Now showing items 1-20 of 655

• #### Development of an ambisonic guitar system GASP: Guitars with ambisonic spatial performance

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.
• #### Impact of social distancing to mitigate the spread of COVID-19 in a virtual environment

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

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.
• #### Research and implementation of intelligent decision based on a priori knowledge and DQN algorithms in wargame environment

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
• #### WiFi probes sniffing: an artificial intelligence based approach for MAC addresses de-randomization

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.
• #### CloudIoT-based Jukebox Platform: a music player for mobile users in Café

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.
• #### A first look at privacy analysis of COVID-19 contact tracing mobile applications

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.
• #### Arabic machine translation: A survey of the latest trends and challenges

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.
• #### MRI brain classification using the quantum entropy LBP and deep-learning-based features

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.
• #### Z-boson production in p-Pb collisions at $$\sqrt{s_{\mathrm{NN}}}$$ = 8.16 TeV and Pb-Pb collisions at $$\sqrt{s_{\mathrm{NN}}}$$ = 5.02 TeV

Measurement of Z-boson production in p-Pb collisions at √sNN = 8.16 TeV and Pb-Pb collisions at √sNN = 5.02 TeV is reported. It is performed in the dimuon decay channel, through the detection of muons with pseudorapidity −4 < ημ < −2.5 and transverse momentum pTμ > 20 GeV/c in the laboratory frame. The invariant yield and nuclear modification factor are measured for opposite-sign dimuons with invariant mass 60 < mμμ < 120 GeV/c2 and rapidity 2.5 < ycmsμμ< 4. They are presented as a function of rapidity and, for the Pb-Pb collisions, of centrality as well. The results are compared with theoretical calculations, both with and without nuclear modifications to the Parton Distribution Functions (PDFs). In p-Pb collisions the center-of-mass frame is boosted with respect to the laboratory frame, and the measurements cover the backward (−4.46 < ycmsμμ < −2.96) and forward (2.03 < ycmsμμ < 3.53) rapidity regions. For the p-Pb collisions, the results are consistent within experimental and theoretical uncertainties with calculations that include both free-nucleon and nuclear-modified PDFs. For the Pb-Pb collisions, a 3.4σ deviation is seen in the integrated yield between the data and calculations based on the free-nucleon PDFs, while good agreement is found once nuclear modifications are considered.
• #### Artificial neural networks training acceleration through network science strategies

The development of deep learning has led to a dramatic increase in the number of applications of artificial intelligence. However, the training of deeper neural networks for stable and accurate models translates into artificial neural networks (ANNs) that become unmanageable as the number of features increases. This work extends our earlier study where we explored the acceleration effects obtained by enforcing, in turn, scale freeness, small worldness, and sparsity during the ANN training process. The efficiency of that approach was confirmed by recent studies (conducted independently) where a million-node ANN was trained on non-specialized laptops. Encouraged by those results, our study is now focused on some tunable parameters, to pursue a further acceleration effect. We show that, although optimal parameter tuning is unfeasible, due to the high non-linearity of ANN problems, we can actually come up with a set of useful guidelines that lead to speed-ups in practical cases. We find that significant reductions in execution time can generally be achieved by setting the revised fraction parameter (ζ) to relatively low values.
• #### Performances analysis of a micro-grid connected multi-renewable energy sources system associated with hydrogen storage

This work highlights the modelling and simulation of a micro-grid connected renewable energy system. It comprises of wind turbine (WT) based on doubly fed induction generator (DFIG), photovoltaic generator (PV), fuel cell (FC) generator, a Hydrogen tank, a water electrolyser used for long-term storage, and a battery bank energy storage system (BBESS) utilized for short-term storage. In this paper, a global control strategy and an energy management strategy are proposed for the overall system. This strategy consists in charging the BBESS and producing hydrogen from the water electrolyser in case of power excess provided from WT-DFIG and photovoltaic generators. Therefore, the FC and the BBESS will be used as a backup generator to supply the demand required power, when the WT-DFIGs and the PV energy are deficient. The effectiveness of this contribution is verified through computer simulations under Matlab/Simulink, where very satisfactory results are obtained.
• #### Recurrent sequences: Key results, applications, and problems

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.
• #### DETN: delay-efficient tolerant network for internet of planet

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.
• #### Experimental validation of fuel cell, battery and supercapacitor energy conversion system for electric vehicle applications

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.
• #### Implementation of fuel cell and photovoltaic panels based DC micro grid prototype for electric vehicles charging station

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.
• #### Proton exchange membrane fuel cell modules for ship applications

In this article, we proposed a more reliable architecture composed of five fuel cell modules (FC), a storage system composed of battery and supercapacitor was also proposed to support the operation of the fuel cell. The main objective of this work is to study the feasibility of using the global system for small marine applications. In this paper, the global system was modeled and then simulated using Matlab/Simulink. The fuel cell is used as the main power source; each fuel cell is connected with a DC bus via a DC–DC boost converter. The Energy Storage System (HESS) is controlled as a fast-bidirectional auxiliary power source, it contains a battery and supercapacitors and each source is connected to the DC bus via a bidirectional buck-boost DC–DC converter (BBDCC). In order to optimize the HESS, the supercapacitors and the batteries are designed to allow high-efficiency operation and minimal weight. The entire system’s energy management algorithm (PMA) is developed to satisfy the energy demand of the boat. Finally, simulation tests are presented in Matlab/Simulink and discussed, where the effectiveness of the proposed system with its control is confirmed.
• #### Acquiring Guideline-enabled data driven clinical knowledge model using formally verified refined knowledge acquisition method

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.
• #### Ubiquitous health profile (UHPr): a big data curation platform for supporting health data interoperability

The lack of Interoperable healthcare data presents a major challenge, towards achieving ubiquitous health care. The plethora of diverse medical standards, rather than common standards, is widening the gap of interoperability. While many organizations are working towards a standardized solution, there is a need for an alternate strategy, which can intelligently mediate amongst a variety of medical systems, not complying with any mainstream healthcare standards while utilizing the benefits of several standard merging initiates, to eventually create digital health personas. The existence and efficiency of such a platform is dependent upon the underlying storage and processing engine, which can acquire, manage and retrieve the relevant medical data. In this paper, we present the Ubiquitous Health Profile (UHPr), a multi-dimensional data storage solution in a semi-structured data curation engine, which provides foundational support for archiving heterogeneous medical data and achieving partial data interoperability in the healthcare domain. Additionally, we present the evaluation results of this proposed platform in terms of its timeliness, accuracy, and scalability. Our results indicate that the UHPr is able to retrieve an error free comprehensive medical profile of a single patient, from a set of slightly over 116.5 million serialized medical fragments for 390,101 patients while maintaining a good scalablity ratio between amount of data and its retrieval speed.
• #### Disrupting resilient criminal networks through data analysis: The case of Sicilian Mafia

Compared to other types of social networks, criminal networks present particularly hard challenges, due to their strong resilience to disruption, which poses severe hurdles to Law-Enforcement Agencies (LEAs). Herein, we borrow methods and tools from Social Network Analysis (SNA) to (i) unveil the structure and organization of Sicilian Mafia gangs, based on two real-world datasets, and (ii) gain insights as to how to efficiently reduce the Largest Connected Component (LCC) of two networks derived from them. Mafia networks have peculiar features in terms of the links distribution and strength, which makes them very different from other social networks, and extremely robust to exogenous perturbations. Analysts also face difficulties in collecting reliable datasets that accurately describe the gangs’ internal structure and their relationships with the external world, which is why earlier studies are largely qualitative, elusive and incomplete. An added value of our work is the generation of two real-world datasets, based on raw data extracted from juridical acts, relating to a Mafia organization that operated in Sicily during the first decade of 2000s. We created two different networks, capturing phone calls and physical meetings, respectively. Our analysis simulated different intervention procedures: (i) arresting one criminal at a time (sequential node removal); and (ii) police raids (node block removal). In both the sequential, and the node block removal intervention procedures, the Betweenness centrality was the most effective strategy in prioritizing the nodes to be removed. For instance, when targeting the top 5% nodes with the largest Betweenness centrality, our simulations suggest a reduction of up to 70% in the size of the LCC. We also identified that, due the peculiar type of interactions in criminal networks (namely, the distribution of the interactions’ frequency), no significant differences exist between weighted and unweighted network analysis. Our work has significant practical applications for perturbing the operations of criminal and terrorist networks.