• 3D sustainable renewable micro power station for smart grid industrial applications

      Komlanvi, Moglo; Shafik, Mahmoud; Ashu, Mfortaw Elvis; University of Derby (2015)
      The supply of clean energy and its security is becoming a global issue for all countries across the world, due to the limitations of fossil fuels resources usages for energy generations, the relative high dependency on imported fuels, their ever climbing prices and its environmental impacts. Power supply must increase as rapidly as demand to ensure sustained growth. This is the rationale upon which Governments, international organizations, researchers are accelerating investments in expanding the power system, its generation and transmission. This paper presents the preliminary research undertaken to design and develop a 3Dimentional (3D) sustainable renewable micro power station model for smart grid industrial applications. It introduces a solution to challenges in the energy generation sector which do not only refrain only to the safe supply of clean Energy. A major importance for the theoretical study of hybrid systems, based on renewable energy (photovoltaic, wind, hydro system) is the availability of the models that can be utilized to study the behavior of hybrid systems and most important, computer aided design simulation tools. As the available tools are quite limited, this paper would present the most current and up to date model which can be used for the simulation purposes of the 3D sustainable renewable micro power station for smart grid applications as well as for educational purposes.
    • 3ΛH and 3Λ̅H̅ production in Pb–Pb collisions at √sNN = 2.76 TeV

      Alexandre, Didier; Barnby, Lee; Bhasin, Anju; Bombara, Marek; Evans, David; Graham, Katie; Jones, Peter; Jusko, Anton; Krivda, Marian; Lee, Graham; et al. (2016-03-10)
    • Accuracy of no-reference quality metrics in network-impaired video streams

      Vega, Maria Torres; Sguazzo, Vittorio; Mocanu, Decebal Constantin; Liotta, Antonio (ACM, 2015)
    • Achieving dynamic load balancing through mobile agents in small world P2P networks

      Shen, Xiang-Jun; Liu, Lu; Zha, Zheng-Jun; Gu, Pei-Ying; Jiang, Zhong-Qiu; Chen, Ji-Ming; Panneerselvam, John; University of Derby (Elsevier, 2014-05-15)
      Peer-to-Peer (P2P) networks are a class of distributed networking and are being deployed in a wide range of applications. Besides such an importance, P2P networks still incur complexities in the resource location policies and in the load balancing techniques of the nodes, especially in unstructured P2P networks. One potential solution to resolve such issues is to enable the P2P networks to evolve into a self-optimizing overlay network topology by identifying the overloaded peers promptly. This paper introduces a new load balancing method in unstructured P2P networks based on mobile agents and resource grouping techniques. We firstly propose a resource grouping strategy to cluster the nodes which have same set of resources, thereby balancing the load among inter-group nodes. On the other hand, load balancing among intra-group nodes is achieved by using the mobile agents monitoring technique. By using this technique, the mobile agents migrate through the nodes in the same group, for the purpose of identifying the possible network congestion. Thus, queries can reach the desired resources more quickly while congested nodes can be identified promptly. The simulation results show that our proposed network evolves into a group-based small world network significantly. The evolved network exhibits robustness and adaptability under external attacking, high query workload, and higher network churns. The simulation results also illustrate that the proposed model achieves better search performance than the DANTE system.
    • Achieving green IT using VDI in cyber physical society

      Liu, Lu; DaSilva, Don-Anthony; Antonopoulos, Nikolaos; Ding, Zhijun; Zhan, Yongzhao; University of Derby; Tongji University; Jiangsu University (Taiwan Academic Network, 2013-05)
      With rapid advances in Internet technologies and increasing popularity of cyber social networks, the physical world and cyber world are gradually merging to form a new cyber-socio-physical society known as the Cyber Physical Society (CPS). In contrast to the previous research studies in cyber physical society, we are focusing on a different case of CPS-green IT in this paper. The complex cyber physical systems of cloud bring unprecedented challenges in power resource managements. This paper looks at the literature behind virtualization and mainly virtual desktop infrastructure as the solution to these challenges. In this paper, we investigate how to use cutting-edge virtualization technologies to reduce power consumption of IT infrastructure in Cyber Physical Society. This research and the implementation of a testing virtual desktop environment using VMware and Wyse technologies portray the clear improvements that hypervisor and desktop virtualization can be brought to IT infrastructures drawing particular attention to power consumption and the green incentives.
    • Adapting scientific workflow structures using multi-objective optimization strategies

      Habib, Irfan; Anjum, Ashiq; Mcclatchey, Richard; Rana, Omer; University of Derby, UK (Association for Computing Machinery, 2013-04-01)
      Scientific workflows have become the primary mechanism for conducting analyses on distributed computing infrastructures such as grids and clouds. In recent years, the focus of optimization within scientific workflows has primarily been on computational tasks and workflow makespan. However, as workflow-based analysis becomes ever more data intensive, data optimization is becoming a prime concern. Moreover, scientific workflows can scale along several dimensions: (i) number of computational tasks, (ii) heterogeneity of computational resources, and the (iii) size and type (static versus streamed) of data involved. Adapting workflow structure in response to these scalability challenges remains an important research objective. Understanding how a workflow graph can be restructured in an automated manner (through task merge, for instance), to address constraints of a particular execution environment is explored in this work, using a multi-objective evolutionary approach. Our approach attempts to adapt the workflow structure to achieve both compute and data optimization. The question of when to terminate the evolutionary search in order to conserve computations is tackled with a novel termination criterion. The results presented in this article demonstrate the feasibility of the termination criterion and demonstrate that significant optimization can be achieved with a multi-objective approach.
    • An adaptive multilevel indexing method for disaster service discovery

      Wu, Yan; Yan, Chun Gang; Liu, Lu; Ding, Zhijun; Jiang, Changjun; University of Derby (IEEE, 2014-12-05)
      With the globe facing various scales of natural disasters then and there, disaster recovery is one among the hottest research areas and the rescue and recovery services can be highly benefitted with the advancements of information and communications technology (ICT). Enhanced rescue effect can be achieved through the dynamic networking of people, systems and procedures. A seamless integration of these elements along with the service-oriented systems can satisfy the mission objectives with the maximum effect. In disaster management systems, services from multiple sources are usually integrated and composed into a usable format in order to effectively drive the decision-making process. Therefore, a novel service indexing method is required to effectively discover desirable services from the large-scale disaster service repositories, comprising a huge number of services. With this in mind, this paper presents a novel multilevel indexing algorithm based on the equivalence theory in order to achieve effective service discovery in large-scale disaster service repositories. The performance and efficiency of the proposed model have been evaluated by both theoretical analysis and practical experiments. The experimental results proved that the proposed algorithm is more efficient for service discovery and composition than existing inverted index methods.
    • An adaptive secure communication framework for mobile peer-to-peer environments using Bayesian games

      Li, Zhiyuan; Liu, Lu; Chen, Rulong; Bi, Jun-Lei; University of Derby (Springer, 2015-06-12)
      Peers in Mobile P2P (MP2P) networks exploit both the structured and unstructured styles to enable communication in a peer-to-peer fashion. Such networks involve the participation of two types of peers: benign peers and malicious peers. Complexities are witnessed in the determination of the identity of the peers because of the user mobility and the unrestricted switching (ON/OFF) of the mobile devices. MP2P networks require a scalable, distributed and light-weighted secure communication scheme. Nevertheless, existing communication approaches lack the capability to satisfy the requirements above. In this paper, we propose an Adaptive Trusted Request and Authorization model (ATRA) over MP2P networks, by exploiting the limited historical interaction information among the peers and a Bayesian game to ensure secure communication. The simulation results reveal that regardless of the peer’s ability to obtain the other such peer’s trust and risk data, the request peers always spontaneously connect the trusted resource peers and the resource peers always preferentially authorize the trusted request peers. Performance comparison of ATRA with state-of-the-art secure communication schemes over MP2P networks shows that ATRA can: (a) improve the success rate of node typing identification, (b) reduce time required for secure connections found, (c) provide efficient resource sharing, and (d) maintain the lower average cost.
    • Adaptive service discovery on service-oriented and spontaneous sensor systems

      Liu, Lu; Xu, Jie; Antonopoulos, Nikolaos; Li, Jianxin; Wu, Kaigu; University of Derby (Old City Publishing, 2012-03-06)
      Natural and man-made disasters can significantly impact both people and environments. Enhanced effect can be achieved through dynamic networking of people, systems and procedures and seamless integration of them to fulfil mission objectives with service-oriented sensor systems. However, the benefits of integration of services will not be realised unless we have a dependable method to discover all required services in dynamic environments. In this paper, we propose an Adaptive and Efficient Peer-to-peer Search (AEPS) approach for dependable service integration on service-oriented architecture based on a number of social behaviour patterns. In the AEPS network, the networked nodes can autonomously support and co-operate with each other in a peer-to-peer (P2P) manner to quickly discover and self-configure any services available on the disaster area and deliver a real-time capability by self-organising themselves in spontaneous groups to provide higher flexibility and adaptability for disaster monitoring and relief.
    • Addressing robustness in time-critical, distributed, task allocation algorithms.

      Whitbrook, Amanda; Meng, Qinggang; Chung, Paul W. H.; University of Derby; Loughborough University (Springer, 2018-04-18)
      The aim of this work is to produce and test a robustness module (ROB-M) that can be generally applied to distributed, multi-agent task allocation algorithms, as robust versions of these are scarce and not well-documented in the literature. ROB-M is developed using the Performance Impact (PI) algorithm, as this has previously shown good results in deterministic trials. Different candidate versions of the module are thus bolted on to the PI algorithm and tested using two different task allocation problems under simulated uncertain conditions, and results are compared with baseline PI. It is shown that the baseline does not handle uncertainty well; the task-allocation success rate tends to decrease linearly as degree of uncertainty increases. However, when PI is run with one of the candidate robustness modules, the failure rate becomes very low for both problems, even under high simulated uncertainty, and so its architecture is adopted for ROB-M and also applied to MIT’s baseline Consensus Based Bundle Algorithm (CBBA) to demonstrate its flexibility. Strong evidence is provided to show that ROB-M can work effectively with CBBA to improve performance under simulated uncertain conditions, as long as the deterministic versions of the problems can be solved with baseline CBBA. Furthermore, the use of ROB-M does not appear to increase mean task completion time in either algorithm, and only 100 Monte Carlo samples are required compared to 10,000 in MIT’s robust version of the CBBA algorithm. PI with ROB-M is also tested directly against MIT’s robust algorithm and demonstrates clear superiority in terms of mean numbers of solved tasks.
    • Allen’s interval algebra and smart-type environments

      Chuckravanen, Dineshen; Daykin, Jacqueline; Hunsdale, Karen; Seeam, Amar; Aberystwyth University (Mauritius Campus) (IARIA, 2017-03)
      Allen’s interval algebra is a calculus for temporal reasoning that was introduced in 1983. Reasoning with qualitative time in Allen’s full interval algebra is nondeterministic polynomial time (NP) complete. Research since 1995 identified maximal tractable subclasses of this algebra via exhaustive computer search and also other ad-hoc methods. In 2003, the full classification of complexity for satisfiability problems over constraints in Allen’s interval algebra was established algebraically. Recent research proposed scheduling based on the Fishburn-Shepp correlation inequality for posets. This article first reviews Allen’s calculus and surrounding computational issues in temporal reasoning. We then go on to describe three potential temporal-related application areas as candidates for scheduling using the Fishburn-Shepp inequality. We also illustrate through concrete examples, and conclude the importance of Fishburn-Shepp inequality for the suggested application areas that are the development of smart homes, intelligent conversational agents and in physiology with emphasis during time-trial physical exercise. The Fishburn-Shepp inequality will enable the development of smart type devices, which will in turn help us to have a better standard of living.
    • Alwyn Francis Horadam, 1923-2016: A personal tribute to the man and his sequence

      Larcombe, Peter J.; University of Derby (The Institute of Combinatorics and its Applications, 2016-09)
      Having received news of the passing of Alwyn Horadam this last July, I was determined that I should write something in his honour in which my own contact with him is described and combined with some introductory details on what I feel is his major endowment to the community of mathematicians - the so called and pre-eminent Horadam sequence whose specialisations thereof are great in number.
    • AmbiFreeVerb 2—Development of a 3D ambisonic reverb with spatial warping and variable scattering

      Dring, Mark; Wiggins, Bruce; University of Derby (Audio Engineering Society, 2016-07-14)
      In this paper the development of a three dimensional Ambisonic reverb based on the open source FreeVerb algorithm will be presented and discussed. This model is then extended to include processing in over-specified A-format, rather than B-format, variable scattering between channels along with controls for warping the distribution of the reflections to implement a reverb that is able to react to the source position in a spatially coherent way with an acoustical analysis of its performance.
    • An exploratory social network analysis of academic research networks

      Toral, Sergio L.; Bessis, Nik; Martinez-Torres, M. R.; Franc, Florian; Barrero, Federico; Xhafa, Fatos; University of Seville, Spain; University of Derby; University of Toulouse (INSA), France; Polytechnic University of Catalonia (UPC), Spain (IEEE, 2011-11)
      For several decades, academics around the world have been collaborating with the view to support the development of their research domain. Having said that, the majority of scientific and technological policies try to encourage the creation of strong inter-related research groups in order to improve the efficiency of research outcomes and subsequently research funding allocation. In this paper, we attempt to highlight and thus, to demonstrate how these collaborative networks are developing in practice. To achieve this, we have developed an automated tool for extracting data about joint article publications and analyzing them from the perspective of social network analysis. In this case study, we have limited data from works published in 2010 by England academic and research institutions. The outcomes of this work can help policy makers in realising the current status of research collaborative networks in England.
    • Analysis of binaural cue matching using ambisonics to binaural decoding techniques

      Wiggins, Bruce; University of Derby (2017)
      Last year Google enabled spatial audio in head-tracked 360 videos using Ambisonics to binaural decoding on Android mobile devices. There was some early criticism of the 1st order to binaural conversion employed by Google, in terms of the quality of localisation and noticeable frequency response colouration. In this paper, the algorithm used by Google is discussed and the Ambisonics to Binaural conversion using virtual speakers analysed with respect to the resulting inter-aural time, level, and spectrum differences compared to an example HRTF data set. 1st to 35th order Ambisonics using multiple virtual speaker arrays are implemented and analysed with inverse filtering techniques for smoothing the frequency spectrum also discussed demonstrating 8th order decoding correctly reproducing binaural cues up to 4 kHz.
    • Analysis of HEp-2 images using MD-LBP and MAD-bagging

      Schaefer, Gerald; Doshi, Niraj P.; Zhu, Shao Ying; Hu, Qinghua; Loughborough University; University of Derby (IEEE, 2014-08)
      Indirect immunofluorescence imaging is employed to identify antinuclear antibodies in HEp-2 cells which founds the basis for diagnosing autoimmune diseases and other important pathological conditions involving the immune system. Six categories of HEp-2 cells are generally considered, namely homogeneous, fine speckled, coarse speckled, nucleolar, cyto-plasmic, and centromere cells. Typically, this categorisation is performed manually by an expert and is hence both time consuming and subjective. In this paper, we present a method for automatically classifiying HEp-2 cells using texture information in conjunction with a suitable classification system. In particular, we extract multidimensional local binary pattern (MD-LBP) texture features to characterise the cell area. These then form the input for a classification stage, for which we employ a margin distribution based bagging pruning (MAD-Bagging) classifier ensemble. We evaluate our algorithm on the ICPR 2012 HEp-2 contest benchmark dataset, and demonstrate it to give excellent performance, superior to all algorithms that were entered in the competition.
    • Analytical tools for blockchain: review, taxonomy and open challenges.

      Balaskas, Anastasios; Franqueira, Virginia N. L.; University of Derby (IEEE Computer Society, 2018-12-06)
      Bitcoin has introduced a new concept that could feasibly revolutionise the entire Internet as it exists, and positively impact on many types of industries including, but not limited to, banking, public sector and supply chain. This innovation is grounded on pseudo-anonymity and strives on its innovative decentralised architecture based on the blockchain technology. Blockchain is pushing forward a race of transaction-based applications with trust establishment without the need for a centralised authority, promoting accountability and transparency within the business process. However, a blockchain ledger (e.g., Bitcoin) tend to become very complex and specialised tools, collectively called “Blockchain Analytics”, are required to allow individuals, law enforcement agencies and service providers to search, explore and visualise it. Over the last years, several analytical tools have been developed with capabilities that allow, e.g., to map relationships, examine flow of transactions and filter crime instances as a way to enhance forensic investigations. This paper discusses the current state of blockchain analytical tools and presents a thematic taxonomy model based on their applications. It also examines open challenges for future development and research.
    • Anisotropic Flow of Charged Particles in Pb-Pb Collisions at √sNN = 5.02 TeV

      Alexandre, Didier; Barnby, Lee; Evans, David; Graham, Katie; Jones, Peter; Jusko, Anton; Krivda, Marian; Lee, Graham; Lietava, Roman; Zardoshti, Nima; et al. (2016-04-01)
    • Application of Big Data for national security: A practitioner's guide to emerging technologies

      Akhgar, Babak; Saathoff, Gregory B.; Arabnia, Hamid R.; Hill, Richard; Staniforth, Andrew; Bayeri, Saskia; University of Derby (Butterworth-Heinemann, 2015-02-17)
      Application of Big Data for National Security provides users with state-of-the-art concepts, methods, and technologies for Big Data analytics in the fight against terrorism and crime, including a wide range of case studies and application scenarios. This book combines expertise from an international team of experts in law enforcement, national security, and law, as well as computer sciences, criminology, linguistics, and psychology, creating a unique cross-disciplinary collection of knowledge and insights into this increasingly global issue. The strategic frameworks and critical factors presented in Application of Big Data for National Security consider technical, legal, ethical, and societal impacts, but also practical considerations of Big Data system design and deployment, illustrating how data and security concerns intersect. In identifying current and future technical and operational challenges it supports law enforcement and government agencies in their operational, tactical and strategic decisions when employing Big Data for national security •Contextualizes the Big Data concept and how it relates to national security and crime detection and prevention •Presents strategic approaches for the design, adoption, and deployment of Big Data technologies in preventing terrorism and reducing crime •Includes a series of case studies and scenarios to demonstrate the application of Big Data in a national security context •Indicates future directions for Big Data as an enabler of advanced crime prevention and detection
    • Application of Two-Constant Feedback Quasi-Orthogonal Space Time Block Coding in MIMO Communication Systems

      Elazreg, Abdulghani; Elsabae, Ramadan; Shrud, Mohamed; Kharaz, Ahmad; University of Derby; Loughborough University; College of Electronic Technology, Tripoli, Libya (IEEE, 2018-12-03)