• The transparency of binaural auralisation using very high order circular harmonics

      Dring, Mark; Wiggins, Bruce; University of Derby (Institute of Acoustics, 2019-11)
      Ambisonics to binaural rendering has become the de facto format for processing and reproducing spatial sound scenes, but direct capture and software generated output is limited to low orders; limiting the accuracy of psycho-acoustic cues and therefore the illusion of a ‘real-world’ experience. Applying a practical method through the use of acoustic modelling software, this study examines the potential of using very high horizontal only Ambisonic orders (up to 31st) to binaural rendering. A novel approach to the scene capturing process is implemented to realise these very high orders for a reverberant space with head-tracking capabilities. A headphone based subjective test is conducted, evaluating specific attributes of a presented auditory scene to determine when a limit to the perceived auditory differences of varying orders has been reached.
    • A case study on sound level monitoring and management at large-scale music festivals

      Hill, Adam J.; Kok, Marcel; Mulder, Johannes; Burton, Jon; Kociper, Alex; Berrios, Anthony; University of Derby; Murdoch University; dBcontrol; Gand Concert Sound (Institute of Acoustics, 2019-11)
      Sound level management at live events has been made immeasurably easier over the past decade or so through use of commercially-available sound level monitoring software. This paper details a study conducted at a large-scale multi-day music festival in Chicago, USA. The focus was twofold: first to explore how the use of noise monitoring software affects the mix level from sound engineers and second on how crowd size, density and distribution affect the mix level. Additionally, sound levels at various points in the audience were monitored to indicate audience sound exposure over the duration of the festival. Results are presented in relation to those from previous studies with key findings pointing towards recommendations for best practice.
    • Remarks on a family of complex polynomials

      Andrica, Dorin; Bagdasar, Ovidiu; University of Derby (University of Belgrade, 2019-10-30)
      Integral formulae for the coefficients of cyclotomic and polygonal polynomials were recently obtained in [2] and [3]. In this paper, we define and study a family of polynomials depending on an integer sequence m1, . . . , mn, . . . , and on a sequence of complex numbers z1, . . . , zn, . . . of modulus one. We investigate some particular instances such as: extended cyclotomic, extended polygonal-type, and multinomial polynomials, for which we obtain formulae for the coefficients. Some novel related integer sequences are also derived.
    • Privacy verification of photoDNA based on machine learning

      Nadeem, Muhammad Shahroz; Franqueira, Virginia N. L.; Zhai, Xiaojun; University of Derby, College of Engineering and Technology; University of Essex, School of Computer Science and Electronic Engineering (The Institution of Engineering and Technology (IET), 2019-10-09)
      PhotoDNA is a perceptual fuzzy hash technology designed and developed by Microsoft. It is deployed by all major big data service providers to detect Indecent Images of Children (IIOC). Protecting the privacy of individuals is of paramount importance in such images. Microsoft claims that a PhotoDNA hash cannot be reverse engineered into the original image; therefore, it is not possible to identify individuals or objects depicted in the image. In this chapter, we evaluate the privacy protection capability of PhotoDNA by testing it against machine learning. Specifically, our aim is to detect the presence of any structural information that might be utilized to compromise the privacy of the individuals via classification. Due to the widespread usage of PhotoDNA as a deterrent to IIOC by big data companies, ensuring its ability to protect privacy would be crucial. In our experimentation, we achieved a classification accuracy of 57.20%.This result indicates that PhotoDNA is resistant to machine-learning-based classification attacks.
    • Calibration approaches for higher order ambisonic microphones

      Middlicott, Charlie; Wiggins, Bruce; University of Derby; Sky Labs (Audio Engineering Society, 2019-10-08)
      Recent years have seen an increase in the capture and production of ambisonic material due to companies such as YouTube and Facebook utilizing ambisonics for spatial audio playback. Consequently, there is now a greater need for affordable high order microphone arrays due to this uptake in technology. This work details the development of a five-channel circular horizontal ambisonic microphone intended as a tool to explore various optimization techniques, focusing on capsule calibration & pre-processing approaches for unmatched capsules.
    • GORTS: genetic algorithm based on one-by-one revision of two sides for dynamic travelling salesman problems

      Xu, Xiaolong; Yuan, Hao; Matthew, Peter; Ray, Jeffrey; Bagdasar, Ovidiu; Trovati, Marcello; University of Derby; Nanjing University of Posts and Telecommunications, Nanjing, China; Edge Hill University, Ormskirk, UK (Springer, 2019-09-21)
      The dynamic travelling salesman problem (DTSP) is a natural extension of the standard travelling salesman problem, and it has attracted significant interest in recent years due to is practical applications. In this article, we propose an efficient solution for DTSP, based on a genetic algorithm (GA), and on the one-by-one revision of two sides (GORTS). More specifically, GORTS combines the global search ability of GA with the fast convergence feature of the method of one-by-one revision of two sides, in order to find the optimal solution in a short time. An experimental platform was designed to evaluate the performance of GORTS with TSPLIB. The experimental results show that the efficiency of GORTS compares favourably against other popular heuristic algorithms for DTSP. In particular, a prototype logistics system based on GORTS for a supermarket with an online map was designed and implemented. It was shown that this can provide optimised goods distribution routes for delivery staff, while considering real-time traffic information.
    • Integration and evaluation of QUIC and TCP-BBR in longhaul science data transfers

      Lopes, Raul H. C.; Franqueira, Virginia N. L.; Duncan, Rand; Jisc, Lumen House; University of Derby, College of Engineering and Technology; Brunel University London, College of Engineering, Design and Physical Sciences (EDP Sciences, 2019-09-17)
      Two recent and promising additions to the internet protocols are TCP-BBR and QUIC. BBR defines a congestion policy that promises a better control in TCP bottlenecks on long haul transfers and can also be used in the QUIC protocol. TCP-BBR is implemented in the Linux kernels above 4.9. It has been shown, however, to demand careful fine tuning in the interaction, for example, with the Linux Fair Queue. QUIC, on the other hand, replaces HTTP and TLS with a protocol on the top of UDP and thin layer to serve HTTP. It has been reported to account today for 7% of Google’s traffic. It has not been used in server-to-server transfers even if its creators see that as a real possibility. Our work evaluates the applicability and tuning of TCP-BBR and QUIC for data science transfers. We describe the deployment and performance evaluation of TCP-BBR and comparison with CUBIC and H-TCP in transfers through the TEIN link to Singaren (Singapore). Also described is the deployment and initial evaluation of a QUIC server. We argue that QUIC might be a perfect match in security and connectivity to base services that are today performed by the Xroot redirectors.
    • First observation of an attractive interaction between a proton and a cascade baryon

      Acharya, S.; Adamová, D.; Adhya, S. P.; Adler, A.; Adolfsson, J.; Aggarwal, M. M.; Aglieri Rinella, G.; Agnello, M.; Agrawal, N.; Ahammed, Z.; et al. (American Physical Society (APS), 2019-09-13)
      This Letter presents the first experimental observation of the attractive strong interaction between a proton and a multistrange baryon (hyperon) Ξ−. The result is extracted from two-particle correlations of combined p−Ξ−⊕¯p−¯Ξ+ pairs measured in p−Pb collisions at √sNN=5.02 TeV at the LHC with ALICE. The measured correlation function is compared with the prediction obtained assuming only an attractive Coulomb interaction and a standard deviation in the range [3.6, 5.3] is found. Since the measured p−Ξ−⊕¯p−¯Ξ+ correlation is significantly enhanced with respect to the Coulomb prediction, the presence of an additional, strong, attractive interaction is evident. The data are compatible with recent lattice calculations by the HAL-QCD Collaboration, with a standard deviation in the range [1.8, 3.7]. The lattice potential predicts a shallow repulsive Ξ− interaction within pure neutron matter and this implies stiffer equations of state for neutron-rich matter including hyperons. Implications of the strong interaction for the modeling of neutron stars are discussed.
    • A validation of security determinants model for cloud adoption in Saudi organisations’ context

      Alassafi, Madini O.; Atlam, Hany F.; Alshdadi, Abdulrahman A.; Alzahrani, Abdullah I.; AlGhamdi, Rayed A.; Buhari, Seyed M.; University of Southampton (Springer, 2019-08-30)
      Governments across the world are starting to make a dynamic shift to cloud computing so as to increase efficiency. Although, the cloud technology brings various benefits for government organisations, including flexibility and low cost, adopting it with the existing system is not an easy task. In this regard, the most significant challenge to any government agency is security concern. Our previous study focused to identify security factors that influence decision of government organisations to adopt cloud. This research enhances the previous work by investigating on the impact of various independent security related factors on the adopted security taxonomy based on critical ratio, standard error and significance levels. Data was collected from IT and security experts in the government organisations of Saudi Arabia. The Analysis of Moment Structures (AMOS) tool was used in this research for data analysis. Critical ratio reveals the importance of Security Benefits, Risks and Awareness Taxonomies on cloud adoption. Also, most of the exogenous variables had strong and positive relationships with their fellow exogenous variables. In future, this taxonomy model can also be applied for studying the adoption of new IT innovations whose IT architecture is similar to that of the cloud.
    • Towards a trusted unmanned aerial system using blockchain (BUAS) for the protection of critical infrastructure

      Barka, Ezedin; Kerrache, Chaker Abdelaziz; Benkraouda, Hadjer; Shuaib, Khaled; Ahmad, Farhan; Kurugollu, Fatih; College of Information Technology, United Arab Emirates University; Department of Mathematics and Computer Science, University of Ghardaia, Algeria; Cyber Security Research Group, University of Derby, UK (Wiley, 2019-07-29)
      With the exponential growth in the number of vital infrastructures such as nuclear plants and transport and distribution networks, these systems have become more susceptible to coordinated cyber attacks. One of the effective approaches used to strengthen the security of these infrastructures is the use of Unmanned Aerial Vehicles (UAVs) for surveillance and data collection. However, UAVs themselves are prone to attacks on their collected sensor data. Recently, Blockchain (BC) has been proposed as a revolutionary technology which can be integrated within IoT to provide a desired level of security and privacy. However, the integration of BC within IoT networks, where UAV's sensors constitute a major component, is extremely challenging. The major contribution of this study is two-fold. (1) survey of the security issues for UAV's collected sensor data, define the security requirements for such systems, and identify ways to address them. (2) propose a novel Blockchain-based solution to ensure the security of, and the trust between the UAVs and their relevant ground control stations (GCS). Our implementation results and analysis show that using UAVs as means for protecting critical infrastructure is greatly enhanced through the utilization of trusted Blockchain-based Unmanned Aerial Systems (UASs).
    • Analysis and optimal design of a vibration isolation system combined with electromagnetic energy harvester

      Diala, Uchenna; Mofidian, SM Mahdi; Lang, Zi-Qiang; Bardaweel, Hamzeh; University of Sheffield (SAGE Publications, 2019-07-17)
      This work investigates a vibration isolation energy harvesting system and studies its design to achieve an optimal performance. The system uses a combination of elastic and magnetic components to facilitate its dual functionality. A prototype of the vibration isolation energy harvesting device is fabricated and examined experimentally. A mathematical model is developed using first principle and analyzed using the output frequency response function method. Results from model analysis show an excellent agreement with experiment. Since any vibration isolation energy harvesting system is required to perform two functions simultaneously, optimization of the system is carried out to maximize energy conversion efficiency without jeopardizing the system’s vibration isolation performance. To the knowledge of the authors, this work is the first effort to tackle the issue of simultaneous vibration isolation energy harvesting using an analytical approach. Explicit analytical relationships describing the vibration isolation energy harvesting system transmissibility and energy conversion efficiency are developed. Results exhibit a maximum attainable energy conversion efficiency in the order of 1%. Results suggest that for low acceleration levels, lower damping values are favorable and yield higher conversion efficiencies and improved vibration isolation characteristics. At higher acceleration, there is a trade-off where lower damping values worsen vibration isolation but yield higher conversion efficiencies.
    • Realization of blockchain in named data networking-based internet-of-vehicles

      Ahmad, Farhan; Kerrache, Chaker Abdelaziz; Kurugollu, Fatih; Hussain, Rasheed; Cyber Security Research Group, University of Derby, UK; Department of Mathematics and Computer Science, University of Ghardaia, Algeria; Institute of Information Systems, Innopolis University, Russia (IEEE, 2019-07-15)
      The revolution of Internet-of-vehicles (IoV) has stimulated a substantial response from academia, research and industry due to its massive potential to improve overall transportation. Current IoV faces huge challenges due to its reliance on the IP-based network architecture. Therefore, Named Data Networking (NDN) is proposed as a promising architecture to solve issues posed by IP-based systems. Recently, Blockchains (BCs) are utilized within IoV to increase network security. However, the integration of BC within NDN-enabled IoV is still an open research problem. In this study, we proposed a novel tier-based architecture known as “Blockchain in NDN-enabled Internet-of-Vehicles (BINDN)” which can support BC within NDN-enabled IoV. BINDN can be used as a reference architecture to design security solutions in NDN-enabled IoV using BC. Further, it provides an extensive set of applications including IoV security, trust management and privacy enhancements. Moreover, we highlighted major challenges and issues when integrating BC within NDN-enabled IoV.
    • 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.
    • A software platform for noise reduction in sound sensor equipped drones

      Kang, Byungseok; University of Derby (IEEE, 2019-07-08)
      A flying drone provides multiple video capturing options for filming videos. Since a noise is generated by the propellers and rotors of a drone, the quality of sound in the recorded video is quite low. Large drones are used singly in missions while small ones are used in formations or swarms. The small drones are proving to be useful in civilian applications. These are effective with multiple drones. Consideration of small drones for the applications, such as group flight, entertainment, and signal emission lead to deployment of networked drones. To solve the noise problem and develop group display applications, a software platform for these issues in networked drones is proposed. Noise reduction combines the active noise control and spectral subtraction. In addition, drones form group displays for an entertainment and displaying application. We develop a small-scale testbed to measure the service quality of proposed platform. The experimental results show that the proposed noise reduction produces a speech signal with up to 67.5% similarity to the original signal. It outperforms active noise control and spectral subtraction with the similarities of 53.1% and 39.6%, respectively. We see that drone formation can form a group display to show messages effectively.
    • A location aware fast PMIPv6 for low latency wireless sensor networks

      Kang, Byungseok; University of Derby (IEEE, 2019-06-28)
      Recently, mobile sensor networks (MSN) have been actively studied due to the emergence of mobile sensors such as Robomote and robotic sensor agents (RSAs). The research on existing mobile sensor networks mainly focuses on solving the coverage hole, which is a problem that occurs in the existing stationary sensor network (SSN). These studies have disadvantages in that they cannot make the most use of the mobile ability given to the moving sensors. In order to solve this problem, there is a proposal for sensing a wider area than a fixed sensor network by giving the moving sensor continuous mobility. However, the research is still in the early stage, and communication path to the sink node and data transmission problems. In this paper, we propose a location-aware fast PMIPv6 (LA-FPMIPv6) protocol that enables efficient routing and data transmission in a mobile sensor network environment composed of mobile sensors with continuous mobility. In the proposed protocol, the fixed sensor is arranged with the moving sensor so that the fixed sensor transmits the sensing data to the sink node instead of the moving sensor. For performance evaluation, the LA-FPMIPv6 is compared with existing methods through mathematical analysis and computer simulation. The results of the performance evaluation show that the LA-FPMIPv6 effectively reduces the handover latency, signaling cost, and buffering cost compared with the conventional methods.
    • A survey of deep learning solutions for multimedia visual content analysis.

      Nadeem, Muhammad Shahroz; Franqueira, Virginia N. L.; Zhai, Xiaojun; Kurugollu, Fatih; University of Derby; University of Essex (IEEE, 2019-06-24)
      The increasing use of social media networks on handheld devices, especially smartphones with powerful built-in cameras, and the widespread availability of fast and high bandwidth broadband connections, added to the popularity of cloud storage, is enabling the generation and distribution of massive volumes of digital media, including images and videos. Such media is full of visual information and holds immense value in today’s world. The volume of data involved calls for automated visual content analysis systems able to meet the demands of practice in terms of efficiency and effectiveness. Deep Learning (DL) has recently emerged as a prominent technique for visual content analysis. It is data-driven in nature and provides automatic end-to-end learning solutions without the need to rely explicitly on predefined handcrafted feature extractors. Another appealing characteristic of DL solutions is the performance they can achieve, once the network is trained, under practical constraints. This paper identifies eight problem domains which require analysis of visual artefacts in multimedia. It surveys the recent, authoritative, and best performing DL solutions and lists the datasets used in the development of these deep methods for the identified types of visual analysis problems. The paper also discusses the challenges that DL solutions face which can compromise their reliability, robustness, and accuracy for visual content analysis.
    • A comparative analysis of trust models for safety applications in IoT-enabled vehicular networks

      Ahmad, Farhan; Adnane, Asma; Hussain, Rasheed; Kurugollu, Fatih; University of Derby; Loughborough University; Innopolis University (IEEE, 2019-06-13)
      To achieve these goals, VANET requires a secure environment for authentic, reliable and trusted information dissemination among the network entities. However, VANET is prone to different attacks resulting in the dissemination of compromised/false information among network nodes. One way to manage a secure and trusted network is to introduce trust among the vehicular nodes. To this end, various Trust Models (TMs) are developed for VANET and can be broadly categorized into three classes, Entity-oriented Trust Models (ETM), Data oriented Trust Models (DTM) and Hybrid Trust Models (HTM). These TMs evaluate trust based on the received information (data), the vehicle (entity) or both through different mechanisms. In this paper, we present a comparative study of the three TMs. Furthermore, we evaluate these TMs against the different trust, security and quality-of-service related benchmarks. Simulation results revealed that all these TMs have deficiencies in terms of end-to-end delays, event detection probabilities and false positive rates. This study can be used as a guideline for researchers to design new efficient and effective TMs for VANET.
    • Multiclass disease predictions based on integrated clinical and genomics datasets

      Anjum, Ashiq; Subhani, Moeez; University of Derby (IARIA, 2019-06-02)
      Clinical predictions using clinical data by computational methods are common in bioinformatics. However, clinical predictions using information from genomics datasets as well is not a frequently observed phenomenon in research. Precision medicine research requires information from all available datasets to provide intelligent clinical solutions. In this paper, we have attempted to create a prediction model which uses information from both clinical and genomics datasets. We have demonstrated multiclass disease predictions based on combined clinical and genomics datasets using machine learning methods. We have created an integrated dataset, using a clinical (ClinVar) and a genomics (gene expression) dataset, and trained it using instancebased learner to predict clinical diseases. We have used an innovative but simple way for multiclass classification, where the number of output classes is as high as 75. We have used Principal Component Analysis for feature selection. The classifier predicted diseases with 73% accuracy on the integrated dataset. The results were consistent and competent when compared with other classification models. The results show that genomics information can be reliably included in datasets for clinical predictions and it can prove to be valuable in clinical diagnostics and precision medicine.
    • Consumer-facing technology fraud: Economics, attack methods and potential solutions

      Mohammed Aamir, Ali; Muhammad AJmal, Azad; Mario Parreno, Centeno; Feng, Hao; Aad Van, Moorsel; Newcastle University; Derby University; University of Warwick (Elsevier, 2019-05-11)
      The emerging use of modern technologies has not only benefited society but also attracted fraudsters and criminals to misuse the technology for financial benefits. Fraud over the Internet has increased dramatically, resulting in an annual loss of billions of dollars to customers and service providers worldwide. Much of such fraud directly impacts individuals, both in the case of browser-based and mobile-based Internet services, as well as when using traditional telephony services, either through landline phones or mobiles. It is important that users of the technology should be both informed of fraud, as well as protected from frauds through fraud detection and prevention systems. In this paper, we present the anatomy of frauds for different consumer-facing technologies from three broad perspectives - we discuss Internet, mobile and traditional telecommunication, from the perspectives of losses through frauds over the technology, fraud attack mechanisms and systems used for detecting and preventing frauds. The paper also provides recommendations for securing emerging technologies from fraud and attacks.
    • Stability and pinning synchronization analysis of fractional order delayed Cohen–Grossberg neural networks with discontinuous activations

      Pratap, A.; Raja, R.; Cao, J.; Lim, C.P.; Bagdasar, O.; University of Derby (Elsevier, 2019-05-11)
      This article, we explore the asymptotic stability and asymptotic synchronization analysis of fractional order delayed Cohen–Grossberg neural networks with discontinuous neuron activation functions (FCGNNDDs). First, under the framework of Filippov theory and differ- ential inclusion theoretical analysis, the global existence of Filippov solution for FCGNNDDs is studied by means of the given growth condition. Second, by virtue of suitable Lyapunov functional, Young inequality and comparison theorem for fractional order delayed linear system, some global asymptotic stability conditions for such system is derived by limiting discontinuous neuron activations. Third, the global asymptotic synchronization condition for FCGNNDDs is obtained based on the pinning control. At last, two numerical simula- tions are given to verify the theoretical findings.