• 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.
    • 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.
    • Acquiring Guideline-enabled data driven clinical knowledge model using formally verified refined knowledge acquisition method

      Afzal, Muhammad; Malik, Khalid M.; Ali, Taqdir; Ali Khan, Wajahat; Irfan, Muhammad; Jamshrf, Arif; Lee, Sungyoung; Hussain, Maqbool; Sejong University, Seoul, South Korea; Oakland University, Rochester, MI, USA; et al. (Elsevier, 2020-08-19)
      Background and Objective: Validation and verification are the critical requirements for the knowledge acquisition method of the clinical decision support system (CDSS). After acquiring the medical knowledge from diverse sources, the rigorous validation and formal verification process are required before creating the final knowledge model. Previously, we have proposed a hybrid knowledge acquisition method with the support of a rigorous validation process for acquiring medical knowledge from clinical practice guidelines (CPGs) and patient data for the treatment of oral cavity cancer. However, due to lack of formal verification process, it involves various inconsistencies in knowledge relevant to the formalism of knowledge, conformance to CPGs, quality of knowledge, and complexities of knowledge acquisition artifacts.Methods: This paper presents the refined knowledge acquisition (ReKA) method, which uses the Z formal verification process. The ReKA method adopts the verification method and explores the mechanism of theorem proving using the Z notation. It enhances a hybrid knowledge acquisition method to thwart the inconsistencies using formal verification.Results: ReKA adds a set of nine additional criteria to be used to have a final valid refined clinical knowledge model. These criteria ensure the validity of the final knowledge model concerning formalism of knowledge, conformance to GPGs, quality of the knowledge, usage of stringent conditions and treatment plans, and inconsistencies possibly resulting from the complexities. Evaluation, using four medical knowledge acquisition scenarios, shows that newly added knowledge in CDSS due to the additional criteria by the ReKA method always produces a valid knowledge model. The final knowledge model was also evaluated with 1229 oral cavity patient cases, which outperformed with an accuracy of 72.57% compared to a similar approach with an accuracy of 69.7%. Furthermore, the ReKA method identified a set of decision paths (about 47.8%) in the existing approach, which results in a final knowledge model with low quality, non-conformed from standard CPGs.Conclusion: ReKA refined the hybrid knowledge acquisition method by discovering the missing steps in the current validation process at the acquisition stage. As a formally proven method, it always yields a valid knowledge model having high quality, supporting local practices, and influenced by standard CPGs. Furthermore, the final knowledge model obtained from ReKA also preserves the performance such as the accuracy of the individual source knowledge models.
    • An active deep learning approach for minimally supervised polsar image classification

      Xue, Yong; University of Derby; Fudan University, Shanghai, China; X'ian Electronics and Engineering Institute, China (IEEE, 2019-08-01)
      Recently, deep neural networks have received intense interests in polarimetric synthetic aperture radar (PolSAR) image classification. However, its success is subject to the availability of large amounts of annotated data which require great efforts of experienced human annotators. Aiming at improving the classification performance with greatly reduced annotation cost, this paper presents an active deep learning approach for minimally supervised PolSAR image classification, which integrates active learning and fine-tuned convolutional neural network (CNN) into a principled framework. Starting from a CNN trained using a very limited number of labeled pixels, we iteratively and actively select the most informative candidates for annotation, and incrementally fine-tune the CNN by incorporating the newly annotated pixels. Moreover, to boost the performance and robustness of the proposed method, we employ Markov random field (MRF) to enforce class label smoothness, and data augmentation technique to enlarge the training set. We conducted extensive experiments on four real benchmark PolSAR images, and experiments demonstrated that our approach achieved state-of-the-art classification results with significantly reduced annotation cost.
    • 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.
    • 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

      Wiggins, Bruce; Dring, Mark; 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 (2017-09)
      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.
    • 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)
    • Applications of dynamic diffuse signal processing in sound reinforcement and reproduction.

      Moore, Jonathan B.; Hill, Adam J.; University of Derby (Institute of Acoustics, 2017-11-21)
      Electroacoustic systems are subject to position-dependent frequency responses due to coherent interference between multiple sources and/or early reflections. Diffuse signal processing (DiSP) provides a mechanism for signal decorrelation to potentially alleviate this well-known issue in sound reinforcement and reproduction applications. Previous testing has indicated that DiSP provides reduced low-frequency spatial variance across wide audience areas, but in closed acoustic spaces is less effective due to coherent early reflections. In this paper, dynamic implementation of DiSP is examined, whereby the decorrelation algorithm varies over time, thus allowing for decorrelation between surface reflections and direct sounds. Potential applications of dynamic DiSP are explored in the context of sound reinforcement (subwoofers, stage monitoring) and sound reproduction (small-room low-frequency control, loudspeaker crossovers), with preliminary experimental results presented.
    • An approach to optimise resource provision with energy-awareness in datacentres by combating task heterogeneity.

      Panneerselvam, John; Liu, Lu; Antonopoulos, Nikolaos; University of Derby (IEEE, 2018-01-16)
      Cloud workloads are increasingly heterogeneous such that a single Cloud job may encompass one to several tasks, and tasks belonging to the same job may behave distinctively during their actual execution. This inherent task heterogeneity imposes increased complexities in achieving an energy efficient management of the Cloud jobs. The phenomenon of a few proportions of tasks characterising increased resource intensity within a given job usually lead the providers to over-provision all the encompassed tasks, resulting in majority of the tasks incurring an increased proportions of resource idleness. To this end, this paper proposes a novel analytics framework which integrates a resource estimation module to estimate the resource requirements of tasks a priori, a straggler classification module to classify tasks based on their resource intensity, and a resource optimisation module to optimise the level of resource provision depending on the task nature and various runtime factors. Performance evaluations conducted both theoretically and through practical experiments prove that the proposed methodology performs better than the compared statistical resource estimation methods and existing models of straggler mitigation, and further demonstrate the effectiveness of the proposed methodology in achieving energy conservation by postulating appropriate level of resource provisioning for task execution.
    • Approaching the Internet of things (IoT): a modelling, analysis and abstraction framework

      Ikram, Ahsan; Anjum, Ashiq; Hill, Richard; Antonopoulos, Nikolaos; Liu, Lu; Sotiriadis, Stelios; University of Derby (Wiley, 2013)