Vol 53, No 4 (2019)

The article focuses on the development of an on-board system design method for optimal control of an autonomous mobile group of objects. It is assumed that the group consists of a leader and some agents. A new method for the synthesis of an optimal multivariable control system, which is needed for preserving desired position of the agent relatively to the leader, was substantiated in the article. The leader passes along a random trajectory and measurement of the agent position with respect to the leader is accompanied with random noise. All group members experience the action of random disturbances.

Discriminative and generative machine learning algorithms have been successfully used in different classification tasks during the last several decades. They both have some advantages and disadvantages and depending on a problem, one type of algorithm performs better than the other one. In this paper we contribute to the research of combination of both approaches and propose literature based a hybrid discriminative-generative generic model. Also, we propose hybrid model structure finding and building a new algorithm. We present theoretical and practical advantages of the hybrid model over its consisting algorithms, efficiency of the model structure finding algorithm, then perform experiments and compare results.

Website phishing is an online crime for obtaining secret information such as passwords, account numbers, and credit card details. Attackers lure users through attractive hyperlinks, in order to, redirect to the fake websites. Phishing detection through a machine-learning approach has become quite effective nowadays. In this research, the Uniform Resource Locator (URL) based phishing detection approach has been used. Machine-learning classifiers like Naïve Bayes, Iterative Dichotomiser-3 (ID3), K-Nearest Neighbor (KNN), Decision Tree and Random Forest used for the classification of legitimate and illegitimate websites. This classification would help in the detection of phishing websites. However, it has been observed that use of Genetic Algorithms (GAs) for feature selection can improve the detection accuracy. Our experimental results portrayed the use of Iterative Dichotomiser-3 (ID3) along with Yet Another Generating Genetic Algorithm (YAGGA) improves the detection accuracy up to 95%.

The Carrier Frequency Offset (CFO) is considered to be a major drawback of the Orthogonal Frequency-Division Multiplexing (OFDM) signal. So, in many practical situations, specifically with LTE-Advanced downlink introducing carrier aggregation, estimation of the CFO-induced OFDM symbol phase deviation, is of interest. However, this demands complex test equipment, such as e.g. a Vector Signal Analyzer (VSA), which might not be always and everywhere available. Therefore, we applied the link abstraction principle on the Bit Error Rate (BER) that is considered to be determined just by the CFO-caused phase deviation, i.e. as if the channel was noiseless and time-dispersion-free (so that evident bit errors occur just due to the actual CFO). Furthermore, as the CFO-caused squared phase deviation is linear with the instantaneous (per-OFDM-symbol) Peak-to-Average Power Ratio (PAPR), which is related to the Error Vector Magnitude (EVM) and so with BER, we develop a simple model for analytical BER-based estimating of CFO. In this sense, we considered the easy-to-measure BER degradation as resulting just from the according additive white Gaussian noise (AWGN) source, which abstracts the CFO distortion. The proposed analytical model is validated by according Monte-Carlo simulations.

5G cellular mobile networks aspire to accommodate large numbers of users and devices most of which are expected to be mobile. They will also provide much higher data rates than previous generations’ networks. Analyzing how a cell’s performance is impacted by user mobility becomes paramount in an effort to provide the best possible quality of services. In this paper we propose a model of a generic cell where each user is moving based on the random waypoint model. We then adapt this model to a 5G cell to extract meaningful quality of service metrics. To this end and to provide a better perspective of how the model can be adapted to different bandwidth allocation policies, we extend the Engset multirate loss model to obtain recursive but efficient formulas for various performance measures, including call blocking and time congestion probabilities for both the complete sharing and bandwidth reservation policies.