ABSTRACT. Magnetic Resonance Imaging (MRI) is non-invasive and non-ionizing imaging modality. It is very powerful imaging modality due to the ability to obtain images with various contrasts. The biggest drawback, which prevents wider acceptance of MRI and its use in regular population screening, is its price – the cost of an instrument is one million euros per tesla of field strengths. The higher the field strength the higher signal to noise ratio (SNR) is which allows scanning in higher resolution and resolving of smaller structural abnormalities. In this talk, the benefits of going towards the higher fields will be presented as well as the main technical challenges. Future perspectives and development directions of MRI will be discussed as well.

ABSTRACT. A novel CMOS lumped-component filter with on-chip calibration is proposed, using inline topology with resonant coupling between resonators. Calibration network is based on successive-approximation register (SAR) algorithm and free running cross-coupled oscillator and the design freedom given in the synthesis of the filter for choosing the capacitors values [1]. Variation in the 3-dB bandwidth (BW) and also transmission zeroes locations decreased from 1.5GHz to a 100MHz with center frequency of 10 GHz.

ABSTRACT. The modeling of a four-port coupler device using a transmission line-based wave digital (WD) approach involves representing the device as a network of transmission line sections that are connected through topological junctions. In this work, the focus is on expanding the range of microwave circuits modeled with use of WD technique to include circuits with several ports. Different topologies of four-port dual-band couplers with open-circuited, short-circuited or stepped-impedance stubs are investigated here.

ABSTRACT. DRA antennas have several favorable characteristics such as small size, low cost and desirable radiation pattern. These advantages make companies use this antenna in different fields in communication. Our aim is to enhance some properties of the rectangular antenna such as gain, bandwidth, S-parameter, VSWR and directivity. Therefore, three kinds of rectangular DRA antenna designs were selected and their geometrical and material parameters were systematically changed to enhance their performance. Also, the effect of adding more layers into the DRA design to get higher gain and to enhance the efficiency at frequency 28 GHz was studied. The designing and simulating of the DRA antenna were carried out by using the framework of CST.

ABSTRACT. A crossed slot arrays with three different feeding networks for K band applications are designed and compared. The first antenna array consists of eight identical crossed slots, fed by the separate coplanar waveguides (CPW), that form a linear antenna array with equal inter-element spacing and uniform amplitude excitation. The second, corporate feed network consists of CPW feeding lines and CPW quarter-wave impedance transformers enabling uniform power distribution. As a third antenna array, the resonant series fed antenna array is considered with input power that comes from one end of the array and provides in phase feeding of the crossed slots with inter element CPW transmission lines. Simulation results show that the array antenna with corporate feeding achieves the widest impedance bandwidth from 24.75 GHz to 26.75 GHz with the input reflection coefficient better than -10 dB. The antenna array with series fed shows the maximum gain of 16.8 dBi and the highest front to back ratio of 33 dBi in regard to other two ways of antenna feeding.

ABSTRACT. Microstip antenna arrays became increasingly popular in modern antenna applications due to their simplicity, compact size, easiness of manufacturing of mass production. One of the biggest problems in antenna arrays of microstrip antennas is the strong mutual coupling between elements, due to the space and surface waves, that severely impacts the element active impedances and antenna pattern. The present paper discusses an opportunity to mitigate this undesirable impact.

ABSTRACT. Solar observations in radio spectrum are highly important for us. They can bring us valuable information about the dynamic between the sun energetic particles and the Earth and our lives more specifically. The paper discusses the idea behind choosing a LPDA antenna for solar observations, why that would be really useful and what are the expectations.

ABSTRACT. This study focuses on the classification of data obtained from Brain Computer Interface (BCI) using the Bayesian analysis. The aim of the paper is to study the classification of brain signals and the possibilities of reducing the number of channels. Experimental data is obtained by using Emotiv Epoc 14+. The programming language R was used for data processing. The data is classified using the Bayesian analysis in R.

ABSTRACT. Feature selection is essential for identifying beneficial features in data and reducing the curse of dimensionality. In this paper, we analyze the effect of population size on the performance of the adaptive memetic binary optimization algorithm for feature selection. The adaptive memetic binary optimization algorithm is an optimization algorithm that works exclusively with binary values in a discrete search area. The choice of the population size may notably impact the quality of the solution. We conducted experiments on three datasets and used population sizes of 10, 20, 40, and 80 to evaluate the performance of the adaptive memetic binary optimization. Our experimental results show that when the population size is set to 20, the adaptive memetic binary optimization produces the best solutions regarding the fitness function, average selected features, and classification error rate. The convergence graphs analysis results suggest that the adaptive memetic binary optimization algorithm performs better on datasets with larger population sizes, leading to faster convergence and a better solution. These results provide important insights into the impact of population size on the performance of adaptive memetic binary optimization for feature selection.

ABSTRACT. This paper introduces a novel method based on symmetric cryptography relying on Appell’s recurrent hypergeometric functions of two variables. The implementation was done in C# and our method compared to standard methods when it comes to encryption and decryption processing time: DES, 3DES, Caesar cipher and RSA. The achieved results seem promising as it outperforms DES algorithm which represented de facto standard in cryptography for long period.

ABSTRACT. In this paper a multi-objective version of Ali Baba and the forty thieves optimization algorithm is proposed. The algorithm models by the events of the thieves trying to catch Ali Baba, who learned the location of their treasure stores, and Ali Baba's assistant Marjaneh trying to mislead the thieves. As a result of the comparisons made under different conditions, it was seen that the multi-objective Ali Baba and the forty thieves algorithm gives the most successful results compared to the some other multi-objective intelligent optimization algorithms.

ABSTRACT. This study strives to investigate the effectiveness of ensemble learning methods in analyzing the sentiments of menopausal experiences as manifested on Twitter. To achieve this objective, we leveraged data crawling techniques to collect pertinent Twitter data from two different time periods i.e., February 2023 and March 2023, which we analyzed using ensemble learning approaches. By doing so, we aimed to augment the precision and robustness of our sentiment analysis results. Specifically, we collected relevant Twitter data relating to menopause by using specific keywords and applied the term frequency-inverse document frequency (TF-IDF) algorithm to extract features. However, our dataset exhibited class imbalance, which we addressed using the Synthetic Minority Over-sampling Technique (SMOTE). Subsequently, we trained several ensemble learning models, including bagging, boosting, and random forest, using the sci-kit-learn library in Python. We evaluated the efficacy of each model using accuracy, recall, and precision as the performance metric. Our analysis demonstrated that the Random Forest algorithm outperformed other ensemble learning approaches in terms of accuracy, attaining an accuracy of 0.96 and 0.89 respectively for the two datasets collected. Our research is of paramount importance as it provides a comprehensive understanding of the emotional experiences of women during menopause. We underscore the significance of mood changes in comprehending the emotional experiences of menopausal women. Our results can inform the development of personalized interventions for managing menopausal symptoms, which can significantly enhance the quality of life of women undergoing menopause.

ABSTRACT. In the last decade wireless communications experienced exponential growth. Number of connected devices exceed the number of people on earth and the data rates can be as high as several GB/s. 5G communications already incorporate mm-wave frequencies. The next wave in wireless technology will be about sensing the environment and combining with communications. The applications are very versatile starting with driver monitoring, autonomous driving, industry applications related to non-destructive material testing, food control and improving efficiency in agriculture. The 6G standards will further extend the use of mm-wave bands for increasing data rates and for incorporating sensing functionality. From semiconductor technology and design perspective a lot of progress has been made to gain speed, reliability and design flows that guarantee first-time right mm-wave circuits. The idea of this paper is to approach mm-wave communications and sensing from application, system, circuit perspective, identify challenges and finally show examples how to address them from circuit perspective.

ABSTRACT. In this paper, the impact of a real parallel coupled stepped-impedance resonator filter in the linearization circuit on a single stage broadband power amplifier is considered. The amplifier operates at 3.5 GHz central frequency and its linearization is performed in simulation by the digital technique that utilizes the baseband nonlinear signals of the second-order which modulate the signal carrier second harmonic. Results gained in the linearization process were analysed for 5G signal FBMC modulation form with 50 MHz useful bandwidth channel for several signal output power levels. The output spectrum achieved by using the parallel coupled stepped-impedance resonator filter in the linearization circuit is compared to the case when the linearization circuit contains an ideal filter.

ABSTRACT. In this paper, a neural model for estimating the penetration depth of EM waves into soil is proposed based on the determination of its complex relative permittivity by radial basis function (RBF) networks. The model uses two RBF networks to determine the real and imaginary parts of the complex relative permittivity of the soil in a frequency range of interest and for different soil moisture contents. The accuracy of the proposed neural model is investigated through comparison with measured results.

ABSTRACT. Infrared thermography is a non-invasive and harmless method of measuring surface temperature. The aim of the present study was to test the feasibility of using infrared thermography in monitoring the influence of temperature-humidity index (THI) on thigh skin temperature in dairy cows. Correlation coefficients between measured surface temperatures and THI were close to 0.95. The results indicate that infrared thermography could be used as a tool to monitor the effect of THI on skin temperature in dairy cows.

ABSTRACT. The work evaluates the influence of the height from which an intersection with urban traffic is observed with unmanned aerial vehicles. The specific influences of the atmosphere at different parameters are considered. A real measurement of an intersection in the city of Sofia was made.

ABSTRACT. Buffer space management is critical to ensuring full utilization of network resources in present IoT networks. It allows the multiplexing of services with different quality of service requirements. Current IoT technologies supports a wide range of services including medical treatment, preventive equipment maintenance, remote operation of machinery, environmental monitoring, video surveillance and real-time alerts, connected transport as well as multiplexed services consisting of combinations of these. Traffic prioritization plays an important role in quality of service management in all mobile networks. From a network perspective, the aim is to ensure a minimum waiting time for packets of higher priority classes in the buffers of these networks. In order to use the resources correctly, it is necessary for each type of traffic or service to share the traffic capacity depending on the quality of service requirements they have.

ABSTRACT. Multicolored video QR Codes (MCVQRCs) is a new type of QR Code that can encode video data in addition to traditional text and numeric data. It has an immense potential to revolutionize the way video data is stored and shared. In this paper, we present an implementation of MCVQRCs which utilizes a combination of data compression and error correction techniques to effectively encode video data within the confines of a traditional QR Code. We also introduce a new algorithm for generating MCVQRCs that can be customized with different color schemes to enhance their visual appeal and distinguish them from standard QR Codes. Our work showcases the potential of MCVQRCs to provide a convenient and efficient way to store and share video data demonstrating its significant impact on a variety of applications, including video marketing, education, and entertainment.

ABSTRACT. An analytical modeling approach is made to the MARS cloud service. The analysis is based on a real system placed in a production environment with real influences from the rest of the IT infrastructure. Based on the results obtained from the operation of the system, a model is created that enables for service system analysis through the assessment of the values of a number of parameters for certain data protection scenarios.

ABSTRACT. DevOps methodology intensively adopted within software development workflow of almost any competitive organization aims to automatize the activities related to development, testing, continuous integration and deployment. However, high degree of automation leaves space for security flaws, which can lead to catastrophic consequences, when it comes to critical usage domains, like healthcare, public safety and maritime infrastructures. In order to fill the gap introduced by both human mistakes and software errors, a novel sub-field has emerged in this area, often called DevSecOps, tackling the security aspects within DevOps workflows. In this paper, we leverage the trending ChatGPT chatbot solution’s API in Python for purpose of Infrastructure as Code (IaC) script static analysis. Additionally, we aggregate the results and post-process then in order to make them useful for end-users. The proposed solution is evaluated in case studies targeting Terraform and Ansible IaC script case studies. According to outcomes, our approach shows promising results, but is more suitable for usage as auxiliary tool rather than continuous integration/delivery pipeline checker.

ABSTRACT. Every artificial intelligence task requires a particular dataset to train the model and test it. Nowadays, when artificial intelligence is a field in expansion, data is becoming a critical resource. Natural language processing is a specific field in artificial intelligence that needs separate datasets for each task and tor each processed language. This paper describes the process of collecting a dataset for a question answering system in the Serbian language. Data collection was achieved by using the Web scraping method. The Web scraper was implemented in the python programming language. The resulting dataset contains 16374 questions and answers from 6 different fields: history, biology, geography, physics, chemistry, and mathematics.

ABSTRACT. This paper describes research on different methods for implementing part-of-speech taggers for the Serbian language. Experiments were conducted on the SrWaC corpus, which consists of 10 million tokens collected from .rs web domain. Algorithms from NLTK library were used for creating tagging models. The tagset was modified and adapted for semi-deep tagging. The experiments involved preprocessing of the corpus to correct tagging mistakes and reversing the word order in sentences to obtain a reversed corpus, which is used for creating backwards tagging model. To achieve greater accuracy, a tagger that combines n-gram taggers was implemented using the NLTK library. However, the best result achieved during tagging was with the Perceptron tagger, which reached 95% accuracy. The goal of this work is, among other things, to ensure that tagging is fast, as the intention is to use the best models for a syntactic analysis tool for the Serbian language.

ABSTRACT. The focus of this study is to forecast the Global Horizontal Irradiance (GHI) for an hour ahead, using both univariate and multivariate analysis techniques. The forecasting problem is modelled as a supervised learning problem. In order to simplify the forecasting models, a feature selection algorithm is used to identify the highly correlated features. The forecasting is performed by utilizing popular machine learning algorithms viz., Random Forest (RF), K-Nearest Neighbors Regression (KNN), Support Vector Machine (SVM) and Artificial Neural Networks (ANN). The paper evaluates and contrasts the effectiveness of these models for this application. Additionally, the study examines how the forecasting models’ performance varies throughout the year and across seasons.

ABSTRACT. This paper introduces a Morris Screening-based sensitivity analysis (MSSA) for identifying the most influential of the available parameters, namely, real and reactive power of flexible loads and renewable energy resources (RES), aimed to support congestion management and voltage regulation in interconnected transmission systems. The methodology considers cross-border resources (demand side management (DSM) and RES assets) and serves to identify how the ranking order (priority of deployment) varies depending on the operating conditions (network loading and RES generation), the type of the study method (DC or AC-based power flow) and the consideration of the capacity of flexible resources. The results have shown that priority of deployment is highly dependent on RES penetration level as it can visibly change the ranking order (level of influence) of the considered parameters.

ABSTRACT. This paper determines and analyzes the power and location of the global and local maximum power points (MPPs) that can appear on the power-voltage (P-V) curve of photovoltaic (PV) arrays in the case of partial shading. The partial shading effect on the PV array is included using the reduced solar irradiation of the shaded area of its PV module surfaces. Based on the single diode model of the PV cell, considering the required number of cells creating the PV module, the current-voltage (I-V) characteristic of the PV module is created. Using obtained I-V characteristic and the reduced solar irradiance of some PV modules, the (P-V) curve of the PV array in the case of partial shading is determined. Analyzing the shape of the (P-V) curve, the power, location and the number of MPPs, are determined, considering cases with the different shading patterns. Using obtained results probability density function of MPPs location is generated.

ABSTRACT. Integration of renewable energy sources (RES) causes imbalances in the power system, because of their variable generation and stochastic nature of the source. Photovoltaic (PV) and wind power plants (WPP) are dominant RES in the region of Southeast Europe. This paper is analyzing the impact of RES on frequency regulation reserves in North Macedonia. There are and will be a high increase of installed PV capacity in the power system that will have impact on imbalances and frequency regulation reserves. Тhe methodology for the assessment of RES impact on frequency regulation reserves is based on the data available for the authors for the years 2015 and 2016, about hourly deviations (imbalances) of PV, WPP and load. According to these data necessary frequency regulation reserves capacities are calculated for the years 2023, 2025 and 2030. The results and conclusion are presented at the end of the paper.

ABSTRACT. The adoption of photovoltaics will impact the future prices of electricity, with their increasing adoption expected to lead to a reduction in the demand for traditional power sources, resulting in a decrease in electricity prices. As more solar power is used, it is expected that the shape of the daily price of electricity will become much more predictable, with low prices during the middle of the day and higher prices during the morning and evening. This paper analyses the tilt and azimuth angles that will yield maximum production and revenue for fixed-tilt PV systems taking into account the electricity prices on the day ahead markets.

ABSTRACT. The paper aims to determine i) key chemical-kinetic parameters of lavender waste biomass and ii) size distribution of particulate matter (PM) obtained during biomass oxidation. For that purpose, proximate, ultimate, lignocellulosic and thermogravimetric analyses were performed, as well as the calorific value was measured. The PM with different sizes was collected during solid lavender residue combustion in Horizontal Tube Furnace (HTF). The results disclosed that the majority of PM was with size below 2.5 µm.