• iMIG: Toward an adaptive live migration method for KVM virtual machines

      Li, Jianxin; Zhao, Jieyu Zhao; Li, Yi; Cui, Lei; Li, Bo; Liu, Lu; Panneerselvam, John; University of Derby (Oxford University Press, 2014-07-22)
      With the energy and power costs increasing alongside the growth of the IT infrastructures, achieving workload concentration and high availability in cloud computing environments is becoming more and more complex. Virtual machine (VM) migration has become an important approach to address this issue, particularly; live migration of the VMs across the physical servers facilitates dynamic workload scheduling of the cloud services as per the energy management requirements, and also reduces the downtime by allowing the migration of the running instances. However, migration is a complex process affected by several factors such as bandwidth availability, application workload and operating system configurations, which in turn increases the complications in predicting the migration time in order to negotiate the service-level agreements in a real datacenter. In this paper, we propose an adaptive approach named improved MIGration (iMIG), in which we characterize some of the key metrics of the live migration performance, and conduct several experiments to study the impacts of the investigated metrics on the Kernel-based VM (KVM) functionalities, as well as the energy consumed by both the destination and the source hosts. Our results reveal the importance of the configured parameters: speed limit, TCP buffer size and max downtime, along with the VM properties and also their corresponding impacts on the migration process. Improper setting of these parameters may either incur migration failures or causes excess energy consumption. We witness a few bugs in the existing Quick EMUlator (QEMU)/KVM parameter computation framework, which is one of most widely used KVM frameworks based on QEMU. Based on our observations, we develop an analytical model aimed at better predictions of both the migration time and the downtime, during the process of VM deployment. Finally, we implement a suite of profiling tools in the adaptive mechanism based on the qemu-kvm-0.12.5 version, and our experiment results prove the efficiency of our approach in improving the live migration performance. In comparison with the default migration approach, our approach achieves a 40% reduction in the migration latency and a 45% reduction in the energy consumption.
    • Impact of social distancing to mitigate the spread of COVID-19 in a virtual environment

      Marti-Mason, Diego; Kapinaj, Matej; Pinel-Martínez, Alejandro; Stella, Leonardo; University of Derby (The Association for Computing Machinery, 2020-11-01)
      A novel strand of Coronavirus has spread in the past months to the point of becoming a pandemic of massive proportions. In order to mitigate the spread of this disease, many different policies have been adopted, including a strict national lockdown in some countries or milder government policies: one common aspect is that they mostly rely around keeping distance between individuals. The aim of this work is to provide means of visualizing the impact of social distancing in an immersive environment by making use of the virtual reality technology. To this aim, we create a virtual environment which resembles a university setting (we based it on the University of Derby), and populate it with a number of AI agents. We assume that the minimum social distance is 2 meters. The main contribution of this work is twofold: the multi-disciplinary approach that results from visualizing the social distancing in an effort to mitigate the spread of the COVID-19, and the digital twin application in which the users can navigate the virtual environment whilst receiving visual feedback in the proximity of other agents. We named our application SoDAlVR, which stands for Social Distancing Algorithm in Virtual Reality.
    • Impact of transmission power control in multi-hop networks

      Kotian, Roshan; Exarchakos, Georgios; Stavros, Stavrou; Liotta, Antonio (North-Holland, 2017)
    • Implementation of fuel cell and photovoltaic panels based DC micro grid prototype for electric vehicles charging station

      Benyahia, N.; Tamalouzt, S.; Denoun, H.; Badji, A.; Bousbaine, A.; Moualek, R.; Benamrouche, N.; Mouloud Mammeri University, Tizi-Ouzou, Algeria; Abderrahmane Mira University, Bejaia, Algeria; University of Derby (Springer Singapore, 2020-08-20)
      Today, electric vehicle (EV) appears as an evident solution for the future automotive market. The introduction of EV will lead to the reduction of greenhouse gas emissions and decrease the travelling cost. However, electric vehicle is truly an ecological solution only if the production of electricity necessary for its operation is produced from sustainable energy sources. In this paper, an Electric Vehicle Charging Station (EVCS) through sustainable energy sources via a DC micro-grid system has been proposed. The proposed system includes a fuel cell (FC), photovoltaic (PV) panels, storage battery and possibility of a connection to the grid. In this work a low power prototype of a micro-grid based EVCS has been first validated using a numerical simulation under Matlab/Simulink using variable irradiance and number of recharging vehicles. In the second part of this paper, an EVCS prototype has been realized in the laboratory. The tests are realized using an emulator of the PEM fuel cell with the concept of the hardware-in-the-loop (HIL). The objective of this emulation is to evaluate the performances of the whole system without the need for a real fuel cell. The whole system is implemented on the dSPACE 1103 platform and the results of the tests are discussed.
    • Improved aerosol optical depth and ångstrom exponent retrieval over land From MODIS based on the non-lambertian forward model

      Leiku, Yang; Xue, Yong; Guang, Jie; Hassan, Kazemian; Zhang, Jiahua; Li, Chi; Beijing Normal University; Institute of Remote Sensing and Digital Earth; London Metropolitan University (IEEE, 2014-02-28)
      In this letter, an improved algorithm for aerosol retrieval is presented by employing the non-Lambertian forward model (forward model) (NL_FM) in the Moderate Resolution Imaging Spectroradiometer (MODIS) dark target (DT) algorithm to reduce the uncertainties induced when using the Lambertian FM (L_FM). This new algorithm was applied to MODIS measurements of the whole year of 2008 over Eastern China. By comparing the results with that of AERONET, we found that the accuracy of the aerosol optical depth (AOD) retrieval was improved with the regression plots concentrating around the 1 : 1 line and two-thirds falling within the expected error (EE) envelope EE = ±0.05±0.1τ (from 53.6% with L_FM to 68.7% with NL_FM at band 0.55 μm). Surprisingly, more accurate retrieval of the AOD demonstrated significantly improved the Ångstrom exponent (AE) retrieval, which is related to particle size parameters. The regression plots tended to concentrate around the 1 : 1 line, and many more fell within the EE = ±0.4 from 53.6% with L_FM to 80.9% with NL_FM. These results demonstrate that including the NL_FM in the MODIS DT algorithm has the potential to significantly improve both AOD and AE retrievals with respect to AERONET in comparison to the L_FM used in the current MODIS operational retrievals.
    • Improved Kalman filter based differentially private streaming data release in cognitive computing.

      Wang, Jun; Luo, Jing; Liu, Xiaozhu; Li, Yongkai; Liu, Shubo; Zhu, Rongbo; Anjum, Ashiq; University of Derby; South-Central University for Nationalities; Wuhan University of Technology; et al. (Elsevier, 2019-04-04)
      Cognitive computing works well based on volumes of data, which offers the guarantee of unlocking novel insights and data-driven decisions. Steaming data is a major component of aggregated data, and sharing these real-time aggregated statistics has gained a lot of benefits in decision analysis, such as traffic heat map and disease outbreaks. However, original streaming data sharing will bring users the risk of privacy disclosure. In this paper, differential privacy technology is introduced into cognitive system, and an improved Kalman filter based differentially private streaming data release scheme is proposed for privacy requirement of cognitive computing system. The feasibility of the proposed scheme has been demonstrated through analysis of the utility of sanitized data from four real datasets, and the experimental results show that the proposed scheme outperforms the Kalman filter-based method at the same level of privacy preserving.
    • Improving RSS fingerprint-based localization using directional antennas

      Kanaris, Loizos; Kokkinis, Akis; Raspopoulos, Marios; Liotta, Antonio; Stavrou, Stavros (IEEE, 2014)
    • Inclusive quarkonium production at forward rapidity in pp collisions at √s=8 TeV

      Alexandre, Didier; Barnby, Lee; Evans, David; Graham, Katie; Jones, Peter; Jusko, Anton; Krivda, Marian; Lee, Graham; Lietava, Roman; Zardoshti, Nima; et al. (2016-04-05)
    • Increasing the impact of mathematics support on aiding student transition in higher education.

      Gallimore, M.; Stewart, Jill; University of Lincoln (Oxford University Press, 2014-04-10)
      The ever growing gap between secondary and university level mathematics is a major concern to higher education institutions. The increase in diversity of students’ background in mathematics, with entry qualifications ranging from the more traditional A-level programmes to BTEC or international qualifications is compounded where institutions attempt to widen participation. For example, work-based learners may have been out of education for prolonged periods and, consequently, are often unprepared for the marked shift in levels, and catering for all abilities is difficult in the normal lecture, tutorial format. Lack of sufficient mathematical knowledge not only affects students’ achievement on courses but also leads to disengagement and higher drop-out rates during the first 2 years of study. Many universities now offer a maths support service in an attempt to overcome these issues, but their success is varied. This article presents a novel approach to maths support designed and adopted by the University of Lincoln, School of Engineering, to bridge this transition gap for students, offer continued support through Assessment for Learning and Individual Learning Plans, and ultimately increase student achievement, engagement and retention. The article then extends this proven approach and discusses recently implemented enhancements through the use of online diagnostic testing and a ‘student expert’ system to harness mathematical knowledge held by those gifted and talented students (often overlooked by higher education institutions) and to promote peer-to-peer mentoring. The article shows that with the proven system in place, there is a marked increase in student retention compared with national benchmark data, and an increase in student engagement and achievement measured through student feedback and assessments. Although the online enhancements are in the early stages of implementation, it is expected, based on these results, that further improvements will be shown.
    • An inductive content-augmented network embedding model for edge artificial intelligence

      Yuan, Bo; Panneerselvam, John; Liu, Lu; Antonopoulos, Nick; Lu, Yao; University of Derby; Tongji University, Shanghai, China; University of Leicester; Edinburgh Napier University (IEEE, 2019-03-04)
      Real-time data processing applications demand dynamic resource provisioning and efficient service discovery, which is particularly challenging in resource-constraint edge computing environments. Network embedding techniques can potentially aid effective resource discovery services in edge environments, by achieving a proximity-preserving representation of the network resources. Most of the existing techniques of network embedding fail to capture accurate proximity information among the network nodes and further lack exploiting information beyond the second-order neighbourhood. This paper leverages artificial intelligence for network representation and proposes a deep learning model, named inductive content augmented network embedding (ICANE), which integrates the network structure and resource content attributes into a feature vector. Secondly, a hierarchical aggregation approach is introduced to explicitly learn the network representation through sampling the nodes and aggregating features from the higher-order neighbourhood. A semantic proximity search model is then designed to generate the top-k ranking of relevant nodes using the learned network representation. Experiments conducted on real-world datasets demonstrate the superiority of the proposed model over the existing popular methods in terms of resource discovery and the query resolving performance.
    • Inequality indexes as sparsity measures applied to ventricular ectopic beats detection and its efficient hardware implementation.

      Baali, Hamza; Zhai, Xiaojun; Djelouat, Hamza; Amira, Abbes; Bensaali, Faycal; Qatar University; University of Derby (IEEE, 2017-12-27)
      Meeting application requirements under a tight power budget is of a primary importance to enable connected health internet of things (IoT) applications. This paper considers using sparse representation and well-defined inequality indexes drawn from the theory of inequality to distinguish ventricular ectopic beats (VEBs) from non-VEBs. Our approach involves designing a separate dictionary for each arrhythmia class using a set of labelled training QRS complexes. Sparse representation, based on the designed dictionaries of each new test QRS complex is then calculated. Following this, its class is predicted using the winner-takes-all principle by selecting the class with the highest inequality index. Our experiments showed promising results ranging between 80% and 100% for the detection of VEBs considering the patient-specific approach, 80% using cross-validation and 70% on unseen data using independent sets for training and testing respectively. An efficient hardware implementation of the alternating direction method of multipliers (ADMM) algorithm is also presented. The results show that the proposed hardware implementation can classify a QRS complex in 69.3 ms that use only 0.934 W energy.
    • Inexpensive user tracking using boltzmann machines

      Mocanu, Elena; Mocanu, Decebal Constantin; Ammar, Haitham Bou; Zivkovic, Zoran; Liotta, Antonio; Smirnov, Evgueni (IEEE, 2014)
    • Infinitely many minimal classes of graphs of unbounded clique-width.

      Collins, Andrew; Foniok, Jan; Korpelainen, Nicholas; Lozin, Vadim; Zamaraev, Viktor; University of Warwick; Manchester Metropolitan University; University of Derby (Elsevier, 2017-03-23)
      The celebrated theorem of Robertson and Seymour states that in the family of minor-closed graph classes, there is a unique minimal class of graphs of unbounded tree-width, namely, the class of planar graphs. In the case of tree-width, the restriction to minor-closed classes is justified by the fact that the tree-width of a graph is never smaller than the tree-width of any of its minors. This, however, is not the case with respect to clique-width, as the clique-width of a graph can be (much) smaller than the clique-width of its minor. On the other hand, the clique-width of a graph is never smaller than the clique-width of any of its induced subgraphs, which allows us to be restricted to hereditary classes (that is, classes closed under taking induced subgraphs), when we study clique-width. Up to date, only finitely many minimal hereditary classes of graphs of unbounded clique-width have been discovered in the literature. In the present paper, we prove that the family of such classes is infinite. Moreover, we show that the same is true with respect to linear clique-width.
    • Influence discovery in semantic networks: An initial approach

      Trovati, Marcello; Bagdasar, Ovidiu; University of Derby (IEEE, 2014-03-26)
      Assessing the influence between concepts, which include people, physical objects, as well as theoretical ideas, plays a crucial role in understanding and discovering knowledge. Despite the huge amount of literature on knowledge discovery in semantic networks, there has been little attempt to fully classify and investigate the influence, which also includes causality, of a semantic entity on another one as dynamical entities. In this paper we will introduce an approach to discover and assess influence among nodes in a semantic network, with the aim to provide a tool to identify its type and direction. Even though this is still being developed, the preliminary evaluation shows promising and interesting results.
    • The influence of discrete arriving reflections on perceived intelligibility and speech transmission index measurements

      Hammond, Ross; Mapp, Peter; Hill, Adam J.; University of Derby; Peter Mapp Associates (Audio Engineering Society, 2016-09-20)
      The most widely used objective intelligibility measurement method, the Speech Transmission Index (STI), does not completely match the highly complex auditory perception and human hearing system. Investigations were made into the impact of discrete reflections (with varying arrival times and amplitudes) on STI scores, subjective intelligibility, and the subjective “annoyance factor.” This allows the effect of comb filtering on the modulation transfer function matrix to be displayed, as well as demonstrates how the perceptual effects of a discrete delay cause subjective “annoyance,” that is not necessarily mirrored by STI. This work provides evidence showing why STI should not be the sole verification method within public address and emergency announcement systems, where temporal properties also need thoughtful consideration.
    • The influence of discrete arriving reflections on perceived intelligibility and STI measurements

      Hammond, Ross; Mapp, Peter; Hill, Adam J.; University of Derby; Peter Mapp Associates (Audio Engineering Society, 2016-05-26)
      The most widely used objective intelligibility measurement method, the Speech Transmission Index (STI), does not completely match the highly complex auditory perception and human hearing system. Investigations were made into the impact of discrete reflections (with varying arrival times and amplitudes) on STI scores, subjective intelligibility, and the subjective annoyance factor.’ This allows the effect of comb filtering on the modulation transfer function matrix to be displayed, as well as demonstrates how the perceptual effects of a discrete delay cause subjective ‘annoyance,’ that is not necessarily mirrored by STI. This work provides evidence showing why STI should not be the sole verification method within public address and emergency announcement systems, where temporal properties also need thoughtful consideration.
    • Innovation in micro actuators and Big Data technology transform visually impaired daily life activities and Improve their access to information technology resources

      Shafik, Mahmoud; University of Derby (UNSYS Digital, 2014-12)
      It is indeed very alarming when we learn that every five seconds one person in the world goes blind. 285 million people are visually impaired worldwide. 39 million are blind and 246 have low vision, 90% of the world's visually impaired live in developing countries. This blind and visually impaired community Tactile and Braille is the most efficient possible way to read, write and interact with latest information technology resources. There are many outstanding efforts have been done on previous decades to improve this community quality of life. This paper presents the current state of the art of the current micro actuators technology and its latest development for visually impaired information technology access application. It is also presents innovative tactile graphical display using electro rheological fluid micro actuators for the visually impaired people information technology (IT) access application. The display consists of 124x4 dots. Each dot is a micro electro rheological fluid actuator. The micro-actuator is designed based on linear vertical movement principles. An advanced software tools and embedded system based on voltage matrix manipulation are developed, to provide the graphical display near real time control. The actuator design and development process and software control tools is presented in this paper. Prototype size 124x4 dots, on a matrix form, of 2.54mm pitch, was manufactured. The experimental tests carried out into the prototype showed a close agreement with the standard criteria of Tactile Braille applications. The stroke and dynamic time response test showed the practicability of the developed graphical tactile display, for visually impaired IT access applications.
    • InOt-RePCoN: Forecasting user behavioural trend in large-scale cloud environments.

      Panneerselvam, John; Liu, Lu; Antonopoulos, Nikolaos; University of Derby (Elsevier, 2017-06-01)
      Cloud Computing has emerged as a low cost anywhere anytime computing paradigm. Given the energy consumption characteristics of the Cloud resources, service providers are under immense pressure to reduce the energy implications of the datacentres. Forecasting the anticipated future workloads would help the service providers to achieve an optimum energy-efficient scaling of the datacentre resources in accordance with the incoming workloads. But the extreme dynamicity of both the users and their workloads impose several challenges in accurately predicting their future behavioural trend. This paper proposes a novel prediction model named InOt-RePCoN (Influential Outlier Restrained Prediction with Confidence Optimisation), aimed at a tri-fold forecast for predicting the expected number of job submissions, session duration for users, and also the job submission interval for the incoming workloads. Our proposed framework exploits autoregressive integrated moving average (ARIMA) technique integrated with a confidence optimiser for prediction and achieves reliable level of accuracy in predicting the user behaviours by the way of exploiting the inherent periodicity and predictability of every individual jobs of every single users. Performance evaluations conducted on a real-world Cloud trace logs reveal that the proposed prediction model outperforms the existing prediction models based on simple auto-regression, simple ARIMA and co-clustering time-series techniques in terms of the achieved prediction accuracy.
    • Inspiring undergraduates to high achievement in STEM (and other) subjects

      Self, Richard; University of Derby (2016-05)
      This Workshop was presented at the International Journal of Arts and Sciences Multi-disciplinary conference held in Toronto 31May to 3 June 2016 at Ryerson University.