Faculty of Electrical Engineering and Computing in Zagreb

In this paper, we present a pilot-aided sampling and carrier frequency offset estimator in orthogonal frequency-division multiplexing (OFDM) systems. The proposed algorithm enables joint carrier and sampling frequency-offset estimation from a pilot whose duration is only two symbol periods. Furthermore, we propose time-domain signal processing algorithms for carrier and sampling frequency offset correction, which do not require time-consuming signal interpolation, and with which fixed free-running oscillators can be used. The performance of the proposed algorithms is studied through simulations, and compared to the performance of the other algorithms described in the literature.

This paper explores the evolution of Boolean functions for a cryptographic usage, with genetic algorithms and genetic programming. We also experiment with a new mutation operator and a new kind of initialization process. Results obtained show that those modifications can help in obtaining better solutions. The results indicate that it is possible to obtain high quality Boolean functions with algorithms that are not tailor-made for this purpose. Additionally, among the algorithms tested, the best performance was obtained with variations of genetic programming.

Crossover is the most important operator in real-coded genetic algorithms. However, the choice of the best operator for a specific problem can be a difficult task. In this paper we compare 16 crossover operators on a set of 24 benchmark functions. A detailed statistical analysis is performed in an effort to find the best performing operators. The results show that there are significant differences in efficiency of different crossover operators, and that the efficiency may also depend on the distinctive properties of the fitness function. Additionally, the results point out that the combination of crossover operators yields the best results.

Purpose Due to the significant rise in the use of social media in recent years, the purpose of this paper is to investigate who, how and why participates in creating content at political social networking websites utilising a content analysis of posts and comments published on Facebook during the 2015 general election campaign in Croatia. It shows consequences of a transition from traditional to social media campaigns and the effectiveness of social media at activating and moving public opinion during the general election campaign. Design/methodology/approach This study uses a data collection through a social media website, a classification of data set items by content attributes and a statistical analysis of the classified data. Findings Building on an empirical data set from Croatia, this study reveals that different political parties implement different election campaign strategies on social media to influence citizens who, consequently, respond differently to each of them. The results indicate that political messages with positive emotions evocate positive response from citizens, while neutral content is more likely to invoke negative comments and criticism, and support to the opponent. Another implication of the results is that two-way and tolerant communication of political actors increases citizen engagement, whereas unidirectional communication decreases it. Originality/value This paper provides an original insight into qualitative content analysis of posts and user comments published on Facebook during the 2015 general election campaign in Croatia.

There is a dramatic transformation occurring across the world. Efforts to decarbonize the energy sector are leading to an increasingly decentralized energy system. Power, heating, transport, building and industry sectors are progressively more interconnected. In addition, new business models and innovative technological solutions are driving change in the power industry. The new system being unfolded requires a much higher level of flexibility and coordination. The increased complexities can only be mitigated by actively managing the energy supply chain. In this context, artificial intelligence (AI) has the potential to play a key role in numerous areas such as demand forecasting, predictive maintenance, energy management and customer support. Among digital technologies, AI is the one with the highest adoption rate in the power sector. Considering the growing importance of AI, the paper analyses the current status and future prospects of the technology in the power industry. It also explores different issues and barriers to its wider adoption. The review of possible applications is conducted by analysing academic literature and by observing numerous companies around the world that are developing and implementing AI solutions on the grid’s edge.

As the Data Lakes have gained a significant presence in the data world in the previous decade, several main approaches to building Data Lake architectures have been proposed. From the initial architecture towards the novel ones, omnipresent layers have been established, while at the same time new architecture layers are evolving. The evolution of the Data Lake is mirrored in the architectures, giving each layer a distinctive role in data processing and consumption. Moreover, evolving architectures tend to incorporate established approaches, such as Data Vaults, into their layers for more refined usages. In this article, several well-known architecture models will be presented and compared with the goal of identifying their advantages. Next to the architecture models, the topic of Data Governance in the terms of the Data Lake will be covered in order to expound its impact on the Data Lake modeling.

In past, detection of network attacks has been almost solely done by human operators. They anticipated network anomalies in front of consoles, where based on their expert knowledge applied necessary security measures. With the exponential growth of network bandwidth, this task slowly demanded substantial improvements in both speed and accuracy. One proposed way how to achieve this is the usage of artificial intelligence (AI), progressive and promising computer science branch, particularly one of its sub-fields - machine learning (ML) - where main idea is learning from data. In this paper authors will try to give a general overview of AI algorithms, with main focus on their usage for network intrusion detection.

Genetic algorithms (GAs) represent a method that mimics the process of natural evolution in effort to find good solutions. In that process, crossover operator plays an important role. To comprehend the genetic algorithms as a whole, it is necessary to understand the role of a crossover operator. Today, there are a number of different crossover operators that can be used in binary-coded GAs. How to decide what operator to use when solving a problem? When dealing with different classes of problems, crossover operators will show various levels of efficiency in solving those problems. A number of test functions with various levels of difficulty has been selected as a test polygon for determine the performance of crossover operators. The aim of this paper is to present a larger set of crossover operators used in genetic algorithms with binary representation and to draw some conclusions about their efficiency. Results presented here confirm the high-efficiency of uniform crossover and two-point crossover, but also show some interesting comparisons among others, less used crossover operators.

The development of optical coherence tomography (OCT) devices has significantly influenced diagnostics and therapy guidance in ophthalmology. The growing number of available images results in the increasing importance of introducing robust algorithms for automatic segmentation in clinical practice. With advances in computer vision in recent years, development of algorithms for segmentation of the retinal structure and/or pathological biomarkers have intensified. However, we are experiencing a reproducibility crisis due to a lack of openly available databases. In this paper we give an overview of a new openly available Annotated Retinal OCT Image (AROI) database that we have developed as a result of the collaboration of one research institution and one hospital. It consists of 1136 annotated B-scans (from 24 patients suffering from age-related macular degeneration) and associated raw high-resolution images. In each B-scan, three retinal layers and three retinal fluids were annotated by an ophthalmologist. Results for intra- and inter-observer errors are obtained to set a baseline for ML algorithms validation. We believe that the AROI database offers many possibilities for the computer vision research community specialized in retinal images and represents a step towards developing a robust artificial intelligence system in ophthalmology.

Abstract Advances in sequencing technologies have pushed the limits of genome assemblies beyond imagination. The sheer amount of long read data that is being generated enables the assembly for even the largest and most complex organism for which efficient algorithms are needed. We present a new tool, called Ra, for de novo genome assembly of long uncorrected reads. It is a fast and memory friendly assembler based on sequence classification and assembly graphs, developed with large genomes in mind. It is freely available at https://github.com/lbcb-sci/ra . This work has been supported in part by the Croatian Science Foundation under the project Single genome and metagenome assembly (IP-2018-01-5886), and in part by the European Regional Development Fund under the grant KK.01.1.1.01.0009 (DATACROSS). In addition, M.Š. is partly supported by funding from A*STAR, Singapore.

Proactive system monitoring is the search for potential problems in the Information Systems (IS) before they occur. With reference to IS, monitoring is always proactive. Watching for evidence of emerging or potential problems and identifying their source (factor), checking available disk space, the health of essential processes, probing systems to ensure that they are reachable, business requirement fulfilment; these are all examples of detecting problems before they disrupt the work/flow of an IS. Monitoring is not something that should be cavalierly put off until there's nothing else on the development team's schedule to be done. As soon as the IS is put in production, it is time to start to protect it against all possible types of problems.

Abstract: Besides many advantages of wavelet transform, it has several drawbacks, e.g. ringing, shift variance, aliasing and lack of directionality. Some of them can be eliminated by using wavelet packet transform, stationary wavelet transform, complex wavelet transform, adaptive directional lifting-based wavelet transform, or adaptive wavelet filter banks that use either L2 or L1 norm. This paper contains an overview of these methods.

In this paper a class of system transfer functions based on the impulse response symmetry criterion is presented. The class is obtained using nonlinear optimization procedure. Optimization of the second to tenth order system is executed. The time and frequency domain properties of obtained system or filter class are given and compared to commonly known filter approximations.

Design of linear parameter varying control (LPV) for wind turbines is considered in this paper. Multivariable, robust control law, which can guarantee stability and desired performance in the whole operating region of the wind turbine, is obtained LPV controller is compared with a classical controller.

Software defect prediction research relies on data that must be collected from otherwise separate repositories. To achieve greater generalization of the results, standardized protocols for data collection and validation are necessary. This paper presents an exhaustive survey of techniques and approaches used in the data collection process. It identifies some of the issues that must be addressed to minimize dataset bias and also provides a number of measures that can help researchers to compare their data collection approaches and evaluate their data quality. Moreover, we present a data collection procedure that uses a bug-code linking technique based on regular expression. The detailed comparison and root cause analysis of inconsistencies with a number of popular data collection approaches and their publicly available datasets, reveals that our procedure achieves the most favorable results. Finally, we implement our data collection procedure in a data collection tool we name the Bug-Code (BuCo) Analyzer.

Evolutionary computation algorithms represent a range of problem-solving techniques based on principles of biological evolution, like natural selection and genetic inheritance. Such algorithms can be used to solve a variety of difficult problems, among which are those from the area of cryptography. Examples of such an approach include the evolving hash functions or creation of a new block cipher. First results in this area have emerged over 30 years ago, and in recent years there has been an increased interest in this area. Still, some problems like problem formulation and representation remain open. The purpose of this paper is to give a survey of cryptographic applications that can be developed with the help of evolutionary computation methods, and to address their applicability to the real-world scenarios.

The problem of frequency load shedding in an isolated power system is considered. A proper design of the load shedding scheme should minimize consumer disruption in the case of the load excess and help balance the load to the remaining generation in a system. An adaptive strategy, which can reduce total amount of shed close to the theoretical minimum, is proposed and compared to conventional load shedding scheme.

Detection of vegetation in images is a common procedure in remote sensing and is commonly applied to satellite and aerial images. Recently it has been applied to images recorded from within ground vehicles for autonomous navigation in outdoor environments. In this paper we present a method for roadside vegetation detection intended for traffic safety and infrastructure maintenance. While many published methods for vegetation detection are using Near Infrared images which are particularly suitable for vegetation detection, our method uses image features from the visible spectrum allowing the use of common onboard color cameras. Our feature set consists of color features and texture features. One of our specific goals was to identify a useful texture feature set for the problem of vegetation detection. Based on the feature set, the detection is implemented using a Support Vector Machine algorithm. For training and testing purposes we recorded our own image database consisting of different images containing roadside vegetation in various conditions. We are presenting promising experimental results and a discussion of specific problems experienced or expected in real-world application of the method.

In this paper we present a method for real-time detection of human fall from video for support of elderly people living alone in their homes. The detection algorithm has four steps: background estimation, extraction of moving objects, motion feature extraction, and fall detection. The detection is based on features that quantify dynamics of human motion and body orientation. The algorithms are implemented in C++ using the OpenCV library. The method is tested using a single camera and 20 test video recordings showing typical fall scenarios and regular household behaviour. The experimental results show 90% of human fall detection accuracy.

Communication via satellite begins when the satellite is positioned in the desired orbital position. The satellite’s coverage area on the Earth depends on orbital parameters. Ground stations can communicate with LEO (Low Earth Orbiting) satellites only when the satellite is in their visibility region. The duration of the visibility and so the communication duration varies for each satellite pass at the ground station. For low cost LEO satellite ground stations in urban environment it will be a big challenge to ensure communication down to the horizon. The communication at low elevation angles can be hindered through natural barriers or will be interfered by man made noise. This paper discusses the variations of the communication duration between the ground station and LEO satellites and investigates if it is useful to support low elevation passes. For this paper data recorded at the Vienna satellite ground station within the Canadian space observation project “MOST ” (Micro variability and Oscillations of Stars) are applied.