Abstract: Rigorous electromagnetic models and approximations are traditionally based on the Sommerfeld’s resolution for Hertz vector potentials. However, another resolution, based on transverse Hertz vector potentials, also exists. This paper shows that a low frequency approximation, based on this resolution, for wires embedded in a two-layered earth, is more accurate than the existing alternative. The accuracy of proposed approximation is validated for a range of different wire geometries, frequencies and earth characteristics.

Keywords: Electromagnetic model; Grounding; Green’s functions; Modeling

Abstract: The work suggests a potential solution to contemporary Corporate and National security concerns, with regards to the use in the future of 5G technologies. In today’s digital age, individuals, groups and some states, (ab)use the easy access to modern technologies to further their economic and political objectives. In these endeavors, among others, these actors utilize jamming attacks (i.e. electronic warfare tactics exclusively used by the military in the past), in order to produce the well-known effect of DoS (Denial of Service). Up to now there is no communication technology that is immune to the jamming attacks, so there is no expectation that the new 5G concept, that is still under development will be fully resistant to well-known traditional jamming and other types of attacks. The proposed Security and Quality of Service (QoS) framework provides high levels of security features and safety, through high-performance mobile broadband networks, using Mobile Cloud computing. It guarantees QoS provisioning for different broadband services, with present jamming, as well as, other types of distributed DoS (DDoS) attacks. Moreover, the trend of integrating various criminal activities in the cyberspace is also presented. The performance of the proposed security and QoS framework is evaluated through simulations and analysis with multimedia traffic, in a heterogeneous mobile broadband Cloud environment with the coexistence of multiple radio technologies.

Keywords: 5G; Cloud; Cyber; Jamming; DoS; Quality of Service (QoS); Security

Abstract: The emergence of new ICT applications has triggered significant growth in data traffic volumes. As a result, novel flexible and adaptive concepts for optical network management has emerged, in order to meet the heterogeneous and variable traffic demands. Efficient spectrum usage has become one of the most important issues, since the optical spectrum is not an infinite resource. Today’s WDM (Wavelength Division Multiplexing) technology is inappropriate, due to the fixed spectrum grid it uses. The elastic optical networks (EON) will overcome these problems. Finding an efficient RSA (Routing and Spectrum Allocation) algorithm is very important for resourceful use of the network. This paper extends the current research knowledge regarding the intelligent RSA algorithm with support for different Class of Service (CoS). It assumes a coexistence of two traffic categories in the network, along with а central Software Defined Networking (SDN) controller. Furthermore, a dynamic RSA algorithm is used with two controlling coefficients to determine the priority of different classes. The performance of the algorithm and the impact of the controlling coefficients on several performance metrics (e.g. average latency for data traffic, blocking probability, network utilization and network efficiency) has been examined through MATLAB simulations. The obtained results indicate some complex influence of the data traffic load and spectrum-controlling RSA coefficient over the performance metrics. The results can help network operators not only define different CoS in their network, but to also finely tune its utilization, blocking probability and data traffic latency, which is crucial for the emerging 5G communication networks.

Keywords: Elastic Optical Networks; Software Defined Networking; RSA; 5G; MATLAB

Abstract: In this paper, new sufficient Lyapunov-like stability conditions and new Lyapunov-like, stabilizability conditions for nonlinear descriptor systems, with control, are presented along with their applications to quadcopter’s flight stabilization. Most of these results are rather practical because they do not need solving nonlinear differential equations and nonlinear algebraic equations. Thus, the usage of these new results requires only the differentiation of functions. The numerical simulation results prove that the stabilization of the quadcopter is improved, in comparison with pure tracking control.

Keywords: Nonlinear descriptor system; Lyapunov-like stability; Nonlinear hybrid control; Quadcopter stabilization

Abstract: In this work, we focus on the task of multi-label classification (MLC), where every example is associated with a set of labels. We present an algorithm for learning option predictive clustering trees (OPCTs) for MLC, based on the predictive clustering framework. The algorithm addresses the myopia of the standard tree induction algorithm by considering alternative splits in the internal nodes of the tree and introducing option nodes where appropriate. An option tree can be viewed as a compact representation of an ensemble, as well as, used as a pool of candidates from which a single tree can be extracted. This broadens the space of trees that is searched and reduces the myopia, compared to the standard tree induction. We evaluate the proposed OPCTs on 12 benchmark MLC datasets from different domains. Results show that OPCTs as ensembles can achieve performance similar to the bagging ensembles of PCTs, while the single trees extracted from OPCTs can outperform standard PCTs. We also perform parameter sensitivity analysis and provide avenues for future work.

Keywords: predictive clustering trees; option trees; multi-label classification; myopia

Abstract: In this work, we address the task of feature ranking for hierarchical multi-label classification (HMLC). The task of HMLC concerns problems with multiple binary variables, organized into a hierarchy of target attributes. The goal is to train a model to learn and accurately predict all of them, simultaneously. This task is receiving increasing attention from the research community, due to its wide application potential in text document classification and functional genomics. Here, we propose a group of feature ranking methods based on three established ensemble methods of predictive clustering trees: Bagging, Random Forests and Extra Trees. Predictive clustering trees are a generalization of decision trees, towards predicting structured outputs. Furthermore, we propose to use three scoring functions for calculating the feature importance values: Symbolic, Genie3 and Random Forest. We test the proposed methods on 30 benchmark HMLC datasets, show that Symbolic and Genie3 scores return relevant rankings, that all three scores outperform the HMLC-Relief ranking method and are computed in very time-efficient manner. For each scoring function, we find the most appropriate ensemble method and compare the scores to find the best one.

Keywords: hierarchical multi-label classification; feature ranking; ensemble methods; Relief

Abstract: Time series analysis for basic tourism parameters, in the countries of the Balkan Peninsula, have been emphasized in recent research. Moreover, some of them have also shown a trend, aside from the rising variance during the period-heteroscedasticity. All of these characteristics of a time series of tourist demand, result in them being a great challenge for modeling. Therefore, there are different types of models that can be implemented for the modeling of a time series, which include accentuated seasonal components. Throughout this paper, multiple tests are performed using several parameters of the time series, with the ARIMA model, in an attempt to find any influence on the fit and validity of the model. For the accepted models, series are predicted for a year in advance and, in addition, a method of testing the decisions made by authorities in the field of tourism is presented.

Keywords: time series; modelling; parameters; forecasting; testing; decisions