (International Academy, Research and Industry Association, IARIA, 2015-03-01)
      This paper is part of our ongoing research on the ways interaction affects student immersion within a virtual world and, consequently, student engagement with the educational activities that take place within it when a hybrid learning method is used. We confirm and further enhance our hypothesis investigating student feelings and thoughts about the interaction taking place within a virtual world when that is used in higher education. Specifically, 111 university students, both at undergraduate and postgraduate level, who used our "in-house" OpenSim virtual world for roughly 8 weeks, were asked to indicate their opinion and feelings about the virtual world and the various kinds of interaction they had. The results of this study validated our initial hypothesis that interaction plays a crucial role in student engagement, underlying that the nature and the design of the educational activities substantially affects student engagement.
    • Human interaction based Robot Self-Learning approach for generic skill learning in domestic environment

      Cao, Tao; Li, Dayou; Maple, Carsten; Qiu, Renxi; University of Bedfordshire (IEEE Computer Society, 2014-04-17)
      Unstructured domestic environments present a substantial challenge to effective robotic operation. Domestic environment requires service robots to deal with unexpected environment changes, novel objects, and user manipulations. We present an approach to enable service robots to actively learn high-level skills in an unstructured environment. This involves using a combination of processing environment changes, recording and learning user manipulation data, setting up meaningful hypothesis, proactively performing test actions and interacting with user feedback, and logic reasoning. We demonstrate our Robot Self-Learning (RSL) approach by using ROS (Robotic Operating System) and Care-O-bot (COB) 3. These methods enable service robots to learn generalized high-level skills in a sophisticated and changing environment. The RSL approach allows robots to learn new actions imposed by a human and action condition from perception changes from the environment. We also present logic based reasoning engine to speed up the self learning process. © 2013 IEEE.
    • A hybrid approach to combat email-based cyberstalking

      Ghasem, Zinnar; Frommholz, Ingo; Maple, Carsten; University of Bedfordshire; University of Warwick (Institute of Electrical and Electronics Engineers Inc., 2015-10-26)
      Email is one of the most popular Internet applications which enables individuals and organisations alike to communicate and work effectively. However, email has also been used by criminals as a means to commit cybercrimes such as phishing, spamming, cyberbullying and cyberstalking. Cyberstalking is a relatively new surfacing cybercrime, which recently has been recognised as a serious social and worldwide problem. Combating email-based cyberstalking is a challenging task that involves two crucial steps: a robust method for filtering and detecting cyberstalking emails and documenting evidence for identifying cyberstalkers as a prevention and deterrence measure. In this paper, we discuss a hybrid approach that applies machine learning to detect, filter and file evidence. To this end we present a new robust feature selection approach to select informative features, aiming to improve the performance of machine learning within this task.
    • A hybrid method for secure and reliable transmission on industrial automation and control networks in industry 4.0

      Karimireddy, Thanmayee; Zhang, Sijing; University of Bedfordshire (Institute of Electrical and Electronics Engineers Inc., 2019-11-11)
      Industry 4.0 initiated the need of networking the factory automation, control and manufacturing facilities by forming industrial automation and control networks. These industrial networks transmit control packets with stringent security and reliability requirements. In this paper, a hybrid method is proposed to ensure secure and reliable transmission of the packets on these industrial automation and control networks. This method is based on hybrid cryptography and reliable properties are incorporated into it to ensure both secure and reliable delivery of the packets to the receiver. Evaluation of the proposed method is undertaken in this report based on the simulation studies.
    • Identifying Mubasher software products through sentiment analysis of Arabic tweets

      AL-Rubaiee, Hamed Saad; Qiu, Renxi; Li, Dayou; University of Bedfordshire (Institute of Electrical and Electronics Engineers Inc., 2016-05-02)
      Social media has recently become a rich resource in mining user sentiments. In this paper, Twitter has been chosen as a platform for opinion mining in trading strategy with Mubasher products, which is a leading stock analysis software provider in the Gulf region. This experiment proposes a model for sentiment analysis of Saudi Arabic (standard and Arabian Gulf dialect) tweets to extract feedback from Mubasher products. A hybrid of natural language processing and machine learning approaches on building models are used to classify tweets according to their sentiment polarity into one of the classes positive, negative and neutral. Firstly, document's Pre-processing are explored on the dataset. Secondly, Naive Bayes and Support Vector Machines (SVMs) are applied with different feature selection schemes like TF-IDF (Term Frequency-Inverse Document Frequency) and BTO (Binary-Term Occurrence). Thirdly, the proposed model for sentiment analysis is expanded to obtain the results for N-Grams term of tokens. Finally, human has labelled the data and this may involve some mistakes in the labelling process. At this moment, neutral class with generalisation of our classification will take results to different classification accuracy.
    • Identifying pneumonia in chest X-rays: a deep learning approach

      Jaiswal, Amit Kumar; Tiwari, Prayag; Kumar, Sachin; Gupta, Deepak; Khanna, Ashish; Rodrigues, Joel J.P.C.; University of Bedfordshire; University of Padova; South Ural State University; Maharaja Agrasen Institute of Technology; et al. (Elsevier, 2019-06-04)
      The rich collection of annotated datasets piloted the robustness of deep learning techniques to effectuate the implementation of diverse medical imaging tasks. Over 15% of deaths include children under age five are caused by pneumonia globally. In this study, we describe our deep learning based approach for the identification and localization of pneumonia in Chest X-rays (CXRs) images. Researchers usually employ CXRs for the diagnostic imaging study. Several factors such as positioning of the patient and depth of inspiration can change the appearance of the chest X-ray, complicating interpretation further. Our identification model (https://github.com/amitkumarj441/identify_pneumonia) is based on Mask-RCNN, a deep neural network which incorporates global and local features for pixel-wise segmentation. Our approach achieves robustness through critical modifications of the training process and a novel post-processing step which merges bounding boxes from multiple models. The proposed identification model achieves better performances evaluated on chest radiograph dataset which depict potential pneumonia causes.
    • 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)
    • Imaging quality assessment of different AFM working modes on living cancer cells

      Wang, Guoliang; Sun, Baishun; Wu, Xiaomin; Zhang, Wenxiao; Qu, Yingmin; Song, Zhengxun; Wang, Zuobin; Li, Dayou; Changchun University of Science and Technology; University of Bedfordshire (IEEE, 2020-02-02)
      Since the invention of atomic force microscope (AFM) in 1986, its capabilities in biophysical research, such as living cell imaging, molecule imaging and recognition and drug treatment analysis, have been deeply investigated. Various types of working modes of atomic force microscopy have been employed for imaging and analyzing living cells. The physical properties of living cells can be directly illustrated by its good resolution images. In this paper, the applications of three AFM working modes including contact, tapping and quantitative imaging (QI) modes for the investigation of living lung cancer cells (A549) are presented. Meanwhile, the quality of images of the cells obtained by different working modes is compared through the image quality assessment (IQA) methods.
    • Imaging the substructures of individual IgE antibodies with atomic force microscopy

      Hu, Jing; Gao, Mingyan; Wang, Ying; Liu, Mengnan; Wang, Jianfei; Li, Jiani; Song, Zhengxun; Chen, Yujuan; Wang, Zuobin (American Chemical Society, 2019-10-29)
      The interaction between antibodies and substrates directly affects its conformation and thus its immune function. Therefore, it is desirable to study the structure of antibodies at the single molecule level. Herein, the substructures of Immunoglobulin E (IgE) on solid surfaces were investigated. For this purpose, the tapping-mode atomic force microscopy (AFM) was applied to observe the individual IgE substructures adsorbed onto Mg2+ and Na+ modified mica substrates in air. As expected, the AFM images revealed that the IgE antibodies exhibited different conformations on the surface of mica substrate, consisting of the four basic orientations: three domain, two equivalent domain, two unequal domain and single domain morphologies. Moreover, the differences of the different orientations in single IgE antibodies were also identified clearly.
    • IManageCancer: developing a platform for empowering patients and strengthening self-management in cancer diseases

      Graf, Norbert; Hoffman, Stefan; Koumakis, Lefteris; Pravettoni, Gabrielli; Marias, Kostas; Tsiknakis, Manolis; Kiefer, Stefan; Kondylakis, Haridimos; Bucur, Anca; Dong, Feng; et al. (Institute of Electrical and Electronics Engineers Inc., 2017-11-13)
      Cancer research has led to more cancer patients being cured, and many more enabled to live with their cancer. As such, some cancers are now considered a chronic disease, where patients and their families face the challenge to take an active role in their own care and in some cases in their treatment. To this direction the iManageCancer project aims to provide a cancer specific self-management platform designed according to the needs of patient groups while focusing, in parallel, on the wellbeing of the cancer patient. In this paper, we present the use-case requirements collected using a survey, a workshop and the analysis of three white papers and then we explain the corresponding system architecture. We describe in detail the main technological components of the designed platform, show the current status of development and we discuss further directions of research.