In this paper we describe by pseudo-code the “Algorithm of the cyclic-order graph”, which we programmed in MATLAB 2016a and which is also possible to be executed in GNU Octave. We describe program’s functionality and its use. The program implementing this algorithm is an indispensable tool during proofs in the field of graph theory, especially when dealing with crossing numbers for join products of graphs with paths of given numbers of vertices.

The article describes a method for detecting developing defects in high-power oil-filled transformers, based on the results of analysis of dissolved gases in oil. It has been found that an early diagnostic sign of development of ionization processes of aging and thermal degradation in transformers’ isolation is the appearance of a systematic trend in the time dependencies of hydrocarbon gas content. Though, exceeding the concentrations of gases regulated by boundary values (the traditional criterion) does not always reflect the damage to the insulation, it often being reversible.

This paper studies how the use of different coding practices when developing Android applications influence energy consumption. We consider two common Java/Android programming practices, namely string operations and (non) cached image loading, and we show the energy profile of different coding practices for doing them. With string operations, we compare the performance of the usage of the standard String class to the usage of the StringBuilder class, while with our second practice we evaluate the benefits of image caching with asynchronous loading. We externally measure energy consumption of the example applications using the Trepn profiler application by Qualcomm. Our preliminary results show that selected coding practices do significantly affect energy consumption, in the particular cases of our practice selection, this difference varies between 20% and 50%.

Direct Torque Control (DTC) is a control technique in AC drive systems to obtain high performance torque ripple. This paper also proposes improvement of the conventional DTC without voltages zeros using the improvement of the switching table and the application of the Artificial Neural Network (ANN) to minimize the torque ripple, stator flux ripple and Total Harmonic Distortion (THD) value of stator current and to get better performance of the induction motor (1MW) controlled by DTC, by using two-level inverter. The comparison with conventional direct torque control, show that the use of the proposed strategies with ANN, reduced the torque ripple, stator flux ripple and total harmonic distortion value of stator current. The validity of the proposed strategies is confirmed by the simulative results.

This paper deals with an analysis of four phase interleaved DC-DC converter for higher power application in continuous conduction mode. The interleaved topology is widely used due to their advantage as lower input current ripple which means volume reduction of input capacitor. The current ripple equation of an input current for the boost operation mode and the ripple current in individual phases of the interleaved converter using uncoupled inductor are shown. The theoretical equations are supplemented by the simulation results using Spice simulator and by measurement on the interleaved converter.

Universal Filter Multi-Carrier (UFMC) modulation is a potential candidate for next generation wireless communication due to its advantages like better sub carrier separation and less complexity. Higher peak to average power ratio (PAPR) affects the performance of UFMC and suitable PAPR reduction technique is required to mitigate the same. In this work, an improved PAPR reduction technique (CFC-UFMC) has been proposed for UFMC system. Performance of proposed technique is compared with existing Selective Mapping (SLM), Clipping, Companding and Clipping & Filtering methods as applied on UFMC. Simulation results show that proposed technique provides better PAPR reduction with variation in design parameters namely FFT Size, Filter Length and Bits per sub carrier as compared with other techniques. Various PAPR reduction techniques have also been compared on the basis of bit error rate.

This paper deals with an image encryption scheme, which could be used for improving security of steganographic algorithms. Higher level of security is reached by masking information contained in secret data by their encryption. Therefore in the worst case, the attacks would yield encrypted version of secret data in the worst case. After brief description of reasons for usage and drawbacks of already used solutions, the paper presents one of chaotic maps as possible tool for encryption algorithms construction. Properties of this map and proposed algorithms are discussed in detail. The effects caused by application of described encryption scheme are illustrated by performed experiments.

The Internet of Things (IoT) is one of perspective electronic sectors. In the near future a lot of common devices from a refrigerator to a door lock will be connected to the internet. Protection of the IoT devices should not be neglected. The device security is composed of many safety levels, where every countermeasure increases its robustness. The paper describes an implementation of a True Random Number Generator (TRNG) used in many cryptographic algorithms and protocols. It is based on a modern low-cost and low-power STM32F050 ARM-M0 microcontroller, suitable especially for IoT applications. The main motivation for developing of such generator was its absence in lower members of microcontroller families. Integrated TRNG uses common features of the microcontroller, which may be portable across ARM-M0 architecture. A source of randomness is instability of internal RC oscillator, which is acquired using another faster clock and one timer. The paper follows a previous research, but using the modern microcontroller with proposed on-line embedded tests which are designed in order to be simple and effective.

The work presented in this paper is focused on creating of predictive models that help in the process of incident resolution and implementation of IT infrastructure changes to increase the overall support of IT management. Our main objective was to build the predictive models using machine learning algorithms and CRISP-DM methodology. We used the incident and related changes database obtained from the IT environment of the Rabobank Group company, which contained information about the processing of the incidents during the incident management process. We decided to investigate the dependencies between the incident observation on particular infrastructure component and the actual source of the incident as well as the dependency between the incidents and related changes in the infrastructure. We used Random Forests and Gradient Boosting Machine classifiers in the process of identification of incident source as well as in the prediction of possible impact of the observed incident. Both types of models were tested on testing set and evaluated using defined metrics.