• WHAM - Webcam Head-tracked AMbisonics

      Dring, Mark; Wiggins, Bruce; University of Derby (Institute of Acoustics, 2020-11-19)
      This paper describes the development and implementation of a real-time head-tracked auralisation platform using Higher Order Ambisonics (HOA) decoded binaurally based on open-source and freely available web technologies without the need for specialist head-tracking hardware. An example implementation of this work can be found at https://brucewiggins.co.uk/WHAM/.
    • A repairing missing activities approach with succession relation for event logs

      Liu, Jie; Xu, Jiuyun; Zhang, Ruru; Reiff-Marganiec, Stephan; China University of Petroleum; The China Mobile (Suzhou) Software Technology Company, Suzhou, China; University of Derby (Springer, 2020-11-11)
      In the field of process mining, it is worth noting that process mining techniques assume that the resulting event logs can not only continuously record the occurrence of events but also contain all event data. However, like in IoT systems, data transmission may fail due to weak signal or resource competition, which causes the company’s information system to be unable to keep a complete event log. Based on a incomplete event log, the process model obtained by using existing process mining technologies is deviated from actual business process to a certain degree. In this paper, we propose a method for repairing missing activities based on succession relation of activities from event logs. We use an activity relation matrix to represent the event log and cluster it. The number of traces in the cluster is used as a measure of similarity calculation between incomplete traces and cluster results. Parallel activities in selecting pre-occurrence and post-occurrence activities of missing activities from incomplete traces are considered. Experimental results on real-life event logs show that our approach performs better than previous method in repairing missing activities.
    • Intelligent price alert system for digital assets - cryptocurrencies

      Chhem, Sronglong; Anjum, Ashiq; Arshad, Bilal; University of Derby (ACM Press, 2019-12)
      Cryptocurrency market is very volatile, trading prices for some tokens can experience a sudden spike up or downturn in a matter of minutes. As a result, traders are facing difficulty following with all the trading price movements unless they are monitoring them manually. Hence, we propose a real-time alert system for monitoring those trading prices, sending notifications to users if any target prices match or an anomaly occurs. We adopt a streaming platform as the backbone of our system. It can handle thousands of messages per second with low latency rate at an average of 19 seconds on our testing environment. Long-Short-Term-Memory (LSTM) model is used as an anomaly detector. We compare the impact of five different data normalisation approaches with LSTM model on Bitcoin price dataset. The result shows that decimal scaling produces only Mean Absolute Percentage Error (MAPE) of 8.4 per cent prediction error rate on daily price data, which is the best performance achieved compared to other observed methods. However, with one-minute price dataset, our model produces higher prediction error making it impractical to distinguish between normal and anomaly points of price movement.
    • 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.
    • Smart anomaly detection in sensor systems: A multi-perspective review

      Erhan, L.; Ndubuaku, M.; Di Mauro, M.; Song, W.; Chen, M.; Fortino, G.; Bagdasar, Ovidiu; Liotta, A.; University of Derby; University of Salerno, Italy; et al. (Elsevier, 2020-10-15)
      Anomaly detection is concerned with identifying data patterns that deviate remarkably from the expected behavior. This is an important research problem, due to its broad set of application domains, from data analysis to e-health, cybersecurity, predictive maintenance, fault prevention, and industrial automation. Herein, we review state-of-the-art methods that may be employed to detect anomalies in the specific area of sensor systems, which poses hard challenges in terms of information fusion, data volumes, data speed, and network/energy efficiency, to mention but the most pressing ones. In this context, anomaly detection is a particularly hard problem, given the need to find computing-energy-accuracy trade-offs in a constrained environment. We taxonomize methods ranging from conventional techniques (statistical methods, time-series analysis, signal processing, etc.) to data-driven techniques (supervised learning, reinforcement learning, deep learning, etc.). We also look at the impact that different architectural environments (Cloud, Fog, Edge) can have on the sensors ecosystem. The review points to the most promising intelligent-sensing methods, and pinpoints a set of interesting open issues and challenges.
    • The design and optimisation of surround sound decoders using heuristic methods

      Wiggins, Bruce; Berry, Stuart; Lowndes, Val; Paterson-Stephens, Iain; University of Derby (2003-04-09)
      Surround sound has, for a number of years, had the standard of an irregular five-speakers layout (as defined by the ITU), but this is most likely set to expand to 7,9 or more, speaker configurations. The Ambisonic system, pioneered by Micheal Gerzon in the late 1960s, is very well suited to situations where the end system speaker configuration is not fixed in terms of number or position. However, while designing Ambisonic decoders for a regular (e.g. hexagonal) layout is well documented, optimising the decoders for irregular layouts is not a simple task, when optimisation requires the solution of a set of non-linear simultaneous equations [1 – Gerzon & Barton]. This paper describes an alternative approach to the determination of these “optimised coefficients”. This approach, based on a Tabu Search methodology [2 – Berry & Lowndes], efficiently determined sets of alternative optimal settings which were better (in terms of the reviewed parameters) than the results obtained using the standard analytical methods.
    • Research and implementation of intelligent decision based on a priori knowledge and DQN algorithms in wargame environment

      Sun, Yuxiang; Yuan, Bo; Zhang, Tao; Tang, Bojian; Zheng, Wanwen; Zhou, Xianzhong; University of Derby; Nanjing University, China (MDPI AG, 2020-10-13)
      The reinforcement learning problem of complex action control in a multi-player wargame has been a hot research topic in recent years. In this paper, a game system based on turn-based confrontation is designed and implemented with state-of-the-art deep reinforcement learning models. Specifically, we first design a Q-learning algorithm to achieve intelligent decision-making, which is based on the DQN (Deep Q Network) to model complex game behaviors. Then, an a priori knowledge-based algorithm PK-DQN (Prior Knowledge-Deep Q Network) is introduced to improve the DQN algorithm, which accelerates the convergence speed and stability of the algorithm. The experiments demonstrate the correctness of the PK-DQN algorithm, it is validated, and its performance surpasses the conventional DQN algorithm. Furthermore, the PK-DQN algorithm shows effectiveness in defeating the high level of rule-based opponents, which provides promising results for the exploration of the field of smart chess and intelligent game deduction
    • Development of an ambisonic guitar system GASP: Guitars with ambisonic spatial performance

      Werner, Duncan; Wiggins, Bruce; Fitzmaurice, Emma; University of Derby (CRC Press/ Routledge, 2021-01-22)
      Ambisonics, pioneered by Michael Gerzon (1977,1985), is a kernel-based 3D surround sound system. The encoding (recording or panning) of the audio is separated from the decoding (or rendering) of the audio to speaker feeds or, more recently, head tracked headphones (by binaurally decoding the Ambisonic sound field). Audio encoded in this way can be rendered to any number of speakers in almost any position in 3D space, as long as the positions of the speakers are known. Moreover, Ambisonics is a system optimised around a number of psycho-acoustic criteria which, when implemented, reduce the variability of audio no matter what speaker arrangement is used for reproduction. This allows for a `mix once' system where subsequent remixing is not necessary when replayed over different loudspeaker systems or headphones and allows for full 3D reproduction. The Ambisonics system is finally gaining some traction due to its use in Virtual Reality audio, using the ambiX standard (Nachbar et al. 2011) but few instruments exist that make use of this 3D spatial audio format, although previous studies into some aspects of the relationship between instruments, performance and spatialisation is available, for example, see Pysiewica and Weinzierl (2017), Graham and Bridges (2017), Bates (2010), Pukette (2007) and Graham (2012). The system combines custom and off-the-shelf hardware/software to create both a live performance Ambisonic guitar system, and virtual reality (VR) ready, binaural performance instrument. The system comprises of two aspects: firstly as an innovative audio project, fusing the musical with the technical, combining individual string timbralisation with Ambisonic surround sound. And secondly as an artistic musical project, providing alternative experimental surround sound production ideas for the guitarist and music producer, with potential applications in the Sound Arts world as well as commercial musical applications. This paper explores the possibilities of the Guitar as a spatial instrument detailing the technical and artistic processes involved in the production and live performance of the instrument. Key features of the described system include: Multichannel hexaphonic guitar pickups facilitate the guitar system to process individual strings independently for both timbre and spatial location. Guitar production timbre and effects are achieved with Line 6 Helix commercial sound processing software for individual string timbralisation. Ambisonic surround-sound performance: spatial positioning is achieved using our own bespoke WigWare algorithms and can be heard over either an array of circular (2D) or spherical (3D) loudspeakers, alternatively the user can listen to the output with headphones using binaural implementation. Rhythmic gate-switching of individual strings, such that either simple or complex polyrhythms can be programmed or performed live across individual strings (producing similar results to a keyboard controlled arpeggiator). ‘Auditory Scenes’ have been developed for presenting combinations of individual string timbres, spatial, and rhythmic arpeggiator parameters. The system can be applied to post-production sound manipulation, or as a real-time live Ambisonic performance instrument within a concert environment. These two categories can yield differing production possibilities. We have also identified potential applications for guitar training and education.
    • WiFi probes sniffing: an artificial intelligence based approach for MAC addresses de-randomization

      Uras, Marco; Cossu, Raimondo; Ferrara, Enrico; Bagdasar, Ovidiu; Liotta, Antonio; Atzori, Luigi; University of Derby; University of Cagliari; Free University of Bozen, Bolzano, Italy (IEEE, 2020-09-30)
      To improve city services, local administrators need to have a deep understanding of how the citizens explore the city, use the relevant services, interact and move. This is a challenging task, which has triggered extensive research in the last decade, with major solutions that rely on analysing traces of network traffic generated by citizens WiFi devices. One major approach relies on catching the probe requests sent by devices during WiFi active scanning, which allows for counting the number of people in a given area and to analyse the permanence and return times. This approach has been a solid solution until some manufacturer introduced the MAC address randomization process to improve the user’s privacy, even if in some circumstances this seems to deteriorate network performance as well as the user experience. In this work we present a novel techniques to tackle the limitations introduced by the randomization procedures and that allows for extracting data useful for smart cities development. The proposed algorithm extracts the most relevant information elements within probe requests and apply clustering algorithms (such as DBSCAN and OPTICS) to discover the exact number of devices which are generating probe requests. Experimental results showed encouraging results with an accuracy of 65.2% and 91.3% using the DBSCAN and the OPTICS algorithms, respectively.
    • Recurrent sequences: Key results, applications, and problems

      Andrica, Dorin; Bagdasar, Ovidiu; University of Derby (Springer International Publishing, 2020-09)
      This self-contained text presents state-of-the-art results on recurrent sequences and their applications in algebra, number theory, geometry of the complex plane and discrete mathematics. It is designed to appeal to a wide readership, ranging from scholars and academics, to undergraduate students, or advanced high school and college students training for competitions. The content of the book is very recent, and focuses on areas where significant research is currently taking place. Among the new approaches promoted in this book, the authors highlight the visualization of some recurrences in the complex plane, the concurrent use of algebraic, arithmetic, and trigonometric perspectives on classical number sequences, and links to many applications. It contains techniques which are fundamental in other areas of math and encourages further research on the topic. The introductory chapters only require good understanding of college algebra, complex numbers, analysis and basic combinatorics. For Chapters 3, 4 and 6 the prerequisites include number theory, linear algebra and complex analysis. The first part of the book presents key theoretical elements required for a good understanding of the topic. The exposition moves on to to fundamental results and key examples of recurrences and their properties. The geometry of linear recurrences in the complex plane is presented in detail through numerous diagrams, which lead to often unexpected connections to combinatorics, number theory, integer sequences, and random number generation. The second part of the book presents a collection of 123 problems with full solutions, illustrating the wide range of topics where recurrent sequences can be found. This material is ideal for consolidating the theoretical knowledge and for preparing students for Olympiads.
    • CloudIoT-based Jukebox Platform: a music player for mobile users in Café

      Kang, Byungseok; Lee, Joohyun; Bagdasar, Ovidiu; Choo, Hyunseung; University of Derby; Sungkyunkwan University, South Korea (Ministry of Education, Taipei, Taiwan, 2020-09-20)
      Contents services have been provided to people in a variety of ways. Jukebox service is one of the contents streaming which provides an automated music-playing service. User inserts coin and presses a play button, the jukebox automatically selects and plays the record. The Disk Jockey (DJ) in Korean cafeteria (café) received contents desired of customer and played them through the speakers in the store. In this paper, we propose a service platform that reinvented the Korean café DJ in an integrated environment of IoT and cloud computing. The user in a store can request contents (music, video, and message) through the service platform. The contents are provided through the public screen and speaker in the store where the user is located. This allows people in the same location store to enjoy the contents together. The user information and the usage history are collected and managed in the cloud. Therefore, users can receive customized services regardless of stores. We compare our platform to exist services. As a result of the performance evaluation, the proposed platform shows that contents can be efficiently provided to users and adapts IoT-Cloud integrated environments.
    • DETN: delay-efficient tolerant network for internet of planet

      Kang, Byungseok; Malute, Francis; Bagdasar, Ovidiu; Hyunseung, Choo; University of Derby; College of Information and Communication Engineering, Sungkyunkwan University, Suwon, South Korea (IEEE, 2020-08-31)
      The explosion of the internet has resulted in various emerging technologies, as for example the Internet of Things (IoT). IoT is an intelligent technology and service connecting objects in the Internet. IoT facilitates the exchange of information between people and devices that communicate with each other. Beyond IoT, we are now studying a new paradigm called Internet of Planets (IoP), in which planets in a solar system communicate with each other using the Internet. This paper presents our research in the internet communications between planets, detailing benefits, limitations and directions for future work. We propose a time (delay) information-based Delay Efficient Tolerant Networking (DETN) routing scheme for efficient data transmission among mobile nodes. The results of the proposed DTN routing algorithm using NS-3 simulation tools indicate satisfactory levels of routing performance in comparison with existing DTN algorithms.
    • Comparative study of the scaling behavior of the Rényi entropy for He-like atoms

      Farid, M; Abdel-Hady, A; Nasser, I; Farid, Mohsen; University of Derby; Egyptian Chinese University, Cairo, Egypt; King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia (IOP Publishing, 2017-07-11)
      We solve the Schrödinger equation in the spherical or Hylleraas-coordinate systems, and within the framework of the Ritz’s variational principle. The eigenvalues, and the eigenfunctions ψ(r) in r- or Hylleraas-space for the 1s2-state of the He-like atoms as a function of two variational parameters are calculated. Using a simple scaling procedure, we calculate the scaled wavefunction as a function of the nuclear charge Z. Given the density of states, ρ(r), the scaling behavior of the information entropies, e.g., Fisher, Shannon and Rényi’s entropies, with their powers and products, as functions of Z are calculated. Scaled wavefunctions for the 1s2-state of the He-like atoms, with exchange, have been used to study the scaling behavior. Our results agree with the published results. Furthermore, we present a simple logarithmic equation that shows the dependence of information entropies on Z for He-like atoms. The formulation enhances the computational efficiency of the entropies and other related quantities.
    • A first look at privacy analysis of COVID-19 contact tracing mobile applications

      Azad, Muhammad Ajmal; Arshad, Junaid; Akmal, Syed Muhammad Ali; Riaz, Farhan; Abdullah, Sidrah; Imran, Muhammad; Ahmad, Farhan; Birmingham CIty University; NED University; NUST UNiversity; et al. (Institute of Electrical and Electronics Engineers (IEEE), 2020-09-17)
      Today’s smartphones are equipped with a large number of powerful value-added sensors and features such as a low power Bluetooth sensor, powerful embedded sensors such as the digital compass, accelerometer, GPS sensors, Wi-Fi capabilities, microphone, humidity sensors, health tracking sensors, and a camera, etc. These value-added sensors have revolutionized the lives of the human being in many ways such, as tracking the health of the patients and movement of doctors, tracking employees movement in large manufacturing units, and monitoring the environment, etc. These embedded sensors could also be used for large-scale personal, group, and community sensing applications especially tracing the spread of certain diseases. Governments and regulators are turning to use these features to trace the people thought to have symptoms of certain diseases or virus e.g. COVID-19. The outbreak of COVID-19 in December 2019, has seen a surge of the mobile applications for tracing, tracking and isolating the persons showing COVID-19 symptoms to limit the spread of disease to the larger community. The use of embedded sensors could disclose private information of the users thus potentially bring threat to the privacy and security of users. In this paper, we analyzed a large set of smartphone applications that have been designed to contain the spread of the COVID-19 virus and bring the people back to normal life. Specifically, we have analyzed what type of permission these smartphone apps require, whether these permissions are necessary for the track and trace, how data from the user devices is transported to the analytic center, and analyzing the security measures these apps have deployed to ensure the privacy and security of users.
    • Big earth data: a comprehensive analysis of visualization analytics issues

      Merritt, Patrick; Bi, Haixia; Davis, Bradley; Windmill, Christopher; Xue, Yong; University of Derby (Taylor and Francis, 2019-02-26)
      Big Earth Data analysis is a complex task requiring the integration of many skills and technologies. This paper provides a comprehensive review of the technology and terminology within the Big Earth Data problem space and presents examples of state-of-the-art projects in each major branch of Big Earth Data research. Current issues within Big Earth Data research are highlighted and potential future solutions identified.
    • Characterisation of large changes in wind power for the day-ahead market using a fuzzy logic approach

      Martínez-Arellano, Giovanna; Nolle, Lars; Cant, Richard; Lotfi, Ahmad; Windmill, Christopher; Nottingham Trent University (Springer Science and Business Media LLC, 2014-08-21)
      Wind power has become one of the renewable resources with a major growth in the electricity market. However, due to its inherent variability, forecasting techniques are necessary for the optimum scheduling of the electric grid, specially during ramp events. These large changes in wind power may not be captured by wind power point forecasts even with very high resolution numerical weather prediction models. In this paper, a fuzzy approach for wind power ramp characterisation is presented. The main benefit of this technique is that it avoids the binary definition of ramp event, allowing to identify changes in power output that can potentially turn into ramp events when the total percentage of change to be considered a ramp event is not met. To study the application of this technique, wind power forecasts were obtained and their corresponding error estimated using genetic programming and quantile regression forests. The error distributions were incorporated into the characterisation process, which according to the results, improve significantly the ramp capture. Results are presented using colour maps, which provide a useful way to interpret the characteristics of the ramp events.
    • Contextualizing geometric data analysis and related data analytics: A virtual microscope for big data analytics

      Farid, Mohsen; Murtagh, Fionn; University of Derby; University of Huddersfield (Le Centre pour la Communication Scientifique Directe, 2017-02-06)
      The relevance and importance of contextualizing data analytics is described. Qualitative characteristics might form the context of quantitative analysis. Topics that are at issue include: contrast, baselining, secondary data sources, supplementary data sources, dynamic and heterogeneous data. In geometric data analysis, especially with the Correspondence Analysis platform, various case studies are both experimented with, and are reviewed. In such aspects as paradigms followed, and technical implementation, implicitly and explicitly, an important point made is the major relevance of such work for both burgeoning analytical needs and for new analytical areas including Big Data analytics, and so on. For the general reader, it is aimed to display and describe, first of all, the analytical outcomes that are subject to analysis here, and then proceed to detail the more quantitative outcomes that fully support the analytics carried out.
    • MRI brain classification using the quantum entropy LBP and deep-learning-based features

      Hasan, Ali M.; Jalab, Hamid A.; Ibrahim, Rabha W.; Meziane, Farid; AL-Shamasneh, Ala’a R.; Obaiys, Suzan J.; Al-Nahrain University, Baghdad 10001, Iraq; University of Malaya, Kuala Lumpur 50603, Malaysia; Ton Duc Thang University, Ho Chi Minh City 758307, Vietnam; University of Derby; et al. (MDPI AG, 2020-09-15)
      Brain tumor detection at early stages can increase the chances of the patient’s recovery after treatment. In the last decade, we have noticed a substantial development in the medical imaging technologies, and they are now becoming an integral part in the diagnosis and treatment processes. In this study, we generalize the concept of entropy di erence defined in terms of Marsaglia formula (usually used to describe two di erent figures, statues, etc.) by using the quantum calculus. Then we employ the result to extend the local binary patterns (LBP) to get the quantum entropy LBP (QELBP). The proposed study consists of two approaches of features extractions of MRI brain scans, namely, the QELBP and the deep learning DL features. The classification of MRI brain scan is improved by exploiting the excellent performance of the QELBP–DL feature extraction of the brain in MRI brain scans. The combining all of the extracted features increase the classification accuracy of long short-term memory network when using it as the brain tumor classifier. The maximum accuracy achieved for classifying a dataset comprising 154 MRI brain scan is 98.80%. The experimental results demonstrate that combining the extracted features improves the performance of MRI brain tumor classification.
    • Arabic machine translation: A survey of the latest trends and challenges

      Ameur, M.S.H.; Meziane, Farid; Guessoum, Ahmed; University of Science and Technology Houari Boumediene (USTHB), Algeria; University of Derby (Elsevier, 2020-09-15)
      Given that Arabic is one of the most widely used languages in the world, the task of Arabic Machine Translation (MT) has recently received a great deal of attention from the research community. Indeed, the amount of research that has been devoted to this task has led to some important achievements and improvements. However, the current state of Arabic MT systems has not reached the quality achieved for some other languages. Thus, much research work is still needed to improve it. This survey paper introduces the Arabic language, its characteristics, and the challenges involved in its translation. It provides the reader with a full summary of the important research studies that have been accomplished with regard to Arabic MT along with the most important tools and resources that are available for building and testing new Arabic MT systems. Furthermore, the survey paper discusses the current state of Arabic MT and provides some insights into possible future research directions.
    • Experimental validation of fuel cell, battery and supercapacitor energy conversion system for electric vehicle applications

      Moualek, R.; Benyahia, N.; Bousbaine, A.; Benamrouche, N.; Mouloud Mammeri University, Tizi-Ouzou, Algeria; University of Derby (Springer Singapore, 2020-08-20)
      Due to the increasing air pollution and growing demand for green energy, the most of research is focused on renewable and sustainable energy. In this work, the PEM fuel cell is proposed as a solution to reduce the impact of the internal combustion engines on air pollution. In this paper a PEM fuel cell, battery and supercapacitor energy conversion system is proposed to ensure the energy demand for an electric vehicle is achieved. The storage system consisting of a battery and supercapacitor offers good performance in terms of autonomy and power availability. In this paper, an energy management of the PEM fuel cell electric vehicle has been first simulated in Matlab/Simulink environment and the results are discussed. Second, a Realtime experimental set up is used to test the performance of the proposed PEM fuel cell electric vehicle system. Experimental results have shown that the proposed system is able to satisfy the energy demand of the electric vehicle.