ABSTRACT. In the last few years, there has been a lot of controversy about the possible adverse health effects of the 5G communication systems, especially at millimeter-wave frequencies. Together with the widespread deployment of the new technology, it has triggered significant public concern. The scope of conducted research studies in this area is still not high, and there are many open questions concerning their results. In this paper, an overview of the scientific literature related to the effects of exposure to 5G electromagnetic fields (EMF) is given. Taking into account the existing research results, EMF safety standards have been adapted to a certain extent to adequately account for new technological developments. The latest changes of ICNIRP and IEEE C95.1 standards are summarized in the paper.

ABSTRACT. The paper provides a comparison of different feature selection methods for a voice Parkinson dataset in order to find an optimal subset with relevant features which gives the higher accuracy. Preliminary, the search space can be reduced using different methods toward searching for relevant features and to decrease the time complexity of the algorithm. Different filter and wrapper methods are applied to this dataset. The performance of each feature selection method is evaluated through the accuracy of three classifiers: Support Vector Machine specifically for the radial kernel basis function, Random Forest, and k-nearest neighbour. Moreover, an optimization algorithm Generalized Simulated Annealing is applied for hyper-parameter tuning of each classifier with the objective of improving its accuracy. Another algorithm, the Genetic Algorithm has been also applied for finding the best optimal subset of features and again is tested with the above-mentioned classifiers. In conclusion, comparisons between different methods were showed taking also into consideration the effect that has Generalized Simulated Annealing in improving the accuracy of each subset generated by each method, including also the execution time.

ABSTRACT. This paper presents a comparison between a complex valued layer based sparse coding and complex valued convolutional neural network for Synthetic Aperture Radar (SAR) patch categorization. Layered sparse coding is based on an optimal dual based $l_1$ analysis, that extends a sparse coding to a multi layer architecture. A contribution of this paper is a framework of layered sparse coding using local spatial pooling layer, normalization layer, map reduction layer and a classification layer. The new method is able to learn several levels of sparse representation of the image which capture features at a variety of abstraction levels and simultaneously preserve the spatial smoothness between the neighboring image patches. Recent progress in convolutional neural networks made a classification and categorization of SAR patches very attractive. In this paper a layer based convolutional neural network consisted of convolutional, drop out, Relu, fully connected, soft max and classification layers were combined to achieve the best classification accuracy. Experimental results showed that the CNN based classification achieved better results compared to the layered based sparse classification.

ABSTRACT. The paper describes an Event-Triggered Non-linear Control (ETNC) approach for Networked Feedback Control System (NFCS) under data-rate constraints. The nonlinear approach is based on the predefined sliding variable defined by the system states with a nonlinear switching function, which ensures the system stability by holding the variable in the prescribed boundary. The stability boundary of the sliding variable is subject to the preselected triggering condition, which selection is a tradeoff of system performance and network data-rate constraints. The primary purpose of triggering conditions is lowering network resources utilization while ensuring the proper performance of the NFCS. Regarding network constraints, the minimum inter-event time of controller update is derived. The efficiency of the proposed NFCS method is confirmed with the results on the real system.

ABSTRACT. This paper introduces a hybrid method based on the combination of two machine learning methods (k-NN and SVM). The novel method is designed to find job titles that are similar based on their description and industry. This method improves the accuracy and time-efficiency of a complex process like selecting the best candidates for a job.

ABSTRACT. We investigate the effect of passivation contouring, surface roughness, and sidewall tapering on the FEM modelling accuracy of mm-wave couplers in CMOS BEOL. Of three effects, sidewall tapering leads to the most significant improvement (0.37 dB) in predicting peak coupling magnitude at V-band. Ultimately, it is found that none of these measures substantially improve on modelling accuracy.

ABSTRACT. Phased array systems have become paramount to the development of next generation wireless communication networks, with errors on phase shifters contributing to beam pointing error. Oscillation-based built-in self-testing (OBIST) of phase shifters may improve on this accuracy. We propose a single-ended oscillation-based test technique for passive RF phase shifters. The method uses a negative resistance oscillator topology, where the phase shifter is used to capacitively load the active negative resistance circuit resulting in a dependency between relative phase shift and output oscillation frequency. Simulation results indicate an average sensitivity of 0.14 MHz/° of phase shift around a nominal 617 MHz output oscillation when testing an X-band reflection-type phase shifter, while the addition of OBT circuitry increases the phase shifter’s mid-band insertion loss by 0.93 dB.

ABSTRACT. Due to the ongoing growth of the IoT applications, Cloud computing is inadequate for transferring huge amounts of data generated by IoT devices. Consequently, Fog and Mist computing emerged as new computing models to meet the requirements of low latency. Fog computing extends the traditional cloud model closer to where the IoT data are created. Mist computing is a lightweight type of fog computing, that brings fog capabilities even closer to the IoT infrastructure. Most of the IoT applications are delay-sensitive, therefore, appropriate resource allocation and scheduling algorithms for real-time applications are required to increase the capacity of cloud, fog and mist computing architectures. In this talk we will highlight novel techniques and solutions to address challenges in resource allocation and scheduling of real-time applications in cloud, fog and mist systems and we will conclude with future research directions in the cloud, fog and mist computing areas.

ABSTRACT. Adaptability and advanced services for ambient intelligence require an intelligent technological support for understanding the current needs and the desires of users in the interactions with the environment for their daily use, as well as for understanding the current status of the environment also in complex situations. This infrastructure constitutes an essential base for smart living. Various technologies are nowadays converging to support the creation of efficient and effective infrastructures for ambient intelligence. Artificial intelligence can provide flexible techniques for designing and implementing monitoring and control systems, which can be configured from behavioral examples or by mimicking approximate reasoning processes to achieve adaptable systems. Machine learning can be effective in extracting knowledge form data and learn the actual and desired behaviors and needs of individuals as well as the environment to support informed decisions in managing the environment itself and its adaptation to the people’s needs. Biometrics can help in identifying individuals or groups: their profiles can be used for adjusting the behavior of the environment. Machine learning can be exploited for dynamically learning the preferences and needs of individuals and enrich/update the profile associated either to such individual or to the group. Biometrics can also be used to create advanced human-computer interaction frameworks. Cloud computing environments will be instrumental in allowing for world-wide availability of knowledge about the preferences and needs of individuals as well as services for ambient intelligence to build applications easily. This talk will analyze the opportunities offered by these technologies to support the realization of adaptable operations and intelligent services for smart living in an ambient intelligent infrastructures

ABSTRACT. Simultaneous transmit and receive (STAR), also known as in-band full-duplex (IBFD), is a key enabling technology for the next-generation wireless networks operating in spectrum congested and contested environments. Transmitting (Tx) and receiving (Rx) at the same time and over the same frequency channel results in significant improvements in communication systems throughput and spectral efficiency. The chief challenge associated with STAR systems is the required high Tx/Rx isolation that may be obtained with transceivers that combine multiple layers of self-interference (SI) cancellation including digital back-end processing. Since transceivers “start and end” with apertures, the antenna layer is therefore critically important for STAR operation. Nowadays, ~40-50% of the required isolation can be achieved with a well-designed IBFD antenna subsystem inching us ever closer to meeting this, not so long ago considered impossible task. This review focuses on the role of antennas in IBFD systems. Performances of various antennas designed to cover different bandwidths, fields of view, and concepts of use are overviewed. What was deemed impossible a decade ago is now likely.

ABSTRACT. In this paper a comparison between the crossed dipole and the corresponding crossed slot antennas intended for K-band applications has been presented. The analysis of the crossed slot and dipole antennas which are complementary structures is carried out to investigate their performances focusing with two objectives: one is to study their advantages as the single radiating elements suitable for energy harvesting applications and the other is to evaluate their applicability as a radiating element in an array. Therefore, both antennas were simulated and optimized at 25.875 GHz on the same substrate with added parallel reflector plate. The simulated results are presented and discussed.

ABSTRACT. In this paper, a quick method for estimating the patch width is presented using CMA. The proposed method can be completed in four steps, based on the relationship between the patch width as the variable and different resonant frequencies, and has been validated using four substrates. This method is advantageous as it facilitates antenna designers in estimating the width of the radiator patch using only CMA without the effects of substrate and excitation. Basically, the generated formula enables the calculation of the actual patch width by simply substituting the width of the patch produced based on one of the CMA’s characteristic angle. This parameter which is originally used to find the natural resonant frequency of the structure makes the proposed method time- and resource-efficient. The set of obtained results have been validated to be realistic with satisfactory accuracy.

ABSTRACT. This paper is devoted to investigate dependence of the radiation pattern on crossed slot antenna geometry. The considered slot, fed by a coplanar waveguide, consists of two rectangular parts positioned at angle α to each other. This study has a primary aim to reveal the optimal angle α to provide the maximum gain of considered antenna as well as of its linear arrays. The simulated radiation properties are discussed in the paper.

ABSTRACT. A Multi Band Microstrip Patch Antenna has been proposed in this paper. This antenna operates efficiently for three resonant frequencies- 25.57 GHz, 28.03 GHz, 30.25 GHz and this proves the successful operation of the antenna in two bands i.e. the K-Band and Ka-Band. To achieve this Multi Band nature, some slots in the front and the rear part of the antenna are utilised. The front patch of the antenna is made of copper and is devised in a peculiar dome shape which provides a novelty to this antenna. The antenna shows some great results when simulated giving an overall efficiency of more than 90%, A high gain is also recorded for the frequencies and an extremely high gain for 30.25 GHz. Furthermore, the return loss dips(S11) for the three resonant frequencies are stated to be below -30 dB and reaching to as low as -55 dB at the second resonant frequency.

ABSTRACT. The paper presents the design, simulation and fabrication of a wideband steerable antenna operating at 3GHz and using microstrip line technology. However, the standard Butler matrix has mainly the disadvantage of a limited bandwidth. This could be solved by using multilayer structures, but will raise the price of the product. Therefore, this paper focuses on the conception of a planar wideband Butler matrix fabricated on a low-loss substrate: Rogers 4003c. To this end, a three-branch line coupler is designed to replace the conventional hybrid coupler. It is then optimized by using loaded stubs T- model to reduce the size. In addition to that, U-shaped Schiffman phase shifters are introduced instead of the conventional phase shifters to obtain better performance in terms of bandwidth and resonance. The simulation results of the wideband steerable antenna array show a good performance. The fabrication of the overall structure is presented by the end of the paper.

ABSTRACT. This paper aims to present the impact of multipath interference on the monopulse secondary radar (MSSR) performance. The theoretical aspects of horizontal and vertical reflectors are presented along with the lobing phenomenon occurring in case of full and partial wave reflection. Real MSSR positioning data is also presented to exemplify the effect of reflections on aircraft positioning.

ABSTRACT. An alternative way, based on Artificial Neural Networks (ANN), for the estimation of Faraday polarization rotation of the electromagnetic wave that propagates in the ionosphere, is presented here. It uses a model developed by Radial Basis Function ANN that determines approximate value of the total content of free electrons on the propagating path of the EM wave according to the latitude of the receiving terminal and daytime instance. Afterwards, this value is used to estimate the angle of Faraday Polarization Rotation. This model provides the estimation of the angle for summer and winter period in the Mediterranean area.

ABSTRACT. This paper presents a framework to implement an Internet of Things (IoT) system using fuzzy logic. This framework provides general fuzzy membership functions that can be particularized for various applications implementing specific fuzzy rules to determine the system output. The motivation of this work is to create and test a flexible framework for the rapid implementation of IoT applications. Generic functions are implemented that are integrated into a commercial hardware platform. The framework is evaluated in terms of computation time and power consumption using a wireless sensor network (WSN) evaluation kit from Analog Devices. Also, a system for determining a person’s health condition is illustrated as an application implementation example using the proposed fuzzy IoT framework.

ABSTRACT. The progression of cryptographic attacks in the ICT era doubtless leads to the development of new cryptographic algorithms and assessment, and evaluation of the existing ones. In this paper, the artificial intelligence application, through the fuzzy analytic hierarchy process (FAHP) implementation, is used to rank criteria and sub-criteria on which the algorithms are based to determine the most promising criteria and optimize their use.

ABSTRACT. Processing and analyzing a vast amount of data, in order to keep computer systems safe from attacks and intrusions, is a very time-consuming process. The solution for this problem is the development of distributed firewalls and antivirus programs that use machine-learning algorithms for data analytics and can perform a job in almost real-time. Distributed firewall which uses the Random Forest algorithm is presented. The proposed solution was implemented and tested over a big dataset in a short time period to measure its speed and accuracy.

ABSTRACT. Monitoring the correctness of the car in modern traffic is very difficult, considering that the condition of the car is constantly changing due to driving and requires constant checking of various parameters. Systems that would deal with this would have to be accurate in their estimates and very fast given that vehicles constantly generate a large amount of data. A Vehicle Fault Detection system has been proposed as a solution to this problem. The system is distributed and autonomous and uses linear regression as a classifier. The proposed solution was implemented and tested and detailed performance and accuracy analysis were performed.

ABSTRACT. In this paper, it is explored how the data about trainings, competitions and weather can be leveraged for result and injury prediction in competitive running disciplines. As outcome, two prediction models based on multilayer neural networks are implemented using PyTorch framework for Python programming language and evaluated on dataset containing realistic data of ultramarathon runner. The first model treats problem as regression to predict the number of kilometers run for given ultramarathon duration, while another one determines whether injury will occur for given running distance or no using classification approach. According to our results, both models show satisfiable performance (up to 2% relative error for regression, 70% correct for classification), while the first one performs better, which can be explained by lack of enough injury records in the second case. Moreover, a companion mobile app developed using AppSheet and Google Apps Script for automated dataset construction is introduced.

ABSTRACT. The outbreak of COVID-19 has shown many negative impacts on industry, especially when it comes to travelling and tourism sector. In this paper, it is explored how mobile applications incorporating the elements of augmented reality (AR) can be leveraged in order to revive tourism in post COVID-19 era. As outcome, we present three prototype case studies aiming tourism support: 1) AR-driven exploration and sightseeing 2) historical 3D reconstruction 3) gamification approach to sightseeing. For multiplatform mobile application implementation, the synergy of AppSheet and Google Apps Script technologies is used, while for the elements of location-based AR we rely on AR.js web-based library. According to our findings, AR applications have huge potential in field of tourism.

ABSTRACT. ScratchJr is a programming tool developmentally appropriate for preschoolers (children age 5 to 7). It is intentionally designed without the possibility of externally adding image and sound files, which can create difficulties for adults (teachers and parents alike) involved in pre-school activities with ScratchJr.

This paper presents the structure of ScratchJr projects and a manner in which they can be modified by adding external images and sounds. It also presents the JSON project information code needed for the proper functioning of the programme with added files.

ABSTRACT. This paper presents implementation of the WebSocket communication method in Internet of Things systems that are based on ESP32 microcontroller. Impacts of using this technique for the real-time data transfer in Wi-Fi networks and its comparison to the standard polling techniques are discussed. For the proper comparison, special circuit is designed in order to stress the hardware part of the system and client-server communication. Also, for the real-time data receipt a web server application is designed and used to visualize received values. Efficiency of the data transfer methods are compared and discussed.

ABSTRACT. The adoption of blockchain necessitates the attention of a wide range of elements, much beyond the primarily technological focus of most present investigations. The present inquiries do not provide appropriate models as to how SMEs ought to go about blockchain adoption. Thus, this research study sort to investigate the key factors that drive blockchain adoption and propose a research model that could be used to inform blockchain adoption by SMEs in the context of South Africa. Drawing on the innovation diffusion theory and technological, organizational and environmental factors a model was proposed. A quantitative study was carried out; survey data was collected from 167 SMEs. SPSS was used to analyze the collected data and AMOS to fit the model. The results revealed that organizational readiness, trading partner pressure, perceived benefits, complexity, top management support, and compatibility could significantly influence the adoption of blockchain. However, in the South African context, competitive pressure and government regulations & support were found to be insignificant.