• Parallel Monte Carlo search for Hough Transform.

      Lopes, Raul; Franqueira, Virginia N. L.; Reid, Ivan D.; Hobson, Peter; Brunel University London; University of Derby (IOP Publishing Ltd, 2017-11)
      We investigate the problem of line detection in digital image processing and in special how state of the art algorithms behave in the presence of noise and whether CPU efficiency can be improved by the combination of a Monte Carlo Tree Search, hierarchical space decomposition, and parallel computing. The starting point of the investigation is the method introduced in 1962 by Paul Hough for detecting lines in binary images. Extended in the 1970s to the detection of space forms, what came to be known as Hough Transform (HT) has been proposed, for example, in the context of track fitting in the LHC ATLAS and CMS projects. The Hough Transform transfers the problem of line detection, for example, into one of optimization of the peak in a vote counting process for cells which contain the possible points of candidate lines. The detection algorithm can be computationally expensive both in the demands made upon the processor and on memory. Additionally, it can have a reduced effectiveness in detection in the presence of noise. Our first contribution consists in an evaluation of the use of a variation of the Radon Transform as a form of improving theeffectiveness of line detection in the presence of noise. Then, parallel algorithms for variations of the Hough Transform and the Radon Transform for line detection are introduced. An algorithm for Parallel Monte Carlo Search applied to line detection is also introduced. Their algorithmic complexities are discussed. Finally, implementations on multi-GPU and multicore architectures are discussed.
    • Particle identification in ALICE

      Alexandre, Didier; Barnby, Lee; Evans, David; Graham, Katie; Jones, Peter; Jusko, Anton; Krivda, Marian; Lee, Graham; Lietava, Roman; Villalobos, Orlando; et al. (2016-05-25)
    • Pattern recognition in narrative: Tracking emotional expression in context

      Ganz, Adam; Murtagh, Fionn; University of Derby (Episciences.org, 2015-05-26)
      Using geometric data analysis, our objective is the analysis of narrative, with narrative of emotion being the focus in this work. The following two principles for analysis of emotion inform our work. Firstly, emotion is revealed not as a quality in its own right but rather through interaction. We study the 2-way relationship of Ilsa and Rick in the movie Casablanca, and the 3-way relationship of Emma, Charles and Rodolphe in the novel {\em Madame Bovary}. Secondly, emotion, that is expression of states of mind of subjects, is formed and evolves within the narrative that expresses external events and (personal, social, physical) context. In addition to the analysis methodology with key aspects that are innovative, the input data used is crucial. We use, firstly, dialogue, and secondly, broad and general description that incorporates dialogue. In a follow-on study, we apply our unsupervised narrative mapping to data streams with very low emotional expression. We map the narrative of Twitter streams. Thus we demonstrate map analysis of general narratives.
    • Performance evaluation and simulation of peer-to-peer protocols for Massively Multiplayer Online Games

      Liu, Lu; Jones, Andrew; Antonopoulos, Nikolaos; Ding, Zhijun; Zhan, Yongzhao; University of Derby (Springer, 2013-09-22)
      Massively Multiplayer Online Games are networked games that allow a large number of people to play together. Classically MMOG worlds are hosted on many powerful servers and players that move around the world are passed from server to server as they pass through the environment. Running a large number of servers can be challenging and there are many considerations for a developer who wants to create a game to enter the MMOG market. If it is possible to use a P2P network to host an MMOG successfully, the costs of running a server farm are taken out of the equation. This will allow for groups with small budgets to enter the MMOG market and help competition in the market place. In this paper, the methods for the design of P2P massively multiplayer game protocols have been presented. Performance bottlenecks have been evaluated and highlighted by using simulations. The business viability has also been discussed in this paper.
    • Performance evaluation of discrete event process algorithms for a two-tier high-performance cloud computing network architecture

      Okafor, K.C.; Ugwoke, F.N.; Oparaku, O.U.; Diala, Uchenna; Federal University of Technology Owerri (2015-02)
    • Performance evaluation of machine learning techniques for fault diagnosis in vehicle fleet tracking modules

      Sepulevene, Luis; Drummond, Isabela; Kuehne, Bruno Tardiole; Frinhani, Rafael; Filho, Dionisio Leite; Peixoto, Maycon; Reiff-Marganiec, Stephan; Batista, Bruno; Federal University of Itajubá, Itajubá, Brazil; Federal University of Mato Grosso do Sul, Ponta Porã, Brazil; et al. (Oxford University Press, 2021-05-14)
      With industry 4.0, data-based approaches are in vogue. However, extracting the essential features is not a trivial task and greatly influences the fi nal result. There is also a need for specialized system knowledge to monitor the environment and diagnose faults. In this context, the diagnosis of faults is signi cant, for example, in a vehicle fleet monitoring system, since it is possible to diagnose faults even before the customer is aware of the fault, minimizing the maintenance costs of the modules. In this paper, several models using Machine Learning (ML) techniques were applied and analyzed during the fault diagnosis process in vehicle fleet tracking modules. Two approaches were proposed, "With Knowledge" and "Without Knowledge", to explore the dataset using ML techniques to generate classi fiers that can assist in the fault diagnosis process. The approach "With Knowledge" performs the feature extraction manually, using the ML techniques: Random Forest, Naive Bayes, Support Vector Machine (SVM) and Multi Layer Perceptron (MLP); on the other hand, the approach "Without Knowledge" performs an automatic feature extraction, through a Convolutional Neural Network (CNN). The results showed that the proposed approaches are promising. The best models with manual feature extraction obtained a precision of 99.76% and 99.68% for detection and detection and isolation of faults, respectively, in the provided dataset. The best models performing an automatic feature extraction obtained respectively 88.43% and 54.98% for detection and detection and isolation of failures.
    • Performance formula-based optimal deployments of multilevel indices for service retrieval.

      Wu, Yan; Xu, Wei; Liu, Lu; Miao, Dejun; Jiangsu University; University of Derby; School of Computer Science and Telecommunication Engineering; Jiangsu University; Zhenjiang Jiangsu 212000 China; Computer Science and Telecommunication Engineering, School of Computer Science and Telecommunication Engineering; Jiangsu University; Zhenjiang Jiangsu 212000 China; University of Derby; Kedleston Road Derby DE22 1GB UK; Department of Computing and Mathematics; University of Derby; Kedleston Road Derby DE22 1GB UK (Wiley, 2017-08-25)
      There are many different index structures for service repositories, such as sequential index, inverted index, and multilevel indices that include three deployments. Different service sets maybe have different characteristics that may affect performance from different aspects. For a given service set, which index structure is the most optimal one? To address these issues, this paper analyses five indexing models and proposes expectation of traversed service count to estimate performance of service retrieval. Based on these expectation formulas, an optimal deployment method can be identified to maximize efficiency of service retrieval. Our experiments first validate correctness of the proposed formulas and then validate the effective of the optimal method.
    • Performance simulation of a context provisioning middleware based on empirical measurements

      Reetz, Eike Steffen; Knappmeyer, Michael; Kiani, Saad Liaquat; Anjum, Ashiq; Bessis, Nik; Tönjes, Ralf; University of the West of England; University of Applied Sciences Osnabrück; University of Derby (2012)
      The evaluation of context middleware systems is a challenging endeavour. On the one hand, testbed investigations suffer from an unrealistic environment in terms of number of users, high implementation effort for changes and questionable portability of results. On the other hand simulation of middleware systems is complex due to the high abstraction of implementation. This article contributes to the understanding of a broker based context provisioning system based on black-box measurements of a testbed which are further utilised to increase the accuracy of a simulation model. Both simulations and measurements help in understanding the complex behaviour of a context provisioning middleware and enable the evaluation of complex distributed systems. The presented investigations identify significant parameters and corresponding models for the response delay of the key components of a context provisioning middleware and discuss their integration into an overall simulation model.
    • Performances analysis of a micro-grid connected multi-renewable energy sources system associated with hydrogen storage

      Tamalouzt, Salah; Benyahia, Nabil; Tounzi, Abdelmounaim; Bousbaine, Amar; University of Bejaia, Algeria; University M/Mammeri of Tizi-Ouzou, Algeria; University of Lille, France; University of Derby (IntechOpen, 2020-09-09)
      This work highlights the modelling and simulation of a micro-grid connected renewable energy system. It comprises of wind turbine (WT) based on doubly fed induction generator (DFIG), photovoltaic generator (PV), fuel cell (FC) generator, a Hydrogen tank, a water electrolyser used for long-term storage, and a battery bank energy storage system (BBESS) utilized for short-term storage. In this paper, a global control strategy and an energy management strategy are proposed for the overall system. This strategy consists in charging the BBESS and producing hydrogen from the water electrolyser in case of power excess provided from WT-DFIG and photovoltaic generators. Therefore, the FC and the BBESS will be used as a backup generator to supply the demand required power, when the WT-DFIGs and the PV energy are deficient. The effectiveness of this contribution is verified through computer simulations under Matlab/Simulink, where very satisfactory results are obtained.
    • Pervasive blood pressure monitoring using Photoplethysmogram (PPG) Sensor

      Riaz, Farhan; Azad, Muhammad; Arshad, Junaid; Imran, Muhammad; Hassan, Ali; Rehmad, Saad; Derby (Elsevier, 2019-03-08)
      Preventive healthcare requires continuous monitoring of the blood pressure (BP) of patients, which is not feasible using conventional methods. Photoplethysmogram (PPG) signals can be effectively used for this purpose as there is a physiological relation between the pulse width and BP and can be easily acquired using a wearable PPG sensor. However, developing real-time algorithms for wearable technology is a significant challenge due to various conflicting requirements such as high accuracy, computationally constrained devices, and limited power supply. In this paper, we propose a novel feature set for continuous, real-time identification of abnormal BP. This feature set is obtained by identifying the peaks and valleys in a PPG signal (using a peak detection algorithm), followed by the calculation of rising time, falling time and peak-to-peak distance. The histograms of these times are calculated to form a feature set that can be used for classification of PPG signals into one of the two classes: normal or abnormal BP. No public dataset is available for such study and therefore a prototype is developed to collect PPG signals alongside BP measurements. The proposed feature set shows very good performance with an overall accuracy of approximately 95\%. Although the proposed feature set is effective, the significance of individual features varies greatly (validated using significance testing) which led us to perform weighted voting of features for classification by performing autoregressive modeling. Our experiments show that the simplest linear classifiers produce very good results indicating the strength of the proposed feature set. The weighted voting improves the results significantly, producing an overall accuracy of about 98%. Conclusively, the PPG signals can be effectively used to identify BP, and the proposed feature set is efficient and computationally feasible for implementation on standalone devices.
    • Petri net-based methods for analyzing structural security in e-commerce business processes

      Ding, Zhijun; Liu, Lu; Wang, Xiaoming; Crossley, Richard David; Yu, Wangyang; University of Derby (Elsevier, 2018-05-30)
      The rapid development of e-commerce worldwide, means more e-commerce business processes adopting the structure of multiple participants; these include shopper clients, merchant and third-party payment platforms (TPPs), banks, and so on. It is a distributed and complex system, where communications among these participants rely on the web services and Application Programming Interfaces (APIs) such as Cashier-as-a-Service or CaaS. This introduces new security challenges due to complex interactions among multiple participants, and any design flaws in procedure structures may result in serious security issues. We study the structural security issues based on Petri nets, and a framework for analyzing structural security in e-commerce business process is proposed. Petri net-based modeling and analysis methods are also provided. Given the specifications of e-commerce business processes, the proposed methods can help designers analyze structural security issues of an e-commerce business process.
    • A Philosophical comparison of Chinese and European models of Computer Science education. (a discussion paper)

      Rosen, Clive C. H.; University of Derby (2015-04-29)
      This paper applies a model of the learning process to a case study of teaching a Masters’ level, industry oriented software development project management course to a mixed group of Chinese and European students. By comparing levels of engagement of each group, the paper postulates the influence of the two academic cultures in their ability to deliver students capable of critical evaluation in an industry oriented environment. The paper concludes that both cultures face obstacles in dealing with academic administrations that adhere to traditional values and have failed to fully adjust to a new, contemporary paradigm.
    • A physically based PM 2.5 estimation method using AERONET data in Beijing Area

      Chen, Guili; Guang, Jie; Xue, Yong; Li, Ying; Che, Yahui; Gong, Shaoqi; Univesity of Derby; Chinese Academy of Sciences; University of Information Science & Technology, Nanjing, China (IEEE, 2018-06)
    • Piezoelectric ultrasonic servo control feed drive renovate electro-discharge machining system industrial applications and transfer the technology into a new era

      Shafik, Mahmoud; University of Derby (UNSYS Digital, 2014-06)
      This paper presents the state of art of the latest development in Electro-Discharge Machining (EDM) system technology. It covers the current and recent development using the electromagentic and piezoelectric ultrasonic servo control feed drive technology. The paper also demonstrates how the ultrasonic technology renovates the system and transfers its industrial applications into a new era. EDM process is one of the most common processes in automotive and aerospace industry. It is mainly used to machine and process alloys and very hard materials for key manufacturing components, such as film cooling holes for Turbine Blades, engine strip sheets, steel sheets for automotive industry, outer vehicle body, … etc. The EDM system that uses electromagnetic servo control feed drive has a number of teething issues and this indicated the necessity to employ a new technology that could overcome these issues and enhance the system level of precision, dynamic time response, machining stability, arcing phenomena and product surface profile. The research undertaken to evaluate both systems showed that the system recently developed using ultrasonic servo control feed drive has a clear improvement in system dynamic time response, stability, a notable reduction in arcing and short-circuiting teething phenomena. This has been verified through the inter-electrode gap voltage variation. The electron microscopic examinations into the machined samples using the ultrasonic system have also indicated a clear improvement in the surface profile of the machined samples.
    • Pion–kaon femtoscopy and the lifetime of the hadronic phase in Pb−Pb collisions at √sNN = 2.76 TeV

      Acharya, S.; Adamová, D.; Adler, A.; Adolfsson, J.; Aggarwal, M.M.; Agha, S.; Aglieri Rinella, G.; Agnello, M.; Agrawal, N.; Ahammed, Z.; et al. (Elsevier BV, 2020-12-17)
      In this paper, the first femtoscopic analysis of pion–kaon correlations at the LHC is reported. The analysis was performed on the Pb–Pb collision data at √sNN = 2.76 TeV recorded with the ALICE detector. The non-identical particle correlations probe the spatio-temporal separation between sources of different particle species as well as the average source size of the emitting system. The sizes of the pion and kaon sources increase with centrality, and pions are emitted closer to the centre of the system and/or later than kaons. This is naturally expected in a system with strong radial flow and is qualitatively reproduced by hydrodynamic models. ALICE data on pion–kaon emission asymmetry are consistent with (3+1)-dimensional viscous hydrodynamics coupled to a statistical hadronisation model, resonance propagation, and decay code THERMINATOR 2 calculation, with an additional time delay between 1 and 2 fm/c for kaons. The delay can be interpreted as evidence for a significant hadronic rescattering phase in heavy-ion collisions at the LHC.
    • Planning in the Cloud: Massively Parallel Planning

      Voorhis, Dave; Thompson, Tommy; University of Derby (IEEE Computer Society, 2014-12)
    • plexi: Adaptive re-scheduling web-service of time synchronized low-power wireless networks

      Exarchakos, Georgios; Oztelcan, Ilker; Sarakiotis, Dimitris; Liotta, Antonio (Elsevier, 2017)
    • Polarimetric SAR image semantic segmentation with 3D discrete wavelet transform and Markov random field

      Bi, Haixia; Xu, Lin; Cao, Xiangyong; Xue, Yong; Xu, Zongben; University of Derby; University of Bristol; Shanghai Em-Data Technology Co., Ltd.; Xi’an Jiaotong University, Xi’an, China; University of Derby (IEEE, 2020-06-02)
      Polarimetric synthetic aperture radar (PolSAR) image segmentation is currently of great importance in image processing for remote sensing applications. However, it is a challenging task due to two main reasons. Firstly, the label information is difficult to acquire due to high annotation costs. Secondly, the speckle effect embedded in the PolSAR imaging process remarkably degrades the segmentation performance. To address these two issues, we present a contextual PolSAR image semantic segmentation method in this paper.With a newly defined channelwise consistent feature set as input, the three-dimensional discrete wavelet transform (3D-DWT) technique is employed to extract discriminative multi-scale features that are robust to speckle noise. Then Markov random field (MRF) is further applied to enforce label smoothness spatially during segmentation. By simultaneously utilizing 3D-DWT features and MRF priors for the first time, contextual information is fully integrated during the segmentation to ensure accurate and smooth segmentation. To demonstrate the effectiveness of the proposed method, we conduct extensive experiments on three real benchmark PolSAR image data sets. Experimental results indicate that the proposed method achieves promising segmentation accuracy and preferable spatial consistency using a minimal number of labeled pixels.
    • Policing as a Service in the Cloud

      Zargari, Shahrzad A.; Smith, Anthony; University of Derby (2013-09)
      Security and Privacy are fundamental concerns in cloud computing both in terms of legal complications and user trust. Cloud computing is a new computing paradigm, aiming to provide reliable, customized, and guaranteed computing dynamic environment for end-users. However, the existing security and privacy issues in the cloud still present a strong barrier for users to adopt cloud computing solutions. This paper investigates the security and privacy challenges in cloud computing in order to explore methods that improve the users' trust in the adaptation of the cloud. Policing As A Service can be offered by the cloud providers with the intention of empowering the users to monitor and guard their assets in the cloud. This service is beneficial both to the cloud providers and the users. However, at first, the cloud providers may only be able to offer basic auditing services due to undeveloped tools and applications. Similar to other services delivered in the cloud, this service can be purchased by the users to gain some control over their data. The sub services of the proposed service can be Privacy As A Service and Forensics As A Service. These services give the cloud users a sense of transparency and having control over their data in the cloud while better security and privacy safeguards are sought.
    • Policing as a Service in the Cloud (Extended)

      Zargari, Shahrzad A.; Smith, Anthony; University of Derby; Sheffiels Hallam University (2014-10-22)
      Security and privacy are fundamental concerns in cloud computing both in terms of legal complications and user trust. Cloud computing is a new computing paradigm, aiming to provide reliable, customized, and guaranteed computing dynamic environment for end users. However, the existing security and privacy issues in the cloud still present a strong barrier for users to adopt cloud computing solutions. This paper investigates the security and privacy challenges in cloud computing in order to explore methods that improve the users’ trust in the adaptation of the cloud. Policing as a Service can be offered by the cloud providers with the intention of empowering users to monitor and guard their assets in the cloud. This service is beneficial both to the cloud providers and the users. However, at first, the cloud providers may only be able to offer basic auditing services due to undeveloped tools and applications. Similar to other services delivered in the cloud, users can purchase this service to gain some control over their data. The subservices of the proposed service can be Privacy as a Service and Forensics as a Service. These services give users a sense of transparency and control over their data in the cloud while better security and privacy safeguards are sought.