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Rapid increase in scholarly publications on the web has posed a new challenge to the researchers in finding highly relevant and important research articles associated with a particular area of interest. Even a highly relevant paper is sometimes missed especially for novice researchers due to lack of knowledge and experience in finding and accessing the most suitable articles. Scholarly recommender system is a very appropriate tool for this purpose that can enable researchers to locate relevant publications easily and quickly. However, the main downside of the existing approaches is that their effectiveness is dependent on priori user profiles and thus, they cannot recommend papers to the new users. Furthermore, the system uses both public and non-public metadata and therefore, the system is unable to find similarities between papers efficiently due to copyright restrictions. Considering the above challenges, in this research work, a novel hybrid approach is proposed that separately combines a Content Based Filtering (CBF) recommender module and a Collaborative Filtering (CF) recommender module. Unlike previous CBF and CF approaches, public contextual metadata and paper-citation relationship information are effectively incorporated into these two approaches separately to enhance the recommendation accuracy. In order to verify the effectiveness of the proposed approach, publicly available datasets were employed. Experimental results demonstrate that the proposed approach outperforms the baseline approaches in terms of standard metrics (precision, recall, F1-measure, mean average precision, and mean reciprocal rank), indicating that the proposed approach is more efficient in recommending scholarly publications.

In this work, breakdown strength and lifetime curve of oil-impregnated paper (OIP) are compared at 50Hz and 1500 Hz. For the ramp breakdown tests, sinusoidal voltage ramp of 1 kVp/s is used. The ramp breakdown experiments is conducted on OIP samples made of single and double sheets of OIP. Weibull plots and interpretation of the results are presented. In order to obtain lifetime curves, a step sinusoidal voltage waveform whose peak value is close to the minimum OIP ramp breakdown voltage is applied on single sheet of OIP samples. Time to breakdown is measured, and each experiment is repeated between 20-30 times to obtain good statistical results. The experiments were focused on four different voltage levels and the lifetime curves are plotted for those two target frequencies. Parameters of the lifetime curves are extracted and the interpretation is given.

Due to the increasing size of s (s), their design and optimization phases (i.e., ) grow increasingly complex. At design time, a large design space needs to be explored to find an implementation that fulfills all specifications and then optimizes metrics like energy, area, delay, reliability, etc. At run time, a large configuration space needs to be searched to find the best set of parameters (e.g., voltage/frequency) to further optimize the system. Both spaces are infeasible for exhaustive search typically leading to heuristic optimization algorithms that find some trade-off between design quality and computational overhead. ML can build powerful models that have successfully been employed in related domains. In this survey, we categorize how () may be used and is used for design-time and run-time optimization and exploration strategies of s. A meta-study of published techniques unveils areas in that are well-explored and underexplored with, as well as trends in the employed algorithms. We present a comprehensive categorization and summary of the state of the art on for. Finally, we summarize remaining challenges and promising open research directions.

This paper presents the recently finished EU-funded RICHFIELDS project aimed to design a new research infrastructure that would foster research in the areas of food and nutrition with a focus on consumers' behavior and lifestyle. In this project, an architecture of a new consumer data platform was designed and discussed from the researchers, business, management, ethical and legal points of view. Also new methodology for supporting big and open data standardization and interoperability was developed.

© 2013 IEEE. We present a novel implementation of a Rock-Paper-Scissors (RPS) game interaction with a social robot. The framework is tailored to be computationally lightweight, as well as entertaining and visually appealing through collaboration with designers and animators. The fundamental gesture recognition pipeline employs a Leap motion device and two separate machine learning architectures to evaluate kinematic hand data on-the-fly. The first architecture is used to recognize and segment human motion activity in order to initialize the RPS play, and the second architecture is used to classify hand gestures into rock, paper or scissors. The employed tabletop robot interacts in the RPS play through unique animated gestural movements and vocalizations designed by animators which communicate the robot's choices as well as cognitive reflection on winning, losing and draw states. Performance of both learning architectures is carefully evaluated with respect to accuracy, reliability and run time performance under different feature and classifier types. Moreover, we assess our system during an interactive RPS play between robot and human. Experimental results show that the proposed system is robust to user variations and play style in real environment conditions. As such, it offers a powerful application for the subsequent exploration of social human-machine interaction.

With the advent of Network Function Virtualization (NFV) techniques, a subset of the Internet traffic will be treated by a chain of virtual network functions (VNFs) during their journeys while the rest of the background traffic will still be carried based on traditional routing protocols. Under such a multi-service network environment, we consider the co-existence of heterogeneous traffic control mechanisms, including flexible, dynamic service function chaining (SFC) traffic control and static, dummy IP routing for the aforementioned two types of traffic that share common network resources. Depending on the traffic patterns of the background traffic which is statically routed through the traditional IP routing platform, we aim to perform dynamic service function chaining for the foreground traffic requiring VNF treatments, so that both the end-to-end SFC performance and the overall network resource utilization can be optimized. Towards this end, we propose a deep reinforcement learning based scheme to enable intelligent SFC routing decision-making in dynamic network conditions. The proposed scheme is ready to be deployed on both hybrid SDN/IP platforms and future advanced IP environments. Based on the real GEANT network topology and its one-week traffic traces, our experiments show that the proposed scheme is able to significantly improve from the traditional routing paradigm and achieve close-to-optimal performances very fast while satisfying the end-to-end SFC requirements.

The electrical insulation in mass-impregnated high voltage direct current (HVDC) cables consists of many layers of paper tapes that are wound helically around the conductor and subsequently impregnated in oil. The oil fills the interstices between the fibers in the paper, as well as the larger gaps ("butt gaps") between the revolutions of the helices. In order to assess the electric field in such insulation, the insulation needs to be modeled either as a homogeneous material, or as a composite of several materials. One approach is to model the butt gaps as oil and the rest as a homogeneous material, without paying special attention to the interfaces between the butt gaps and the rest of the insulation. In this work, scanning electron microscopy (SEM) has been used to assess whether or not such an approach is appropriate. The results show that the internal structures in the impregnated paper tapes are much smaller than the paper-free butt gaps. Moreover, the internal structures contain paper-oil interfaces that are comparable to the interfaces between the butt gaps and the rest of the insulation. This justifies the mentioned approach. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

A new interest in the use of game factors while acquiring new knowledge has emerged, and a number of researchers are investigating the effectiveness of the game-based approach in education systems. Recent research in game-based learning suggests that this approach imparts learning by involving learners in the learning process. The game factors generate affective-cognitive reactions that absorb users in playing the game and positively influence the learning. This paper offers a comparison of the learning processes between the game-based learning and pen-and-paper approaches. In this paper the analysis of both learning approaches is realized through a brain-controlled technology, using the Emotiv EEG Tech headset, by analyzing the stress, excitement, relaxation, focus, interest, and engagement that the learner is experiencing while going through both approaches. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

With a significant amount of software now being developed for use in patient-facing studies, there is a pressing need to consider how to design this software effectively in order to support the needs of both researchers and patients. We posit that a microservice architecture—which offers a large amount of flexibility for development and deployment, while at the same time ensuring certain quality attributes, such as scalability, are present—provides an effective mechanism for designing such software. To explore this proposition, in this work we show how the paradigm has been applied to the design of CONSULT, a decision support system that provides autonomous support to stroke patients and is characterised by its use of a data-backed AI reasoner. We discuss the impact that the use of this software architecture has had on the teams developing CONSULT and measure the performance of the system produced. We show that the use of microservices can deliver software that is able to facilitate both research and effective patient interactions. However, we also conclude that the impact of the approach only goes so far, with additional techniques needed to address its limitations. 10.13039/501100000266-UK Engineering & Physical Sciences Research Council (EPSRC) (Grant Number: EP/P010105/1)

Wireless Sensor Networks (WSNs) allow applications to interact with the physical world using sensing nodes deployed in an Internet of Things (IoT). Many WSN sensing nodes have constrained computing and memory capabilities. This paper details a new cache algorithm suitable for use on constrained nodes and its use in an architecture incorporating caching and the flow of data from sensors to services, possibly Cloud-based. This cache algorithm is influenced by the Clock paging algorithm and manages the leases of cached data in its replacement policy, removing the need for a separate process for this. This paper presents implementations of the algorithm in C on the Contiki OS and Java, compares its performance to LRU and considers its suitability for use on constrained WSN nodes.

Artificial neural networks allow the identification of black-box models. This paper proposes a method aimed at replicating the static and dynamic behavior of a DC-DC power converter based on a recurrent nonlinear autoregressive exogenous neural network. The method proposed in this work applies an algorithm that trains a neural network based on the inputs and outputs (currents and voltages) of a Buck converter. The approach is validated by means of simulated data of a realistic nonsynchronous Buck converter model programmed in Simulink and by means of experimental results. The predictions made by the neural network are compared to the actual outputs of the system, to determine the accuracy of the method, thus validating the proposed approach. Both simulation and experimental results show the feasibility and accuracy of the proposed black-box approach.

The AlphaZero algorithm achieved superhuman levels of play in chess, shogi, and Go by learning without domain-specific knowledge except for game rules. This paper targets stochastic games and investigates whether AlphaZero can learn theoretical values and optimal play. Since the theoretical values of stochastic games are expected win rates, not a simple win, loss, or draw, it is worth investigating the ability of AlphaZero to approximate expected win rates of positions. This paper also thoroughly studies how AlphaZero is influenced by hyper-parameters and some implementation details. The analyses are mainly based on AlphaZero learning with lookup tables. Deep neural networks (DNNs) like the ones in the original AlphaZero are also experimented and compared. The tested stochastic games include reduced and stronglysolved variants of Chinese dark chess and EinStein würfelt nicht!. The experiments showed that AlphaZero could learn policies that play almost optimally against the optimal player and could learn values accurately. In more detail, such good results were achieved by different hyper-parameter settings in a wide range, though it was observed that games on larger scales tended to have a little narrower range of proper hyper-parameters. In addition, the results of learning with DNNs were similar to lookup tables.

This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2023.3278372 © Copyright 2023 The Authors. Metaheuristics are primarily developed to explore optimization techniques in many practice areas. Metaheuristics refer to computational procedures leading to finding optimal solutions to optimization problems. Due to the increasing number of optimization problems with large-scale data, there is an ongoing demand for metaheuristic algorithms and the development of new algorithms with more efficiencies and improved convergence speed implemented by a mathematical model. One of the most popular optimization problems is job shop scheduling problems. This paper develops a novel metaheuristic hybrid Parthenogenetic Algorithm (NMHPGA) to optimize flexible job shop scheduling problems for single-machine and multi-machine job shops and a furnace model. This method is based on the principles of genetic algorithms, underlying the combinations of different types of selections, proposed ethnic GA, and hybrid parthenogenetic algorithm. In this paper, a parthenogenetic algorithm combined with ethnic selection GA is tested; the parthenogenetic algorithm version includes parthenogenetic operators: swap, reverse, and insert. The ethnic selection uses different selection operators such as stochastic, roulette, sexual, and aging; then, top individuals are selected from each procedure and combined to generate an ethnic population. The ethnic selection procedure is tested with the PGA types on a furnace model, single-machine job shops, and multi-machines with tardiness, earliness, and due date penalties. A comparison of obtained results of the established algorithm with other selection procedures indicated that the NMHPGA is achieving better objective functions with faster convergence speed. 10.13039/501100007914-Brunel University London

Recent advancements in vehicle-to-everything (V2X) communication have notably improved existing transport systems by enabling increased connectivity and driving autonomy levels. The remarkable benefits of V2X connectivity come inadvertently with challenges which involve security vulnerabilities and breaches. Addressing security concerns is essential for seamless and safe operation of mission-critical V2X use cases. This paper surveys current literature on V2X security and provides a systematic and comprehensive review of the most relevant security enhancements to date. An in-depth classification of V2X attacks is first performed according to key security and privacy requirements. Our methodology resumes with a taxonomy of security mechanisms based on their proactive/reactive defensive approach, which helps identify strengths and limitations of state-of-the-art countermeasures for V2X attacks. In addition, this paper delves into the potential of emerging security approaches leveraging artificial intelligence tools to meet security objectives. Promising data-driven solutions tailored to tackle security, privacy and trust issues are thoroughly discussed along with new threat vectors introduced inevitably by these enablers. The lessons learned from the detailed review of existing works are also compiled and highlighted. We conclude this survey with a structured synthesis of open challenges and future research directions to foster contributions in this prominent field. This work is supported by the H2020-INSPIRE-5Gplus project (under Grant agreement No. 871808), the ”Ministerio de Asuntos Económicos y Transformacion Digital” and the European Union-NextGenerationEU in the frameworks of the ”Plan de Recuperación, Transformación y Resiliencia” and of the ”Mecanismo de Recuperación y Resiliencia” under references TSI-063000-2021-39/40/41, and the CHIST-ERA-17-BDSI-003 FIREMAN project funded by the Spanish National Foundation (Grant PCI2019-103780).

This paper comprehensively utilizes three control freedoms, i.e., switching state selection, zero sequence injection(ZSI) and redundant level modulation(RLM), to maintain capacitor voltage balance in two five-level three-phase converters over the full range of modulation index and power factor. The calculation process of the proposed control method is explained in detail. This method is easy to implement and can be used in other multilevel converters. The first topology is proposed for the first time in this paper. The capacitor voltage controllable region of the second topology has been extended to full operation range without extra circuits. Voltage jumps are found during the deadtime. The performance of the proposed topology and control method is verified by simulation and experiment.

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works This paper proposes a new sliding mode control based on a non-linear switching surface that provides a suboptimal current-limited start-up response in step-down converters. The main idea behind the design consists in combining different switching surfaces. The first one forces the system trajectories to track a constant current path until the output voltage error is small. A non-singular terminal switching surface then drives the trajectories to the vicinity of the steady state operating point. Finally, a first order switching surface regulates the steady state providing very good rates of load and line regulation. The paper presents the design guidelines of the suboptimal sliding mode control and the experimental evaluation that confirms the effectiveness of the proposal. This work was partially supported by the Government of Spain through the Agencia Estatal de Investigacion´ Project DPI2017-85404-P and by the Generalitat de Catalunya through the Project 2017 SGR 872.

The classification of encrypted traffic is becoming ever more relevant in the field of network security management and cybersecurity. Most users are currently using encrypted traffic, which can easily lead to privacy threats, and attackers can identify user behavior through the information obtained. VPN encrypted tunnel is the most popular encrypted tunnel method at present. This paper proposes to use the XGBoost model to classify VPNs and Non-VPNs, normalizing the features extracted from encrypted traffic. Experiments are performed on the public dataset ISCX VPN-nonVPN, and the results show that the XGBoost model has an accuracy of 92.4%. To illustrate the advantages of this model, it is compared with the other 5 classification algorithms. At the same time, this paper applies differential privacy technology to the classification model of encrypted traffic and reduces privacy threats by obfuscating data features.

This paper deals with the design of power efficient switching regulators intended for linear power amplifiers employing envelope tracking techniques in wideband wireless standards. The bottlenecks in existing envelope tracking solutions involve a tradeoff between ripple voltage, slew rate and bandwidth. The slew rate limitation is identified as the main challenge and a `bang-bang' slew-enhancement technique is proposed for overcoming it. This approach enables the use of efficient supply modulators in wideband power amplifiers. The proposed scheme does not significantly degrade PA efficiency and improves the stability of the switching regulator. The prototype has been implemented using the TSMC 0.18 μm technology; schematic simulation results in Cadence® demonstrate the feasibility of the proposed technique.

The emergence of the Internet of Things (IoT) has enabled billions of devices that collect large amounts of data to be connected. Therefore, IoT security has fundamental requirements. One critical aspect of IoT security is data integrity. Cryptographic hash functions are cryptographic primitives that provide data integrity services. However, due to the limitations of IoT devices, existing cryptographic hash functions are not suitable for all IoT environments. As a result, researchers have proposed various lightweight cryptographic hash function algorithms. In this paper, we discuss advanced lightweight cryptographic hash functions for highly constrained devices, categorize design trends, analyze cryptographic aspects and cryptanalytic attacks, and present a comparative analysis of different hardware and software implementations. In the final section of this paper, we highlight present research challenges and suggest future research topics related to the design of lightweight cryptographic hash functions.

The rise of Telemedicine has revolutionized how patients are being treated, leading to several advantages such as enhanced health analysis tools, accessible remote healthcare, basic diagnostic of health parameters, etc. The advent of the Internet of Things (IoT), Artificial Intelligence (AI) and their incorporation into Telemedicine extends the potential of health benefits of Telemedicine even further. Therefore, the synergy between AI, IoT, and Telemedicine creates diverse innovative scenarios for integrating cyber-physical systems into medical health to provide remote monitoring and interactive assistance to patients. Data from World Health Organization reports that 7.4 million people died because of Atrial Fibrillation (AF), recognizing the most common arrhythmia associated with human heart rate. Causes like unhealthy diet, smoking, poor resources to go to the doctor and based on research studies, about 12 and 17.9 million of people will be suffering the AF in the USA and Europe, in 2050 and 2060, respectively. The AF as a cardiovascular disease is becoming an important public health issue to tackle. By using a systematic approach, this paper reviews recent contributions related to the acquisition of heart beats, arrhythmia detection, IoT, and visualization. In particular, by analysing the most closely related papers on Convolutional Neural Network (CNN) and IoT devices in heart disease diagnostics, we present a summary of the main research gaps with suggested directions for future research.

With the use of sensorless control strategy, mechanical position sensors can be removed from the gearbox, so as to decrease the maintenance costs and enhance the system robustness. In this paper, a switching PI control based model reference adaptive system (MRAS) observer using Fuzzy-Logic-Controller (FLC) is introduced for sensorless control of permanent magnet synchronous motor (PMSM) drives. The main work and innovation of this paper include: 1) A disturbance observer based voltage compensation method is proposed to solve the problems of voltage distortion in voltage source inverter (VSI) fed PMSM systems. 2) A double closed-loop FLC is designed for speed and current control. The response performance of speed regulation and the accuracy of the command voltage are improved, and the difficulty of manual adjustment of control parameters is avoided. 3) A sensorless control strategy of a switching PI control based MRAS observer is proposed for stator resistance tracking. By setting different thresholds, the proposed MRAS observer can choose the appropriate PI adaptive mechanism based on the amplitude of the current error variable, which can improve the accuracy of resistance estimation and improve the dynamic performance of sensorless control. 4) The experimental results are given to verify the availability of the proposed scheme.

Site specific propagation models for highly dynamic vehicle-to-vehicle scenarios become time consuming as the identification of visible surfaces must be performed independently for each transmitter location. Moreover the validation of higher order of ray interactions presents a significant computational overhead. This paper presents an efficient ray-tracing algorithm for vehicle-to-vehicle propagation prediction. The model presented in this paper extends a previous model that uses a pre-computed database of intra-visibility of walls and edges in the environment. The model computes the list of faces and edges that are directly visible to the mobile receiver. This visibility information is then used to reduce the image-tree thereby accelerating the ray validation. The paper also presents an efficient technique to compute diffuse scattered rays in a timely manner. The validation results show considerable time saving over previous models.

Failure risk must be tiny in high-integrity systems, such as those in cars, satellites and aircraft. Hence, safety measures must be deployed to avoid a single fault leading to a failure. Redundancy has been often used to address this concern, but it has been proven insufficient if a single fault can cause the same error in all redundant elements, which defeats the purpose of redundancy for error detection. Hence, to avoid this scenario, diversity is implemented along with redundancy, being lockstep execution the most popular diverse redundancy solution for computing cores. However, classic lockstep solutions have non-negligible limitations if implemented in hardware (e.g., half of the cores can only be used for redundant execution and are not even visible at user level), or in software (e.g., the software loop to enforce staggering is long and costs performance). This paper tackles the limitations of classic lockstep solutions by providing an extended analysis and evaluation of SafeDE, a Diversity Enforcement hardware module combining the short loop to enforce diversity of hardware solutions, and the nonintrusiveness of software solutions. Hence, cores can operate in lockstep mode efficiently or run independent tasks. In this paper, we present SafeDE and its rationale, its application to N-modular systems, its hardware and software integration, and an evaluation showing its performance and area efficiency, and its behavior in the presence of faults. This work was supported in part by the European Union’s Horizon 2020 Research and Innovation Programme under Grant 871467, and in part by the Spanish Ministry of Science and Innovation under Grant PID2019-107255GB-C21/AEI/10.13039/501100011033.

This paper presents a design of water-based flexible, optically transparent and frequency reconfigurable antenna having a unidirectional radiation pattern. The proposed antenna incorporates a dipole radiator on top of a reflector plane that makes the radiation unidirectional. The reflector is constructed from pure water enclosed inside a circular cavity made of polydimethylsiloxane (PDMS), which consists of several ring chambers arranged concentrically. Frequency tuning operation is achieved by an innovative mechanical tuning mechanism involving controlling the water configuration inside the circular cavity. Depending upon the presence or absence of the water in different chambers, the resonance frequency changes accordingly while maintaining the radiation pattern unidirectional. Detail simulation investigation of the proposed technique is presented in this paper.

With the rapid development of mobile computing, mobile edge computing has increasingly become an essential means to meet the computing power requirements of intelligent networked vehicles. However, users with high mobility and coupled dynamics are rarely considered in the edge computing paradigms. In this paper, we studied a UAV-assisted mobile edge computing system with multi-platoon vehicles. Our paper aims to maximize the system’s weighted global energy efficiency, which can flexibly adjust each vehicle’s energy consumption according to user preferences and system needs. In particular, we design a controller for platooning vehicles based on a two-dimensional path-following model and Frenet frames, and model the coupled characteristics of air-to-ground communications and onboard computation. Furthermore, due to the non-convexity of the objective function and constraints of the optimization problem, we propose an optimization algorithm based on the sequential quadratic programming method. The simulation results show that the proposed method significantly surpasses conventional schemes.

Public hospitals receive and triage a large volume of medical referrals for otorhinolaryngology annually and it can be a challenge to derive knowledge from them as they are written in unstructured text and may be unavailable in electronic formats. Acquiring knowledge and insights from these referrals are important to public health management and policymakers. Triaging of general practitioner (GP) referrals for ear, nose, and throat (ENT) specialists is a manual process performed by experienced clinicians, but it is time-consuming. This paper proposes utilising machine learning and data mining to automate the process of referrals. In this study, an ensemble of machine learning algorithms to perform clinical text mining against the unstructured referral text in order to derive the relationship among the discovered medical terms was proposed and implemented. A set of comprehensive term sets' association rules which describe the entire referral dataset's characteristics was obtained from the association rule mining experiments. The neural network-based text classification model that can classify referrals with high accuracy was developed, tested and reported in this paper.

This paper envisions to augment the Radio Access Network (RAN) infrastructure in Beyond 5G(B5G) systems by exploiting relaying capabilities of user equipment (UE) as a way to improve the coverage, capacity and robustness. Despite the concept and enabling technologies have been in place for some time, their efficient realization requires the conception and development of new features in B5G systems. Among them, this paper focuses on the Relay UE (RUE) activation decision making, in charge of deciding where and when a UE is suitable to be activated to relay traffic from other UEs. Specifically, the paper analyses seven RUE activation strategies that differ on the criteria and the type of context information considered for this decision-making problem. The considered strategies are evaluated through system level simulations in a realistic urban scenario with the objective of assessing the value of each type of context information. Results reveal that the most efficient strategies from the perspective of outage probability reduction are those that account for the number of UEs that would be served by a RUE based on the experienced spectral efficiency. This paper is part of ARTIST project (ref. PID2020-115104RB-I00) funded by MCIN/AEI/10.13039/ 501100011033.

The H2020 EIC-FTI De-RISC project develops a RISC-V space-grade platform to jointly respond to several emerging, as well as longstanding needs in the space domain such as: (1) higher performance than that of monocore and basic multicore space-grade processors in the market; (2) access to an increasingly rich software ecosystem rather than sticking to the slowly fading SPARC and PowerPC-based ones; (3) freedom (or drastic reduction) of export and license restrictions imposed by commercial ISAs such as Arm; and (4) improved support for the design and validation of safety-related real-time applications, (5) being the platform with software qualified and hardware designed per established space industry standards. De-RISC partners have set up the different layers of the platform during the first phases of the project. However, they have recently boosted integration and assessment activities. This paper introduces the De-RISC space platform, presents recent progress such as enabling virtualization and software qualification, new MPSoC features, and use case deployment and evaluation, including a comparison against other commercial platforms. Finally, this paper introduces the ongoing activities that will lead to the hardware and fully qualified software platform at TRL8 on FPGA by September 2022. This project has received funding from the European Union’s Horizon 2020 Research and Innovation programme under Grant Agreement EIC-FTI 869945. BSC work has also been partially supported by the Spanish Ministry of Science and Innovation under grant PID2019-07255GBC21/AEI/10.13039/501100011033.

This paper considers hybrid beamforming consisting of analog beamforming (ABF) coupled with digital baseband beamforming (DBF) which is designed for multi-user (MU) multiple input multiple output (MIMO) millimeter-wave (mmWave) communications. ABF uses a limited number of radio frequency (RF) chains and finite-resolution phase-shifters to alleviate the power consumption at the base station (BS), while DBF uses either zero-forcing beamforming (ZFB) or regularized zero forcing beamforming (RZFB) to restrain MU interference. The joint design of ABF and DBF constitutes a computationally challenging mixed discrete continuous optimization problem. The paper develops efficient algorithms for its solution, which iterate scalable-complex expressions. Furthermore, we conceive a new class of MU RZFB for attaining higher rates. Simulations are provided to demonstrate the viability of the proposed algorithms and the advantages of the conceived RZFB.

This paper presents a deep learning approach to aid dead-reckoning (DR) navigation using a limited sensor suite. A Recurrent Neural Network (RNN) was developed to predict the relative horizontal velocities of an Autonomous Underwater Vehicle (AUV) using data from an IMU, pressure sensor, and control inputs. The RNN network is trained using experimental data, where a doppler velocity logger (DVL) provided ground truth velocities. The predictions of the relative velocities were implemented in a dead-reckoning algorithm to approximate north and east positions. The studies in this paper were twofold I) Experimental data from a Long-Range AUV was investigated. Datasets from a series of surveys in Monterey Bay, California (U.S) were used to train and test the RNN network. II) The second study explore datasets generated by a simulated autonomous underwater glider. Environmental variables e.g ocean currents were implemented in the simulation to reflect real ocean conditions. The proposed neural network approach to DR navigation was compared to the on-board navigation system and ground truth simulated positions.

Reinforcement Learning (RL)-based algorithmic solutions have been profusely proposed in recent years for addressing multiple problems in the Radio Access Network (RAN). However, how RL algorithms have to be trained for a successful exploitation has not received sufficient attention. To address this limitation, which is particularly relevant given the peculiarities of wireless communications, this paper proposes a functional framework for training RL strategies in the RAN. The framework is aligned with the O-RAN Alliance machine learning workflow and introduces specific functionalities for RL, such as the way of specifying the training datasets, the mechanisms to monitor the performance of the trained policies during inference in the real network, and the capability to conduct a retraining if necessary. The proposed framework is illustrated with a relevant use case in 5G, namely RAN slicing, by considering a Deep Q-Network algorithm for capacity sharing. Finally, insights on other possible applicability examples of the proposed framework are provided. © 2022 IEEE. This paper is part of ARTIST project (ref. PID2020-115104RB-I00) funded by MCIN/AEI/10.13039/ 501100011033 and PORTRAIT project (ref. PDC2021-120797-I00) funded by MCIN/AEI/10.13039/501100011033 and by European Union Next GenerationEU/PRTR.

The reconfigurable intelligent surface (RIS) technology has gained increased attention for improving the performance and efficiency of the fifth-generation (5G) millimeter-wave (mmWave) wireless communication by obviating the propagation and blockage issues. On the other hand, the great flexibility of unmanned aerial vehicles (UAVs) has made them effective gadgets to enhance the coverage of wireless communication networks. Combining these two emerging technologies, the RIS-outfitted UAV (RISoUAV) is a promising solution for providing line-of-sight (LoS) wireless links for mobile targets (MTs) in obstructed high-dense urban areas. In this light, high-speed real-time communication is essential for some vital municipal services like ambulances, fire engines, security guards, police, etc. This important goal is achievable thanks to the RISoUAV-assisted 5G/quasi-optic wireless communication. This paper develops a framework for optimal navigation of a team of RISoUAVs for maintaining LoS links with a team of ground vehicles in a dense urban area. The trajectories of the RISoUAVs are optimized considering the energy efficiency, communication channel gains, and constraints associated with RISoUAVs motion and LoS service. A consensus-based coordinating approach is adopted to coordinate the RISoUAVs navigation to cover all MTs under a good quality of service. Simulation results show the effectiveness of the method. Note to Practitioners—In this paper, we consider a scenario where vehicles need to have high-speed, uninterrupted data links in obstructed, highly dense urban environments. Due to spectrum crunch, the data links are increasingly likely to rely on a high-frequency spectrum, including mmWave with quasi-optic nature, visible light communications, or even laser. However, the obstructed LoS and propagation are critical issues with the 5G and beyond as they rely on the availability of an unobstructed path (e.g., the LoS or a quality reflective path). On the other hand, the RIS performs as a passive reflective element that provides an indirect LoS link, a one-bounce channel, to improve the performance and efficiency of mmWave, and beyond, wireless communication networks. UAVs equipped with RISs are suggested in this paper to be adopted as aerial transponders to reflect signals and facilitate communication in high-density environments. Therefore, the problem of UAV navigation and 3D trajectory planning should be addressed regarding the application, particularly, for providing LoS service for mobile vehicles with arbitrary directions. Autonomous navigation of RISoUAVs for LoS wireless communication is a natural multi-dimensional extension of autonomous navigation with obstacle avoidance where regions in which LoS communication is lost are viewed as obstacles to avoid. However, the environment and valid LoS links can change dynamically due to moving vehicles in the obstructed environment. This makes the navigation design an NP-hard problem that is tackled in this paper by developing an effective navigation program.

This paper compares four sliding-mode control strategies for a dual active bridge. The proposed control algorithms define different switching functions resulting in first-and second-order controllers, and using both discontinuous and continuous sliding-mode algorithms. The paper details the design stage and includes simulation and experimental tests to better compare the performance for each control scheme.

Peer-to-Peer (P2P) energy trading platforms envisioned energy sectors to satisfy the increasing demand for energy. The vision of this paper is not only to trade energy but also to have part of it being shared. Therefore, this paper presents FederatedGrids which is a P2P energy trading and sharing platform inside and across microgrids. Energy sharing allows exchanging energy between the categories of consumers and prosumers in return for future benefits. FederatedGrids platform uses blockchain and federated learning to enable autonomous activities while providing trust and privacy among all participants. Indeed, based on various smart contracts using federated learning, FederatedGrids calculates a prediction of the future energy production and demand allowing the system to autonomously switch between trading and sharing, and enabling the prosumers to make decisions related to their participation in the energy sharing process. Up to our knowledge, this work is the first attempt to create a hybrid energy trading and sharing platform, with the real sharing meaning, and that uses federated learning over the smart contract for energy demand prediction. The experimental results showed a 17.8% decrease in energy cost for consumers and a 76.4% decrease in load over utility grids., NA

The variable switching frequency space vector pulse width modulation technique based on current ripple prediction (CRP-VSFSVPWM) can effectively suppress electromagnetic noise, but its effectiveness depends on the accuracy of load parameters. In this paper, a virtual flux ripple prediction model is developed. Similar to the current ripple prediction model, it is also a time-domain ripple prediction model, but it does not require load parameters and has low complexity. Furthermore, a variable switching frequency space vector modulation strategy based on three-phase virtual flux ripple is designed in this paper. The proposed method is fully validated by comparison with the fixed switching frequency space vector modulation and CRP-VSFSVPWM on a three-phase inverter. Experimental results confirm that the proposed technique can significantly suppress electromagnetic noise while maintaining current harmonic performance, and its performance is not affected by load parameters.

In this paper, a brief survey of data preprocessing methods is presented. Specifically, the data preprocessing methods used in the smart grid (SG) domain are surveyed. Also, with the advent of SG, data collection on a large scale became possible. The data is essential for electricity demand, generation and price forecasting, which plays an important role in making energy efficient decisions, and long and short term predictions regarding energy generation, consumption and storage. However, the forecasting accuracy decreases when data is used in raw form. Hence, data preprocessing is considered essential. This paper provides an overview of the data preprocessing methods and a detailed discussion of the methods used in the existing literature. A comparison of the methods is also given. A survey of closely related survey papers is also presented and the papers are compared based on their contributions. Moreover, based on the discussion of the data preprocessing methods, a narrative is built with a critical analysis. Finally, future research directions are discussed to guide the readers.

The global teleoperation and telerobotics market is projected to reach 98.3billion (USD) by 2027. With the benefits of 5G coverage almost anywhere in the world will allow teleoperated robots to be implemented globally via the cloud. Remote Guided Robotics has excellent potential in existing markets, attempting to remove operators from dangerous tasks and providing access to labour in remote areas, where labour is scarce. This research introduces a configuration of technology that reduces the knowledge gap of what is required to successfully achieve high accuracy and realistic control of a robot in real-time. The system implemented in this paper utilises an infrared camera from Leap Motion to capture and track hand gestures and movements, a Kuka 6 DOF robot and software packages to follow the hand movements from the UltraLeap Leap Motion Controller (LMC) in real-time, and an HTC VR headset for a mixed reality experience where two cameras are placed in the robot work-cell to send back a 3D stereoscopic image to allow the operator to have an immersed experience when remotely controlling the robot. The system proved to have a repeatability of approx. ± 10 mm, and an accuracy of 0.1mm. The gripper was able to successfully pick up 86% of the time, with a final test of gripping an item and guiding it through a bandsaw at an average rate of 24.2 seconds. Testings and simulations completed in this paper outline the level of accuracy and repeatability of the remote guide robotic system. This teleoperated robotic system has the ability to protect operators' safety and meet the explosive growth of needs in the various industries.

This paper envisages a Beyond 5G (B5G) Radio Access Network (RAN) in which the relaying capabilities offered by user equipment (UE) are used as a way to improve the coverage and robustness of the network. The paper proposes and develops the functional framework for supporting the activation of the suitable relay UEs (RUEs) in coverage constrained scenarios. It is based on characterizing each potential RUE through a utility metric that measures the coverage enhancements brought to the network when the RUE is activated. To derive this metric for all the candidate RUEs, the framework considers the use of a Network Digital Twin that allows the offline analysis of different configurations in a fast and safe way. Using the proposed framework, a RUE activation algorithm is proposed and evaluated. The obtained results reflect that significant outage probability reductions can be obtained in the scenario under different traffic distributions thanks to the activation of the RUEs with the highest utility. This paper is part of ARTIST project (ref. PID2020- 115104RB-I00) funded by MCIN/AEI/10.13039/ 501100011033. The work is also funded by the Spanish Ministry of Economic Affairs and Digital Transformation and the European Union - NextGenerationEU under project OPTIMAIX_NDT (Ref. TSI-063000-2021-35).

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works The presence of unbalanced loads in power systems creates voltage imbalances that perturb the operation of sensitive loads like induction motors, power electronics converters and adjustable speed drives. This paper presents a novel control scheme that achieves accurate voltage balance at any node of an islanded AC microgrid with seamless integration with the existing hierarchical control of the microgrid. A master-slave architecture is adopted for the unbalance compensation due to its low network traffic requirements and its modular conception. The unbalance compensation follows a collaborative principle where current injections are computed as a function of the current rating of each inverter thus avoiding the overcurrent stress of the unbalance compensation by a single inverter. The paper presents the stability analysis and the design guidelines for the control parameters. The performance of the control proposal has been successfully tested in a laboratory microgrid. Selected experimental results are presented.

This paper considers an unmanned aerial vehicles (UAV) communication network, where UAV operate as an aerial mobile relay between a source and a destination of the network. To capture the “timelines” of the received information at the destination node, a new performance metric named age of information (AoI) is considered. In addition, a short packet communication scheme maintains low latency in the proposed UAV wireless communication system. The finite block-length theory investigates the performances of short packet communications scheme in the UAV-assisted wireless communications system. In this paper, the Average Age of Information (AAoI) is estimated by applying the Stochastic Hybrid Systems (SHS) model. The SHS model comprises discrete states represents events that reset the AoI process and continuously dynamics that represent linearly growing age processes. Finally, a closed-form expression for the AAoI of the proposed UAV wireless communication system is derived and it can be used to estimate the optimal altitude, block length and other parameters. peerReviewed

Learning to program is often reported as challenging. Difficulties might be connected to, e.g., acquisition of core concepts like iteration, specific language constructs, and program design. Additionally, mastery of a programming language’s constructs does not consequently translate into solving new programming problems. These challenges have to be taken into account in the context of in-service teachers’ professional development (PD). In this paper, we address challenges teachers face when learning to program, considering these closely related questions: What do teachers perceive as difficult and how does this impact the perception of challenges their students will face? How does this influence the perception of teaching programming? The paper is based on the analysis of 178 reflection notes delivered by in-service teachers attending a university-level course on programming to identify issues related to the research questions. Out of the topics that we have selected from the literature as challenging for newcomers, the most difficult ones for teachers seem to be writing code and pseudocode to solve a specific problem. The easiest ones are understanding variable initialization and if statements. Also, teachers express that pupils will struggle with many aspects of programming and point to the complexity and students’ misconceptions inherent in several programming concepts such as loops. Our results suggest that it is not necessarily difficult to learn to program if in-service teachers are given paid time to learn. Computer Science in Schools: A Literature Mapping of Professional Development for In-Service Teachers

Human-Robot Interaction (HRI) is one of the most rapidly emerging fields in robotic applications over the years. One direction of the improvements in the HRI field is by adding the capability of emotional understanding as a fundamental part of human-human interaction necessities. Human emotion understanding has been studied through the well-known Heart Rate Variability (HRV) analysis recently. In this paper, two different methods of classification are proposed to find the relations between activity, heart rate, and emotional states. Two individual k-Nearest Neighborhood (kNN)-based classifications used in the first method and implemented for each dataset of pre- processed accelerometer data and HRV data where both aim to estimate the user's emotion and activity data at the same time. The features of the frequency domain-based HRV data and the user's activity data are combined into a new dataset and two different classifiers of Multilayer Perceptron (MLP) and Support Vector Machines (SVM) were used in the experimental evaluations. Performance comparisons are presented to show the efficiency. Results from both methods are analyzed and reported in this paper.

This paper proposes a computationally efficient method for pose-graph optimization that makes use of a multi-resolution representation of pose-graph transformation constructed on a spanning tree. It is shown that the proposed spanning tree-based hierarchy has a number of advantages over the previously known serial chain-based hierarchy in terms of preservation of sparsity and compatibility with parallel computation. It is demonstrated in numerical experiments using several public datasets that the proposed method outperforms a state-of-the-art solver for large-scale datasets.

The commercial fifth-generation (5G) wireless communications networks have already been deployed with the aim of providing high data rates. However, the rapid growth in the number of smart devices and the emergence of the Internet of Everything (IoE) applications, which require an ultra-reliable and low-latency communication, will result in a substantial burden on the 5G wireless networks. As such, the data rate that could be supplied by 5G networks will unlikely sustain the enormous ongoing data traffic explosion. This has motivated research into continuing to advance the existing wireless networks toward the future generation of cellular systems, known as sixth generation (6G). Therefore, it is essential to provide a prospective vision of the 6G and the key enabling technologies for realizing future networks. To this end, this paper presents a comprehensive review/survey of the future evolution of 6G networks. Specifically, the objective of the paper is to provide a comprehensive review/survey about the key enabling technologies for 6G networks, which include a discussion about the main operation principles of each technology, envisioned potential applications, current state-of-the-art research, and the related technical challenges. Overall, this paper provides useful information for industries and academic researchers and discusses the potentials for opening up new research directions.

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The popularity of mobile broadband connectivity continues to grow and thus, the future wireless networks are expected to serve a very large number of users demanding a huge capacity. Employing larger spectral bandwidth and installing more access points to enhance the capacity is not enough to tackle the stated challenge due to related costs and the interference issues involved. In this way, frequency resources are becoming one of the most valuable assets, which require proper utilization and fair distribution. Traditional frequency resource management strategies are often based on static approaches, and are agnostic to the instantaneous demand of the network. These static approaches tend to cause congestion in a few cells, whereas at the same time, might waste those precious resources on others. Therefore, such static approaches are not efficient enough to deal with the capacity challenge of the future network. Thus, in this paper we present a dynamic access-aware bandwidth allocation approach, which follows the dynamic traffic requirements of each cell and allocates the required bandwidth accordingly from a common spectrum pool, which gathers the entire system bandwidth. We perform the evaluation of our proposal by means of real network traffic traces. Evaluation results presented in this paper depict the performance gain of the proposed dynamic access-aware approach compared to two different traditional approaches in terms of utilization and served traffic. Moreover, to acquire knowledge about access network requirement, we present a machine learning-based approach, which predicts the state of the network, and is utilized to manage the available spectrum accordingly. Our comparative results show that, in terms of spectrum allocation accuracy and utilization efficiency, a well designed machine learning-based bandwidth allocation mechanism not only outperforms common static approaches, but even achieves the performance (with a relative error close to 0.04) of an ideal dynamic system with perfect knowledge of future traffic requirements. This work was supported in part by the EU Horizon 2020 Research and Innovation Program (5GAuRA) under Grant 675806, and in part by the Secretaria d’Universitats i Recerca del Departament d’Empresa i Coneixement from the Generalitat de Catalunya under Grant 2017 SGR 376.

Quality of transmission (QoT) prediction, one of the essential technologies to enable low-margin automatic optical networks, has recently attracted increasing interest among researchers [1]. Analytical model-based QoT estimation, such as the GNPy software, provides system-level optimization for static operations [2]. However, in low-margin dynamic optical networks, QoTs should be predicted based on traffic payload, link configurations, and other parameters to obtain precise QoTs for dynamic configurations with further reduced link margins. In [3], a point-to-point multi-channel QoT prediction based on artificial neural networks(ANN) was proposed, however, lacking end-to-end solutions in terms of training and deployment. P. Safari, etc., developed a network-wide modelfor QoT classification based on a deep convolutional neural network (DCNN) model [4]. However, the complex DCNN model strongly depends on network typologies and requires a huge amount of data from the whole network. which makes it infeasible to train and deploy the model in a practical optical network. In this paper, two cascaded multi-channel ANN models are developed, co-trained, and demonstrated to predict link penalties over a 986-km field-trial testbed consisting of two links and ROADMs. With the two-cascaded ANN setting, the QoT of a channel over the two links is predicted considering traffic payloads in two links with precision of ± 0.16 dB. The co-training of the two cascaded penalty prediction models also provides a feasible solution to provide network-level QoT predictions.

Data-flow programming models have become a popular choice for writing parallel applications as an alternative to traditional work-sharing parallelism. They are better suited to write applications with irregular parallelism that can present load imbalance. However, these programming models suffer from overheads related to task creation, scheduling and dependency management, limiting performance and scalability when tasks become too small. At the same time, many HPC applications implement iterative methods or multi-step simulations that create the same directed acyclic graphs of tasks on each iteration. By giving application programmers a way to express that a specific loop is creating the same task pattern on each iteration, we can create a single task directed acyclic graph (DAG) once and transform it into a cyclic graph. This cyclic graph is then reused for successive iterations, minimizing task creation and dependency management overhead. This paper presents the taskiter, a new construct we propose for the OmpSs-2 and OpenMP programming models, allowing the use of directed cyclic task graphs (DCTG) to minimize runtime overheads. Moreover, we present a simple immediate successor locality-aware heuristic that minimizes task scheduling overhead by bypassing the runtime task scheduler. We evaluate the implementation of the taskiter and the immediate successor heuristic in 8 iterative benchmarks. Using small task granularities, we obtain a geometric mean speedup of 2.56x over the reference OmpSs-2 implementation, and a 3.77x and 5.2x speedup over the LLVM and GCC OpenMP runtimes, respectively. This work was supported in part by the European Union’s Horizon 2020/EuroHPC Research and Innovation Programme (DEEP-SEA) under Grant 955606; in part by the Spanish State Research Agency—Ministry of Science and Innovation, Generalitat de Catalunya, under Project PCI2021121958 and Project 2021-SGR-01007; in part by the Spanish Ministry of Science and Technology under Contract PID2019-107255GB; and in part by Severo Ochoa under Grant CEX2021-001148-S/MCIN/AEI/10.13039/501100011033.

This paper is concerned with the problem of finite-horizon energy-to-peak state estimation for a class of networked linear time-varying systems. Due to the inherent vulnerability of network-based communication, the measurement signals transmitted over a communication network might be intercepted by potential eavesdroppers. To avoid information leakage, by resorting to an artificial-noise-assisted method, we develop a novel encryption-decryption scheme to ensure that the transmitted signal is composed of the raw measurement and an artificial-noise term. A special evaluation index named secrecy capacity is employed to assess the information security of signal transmissions under the developed encryption-decryption scheme. The purpose of the addressed problem is to design an encryption-decryption scheme and a state estimator such that: 1) the desired secrecy capacity is ensured; and 2) the required finite-horizon $l_{2}-l_{\infty}$ performance is achieved. Sufficient conditions are established on the existence of the encryption-decryption mechanism and the finite-horizon state estimator. Finally, simulation results are proposed to show the effectiveness of our proposed encryption-decryption-based state estimation scheme. 10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62273087,61933007,62273088,U21A2019,62073180).

In this paper, to accelerate data acquisition and image reconstruction procedures in a multistatic short-range microwave imaging scenario, an orthogonal coding approach with Fourier domain processing is presented. First, a special two-dimensional (2D) multiple-input multiple-output (MIMO) structure is introduced to fully electronically synthesize the 2D aperture. Then, the model of the transmitted and received signals by a MIMO stepped-frequency-modulated radar is presented, with special considerations about orthogonal, balanced and optimal sequences. On the receiver side, the backscatter frequency response extraction process is formulated with the aim of obtaining individual information of all channels. Finally, based on the introduced model, a fast Fourier-based algorithm with reduced dimensions, named MIMO coded generalized reduced dimension Fourier (CGRDF), is mathematically derived. It includes extracting phase and amplitude compensators with the aim of mapping 4D to 2D spatial data, transferring the backscatter transfer function from the spatial domain to the wavenumber domain, extracting the smoothing filter, compensating the curvature of the wavefront of all scatterers, extracting the reflectivity function and an additional range compensator. The results of numerical simulations show the satisfactory and reliable performance of the proposed approach in terms of the information retrieval process and processing speed.