• Empirical investigation of factors that impact e-government adoption in Nigeria

      Chukwu, Joshua; Conrad, Marc; Crosbie, Tess (IADIS Press, 2019-12-31)
      This paper is a review of the impact of data protection/privacy, website usability and culture on e-government adoption. Along with Hofstede's cultural dimensions and technology acceptance theory, the paper uses these two theories to analyse the highly anticipated era of electronic government, an aspect through which government communicates with agencies and business organisation through information communication technology. We examine the adoption process, its challenges and difficulties, especially in developing countries like Nigeria considering fundamental deficiencies in basic infrastructure, human capacity, political and cultural constraints. This paper can help Nigerian government policy and decision makers develop strategies to adopt e-government services and improve the further development of these services. The paper does not only provide empirical support to previous research, but it validates and improve the results of similar studies in the field.
    • Enable a facile size re-distribution of MBE-grown Ga-droplets via in situ pulsed laser shooting

      Geng, Biao; Shi, Zhenwu; Chen, Chen; Zhang, Wei; Yang, Linyun; Deng, Changwei; Yang, Xinning; Miao, Lili; Peng, Changsi; Soochow University; et al. (Springer, 2021-08-04)
      A MBE-prepared Gallium (Ga)-droplet surface on GaAs (001) substrate is in situ irradiated by a single shot of UV pulsed laser. It demonstrates that laser shooting can facilely re-adjust the size of Ga-droplet and a special Ga-droplet of extremely broad size-distribution with width from 16 to 230 nm and height from 1 to 42 nm are successfully obtained. Due to the energetic inhomogeneity across the laser spot, the modification of droplet as a function of irradiation intensity (IRIT) can be straightly investigated on one sample and the correlated mechanisms are clarified. Systematically, the laser resizing can be perceived as: for low irradiation level, laser heating only expands droplets to make mergences among them, so in this stage, the droplet size distribution is solely shifted to the large side; for high irradiation level, laser irradiation not only causes thermal expansion but also thermal evaporation of Ga atom which makes the size-shift move to both sides. All of these size-shifts on Ga-droplets can be strongly controlled by applying different laser IRIT that enables a more designable droplet epitaxy in the future.
    • Energy efficiency and superlative TTT for equitable RLF and Ping Pong in LTE networks

      Kanwal, Kapil; Safdar, Ghazanfar Ali; University of Bedfordshire (Springer, 2018-06-02)
      Data hungry users engage radio resources over long periods of time thus resulting into higher energy consumption by Base Stations (BSs). Mobile operators’ operational expenditure (OPEX) is directly affected by augmented electricity bills due to increased power consumption, thereby ensuing reduced economic and environmental benefits, i.e. profitability of vendors and green communication accordingly. This work provides performance analysis of our proposed reduced early handover (REHO) scheme which results in increased energy efficiency. Impact of reduced energy consumption is shown on OPEX, as well as greener aspects are investigated by inclusion of real life commercial tariffs adopted by one of the mobile operators in the UK. Performance analysis revealed that varying time to trigger (TTT) values significantly impact radio link failure (RLF), ping pong effect as well as call drop ratio (CDR) and Handover ratio (HOR), at changing users’ velocities. Paper investigates and provides a very useful insight for superlative value of TTT for unbiased RLF and Ping Pong, which can help vendors not only to achieve increased energy efficiency, but also maintain other salient performance parameters within acceptable limits. The work also achieves the fact that the time difference in terms of transmission time intervals (TTIs) for reduced early handover in REHO, always remain the same irrespective of the value of TTT, thus ensuring that REHO continuously achieves increased energy efficiency compared to LTE standard.
    • Energy efficiency in smartphones: a survey on modern tools and techniques

      Shah, Munam Ali; Naheed, Naila; Zhang, Sijing; COMSATS Institute of Information Technology; University of Bedfordshire (Institute of Electrical and Electronics Engineers Inc., 2015-11-02)
      Smartphone is nowadays in the use of the majority of the people. It has different features. It offers power consuming technologies like GPS, 3G, games, apps etc. which creates power management problem faced by the users. In this paper, our contribution is twofold. Firstly, we evaluate different tools and techniques that can be used to optimize the usage of smartphone battery. Secondly, we survey the latest research that has been published in 2010 to 2014 which helps users to find the best technique or tool to achieve energy efficiency in smartphones.
    • Energy efficiency led reduced CO2 emission in green LTE networks

      Kanwal, Kapil; Safdar, Ghazanfar Ali; Ur-Rehman, Masood; University of Bedfordshire (European Alliance for Innovation, 2017-10-04)
      The technological advancements in smart phones and their applications have rapidly raised the number of users and their data demands. To fulfil enlarged user's data requirements, Basestation (BS) engages their resources over prolong time intervals at the cost of increased power consumption. In parallel, operators are expanding network infrastructure by employing additional BSs which also adds in power consumption. This directly increases carbon emission (CO2) thus results in to more global warming. Therefore, Information and Communication Technology (ICT) has become major contributor in global warming while mobile communication is one of the key contributors within ICT. This paper investigates reduced CO2 emission through decreased power consumption in LTE networks. Proposed energy saving scheme is validated through the analysis of various performance related parameters in MATLAB. Results have proven that proposed scheme reduces CO2 emission by 2.10 tonnes per BS.
    • Energy-efficient GCSA medium access protocol for infrastructure-based cognitive radio networks

      Syed, Tazeen Shabana; Safdar, Ghazanfar Ali; University of Bedfordshire (IEEE, 2019-06-05)
      The major challenge encountered by battery-driven wireless local area networking devices is to conserve their energy for prolonged operation. This paper presents an energy-efficient medium access mechanism called group control slot allocation (GCSA) protocol for cognitive radio (CR) networks. GCSA utilizes the group priority allocation algorithm to allocate stations (STAs) into groups; subsequently STAs that have traffic buffered in an access point are assigned to a higher priority group. A management frame, namely group monitoring pointer, optimizes the sleep-awake cycle of the STAs by allocating transmission opportunities to groups based on their priority. GCSA employs publisher-subscriber and point-to-point messaging models for communication between the base station and STAs, respectively. Performance analysis of GCSA demonstrates that increasing the number of STAs which enter into sleep mode augments the percentage of energy saved. The overall system-level results show that energy saved is around 20% higher for GCSA than the IEEE 802.11e standard hybrid coordination function power-saving mode. Since GCSA benefits from history-assisted-led spectrum sensing, the paper also presents the relationship of the local storage of CRs with respect to history and suggests a hybrid approach as best option to keep a balance between the sensed data and its sharing with analytical engine database for history enrichment leading toward improved energy efficiency.
    • Engineering a mobile VR experience with MEMS 9DOF motion controller

      Jayaraj, Lionel; Wood, Jim; Gibson, Marcia; University of Bedfordshire (IEEE, 2018-11-01)
      It has been argued that the Virtual Reality (VR) experience is only complete when it includes appropriate motion controls. To enhance immersion, the player's field of vision, movement and any actions they perform should be constrained as little as possible. In this paper, we discuss the engineering and user experience testing of a novel mobile VR compatible controller which seeks to address limitations of typical VR controllers and joysticks in terms of the freedom of interactivity offered. The resulting artefact is a 9 DOF (Degrees of freedom) motion controller which uses Bluetooth to achieve connectivity to a mobile device. Sensor-based tracking is achieved by reengineering the existing MEMS (Micro Electro-Mechanical System) available in some smart phone models to track motion. A framework and heuristics to measure immersion is developed and user experience tested comparatively during gameplay in a mobile VR sports simulation. Sensor efficiency is also tested via a graphing-simulator.
    • Enhancing cell-edge performance using multi-layer soft frequency reuse scheme

      Hossain, Md. S.; Tariq, Faisal; Safdar, Ghazanfar Ali; Radio Access Group, Bangladesh; Queen Mary University of London; University of Bedfordshire (Institution of Engineering and Technology, 2015-10-29)
      In cellular systems, maintaining data rate at the cell edge has been a challenging task due to strong co-channel interference from neighbouring cells. Several techniques have been proposed to tackle the issue, among which soft frequency reuse (SFR) is the most widely used. A novel multi-layer SFR scheme combined with cell sectoring is proposed to improve the performance in cell-edge region. Then, a spectrum allocation scheme in a three-cell reuse system is designed to ensure the maximisation of the efficiency. A generic expression for power allocation in different regions along with the signal-to-noise ratio of multi-layer SFR in sectored cell is derived. Finally, systemlevel simulation has been carried out to demonstrate the efficiency of the proposed resource allocation scheme. It is shown that the spectral efficiency at cell-edge area improves by ∼10% which is significant for the cell-edge region.
    • Enhancing user fairness in OFDMA radio access networks through machine learning

      Comşa, Ioan-Sorin; Zhang, Sijing; Aydin, Mehmet Emin; Kuonen, Pierre; Trestian, Ramona; Ghinea, Gheorghiţă; Brunel University; University of Bedfordshire; University of the West of England; HEIA-FR; et al. (IEEE, 2019-06-13)
      The problem of radio resource scheduling subject to fairness satisfaction is very challenging even in future radio access networks. Standard fairness criteria aim to find the best trade-off between overall throughput maximization and user fairness satisfaction under various types of network conditions. However, at the Radio Resource Management (RRM) level, the existing schedulers are rather static being unable to react according to the momentary networking conditions so that the user fairness measure is maximized all time. This paper proposes a dynamic scheduler framework able to parameterize the proportional fair scheduling rule at each Transmission Time Interval (TTI) to improve the user fairness. To deal with the framework complexity, the parameterization decisions are approximated by using the neural networks as non-linear functions. The actor-critic Reinforcement Learning (RL) algorithm is used to learn the best set of non-linear functions that approximate the best fairness parameters to be applied in each momentary state. Simulations results reveal that the proposed framework outperforms the existing fairness adaptation techniques as well as other types of RL-based schedulers.
    • Entity-aware capsule network for multi-class classification of big data: a deep learning approach

      Jaiswal, Amit Kumar; Tiwari, Prayag; Garg, Sahil; Hossain, M. Shamim; University of Bedfordshire; University of Padova; École de technologie supérieure, Montréal; King Saud University (Elsevier B.V., 2020-11-20)
      Named entity recognition (NER) is one of the most challenging natural language processing (NLP) tasks, as its performance is related to constantly evolving languages and dependency on expert (human) annotation. The diverse and dynamic content on the web significantly raises the need for a more generalized approach—one that is capable of correctly classifying terms in a corpus and feeding subsequent NLP tasks, such as machine translation, query expansion, and many other applications. Although extensively researched in recent times, the variety of public corpora available nowadays provides room for new and more accurate methods to tackle the NER problem. This paper presents a novel method that uses deep learning techniques based on the capsule network architecture for predicting entities in a corpus. This type of network groups neurons into so-called capsules to detect specific features of an object without reducing the original input unlike convolutional neural networks and their ‘max-pooling’ strategy. Our extensive evaluation on several benchmarked datasets demonstrates how competitive our method is in comparison with state-of-the-art techniques and how the usage of the proposed architecture may represent a significant benefit to further NLP tasks, especially in cases where experts are needed. Also, we explore NER using a theoretical framework that leverages big data for security. For the sake of reproducibility, we make the codebase open-source.
    • Establishing an optimized method for the separation of low and high abundance blood plasma proteins

      Yang, Henian; Wang, Guijie; Zhang, Tiantian; Beattie, John H.; Zhou, Shaobo; University of Bedfordshire; Bournemouth University; University of Aberdeen (PeerJ, 2020-02-01)
      The study tested the efficiency and reproducibility of a method for optimal separation of low and high abundant proteins in blood plasma. Firstly, three methods for the separation and concentration of eluted (E: low abundance), or bound (B: high abundance) proteins were investigated: TCA protein precipitation, the ReadyPrep™ 2-D cleanup Kit and Vivaspin Turbo 4, 5 kDa ultrafiltration units. Secondly, the efficiency and reproducibility of a Seppro column or a ProteoExtract Albumin/IgG column were assessed by quantification of E and B proteins. Thirdly, the efficiency of two elution buffers, containing either 25% or 10% glycerol for elution of the bound protein, was assessed by measuring the remaining eluted volume and the final protein concentration. Compared to the samples treated with TCA protein precipitation and the ReadyPrep™ 2-D cleanup Kit, the E and B proteins concentrated by the Vivaspin4, 5 kDa ultrafiltration unit were separated well in both 1-D and 2-D gels. The depletion efficiency of abundant protein in the Seppro column was reduced after 15 cycles of sample processing and regeneration and the average ratio of E/(B + E) × 100% was 37 ± 11(%) with a poor sample reproducibility as shown by a high coefficient of variation (CV = 30%). However, when the ProteoExtract Albumin/IgG column was used, the ratio of E/(B + E) × 100% was 43 ± 3.1% (n = 6) and its CV was 7.1%, showing good reproducibility. Furthermore, the elution buffer containing 10% (w/v) glycerol increased the rate of B protein elution from the ProteoExtract Albumin/IgG column, and an appropriate protein concentration (3.5 µg/µl) for a 2-D gel assay could also be obtained when it was concentrated with Vivaspin Turbo 4, 5 kDa ultrafiltration unit. In conclusion, the ProteoExtract Albumin/IgG column shows good reproducibility of preparation of low and high abundance blood plasma proteins when using the elution buffer containing 10% (w/v) glycerol. The optimized method of preparation of low/high abundance plasma proteins was when plasma was eluted through a ProteoExtract Albumin/IgG removal column, the column was further washed with elution buffer containing 10% glycerol. The first and second elution containing the low and high abundance plasma proteins, respectively, were further concentrated using Vivaspin® Turbo 4, 5 kDa ultrafiltration units for 1 or 2-D gel electrophoresis.
    • Estimation of collision probability in a saturated vehicular Ad-Hoc networks

      Eze, Elias Chinedum; Zhang, Sijing; Liu, Enjie; University of Bedfordshire (Institute of Electrical and Electronics Engineers Inc., 2015-10-26)
      It is envisioned that WAVE standard, the technology for future DSRC communication in vehicular environments will support vehicular communication networks in order to provide safety and infotainment services as part of ITS. However, the defined parameter set for the EDCA used in 802.11p is capable of prioritizing messages, and when number of vehicles sending AC3 increases, packet collision probability will undeniably increase significantly. Since transmission collisions can only be detected after a transmission if at all, high percentage of packet collision probability will lead to many dead channel access times with no useful data exchange. We proposed CODER scheme which uses network coding to increase packet content and mitigate the problem of high collision probability which is inherent in WAVE standard by minimizing contention. Results of the theoretical analysis and simulation experiments show that CODER scheme has a performance gain over WAVE in terms of reduced collision rate.
    • Euclidean geometry axioms assisted target cell boundary approximation for improved energy efficacy in LTE systems

      Safdar, Ghazanfar Ali; Kanwal, Kapil; University of Bedfordshire (Institute of Electrical and Electronics Engineers Inc., 2017-10-23)
      Long Term Evolution (LTE) facilitates users with high data rate at the cost of increased energy consumption. The base station, also known as eNodeB, is the main energy hungry elements in LTE networks. Since power consumption directly affects the operational expenditure, thus the provision of cost-effective services with adequate quality of service has become a major challenge. This paper investigates reduced early handover (REHO) scheme aimed at increased energy efficiency in LTE systems. REHO, compared to standard LTE A3 event, initiates early handover, thereby resulting into reduced energy consumption. Axioms of Euclidean geometry are employed to estimate the target cell boundary towards calculation of the time difference ΔT between standard LTE and REHO. Performance analysis involved comparison of standard LTE with REHO in the presence of varying velocity and Hysteresis values. Early handover ΔT in REHO is calculated in terms of transmission time intervals and results into improved energy efficiency at the cost of slightly increased radio link failure (RLF). The key finding of the work is the nonsensitivity of users towards velocity in standard LTE, whereas REHO leads to considerably improved energy efficiency at low velocity thereby making it an advantageous scheme for urbanised densely deployed LTE networks. Outcomes provided also deliver a guideline for vendors to choose suitable value of hysteresis, while achieving appropriate results of energy saving and RLF.
    • Evaluating automatically parallelized versions of the support vector machine

      Codreanu, Valeriu; Dröge, Bob; Williams, David; Yasar, Burhan; Yang, Po; Liu, Baoquan; Dong, Feng; Surinta, Olarik; Schomaker, Lambert R.B.; Roerdink, Jos B.T.M.; et al. (John Wiley and Sons Ltd, 2014-10-09)
      The support vector machine (SVM) is a supervised learning algorithm used for recognizing patterns in data. It is a very popular technique in machine learning and has been successfully used in applications such as image classification, protein classification, and handwriting recognition. However, the computational complexity of the kernelized version of the algorithm grows quadratically with the number of training examples. To tackle this high computational complexity, we have developed a directive-based approach that converts a gradient-ascent based training algorithm for the CPU to an efficient graphics processing unit (GPU) implementation. We compare our GPU-based SVM training algorithm to the standard LibSVM CPU implementation, a highly optimized GPU-LibSVM implementation, as well as to a directive-based OpenACC implementation. The results on different handwritten digit classification datasets demonstrate an important speed-up for the current approach when compared to the CPU and OpenACC versions. Furthermore, our solution is almost as fast and sometimes even faster than the highly optimized CUBLAS-based GPU-LibSVM implementation, without sacrificing the algorithm's accuracy.
    • Evaluating urban surface water quality in Luton

      Ajmal, Tahmina; Anyachebelu, Tochukwu Kene; Conrad, Marc; Rawson, David M.; University of Bedfordshire (Springer, 2019-02-09)
      Using a single numerical value to indicate the quality of water, a so-called Water Quality Index (WQI) is a well-established way of rating the overall water quality status of a given water body. During the last few years, researchers in the water sector have developed different such indices to address their specific needs. In this study, we attempt to obtain a WQI formula suited for evaluating the water quality of the River Lea. We have selected four different sites on the River Lea and explore the possibility of monitoring using a minimum number of parameters only. The results obtained are very encouraging and provide a strong indication that only three parameters are enough to indicate water quality of a water body.
    • Evaluation of autoparallelization toolkits for commodity GPUs

      Williams, David; Codreanu, Valeriu; Yang, Po; Liu, Baoquan; Dong, Feng; Yasar, Burhan; Mahdian, Babak; Chiarini, Alessandro; Zhao, Xia; Roerdink, Jos B.T.M.; et al. (Springer Verlag, 2014-12-31)
      In this paper we evaluate the performance of the OpenACC and Mint toolkits against C and CUDA implementations of the standard PolyBench test suite. Our analysis reveals that performance is similar in many cases, but that a certain set of code constructs impede the ability of Mint to generate optimal code. We then present some small improvements which we integrate into our own GPSME toolkit (which is derived from Mint) and show that our toolkit now out-performs OpenACC in the majority of tests.
    • Evaluations of 5-fluorourcil treated lung cancer cells by atomic force microscopy

      Jiang, Xiaolin; Ma, Ke; Hu, Cuihua; Gao, Mingyan; Zhang, Jiashuo; Wang, Ying; Chen, Yujuan; Song, Zhengxun; Wang, Zuobin; Changchun University of Science and Technology; et al. (The Royal Society of Chemistry, 2019-09-06)
      Atomic force microscopy (AFM) can be used to obtain the physical information of single live cancer cells; however, the physical changes in live cells with time based on AFM remain to be studied, which play a key role in the evaluation of the efficacy and side effects of drugs. Herein, the treatment of the A549 cell line with the anticarcinogen 5-fluorouracil has been discussed based on the AFM analysis of their continuous physical changes, including their surface morphology, height, adhesion and Young's modulus, with time. In comparison, the African green monkey kidney (Vero) cell line was tested as normal cells to determine the side effects of 5-fluorouracil. The results show that the optimal concentration of 5-fluorouracil is about 500 μM, which presents the best anticancer effect and mild side effects.
    • Evidence of power-law behavior in cognitive IoT applications

      Bebortta, Sujit; Senapati, Dilip; Rajput, Nikhil Kumar; Singh, Amit Kumar; Rathi, Vipin Kumar; Pandey, Hari Mohan; Jaiswal, Amit Kumar; Qian, Jia; Tiwari, Prayag; College of Engineering and Technology, Bhubaneshwar; et al. (Springer, 2020-02-03)
      The motivations induced due to the presence of scale-free characteristics of neural systems governed by the well-known power-law distribution of neuronal activities have led to its convergence with the Internet of things (IoT) framework. The IoT is one such framework, where the self-organization of the connected devices is a momentous aspect. The devices involved in these networks inherently relate to the collection of several consolidated devices like the sensory devices, consumer appliances, wearables, and other associated applications, which facilitate a ubiquitous connectivity among the devices. This is one of the most significant prerequisites of IoT systems as several interconnected devices need to be included in the convolution for the uninterrupted execution of the services. Thus, in order to understand the scalability and the heterogeneity of these interconnected devices, the exponent of power-law plays a significant role. In this paper, an analytical framework to illustrate the ubiquitous power-law behavior of the IoT devices is derived. An emphasis regarding the mathematical insights for the characterization of the dynamic behavior of these devices is conceptualized. The observations made in this direction are illustrated through simulation results. Further, the traits of the wireless sensor networks, in context with the contemporary scale-free architecture, are discussed.
    • Evolving polynomial neural networks for detecting abnormal patterns

      Nyah, Ndifreke; Jakaite, Livija; Schetinin, Vitaly; Sant, Paul; Aggoun, Amar; University of Bedfordshire (Institute of Electrical and Electronics Engineers Inc., 2016-11-10)
      Abnormal patterns, existing e.g. in raw data, affect decision making process and have to be accurately detected and removed in order to reduce the risk of making wrong decisions. Existing Machine Learning (ML) approaches known from the literature require the user to set and experimentally adjust parameters of a decision model to achieve the best result. When artificial neural networks (ANNs) are employed, a typical problem is setting of a proper network structure and learning parameters that are required to minimise possible over-fitting. We propose a new evolutionary strategy of learning an ANN structure of a near optimal connectivity from the given data and show that such structures are less prone to over-fitting. The proposed method starts to learn with one input variable and one neuron and then adds a new input and a new neuron to the network while its validation error decreases. The resultant ANN consists of a reasonably small number of neurons that are concisely described by a set of short-term polynomial functions of variables that make a distinct contribution to the output. The proposed technique has been tested on the ML benchmarks and the results showed that the performance is comparable with that obtained by the conventional ML methods that require ad hoc tuning.
    • Examining sensor-based physical activity recognition and monitoring for healthcare using Internet of Things: a systematic review

      Qi, Jun; Yang, Po; Waraich, Atif; Deng, Zhikun; Zhao, Youbing; Yang, Yun; Yunnan University; Liverpool John Moore University; University of Bedfordshire (Elsevier, 2018-09-26)
      Due to importantly beneficial effects on physical and mental health and strong association with many rehabilitation programs, Physical Activity Recognition and Monitoring (PARM) have been considered as a key paradigm for smart healthcare. Traditional methods for PARM focus on controlled environments with the aim of increasing the types of identifiable activity subjects complete and improving recognition accuracy and system robustness by means of novel body-worn sensors or advanced learning algorithms. The emergence of the Internet of Things (IoT) enabling technology is transferring PARM studies to open and connected uncontrolled environments by connecting heterogeneous cost-effective wearable devices and mobile apps. Little is currently known about whether traditional PARM technologies can tackle the new challenges of IoT environments and how to effectively harness and improve these technologies. In an effort to understand the use of IoT technologies in PARM studies, this paper will give a systematic review, critically examining PARM studies from a typical IoT layer-based perspective. It will firstly summarize the state-of-the-art in traditional PARM methodologies as used in the healthcare domain, including sensory, feature extraction and recognition techniques. The paper goes on to identify some new research trends and challenges of PARM studies in the IoT environments, and discusses some key enabling techniques for tackling them. Finally, this paper consider some of the successful case studies in the area and look at the possible future industrial applications of PARM in smart healthcare.