• Handbook of cluster analysis

      Hennig, Christian; Meila, Marina; Murtagh, Fionn; Rocci, Roberto; University College London; University of Washington; University of Derby; University of Rome Tor Vergata (CRC Press, 2015-12-01)
      Handbook of Cluster Analysis provides a comprehensive and unified account of the main research developments in cluster analysis. Written by active, distinguished researchers in this area, the book helps readers make informed choices of the most suitable clustering approach for their problem and make better use of existing cluster analysis tools. The book is organized according to the traditional core approaches to cluster analysis, from the origins to recent developments. After an overview of approaches and a quick journey through the history of cluster analysis, the book focuses on the four major approaches to cluster analysis. These approaches include methods for optimizing an objective function that describes how well data is grouped around centroids, dissimilarity-based methods, mixture models and partitioning models, and clustering methods inspired by nonparametric density estimation. The book also describes additional approaches to cluster analysis, including constrained and semi-supervised clustering, and explores other relevant issues, such as evaluating the quality of a cluster. This handbook is accessible to readers from various disciplines, reflecting the interdisciplinary nature of cluster analysis. For those already experienced with cluster analysis, the book offers a broad and structured overview. For newcomers to the field, it presents an introduction to key issues. For researchers who are temporarily or marginally involved with cluster analysis problems, the book gives enough algorithmic and practical details to facilitate working knowledge of specific clustering areas.
    • Hardware acceleration of an image processing system for dielectrophoretic loading of single neurons inside micro-wells of microelectrode arrays

      Zhai, Xiaojun; Jaber, Fadi; Bensaali, Faycal; Mishra, Arti; University of Derby (IEEE, 2015-03-25)
      This paper describes an image processing algorithm and its efficient architecture. The proposed architecture is used to process images of microelectrode arrays (MEAs) and micro-wells captured by a microscope camera in a dielectrophoresis (DEP)-based system which consists as well of digital switches for turning the DEP force 'on' or 'off'. The images are processed in order to determine if a neuron has entered any of the micro-wells in which case the corresponding switch turns 'off' the DEP force. This process must be in real-time to avoid more than one cell to be loaded in a micro-well. The proposed architecture has been successfully implemented and tested on a Zynq SoC. Results achieved have shown that the system can process one image in 9 ms which meets the minimum real-time requirements of this DEP system.
    • HD number plate localization and character segmentation on the Zynq heterogeneous SoC.

      Al-Zawqari, Ali; Hommos, Omar; Al-Qahtani, Abdulhadi; Farhat, Ali; Bensaali, Faycal; Zhai, Xiaojun; Amira, Abbes; Qatar University; University of Derby (Springer, 2018-02-06)
      Automatic number plate recognition (ANPR) systems have become widely used in safety, security, and commercial aspects. A typical ANPR system consists of three main stages: number plate localization (NPL), character segmentation (CS), and optical character recognition (OCR). In recent years, to provide a better recognition rate, high-definition (HD) cameras have started to be used. However, most known techniques for standard definition (SD) are not suitable for real-time HD image processing due to the computationally intensive cost of processing several-folds more of image pixels, particularly in the NPL stage. In this paper, algorithms suitable for hardware implementation for NPL and CS stages of an HD ANPR system are presented. Software implementation of the algorithms was carried on as a proof of concept, followed by hardware implementation on a heterogeneous system-on-chip (SoC) device that contains an ARM processor and a field-programmable gate array (FPGA). Heterogeneous implementation of these stages has shown that this HD NPL algorithm can localize a number plate in 16.17 ms, with a success rate of 98.0%. The CS algorithm can then segment the detected plate in 0.59 ms, with a success rate of 99.05%. Both stages utilize only 21% of the available on-chip configurable logic blocks.
    • HD Qatari ANPR system

      Hommos, Omar; Al-Qahtani, Abdulhadi; Al-Zawqari, Ali; Bensaali, Faycal; Amira, Abbes; Zhai, Xiaojun; University of Derby (IEEE, 2016-03-13)
    • HD-Video Streaming over an Inexpensive In-Building Radio-over-MMF System

      Torres Vega, M.; Zou, S.; Tangdiongga, E.; Koonen, A. M. J.; Liotta, Antonio (IEEE, 2014)
    • Head tracked audio for all - the 3D audio VR revolution

      Wiggins, Bruce; University of Derby (Institute of Acoustics, 2016-11-16)
      Virtual Reality is this year’s buzz-word and must have technology which, through the push of maximum immersion, has given a new focus and interest in true 3D audio. To this end, YouTube has kick-started the resurgence of Ambisonics via head-tracked binaural decoding on the mobile phone powered Google Cardboard platform which has been closely followed by Facebook (Two Big Ears, now Facebook Spatial Audio Workstation) and the other major VR players. In this talk, the technologies involved in 3D audio production for VR will be discussed along with the compromises and issues with current systems that has led to some early criticism of YouTube’s implementation. Google Cardboard headsets will available for demonstrations.
    • Heterogeneous implementation of ECG encryption and identification on the Zynq SoC

      Ait Si Ali, Amine; Zhai, Xiaojun; Amira, Abbes; Bensaali, Faycal; Ramzan, Naeem; University of Derby (IEEE, 2016-05-01)
      This paper presents an innovative and safe connected health solution for human identification. The system consists of the encryption and decryption of ECG signals using the advanced encryption standard (AES) as well as the recognition of individuals based on ECG biometrics. Heterogeneous and efficient implementation of the proposed system has been performed on a Xilinx ZC702 Zynq based prototyping board. Various IP-cores have been created based on the high level synthesis (HLS) implementation of the AES cipher, AES decipher and ECG identification blocks. The proposed hardware implementation has shown promising results since it met the real-time requirements and outclassed current field programmable gate array (FPGA) based systems in multiple key metrics including power consumption, processing time and hardware resources usage. The implemented system needs 10.71 ms to process one ECG sample and consumes 107mW while using only 30% of all available on-chip resources.
    • Hierarchical cluster analysis in clinical research with heterogeneous study population: highlighting its visualization with R

      Zhang, Zhongheng; Murtagh, Fionn; Van Poucke, Sven; Lin, Su; Lan, Peng; Zhejiang University; University of Derby; Ziekenhuis Oost-Limburg; Fujian Medical University (AME Publishing Company, 2017-02)
      Big data clinical research typically involves thousands of patients and there are numerous variables available. Conventionally, these variables can be handled by multivariable regression modeling. In this article, the hierarchical cluster analysis (HCA) is introduced. This method is used to explore similarity between observations and/or clusters. The result can be visualized using heat maps and dendrograms. Sometimes, it would be interesting to add scatter plot and smooth lines into the panels of the heat map. The inherent R heatmap package does not provide this function. A series of scatter plots can be created using lattice package, and then background color of each panel is mapped to the regression coefficient by using custom-made panel functions. This is the unique feature of the lattice package. Dendrograms and color keys can be added as the legend elements of the lattice system. The latticeExtra package provides some useful functions for the work.
    • High performance video processing in cloud data centres

      Yaseen, Muhammad Usman; Zafar, Muhammad Sarim; Anjum, Ashiq; Hill, Richard; University of Derby (IEEE, 2016-03)
      Mobile phones and affordable cameras are generating large amounts of video data. This data holds information regarding several activities and incidents. Video analytics systems have been introduced to extract valuable information from this data. However, most of these systems are expensive, require human supervision and are time consuming. The probability of extracting inaccurate information is also high due to human involvement. We have addressed these challenges by proposing a cloud based high performance video analytics platform. This platform attempts to minimize human intervention, reduce computation time and enables the processing of a large number of video streams. It achieves high performance by optimizing the occupancy of GPU resources in cloud and minimizing the data transfer by concurrently processing a large number of video streams. The proposed video processing platform is evaluated in three stages. The first evaluation was performed at the cloud level in order to evaluate the scalability of the platform. This evaluation includes fetching and distributing video streams and efficiently utilizing available resources within the cloud. The second valuation was performed at the individual cloud nodes. This evaluation includes measuring the occupancy level, effect of data transfer and the extent of concurrency achieved at each node. The third evaluation was performed at the frame level in order to determine the performance of object recognition algorithms. To measure this, compute intensive tasks of the Local Binary Pattern (LBP) algorithm have been ported on to the GPU resources. The platform proved to be very scalable with high throughput and performance when tested on a large number of video streams with increasing number of nodes.
    • High-performance time-series quantitative retrieval from satellite images on a GPU cluster

      Xue, Yong; Liu, Jia; Ren, Kaijun; Song, Junqiang; Windmill, Christopher; Merritt, Patrick; University of Derby (IEEE, 2019-07-12)
      The quality and accuracy of remote sensing instruments continue to increase, allowing geoscientists to perform various quantitative retrieval applications to observe the geophysical variables of land, atmosphere, ocean, etc. The explosive growth of time-series remote sensing (RS) data over large-scales poses great challenges on managing, processing, and interpreting RS ‘‘Big Data.’’ To explore these time-series RS data efficiently, in this paper, we design and implement a high-performance framework to address the time-consuming time-series quantitative retrieval issue on a graphics processing unit cluster, taking the aerosol optical depth (AOD) retrieval from satellite images as a study case. The presented framework exploits the multilevel parallelism for time-series quantitative RS retrieval to promote efficiency. At the coarse-grained level of parallelism, the AOD time-series retrieval is represented as multidirected acyclic graph workflows and scheduled based on a list-based heuristic algorithm, heterogeneous earliest finish time, taking the idle slot and priorities of retrieval jobs into account. At the fine-grained level, the parallel strategies for the major remote sensing image processing algorithms divided into three categories, i.e., the point or pixel-based operations, the local operations, and the global or irregular operations have been summarized. The parallel framework was implemented with message passing interface and compute unified device architecture, and experimental results with the AOD retrieval case verify the effectiveness of the presented framework.
    • High-throughput geocomputational workflows in a grid environment

      Liu, Jia; Xue, Yong; Palmer-Brown, Dominic; Chen, Ziqiang; He, Xingwei; Chinese Academy of Sciences; London Metropolitan University (IEEE, 2015-11-13)
      A grid-computing platform facilitates geocomputational workflow composition to process big geosciences data while fully using idle resources to accelerate processing speed. An experiment with aerosol optical depth retrieval from satellite data shows a 25 percent improvement in runtime over a single high-performance computer.
    • Higher harmonic flow coefficients of identified hadrons in Pb-Pb collisions at √sNN = 2.76 TeV

      ALICE Collaboration; Barnby, Lee; University of Birmingham, United Kingdom; European Organization for Nuclear Research (CERN) (SpringerSCOAP3, 2016-09-28)
      The elliptic, triangular, quadrangular and pentagonal anisotropic flow coefficients for π±, K± and p+p̅ in Pb-Pb collisions at √sNN=2.76 TeV were measured with the ALICE detector at the Large Hadron Collider. The results were obtained with the Scalar Product method, correlating the identified hadrons with reference particles from a different pseudorapidity region. Effects not related to the common event symmetry planes (non-flow) were estimated using correlations in pp collisions and were subtracted from the measurement. The obtained flow coefficients exhibit a clear mass ordering for transverse momentum (pT) values below ≈ 3 GeV/c. In the intermediate pT region (3 < pT< 6 GeV/c), particles group at an approximate level according to the number of constituent quarks, suggesting that coalescence might be the relevant particle production mechanism in this region. The results for pT< 3 GeV/c are described fairly well by a hydrodynamical model (iEBE-VISHNU) that uses initial conditions generated by A Multi-Phase Transport model (AMPT) and describes the expansion of the fireball using a value of 0.08 for the ratio of shear viscosity to entropy density (η/s), coupled to a hadronic cascade model (UrQMD). Finally, expectations from AMPT alone fail to quantitatively describe the measurements for all harmonics throughout the measured transverse momentum region. However, the comparison to the AMPT model highlights the importance of the late hadronic rescattering stage to the development of the observed mass ordering at low values of pT and of coalescence as a particle production mechanism for the particle type grouping at intermediate values of pT for all harmonics.
    • HiTrust: building cross-organizational trust relationship based on a hybrid negotiation tree

      Li, Jianxin; Liu, Xudong; Liu, Lu; Sun, Dazhi; Li, Bo; Beihang University; University of Derby (2011-10-20)
      Small-world phenomena have been observed in existing peer-to-peer (P2P) networks which has proved useful in the design of P2P file-sharing systems. Most studies of constructing small world behaviours on P2P are based on the concept of clustering peer nodes into groups, communities, or clusters. However, managing additional multilayer topology increases maintenance overhead, especially in highly dynamic environments. In this paper, we present Social-like P2P systems (Social-P2Ps) for object discovery by self-managing P2P topology with human tactics in social networks. In Social-P2Ps, queries are routed intelligently even with limited cached knowledge and node connections. Unlike community-based P2P file-sharing systems, we do not intend to create and maintain peer groups or communities consciously. In contrast, each node connects to other peer nodes with the same interests spontaneously by the result of daily searches.
    • Horadam sequences: A survey update and extension.

      Larcombe, Peter J.; University of Derby (The Institute of Combinatorics and its Applications (ICA), 2017)
      We give an update on work relating to Horadam sequences that are generated by a general linear recurrence formula of order two. This article extends a first ever survey published in early 2013 in this Bulletin, and includes coverage of a new research area opened up in recent times.
    • A Horadam-based pseudo-random number generator

      Bagdasar, Ovidiu; Chen, Minsi; University of Derby (IEEE, 2014-03-26)
      Uniformly distributed pseudo-random number generators are commonly used in certain numerical algorithms and simulations. In this article a random number generation algorithm based on the geometric properties of complex Horadam sequences was investigated. For certain parameters, the sequence exhibited uniformity in the distribution of arguments. This feature was exploited to design a pseudo-random number generator which was evaluated using Monte Carlo π estimations, and found to perform comparatively with commonly used generators like Multiplicative Lagged Fibonacci and the 'twister' Mersenne.
    • 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.