Search

This paper revisits the analysis of the well-known infeed effect on transmission lines distance protection. A new adaptive algorithm to overcome this drawback is proposed, which requires solely impedance data and the operational status of power apparatus to perform overreaching distance zones settings. A comparison of the proposed strategy and the traditional one is performed. The obtained results demonstrate that the proposed settings ensure the correct operation of overreaching zones. Thereby, the backup coverage is improved significantly, besides avoiding distance zones overlapping between adjacent lines. It reveals the proposed strategy usefulness and value from the practical point of view, since it can be implemented with technology readily available on the market.

Many recent research has focused on graphic design and shape of the packaging, and their effects on the customer's decision-making process as two separate categories. Since the shape of the packaging along with the graphic design plays an important role in the visual appearance of the packaging and attracts the customer's attention, the main objective of this research was to find out which packaging shapes and graphic elements customers find most attractive, as well as to understand the general opinion of customers about the influence of packaging aesthetics on their decision-making process when purchasing a product. Methodology of this research was conducted in two stages, from which the first one included creating three different graphic design solutions combined with three shape categories, and three variations of added value, for the same food packaging. The second part of the research covered an online questionnaire, in which a group of 50 people participated. This questionnaire focused on three variables: graphic design of the packaging, packaging shape and it’s added value. These variables were evaluated using the Likert scale. Through the questionnaire, participants were asked to answer general questions about the impact of the packaging visual appearance and added value, on their decision-making process when buying a product, as well as to choose the most appealing packaging between the given options. The research results showed that customers preferred non-standard packaging shapes and simple graphic design, as well as packaging with added value. The results of this research prove that the aesthetics of packaging has an impact on the customer's perception of a product, which means that packaging plays an important role in product marketing.

Emotion recognition using electroencephalogram (EEG) signals is getting more and more attention in recent years. Since the EEG signals are noisy, non-linear and have non-stationary properties, it is a challenging task to develop an intelligent framework that can provide high accuracy for emotion recognition. In this paper, we propose a new model for emotion recognition that will be based on the creation of feature maps based on the topographic (TOPO-FM) and holographic (HOLO-FM) representation of EEG signal characteristics. Deep learning has been utilized as a feature extractor method on feature maps, and afterward extracted features are fused together for the classification process to recognize different kinds of emotions. The experiments are conducted on the four publicly available emotion datasets: DEAP, SEED, DREAMER, and AMIGOS. We demonstrated the effectiveness of our approaches in comparison with studies where authors used EEG signals that classify human emotions in the two-dimensional space. Experimental results show that the proposed methods can improve the emotion recognition rate on the different size datasets.

To accurately predict motor performance, considering the effects of core losses is of great importance, especially at speeds much higher than the base where the core losses represent a considerable portion of the total drive losses. Neglecting the effects of core losses leads to an error in the switched reluctance motor (SRM) performance prediction. In this study, a previously developed non‐linear SRM model with multiphase coupling is extended by considering the instantaneous effects of core losses on the SRM performance. Using the new model, the nature of these effects is determined. A comparison of the simulation results of the proposed, basic, and finite element method (FEM) model confirms the importance of considering the effects of core losses on motor performance. Besides the FEM model, the accuracy of the proposed model is verified by experiments.

Modern social trends are intensively transforming supply chains and the last mile as their most complex and most expensive segment. For the realization of the last mile, various solutions can be defined which combine initiatives, technologies, and concepts of city logistics. The successful implementation of these solutions depends on the characteristics of the city, the goals of stakeholders, and the ability to achieve economic, social, and environmental sustainability. In accordance with that, this paper defines innovative sustainable last mile solutions and evaluates their potential application in the real-life logistics system of the city. As evaluation requires consideration of a large number of criteria, this is a problem of multicriteria decision-making, and for solving it, a novel hybrid model that combines Delphi, FARE (Factor Relationship), and VIKOR (Višekriterijumska Optimizacija i Kompromisno Rešenje) methods in the fuzzy environment has been developed. The applicability of the model is demonstrated in the example of evaluating the last mile solution for the central business district of the City of Belgrade. A combination of microconsolidation centers and autonomous vehicles is obtained as the most favorable solution.

The paper depicts agricultural robots that can perform complex tasks. Fast development and application of agricultural robotics is a result of increased development of agricultural machinery. Robots are complex and intelligent systems with a significant role in agriculture that are becoming an integral part of both the technological and scientific progress. The paper presents some important roles of robots and robotic systems in various agricultural areas and explains the deployment of new technologies supported by the examples of their application in arable farming, horticulture, and forestry. Robotics application decreases the deployment of human resources, enables significant production cost savings, and increases production capacity. The application of robotic systems facilitates high precision levels and repetition speed regarding time and space, which cannot be replicated by farmers

Pedestrians are one of the most endangered and vulnerable groups of participants in traffic. In order to increase their safety, different kinds of management measures are applied. Exclusive pedestrian phase represents a measure from a group of technical traffic regulations and implies complete time separation of pedestrian flow from wheel flow. The object of this paper is to analyse world experience in applying exclusive pedestrian phase in order to understand effects on safety of pedestrians in traffic and to apply it in our conditions.

Fertilization is one of the most important components of precision agriculture, ensuring high and stable crop yields. The process of spatial interpolation of soil sample data is recognized as a reliable method of determining the prescription rates for precise fertilization. However, the application of a free open-source geographic information system (GIS) software was often overlooked in the process. In this study, a method of precise fertilization prescription map creation was developed using an open-source GIS software to enable a wider and cheaper availability of its application. The study area covered three independent locations in Osijek-Baranja County. A method was developed for the fertilization of sugar beet with phosphorous pentoxide, but its application is universal with regard to the crop type. An ordinary kriging was determined as an optimal interpolation method for spatial interpolation, with the mean RMSE of 1.8754 and R2of 0.6955. By comparing the precision fertilization prescription rates to a conventional approach, the differences of 4.1 kg ha-1 for Location 1, 15.8 kg ha-1 for Location 2, and 11.2 kg ha-1 for Location 3 were observed. These values indicate a general deficit in soil phosphorous pentoxide, and precise fertilization could ensure its optimal content in the future sowing seasons.

Extracellular vesicle (EV)-mediated signaling is a highly evolutionary conserved intercellular mechanism in which the biological information is packaged, transported, and delivered to the target cells. The potential of EVs for diagnostic and therapeutic applications has recently received significant attention. To enhance the potential of EVs for more extensive functionalities, here we motivate the concept of engineered EV-mediat-ed communication nanonetworks. Corresponding with the EV-mediated intercellular signaling network components that naturally extend the limited ability of individual cells to perform the intricate vital functions, we propose a fundamental molecular communication network framework and review the potential experimental techniques to obtain data for its validation. The proposed framework paves the way for the development of engineered EV-mediated communication networks and enables novel approaches, perspectives, tools, and metrics to reveal the principles and emergent properties of biological EV-mediat-ed signaling networks the understanding of which is still obscure.

A new faulty section identification and fault distance estimation technique for three-terminal transmission line (TTL) is proposed in this paper. It is based on the derivation of three indices by solving non-linear equations with iterative method utilizing synchronized measurements from three ends of the line. In the absence of synchronization, an angle synchronization operator is derived for building a common reference among the three terminals. The efficacy of the technique is verified by generating several fault cases on an existing Indian 400 kV TTL using PSCAD/EMTDC software. Reported results demonstrate the capability of the presented technique for all types of fault on three-terminal transposed/untransposed as well as the non-homogeneous transmission line. It also provides accurate fault location under comprehensive variation in type of fault, line length, fault inception angle, complex fault impedance, source impedance, and current transformer (CT) saturation condition. Its accuracy remains almost constant despite errors in line parameters, as well as synchronization and noise in the measured signals. The initial guess has a minimal impact on the accuracy and convergence of the presented technique. The attained results reveal higher accuracy and better robustness of the proposed technique in comparison with those of several existing techniques.

Event classification is one of the central components of automated disturbance analysis based on phasor measurement unit (PMU) measurements. Obtaining high-quality event labels remains a challenge for supervised learning-based classification of local and system-wide events in power grids due to its labor-intensive requirement. We present a sensitivity study considering rapidly refined, partially, and fully inspected event labels that leads to evidence that hierarchical convolutional neural networks (HCNNs) outperform traditional classification models regardless of the quality of the available event labels. It is demonstrated that performance similar to the one obtained using entirely domain-driven labeling can be achieved as long as the involved expert does not mislabel more than ~5% of the event data captured by PMU measurements.

It is every parent’s wish to protect their children from online pornography, cyber bullying and cyber predators. Several existing approaches analyze a limited amount of information stemming from the interactions of the child with the corresponding online party. Some restrict access to websites based on a blacklist of known forbidden URLs, others attempt to parse and analyze the exchanged multimedia content between the two parties. However, new URLs can be used to circumvent a blacklist, and images, video, and text can individually appear to be safe, but need to be judged jointly. We propose a highly modular framework of analyzing content in its final form at the user interface, or Human Computer Interaction (HCI) layer, as it appears before the child: on the screen and through the speakers. Our approach is to produce Children’s Agents for Secure and Privacy Enhanced Reaction (CASPER), which analyzes screen captures and audio signals in real time in order to make a decision based on all of the information at its disposal, with limited hardware capabilities. We employ a collection of deep learning techniques for image, audio and text processing in order to categorize visual content as pornographic or neutral, and textual content as cyberbullying or neutral. We additionally contribute a custom dataset that offers a wide spectrum of objectionable content for evaluation and training purposes. CASPER demonstrates an average accuracy of 88% and an F1 score of 0.85 when classifying text, and an accuracy of 95% when classifying pornography.

Event detection in electrical grids is a challenging problem for machine learning methods due to spatiotemporally nonstationary systems and the inability to automate event labeling in high-volume data such as PMU measurements. As a result, the existing historical event logs created manually do not correlate well with the corresponding PMU measurements due to scarce and temporally imprecise labels. Trying to overcome this problem by extending event logs to a complete set of labeled events is very costly and often infeasible. We focused on utilizing a transfer learning model to reduce the need for additional data labeling by leveraging some labeled data instances available from a small number of well-defined event detection task. To demonstrate the feasibility, we tested our approach on a large dataset collected by 38 PMUs from the Western Interconnection of the U.S.A. over two years. The model evaluation performed based on varying percentages of labeled source data corresponding to $\sim 20$ -700 characteristic events on different sizes of time windows ranging from 2-seconds to 1-minute demonstrates that the developed method can significantly improve automated event detection based on PMU measurements when extensive labeling is costly or impossible to obtain. When compared to the state-of-the-art machine learning algorithms (unsupervised, semi-supervised, and supervised), the results show that the transfer learning method has significantly better performances when detecting events by learning from as low as 20 representative labeled data instances.

This paper presents grey wolf optimization - GWO. After presenting the biological basis of GWO, it explains the method itself and then the main algorithms of the GWO method as well as their mathematical models. The Grey Wolf Algorithm (GWO) is presented in detail as well as the manner of its operation and it application to optimization examples of engineering problems, such as: optimization of speed reducer, pressure vessel, spring, car side impact, cone coupling and cantilever beam. At the end, the results obtained by the GWO method are compared to the results previously obtained by other methods.

Introduction/purpose: Nowadays, user identification systems play a very important role in modern society. Complex security requirements have led experts to explore ways in which biometric data can be used to identify user identities. This paper presents an overview of biometric standards and methods which can be used to identify users in biometric systems, and therefore to protect information and communication systems. Methods: This paper deals with the problem of standardization in the field of biometrics. The first part of the paper presents concrete examples of the most widely used biometric standards. The second part of the paper gives an overview of the most used biometric methods. Results: The obtained results show that the development of biometric systems and biometric sensors contributes to better protection of identity from misuse, because biometric technologies have great potential for improving the security and accuracy of system operation. Biometric systems improve the security of users and also provide much greater precision in establishing identity. Conclusion: The development of biometric standards should focus on their interconnectivity, as well as on increasing connectivity with other IT standards.

Mergers and acquisitions (M&A) represent one of the most common ways for firms to achieve non-organic growth, and one of the main stages in the typical M&A transaction is due diligence of the target company. The aim of this paper is to provide a guideline for conducting due diligence process in the digital environment. After a brief introduction on the nature, purpose and elements of the due diligence, the paper elaborates various risks, and psychological and contextual hazards often occurred during due diligence process. Special attention has been paid to the elaboration of the role and importance of the digital due diligence as a new concept in today’s contemporary ‘digital’ economy. The outcome of this paper is to provide a comparison between ‘traditional’ and ‘digital’ due diligence, digital due diligence methodology, and easy understandable check list for conducting the process of digital due diligence.

This paper presents an application of an open architecture control system implemented on a multi-axis wood computer numerical control milling machining center, as a digital twin control. The development of the digital twin control system was motivated by research and educational requirements, especially in the field of configuring a new control system by “virtual commissioning”, enabling the validation of the developed controls, program verification, and analysis of the machining process and monitoring. The considered wood computer numerical control (CNC) machining system is supported by an equivalent virtual machine in a computer-aided design and computer-aided manufacturing (CAD/CAM) environment, as well as in the control system, as a digital twin. The configured virtual machines are used for the verification of the machining program and programming system via machining simulation, which is extremely important in multi-axis machining. Several test wood workpieces were machined to validate the effectiveness of the developed control system based on LinuxCNC.

The paper emphasizes the basic attitudes of complex technical system maintenance programming. Particularly it is performed an analysis and a selection of strategies for condition-based maintenance. The assessment of the established maintenance program varieties effectiveness was carried out by comparison with their basic indicators of effectiveness (for corrective and preventive maintenance).

The impact of uncoordinated charging of electrical vehicles (EVs) under high penetration on distribution transformers is studied. It was shown that EV chagrining may cause prolonged transformer overload condition, that may in turn result in transformer loss of life and increased hazard of failure. To mitigate the impact, a fuzzy logic-based system for determining EV charging schedule is devised. It uses four main inputs: 1) EV battery state of charge; 2) required state of charge for the next trip; 3) estimated time of EV departure; and 4) customer comfort level. The resulting output is a performance index that the distribution system operators can utilize in a decision-making tool to determine whether to delay the charging of given EV and pay the incentive to the EV owner. The data for the city of College Station, Texas, USA including temperature, price of electricity and load profile are collected from various sources to simulate different use cases. The example illustrates how the proposed EV management approach could mitigate the impact of EV charging on the transformer loss of life and hazard of failure. The main advantage of the proposed approach is the low cost due to simple design implementation. The information that needs to be sent from the consumer to the distribution system operator is minimized, which helps in maintaining costumers’ privacy.

This study presents the potential of multiple criteria decision aiding (MCDA) as a tool for prediction analysis. We apply MCDA methodology for the determination of internal migration potential of regions within a country. The ELECTRE Tri-C together with the Multiple Criteria Hierarchy Process (MCHP), the imprecise SRF method and the Stochastic Multicriteria Acceptability Analysis (SMAA) will be applied to investigate the case of the internal migrations in Serbia. We shall give recommendations regarding the migration potential of Serbian municipalities in the following years. In particular, the ELECTRE Tri-C will be applied to classify the regions in four classes ordered with respect to regions’ migration potential, MCHP will be used to take into account the hierarchical structure of criteria on which the municipalities are evaluated. Finally, the imprecise SRF method, as well as the SMAA methodology, will be used to take into account the preferences of numerous economic agents.

In this paper, a new approximate discrete cosine transform (DCT) architecture for an H.265/HEVC video compression codec is proposed. In order to reduce the computational complexity, a configuration ...

Purpose This paper aims to highlight the importance of braille for visually impaired people and the possibility of printing it by using the ultra-violet (UV) inkjet printing technique. Design/methodology/approach The vision impairment, definitions and statistics, as well as braille letter and standards, are first reviewed. Then, the methods of printing braille have been discussed, with a focus on UV ink-jet printing. Finally, this study indicates a significant role of UV ink-jet printed Braille, its possibilities and its advantages. Findings Research studies in the field of UV ink-jet printed braille are showing that it is possible to print good legible braille of the adequate height of dots. This paper highlights some advantages of printing braille with UV ink-jet, like the possibility of achieving greater dot height than embossing and no need for printing form. Printing of additional elements on top of braille dot has also been tested, and possible use for differentiation of the written notation is shown. This paper also indicates the importance of technological and operating conditions in UV inkjet braille printing. It predicts a significant role of UV inkjet in braille printing. Originality/value This paper provides a detailed review of UV ink-jet printed braille: possibilities, advantages and application.

Partial discharge (PD) has been identified as one of the root causes of stator insulation failure even for low voltage (LV) ac motors. For reliable operation of variable frequency drive motors, a standard acceptance test procedure (IEC STD 60034-18-41) to ensure PD-free operation of LV motors was developed. Although the test can verify whether PD exists in the stator for a surge with predetermined voltage level and risetime, the information on where PD activity is present in the stator insulation is not provided. In this article, a test method based on a new variable risetime surge generator is proposed for identifying the component of stator insulation where PD occurs in LV random wound stators. The proposed test allows the user to locate whether PD is in the turn, phase, or ground insulation. This information is critical for lowering the PD inception voltage for improving the motor reliability through insulation system design/manufacturing or motor/drive system reconfiguration. The test is implemented with a 4.5 kV variable risetime surge generator prototype, and seven LV ac motors are tested to show where PD is likely to occur in commercial LV motors.

Non-linear finite element analyses have intrinsic model and user factors that influence the results of the analyses. However, non-linear finite element analysis can provide a tool to assess safety using realistic descriptions of material behaviour with actual material properties. A realistic estimation of the existing safety and capacity of slender column elements can be achieved by means of "true" material properties. Nevertheless, it seems that for some structural components, such as slender columns, non-linear finite element analyses can, due to its complexity and its various setting parameters, cause the risk of overestimating the real performance of analysed components or systems. Hence, an invited expert group has carried out an investigation into the experimental testing and the prediction of the bearing capacity of slender columns by performing independent non-linear finite element analyses in order to determine the practical applicability, and its inconsistencies, with respect to the stability failure of slender columns. This work aims the characterization of modelling uncertainties, concerning the prediction of slender columns stability when forecasted by non-linear finite element analysis., This paper was partly carried out during research exchanges at TU Brno (BUT), Lehigh University (LU). The authors acknowledge also the financial support provided by the SAFEBRIDGE ATCZ190 EU Interreg project, the Scientific Grant Agency of the Ministry of Education of Slovak Republic, the Slovak Academy of Sciences VEGA No. 1/0696/14, and Slovak Research and Development Agency under the contract No. APVV-150658. The computational results presented have been achieved [in part] using the Vienna Scientific Cluster (VSC).

The design and construction of roundabouts, as a solution that will replace the classic intersection, has great application in our area, because it has proven to be the best traffic solution. The aim of this paper is to determine whether the roundabout will provide better traffic conditions than a traffic light. Baseline data were collected by counting traffic at the intersection in question and then processed in the SIDRA INTERSECTION software program. The program analysis covers the following criteria: service level, time losses, costs and emissions. Reducing speed has increased the safety of all road users, which in addition to the environmental aspect is the most important positive effect. With traffic congestion, traffic intensity and service levels have been raised. The construction of the roundabout would continue the trend of construction of roundabouts in Banja Luka, which, in addition to traffic amenities, gives this city an aesthetically pleasing new dimension.

A method for estimation of rubber bushing stiffness parameters is presented. Four individual rubber bushings, mounted in a car rear subframe are considered. A traditional model of the bushing elements using a generalised spring model, known as a CBUSH element in Nastran, is compared to a geometrically more realistic approach where the bushing is modelled with solid elements and a linear elastic material model. Each bushing is mass loaded to better reveal the bushing’s dynamic behaviour in a lower frequency range of interest. In an initial step, the overall subframe model is updated towards test data. In a second step, the bushing parameters are updated. Three nominally identical components are used to investigate the spread between the identified parameters. The model updating procedure is based on frequency responses and equalised damping. The undamped behaviour at frequencies below 300 Hz are considered. To quantify the parameter uncertainty, with respect to measurement noise for each individual, an uncertainty quantification procedure is proposed, using a linear-in-parameters surrogate model with bootstrapping.

The goal of sparse FIR filter design is to minimize the number of nonzero filter coefficients, while keeping its frequency response within specified boundaries. Such a design can be formally expressed via minimization of l0-norm of filter’s impulse response. Unfortunately, the corresponding minimization problem has combinatorial complexity. Therefore, many design methods are developed, which solve the problem approximately, or which solve the approximate problem exactly. In this paper, we propose an approach, which is based on the approximation of the l0-norm by an lp-norm with 0 < p < 1. We minimize the lp-norm using recently developed method for signomial programming (SGP). Our design starts with forming a SGP problem that describes filter specifications. The optimum solution of the problem is then found by using iterative procedure, which solves a geometric program in each iteration. The filters whose magnitude responses are constrained in minimax sense are considered. The design examples are provided illustrating that the proposed method, in most cases, results in filters with higher sparsity than those of the filters obtained by recently published methods.

Information theory provides maximum possible information transfer over communication channels, including neural channels recently emerged as remarkable for disruptive nano-networking applications. Information theory was successfully applied to quantify the ability of biological sensory neurons to transfer the information from dynamic stimuli. However, a little of information theory has been subjected to quantify the reliability of neuro-transmission between synaptically coupled neurons. Neuro-transmission, regarded as molecular synaptic communication, relays information between neurons and significantly affects the overall brain processing performance. In this study, we use concepts from information theory to provide the framework based on closed-form expressions that quantify the information rate allowing assessment of neuro-transmission when the parameters are provided for any type of neurons. Considering Poissonian statistics and the rate coding model of neural communication, we show how the information transferred between cortical neurons depend on the molecular, physiological and morphological diversity of cells, the firing rate, and the synaptic wiring. With synaptic redundancy, we infer the ability of an isolated post-synaptic neuron to reliably convey information encoded in the spike train from a pre-synaptic neuron. Estimating information rate between neurons primarily serves in the evaluation of the overall performance of biological neural nano-networks and the development of artificial nano-networks.

Novel non-invasive procedures, such as targeted drug delivery may enhance the efficacy of treatments of cardiac disorders. A possible solution is to utilize a molecular communication paradigm based on cell-derived nano-sized vesicles --- extracellular vesicles (EVs) --- that can be used as vehicles for therapeutic biological cargo. EVs can be engineered with specific cell-targeting transmembrane proteins to improve their pharmacokinetic properties. Here, we study the transport of EVs in a non-cellular component of cardiac tissue referred to as the cardiac extracellular matrix (ECM). We inspect EV diffusion and advection in cardiac ECM by considering 1) tortuosity, which describes the convoluted pathway of EV transportation, 2) volume fraction, which characterizes the porosity of the cardiac ECM; and 3) EV degradation according to their half-life in the body. We analytically describe the EV transportation dynamics via a partial differential equation and solve it numerically using finite element methods. The presented findings indicate that EV propagation is dependent on the cardiac ECM hindrance sources. Bulk flow in the cardiac ECM, however, can mediate the EVs to reach their distant target cells.

Internet of things (IoT) technologies have recently gained much interest from numerous industries, where devices, machines, sensors, or simply things are linked with each other over open communication networks. However, such an operation environment brings new security threats and technology challenges in securing and stabilizing such large systems in the IoT world. Device identity in such an environment is an essential security requirement as a secure anchor for most applications towards clone-resistant resilient operational security. This paper analyzes different contemporary authenticated identification techniques and discusses possible future technologies for physically clone-resistant IoT units. Two categories of identification techniques to counteract cloning IoT units are discussed. The first category is inherently cloneable and includes the classical identification mechanisms based on secret and public key cryptography. Such techniques deploy mainly secret keys stored permanently somewhere in the IoT devices as classical means to make units clone-resistant. However, such techniques are inherently cloneable as the manufacturer or device personalizers can clone them by re-using the same secret key (which must be known to somebody) or reveal keys to third parties to create cloned entities. In contrast, the second, more resilient category is inherently unclonable because it deploys unknown and hard to predict born analog modules such as physical unclonable functions (PUFs) or mutated digital modules and so-called secret unknown ciphers (SUCs). Both techniques are DNA-like identities and hard to predict and clone even by the manufacturer itself. Born PUFs were introduced two decades ago, however, PUFs as analog functions failed to serve as practically usable unclonable electronic identities due to being costly, unstable/inconsistent, and non-practical for mass application. To overcome the drawbacks of analog PUFs, SUCs techniques were introduced a decade ago. SUCs, as mutated modules, are highly consistent, being digital modules. However, as self-mutated digital modules, they offer only clone-resistant identities. Therefore, the SUC technique is proposed as a promising clone-resistant technology embedded in emerging IoT units in non-volatile self-reconfiguring devices. The main threats and expected security requirements in the emerging IoT applications are postulated. Finally, the presented techniques are analyzed, classified, and compared considering security, performance, and complexity given future expected IoT security features and requirements.

Protocol standards, defined by the Internet Engineering Task Force (IETF), are crucial to the successful operation of the Internet. This paper presents a large-scale empirical study of IETF activities, with a focus on understanding collaborative activities, and how these underpin the publication of standards documents (RFCs). Using a unique dataset of 2.4 million emails, 8,711 RFCs and 4,512 authors, we examine the shifts and trends within the standards development process, showing how protocol complexity and time to produce standards has increased. With these observations in mind, we develop statistical models to understand the factors that lead to successful uptake and deployment of protocols, deriving insights to improve the standardisation process.

This paper presents the usage of artificial neural networks (NNs) in bicycle route planning. This research aimed to check the possibility of NNs to transfer human expertise in bicycle route design by training the NN on an already established set of bicycle routes and then using the trained NN to design the routes on the novel area. We created two NNs capable of choosing the best route among the given road network by training them on two different areas. The bicycle routes produced by NNs were the same at best and had 75% overlap at the worst compared to those produced by human experts. Furthermore, the mean square error for all of our NN models varied from 0.015 and 0.081. We compared this new approach to the traditional multicriteria GIS (geographic information system) analysis (MA) that requires the human expert to define the bicycle route selection criteria. The benefit of using NN over the MA was that the NN directly transfers the human expertise to a model. In contrast, the MA needs the expert to select multiple criteria and adjust their weights carefully.

People detection in image and video is challenging problem that has great importance in different applications such as surveillance systems, autonomous driving systems, sports video analysis etc. Player detection task, as a subproblem of people detection, is one of the fundamental steps in football video analysis. In this paper we propose a method for player detection based on a fully convolutional neural network. Novelty in our approach is usage of U-Net architecture for generation of player probability map. U-Net consists of contracting path that has typical convolutional neural network architecture and extracting path where upsampled feature maps are combined with features from contracting path to obtain more precise segmentation. Next step is thresholding of player probability map followed by connected component analysis that gives player bounding boxes. Experimental results show promising performance on football field images including distant views, motion blur, complex background etc.

The number of always-online vehicles continuously increases, and these vehicles will form an immense mobile sensor network. For example, cars can upload live temperature and precipitation information to enhance weather forecasting, and also transmit live cellular network measurements to the cloud. We leverage this vast amount of data, particularly the reference signal received power, to estimate the channel distribution information (CDI) for the vehicular environment. In particular, the proposed CDI maps depict the small-scale fading statistics for spatially separated regions, in contrast to the large-scale fading averages of classical radio maps (path loss and shadow fading). Our map generation framework includes a heuristic for clustering and predicts the fast-fading density per cluster via the Dirichlet process mixture model. This Bayesian nonparametric approach allows for modeling any fast-fading distribution (e.g., Rayleigh, Rice) without prior knowledge. We justify the choice of this approach by benchmarking it against other density estimation methods. Moreover, we support our assumption of local medium-term channel stationarity by measurements and show the framework’s effectiveness for anticipatory networking.

Configuring and setting up a remote access laboratory for an advanced online school on fully programmable System-on-Chip (SoC) proved to be an outstanding challenge. The school, jointly organized by the International Centre for Theoretical Physics (ICTP) and the International Atomic Energy Agency (IAEA), focused on SoC and its applications to nuclear and scientific instrumentation and was mainly addressed to physicists, computer scientists and engineers from developing countries. The use of e-learning tools, which some of them adopted and others developed, allowed the school participants to directly access both integrated development environment software and programmable SoC platforms. This facilitated the follow-up of all proposed exercises and the final project. During the four weeks of the training activity, we faced and overcame different technology and communication challenges, whose solutions we describe in detail together with dedicated tools and design methodology. We finally present a summary of the gained experience and an assessment of the results we achieved, addressed to those who foresee to organize similar initiatives using e-learning for advanced training with remote access to SoC platforms.

Autonomous driving will rely on several safety-related connected applications that coexist with infotainment services for passenger entertainment. The simultaneous provisioning of resources to services with different quality of service requirements poses an immense challenge for future cellular networks. While most safety-related applications require low latencies, infotainment services usually necessitate a high average throughput. We propose a multi-cell, distributed predictive resource allocation framework with interference coordination based on channel distribution information to address the coexistence challenges. The approach first shifts packet transmission times in the so-called statistical look-ahead scheduling (SLAS) step, leveraging service properties. Inter-cell interference is coordinated by base station communication and a low-complexity fractional interference approximation. Lastly, packets are forwarded to an online scheduler according to the found transmission schedule. Moreover, we present a convolutional neural network to reduce the computational complexity in the SLAS step. Simulations show that the distributed approach performs very close to a central-controller solution at significantly lower computational complexity. It outperforms state-of-the-art schedulers in terms of transmission reliability and spectral efficiency.

Moderation of reader comments is a significant problem for online news platforms. Here, we experiment with models for automatic moderation, using a dataset of comments from a popular Croatian newspaper. Our analysis shows that while comments that violate the moderation rules mostly share common linguistic and thematic features, their content varies across the different sections of the newspaper. We therefore make our models topic-aware, incorporating semantic features from a topic model into the classification decision. Our results show that topic information improves the performance of the model, increases its confidence in correct outputs, and helps us understand the model's outputs., Accepted to RANLP 2021

We simplify the construction of projection complexes due to Bestvina-Bromberg-Fujiwara. To do so, we introduce a sharper version of the Behrstock inequality, and show that it can always be enforced. Furthermore, we use the new setup to prove acylindricity results for the action on the projection complexes. We also treat quasi-trees of metric spaces associated to projection complexes, and prove an acylindricity criterion in that context as well.

In this paper, a novel modular power converter for four-phase switched reluctance motor (SRM) is proposed. Firstly, a converter with motor phases grouped in pairs which share one common converter phase is introduced. This discrete-type converter is equivalent to the asymmetrical half-bridge converter (AHBC) in terms of the number of semiconductor components and functionality. The common converter phase concept enables modularization using two standard, commercially available six-pack IGBT modules, thus simplifying mass production. Compared to existing modular topologies, the proposed modular converter has the advantage of enabling an overlap of current pulses, which is a key feature for the SRM performance at elevated speeds while retaining a minimal number of standard power switch modules. In addition, owing to inherent operating regimes, the proposed modular converter enhances the motor performance over almost the entire range of the exploitation characteristics compared to AHBC and existing modular topologies. The advantages of the proposed modular topology were determined by means of finite element method (FEM) simulations. Experiments confirm the conclusions obtained from FEM analysis and verify the feasibility of the proposed modular converter.

Intrusions into the computer systems are becoming increasingly sophisticated. Command and Control (C2) infrastructure, which enables attackers to remotely control infected devices, is a critical component. Malware is set to connect to C2 servers to receive commands and payloads, or upload logs or stolen files. Since techniques for detecting traditional C2 servers are also advancing, attackers look for ways to make C2 communication stealth and resilient. Increasingly, they hide C2 communications in plain sight, in particular on social media and other cloud-based public services. In this paper, we identify several emerging trends in the use of social media for C2 communications by providing a review of the existing research, discuss how attackers could combine these trends in the future to create a stealth and resilient server-less C2 model, look at possible defence aspects, and suggest further research.

Cell-based approximations of PDE traffic models are widely used for traffic prediction and control. However, in order to represent the traffic state with good resolution, cell-based models often require a short cell length, which results in a very large number of states. We propose a new transition system traffic model, based on the front tracking method for solving the LWR PDE model. Assuming piecewise-linear flux function and piecewise-constant initial conditions, this model gives an exact solution. Furthermore, it is easier to extend, has fewer states and, although its dynamics are intrinsically hybrid, is faster to simulate than an equivalent cell-based approximation. The model is extended to enable handling moving bottlenecks as well as probabilistic traffic breakdowns and capacity drops at static bottlenecks. A control strategy that utilizes controlled moving bottlenecks for bottleneck decongestion is described and tested in simulation. It is shown that we are able to keep the static bottleneck in free flow by creating controlled moving bottlenecks at specific instances along on the road, and using them to regulate the incoming traffic flow.

Accurate source localization is an important problem in many research areas as well as practical applications in wireless communications and acoustic signal processing. This paper presents a passive three-dimensional sound source localization (SSL) method that employs a geometric configuration of three soundfield microphones. Two methods for estimating the angle of arrival (AOA) and time difference of arrival (TDOA) are proposed based on Ambisonics A and B format signals. The closed-form solution for sound source location estimation based on two TDOAs and three AOAs is derived. The proposed method is evaluated by simulations and physical experiments in our anechoic chamber. Simulations demonstrate that the estimation method can theoretically obtain Cramér-Rao lower bound for a small Gaussian noise present in AOA and TDOA observations. Investigation on the uncertainty of TDOA and AOA measurements depending on the length of measurement interval is also conducted. Experimental results in terms of RMSE indicate that the proposed solution can be used to accurately find a 3D position of the sound source in free-field environment. Performance evaluation regarding the number of estimation steps shows that higher accuracy can be achieved by longer observations of stationary sound source.

Introduction/purpose: The OTHR simulator presented in this paper is developed and used in practice, with the aim of emulating radar signal environment, but also optimizing the radar parameters in real applications such as: radiated power, antenna array gain, path loss, radar cross section, external interference, and noises. Methods: In this paper, the methodology of mathematical modeling is used as well as simulations . Results: Based on the performed analysis, the output data from the OTHR simulator is presented and discussed. Conclusion: The usage of the presented OTHR simulator makes assessing the reliability of a potential radar at predetermined locations automated, controllable and efficient, with results closely matching radar behavior in real operation.

The characteristics of combined and comb jamming of RCIED activation messages are presented in this paper. Combined jamming signal is formed as the sum of swept sinusoidal signal and noise signal, while comb jamming signal means that its spectrum has a number of simultaneous discrete equally distant sinusoidal components, eventually further swept. The performances of combined jamming are compared to pure sweep and pure barrage jamming. They are determined on the base of originally developed simulation program implementation. Comb jamming is analyzed based on its generation from rectangular pulse train. It is presented how the selection of ratio of pulse duration to its period and the number of frequency components in comb signal influence the obtained jamming signal characteristics. It is proved that comb jamming is especially advantageous in relation to sweep jamming when RCIED activation message duration is significantly lower than the sweep cycle period.