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11:45-12:15 Session 2: Plenary Session
Higher Order Computational Electromagnetics, Uncertainty Quantification, and Meshing Techniques with Applications in Wireless Communication, Medicine, and Meteorology

ABSTRACT. Electromagnetics-related, antenna, RF, microwave, radar, microelectronics, wireless, and lightwave, technologies are exploding! The importance of computational electromagnetics (CEM) to these technologies can hardly be overstated. This plenary talk presents some advances in several major components of CEM, including (1) higher order method of moments, finite element method, ray-tracing, and hybrid techniques, (2) uncertainty quantification techniques for CEM modeling of RF and microwave devices and systems featuring a posteriori error estimation, sensitivity analysis, and intelligent model refinement based on adjoint methods, and (3) automatic surface meshing in CEM by the discrete surface Ricci flow method enabling generation of high quality meshes and adaptive iterative mesh refinement. The talk also shows how these methodologies and techniques can be effectively applied to solving general real-world problems with impacts on wireless communication, medicine, and meteorology. The applications include (A) smart underground mining with an integrated wireless cyber-physical framework using CEM modeling and measurements of wireless propagation in underground mines, (B) design of RF coils for next-generation high and ultra-high field magnetic resonance imaging (MRI) scanners based on CEM and MRI experiments, and (C) accurate characterization of winter precipitation using multi-angle snowflake camera, visual hull image processing, advanced scattering methods, and polarimetric radar.

12:30-13:30TELSIKS Welcome Cocktail Party
14:00-16:00 Session 3A: RF and Microwave Technique (RFMT)
Location: Room A
A Comprehensive and Critical Overview of the Kink Effect in S22 for HEMT Technology (Invited paper)

ABSTRACT. This invited contribution is aimed at presenting a thorough knowledge, a critical understanding, and new insights into the kink effect in the output reflection coefficient (S22) of field-effect transistors (FETs). To accomplish this challenging goal, we report a measurement-based investigation, using the high-electron-mobility transistor (HEMT) technology as a case study. The kink effect is investigated over a wide range of operating conditions, by changing bias point, ambient temperature, device size, and semiconductor technology. The origin of the kink phenomenon and its dependency on the operating condition are analyzed by using a lumped-element equivalent-circuit model. The achieved findings represent a powerful know-how to entitle microwave engineers to take properly the kink effect into account in fabrication, modeling, and design phases.

Resonant devices and gas sensing: from low frequencies to microwave range (Invited paper)
PRESENTER: Nicola Donato

ABSTRACT. This work exploits the development and characterization of Bulk Acoustic Wave (BAW), Surface Acoustic Wave (SAW) and microwave resonant gas sensors performed at the University of Messina laboratories. The BAW and SAW sensors are characterized towards ethanol at several concentration values. The first type at few ppm, the second one at thousands of ppm covering a wide range of concentration values. The Microwave ones are tested for oxygen monitoring and they are able to work in combined mode both as resonators and as resistive sensor in DC mode. All the reported devices are able to work at room temperature with a reduced power consumption.

Wave Digital Model of Edge Coupled Filter Based on Halfwave Coupled Resonator with Dumbbell-Shaped DGS

ABSTRACT. This research investigates the application of a novel halfwave coupled resonator employing dumbbell placed in the ground plane (DGS) that is used to design wideband filter. A type of microstrip bandpass filter (BPF) based on edge coupled DGS resonators is designed using Chebyshev approximation. By using admittance inverters, a wave digital model of microstrip bandpass filter with only one kind of resonator can be generated more conveniently. In order to validate the theoretical predictions and simulated results, a fifth order BPF is fabricated and experimentally verified. The measured and wave-based results have shown a good agreement.

Temperature Dependent Small-Signal Neural Modeling of High-Periphery GaN HEMTs

ABSTRACT. This paper analyzes the thermal dependence of high-periphery GaN-on-SiC HEMT performance. The proposed approach is based on artificial neural networks (ANNs) that are used to model the scattering parameters versus temperature and frequency under a high dissipated power condition for a GaN HEMT with a gate width of 1.5 mm. The modeling results agree very well with measurements up to 65 GHz in the whole considered temperature range going from 35°C to 200°C, confirming the high accuracy and the good generalization capability of the proposed ANN approach.

Dual Band Power Amplifier Linearization

ABSTRACT. The linearization of a dual band power amplifier designed to maintain high power efficiency at two different frequencies, 2.1 GHz and 5 GHz, is performed. Synthesis of the amplifier dual band matching network includes two separate phases. In the initial phase, the required complex impedances at the two working frequencies are transformed into real impedance that is equal at both frequencies. In the second phase, a dual band filter is synthesized to ensure the real impedance to be matched to the termination impedance. The linearization of the dual band amplifier is performed by the 2nd order nonlinear signals of the fundamental signals driven at the gate and drain of the amplifier transistor. The linearization effects are tested for the two sinusoidal signals shifted in frequency by diverse intervals up to 80 MHz at different output power levels of 25 dBm to 35 dBm for both operating frequencies.

A Compact Active Phaser with Enhanced Linearity of Group Delay for Analog Signal Processing

ABSTRACT. This paper presents a wideband compact phaser with a linear group delay, a key block to many Analog Signal Processing applications as Real Time Fourier Transforming systems (RTFT). The phaser, designed in a 130 nm BiCMOS technology, presents a group delay dispersion of 3 ns, a layout area of only 30 µm by 330 µm and a power consumption of the of 7.2 mW. Between 300MHz and 2.5 GHz the linearity error of the group delay is lower than 5%. The enhanced linearity of the group delay has been obtained thanks to an association of active first order all-pass filters and active second order all-pass filters. The benefit of using exclusively active components are the reduced layout area and the amplification achieved by the phaser.

14:00-16:15 Session 3B: Internet Technologies (IT)
Location: Room B
User Engagement for Large Scale Pilots in the Internet of Things (Invited paper)

ABSTRACT. With an expected 50 billion connected devices by 2020, the Internet of Things (IoT) will reshape our environment with great economic opportunities. However, the IoT market evolution will depend directly on the end-user adoption, so it is necessary to support the Large Scale Pilots (LSPs) in order to actively engage end-users in the large scale pilot design, deployment and assessment. In this paper we are presenting end-user engagement methods, including co-creative workshops, crowdsourcing, Living Labs, and developed online tools and resources for end-user engagement, crowdsourcing and personal data protection.

Leveraging Linear Programming for Deployment of Container-based Applications within Softwarized Networks in Fog Computing

ABSTRACT. Softwarization and virtualization of both computing resources and networking assets have made deployment-related procedures more flexible. In this paper, it is explored how to leverage optimization techniques based on linear programming for optimal deployment and network traffic shaping under the given constraints. A model is proposed, implemented in AMPL mathematical programming language and illustrated with example scenarios in context of Fog Computing.

Approach to Dynamic Adaptivity Simulation in Fog Computing Scenarios

ABSTRACT. Fog Computing overcomes the problems of huge latencies in time-critical scenarios of data-intensive applications by leveraging the computation power of devices residing within the Edge instead of offloading all the data to the Cloud for processing. However, the complexity of computing at the Edge makes the resource planning and provisioning increasingly difficult for service providers, especially when it comes to the maintenance of a satisfactory-level Quality of Service (QoS). In this paper, a simulation approach to Fog Computing use cases involving the dynamic adaptivity mechanisms is presented. The simulation tool is implemented as an application run in web browser, relying on existing modeling tool and semantic-driven code generator.

Processing of Big DNA sequence alignment on Hadoop cluster

ABSTRACT. The algorithms for sequence alignment are among the main tools of bioinformatics. They are used for sequence searches and alignment of similar sequences. In this paper, we present research work related to processing of Big DNA (Deoxyribonucleic acid) sequence alignment using Apache Hadoop framework. We present different algorithms for pattern matching and their usability in DNA sequence alignment. We propose the Hadoop implementation of these algorithms and test them on a cluster of commodity computers for various Big data size. The experimental evaluation are performed and results demonstrate the feasibility of our approach

Sensor Fusion and Big Mobility Data Analytics for Activity Recognition

ABSTRACT. With the widespread use of mobile devices, such as smart phones, smart watches, bracelets, equipped with plenty of integrated sensors, massive amounts of data are collected and stored, in an edge and a cloud infrastructure. For the purpose of monitoring of people health and activities, such Big mobility data must be properly processed and analyzed. In this paper, the RemoteHealth platform is presented, developed using contemporary mobile and Big Data technologies. The application implemented on RemoteHealth platform is based on sensor fusion and provides processing and analytics of Big sensor data originated from smart personal devices with the aim to detect and recognize human activities and behavior. The experimental evaluation shows that the sensor fusion increase the accuracy of activity recognition comparing to single sensor use.

Liberty: A Novel Encryption Algorithm Using Pseudo-Random Number Distribution

ABSTRACT. In this paper, we describe the basic model of the new encryption concept and algorithm, named Liberty. The creation of pseudo-random numbers and their distribution through coded messages is given, as well as the avalanche effect that they produce. Elimination of the secure channel for key exchange is also presented. Due to its structure and a different approach to encryption, some of the basics of various fields are given: pseudo-random numbers, modular arithmetic, modular exponentiation, etc. Also, basic software implementation is explained along with the preliminary results, based on the most simplified model, without any optimization.

Logic Differential Calculus in Time-Dependent Importance Analysis Based on Minimal Cut Vectors

ABSTRACT. One of the important tasks in reliability analysis is quantification of influence of system components on operation of the system. This task is addressed in importance analysis, which uses various importance measures to solve this problem. Some of the most commonly known are Birnbaum’s importance and Fussell-Vesely’s Importance (FVI). The former is based on quantification of situations in which a failure/repair of the component results in a failure/repair of the system, while the latter focuses on minimal scenarios that results in a repair of a nonfunctioning system. These scenarios are known as minimal cut vectors. In this paper, we present how time-dependent FVI can be computed without a priori knowledge of these scenarios. Logic differential calculus together with a theory of stochastic processes is used for this task.

Use of Binary Decision Diagrams in Importance Analysis Based on Minimal Cut Vectors

ABSTRACT. Reliability is a key characteristic of any technical system. A current issue in reliability engineering is analysis of complex systems consisting of many components. Examples of such systems are various network systems, such as distribution networks, telecommunication networks or computer networks. Investigation of these and similar systems in a reasonable time requires such a mathematical description of the system that can be efficiently processed by a computer. One of the prospective approaches is to express the structure of the system using a decision diagram. Application of this data structure in reliability analysis allows developing efficient algorithms for calculation of various reliability characteristics, such as importance measures, which allow evaluating influence of individual components of the system on its operation. In this paper, we focus on one special measure, which is known as Fussell-Vesely’s importance and which quantifies how a failure of a component contributes to a failure of the system. This measure can be defined using a concept of minimal cut vectors whose identification might not be an easy task. Therefore, in this paper, we develop a new method for calculation of Fussell-Vesely’s importance, which is based on use of binary decision diagrams. The method is illustrated using an example of a distributed computing system.

16:00-17:00Coffee Break
17:00-19:00 Session 4A: Poster Session (PO1) - RF & Microwaves Technique, Computational Electromagnetics
Location: Room C
Electron Transport Through 2D Waveguide with Using QTBM

ABSTRACT. Simulation of Electron Transport through two dimensional(2D) waveguide using Quantum Transport Boundary Method (QTBM) is done. Specifically, as an example the results of modeling L-shaped contact for a rectangular waveguide are presented. 2D-QTBM approach can be used in any scenario where the vertical axis of the device structure is significantly large and therefore the 2D approximation would be considered valid. This method elegantly generalizes to arbitrary shaped waveguides.

A Laser Beam for Boosting the Power Added Efficiency of an X-Band GaN MMIC Amplifier

ABSTRACT. This paper deals with a new method for improving the power added efficiency of an amplifier by exploiting a blue-ray laser beam. The active device of the tested amplifier is an AlGaN/GaN HEMT on SiC whose dc performance has been prior analyzed with and without applying the laser beam. Thereafter, the effect of the optical radiation on the power added efficiency of the amplifier has been investigated and the relevant results have been reported. This contribution follows an intense experimental activity of the authors in this field and points out this beneficial feature of the optical radiation.

Cryogenic characterization of SAW resonators
PRESENTER: Nicola Donato

ABSTRACT. In this paper the authors report about the RF characterization of surface acoustic wave (SAW) resonators down to cryogenic temperatures (20 K). The characterization campaign is performed with the dual purpose of evaluating their use as temperature sensors or in low noise electronics. The characterized devices are SAW resonators at 423.2 MHz produced by Murata with TO-39 metal package. The RF characterization is performed by recording the Scattering parameters with an Agilent 8753ES Vector Network Analyzer (VNA) and a connection board developed ad hoc. The recorded parameters show a strong dependence of the resonance frequency values towards temperature.

Microfluidic Biosensor for Bioengineering: High-frequency Equivalent-Circuit Modeling of Interdigital Capacitor

ABSTRACT. Microwave biosensing is a rapidly growing field of bioengineering. The progress in micro- and nanotechnologies and the recent advances in microwave dielectric spectroscopy have allowed a rapid development in the miniaturization of high-frequency biosensors. There has therefore been intensive research during the last few years on investigating miniature microwave biosensors for liquid characterization. To contribute to the advancement of this challenging and stimulating field of research, the present contribution is devoted to the analysis of a microfluidic sensor based on a one-port coplanar interdigital capacitor (IDC). The high-frequency performance of the studied sensor is achieved by using the 3D finite-element method (FEM) by Ansoft’s high frequency structure simulator (HFSS). The simulations are used for extracting and validating an equivalent-circuit model that can be further exploited for complex permittivity extraction of the material under test.

Artificial Neural Network Model of Zero-Bias Schottky Diode for Energy Harvesting

ABSTRACT. In this paper, the S-parameters of SMS 7630 zero-bias Schottky diode are modeled with artificial neural network (ANN). A single network is trained with the data obtained from the measurements using a vector network analyzer. The frequency band of those measurements is 0.5 – 5 GHz, and input power is ranging from -25 dBm to 5 dBm. The learning and generalization capabilities of the developed ANN were tested, and results turned up to be satisfactory and useful, especially considering a limited number of different input powers for network training.

On the Introduction of Neural Network-based Optimization Algorithm in an Automated Calibration System

ABSTRACT. This work presents the introduction of a neural network-based optimization approach in the tuning of voltage-controlled circuits (such as active filters). A custom calibration system has been already presented by the same Authors. It was realized with a hardware interface and a dedicated software based on a modified version of a Differential Evolution algorithm. In this paper the implemented algorithms are described in detail together with a possible integration of the neural network synthesis to further enhance performance of the proposed system. As the first step in exploiting neural networks, in this paper they are used as a tool for speeding up the choice of initial values of the filter control voltages. Neural networks are used to replace a look-up table representing the relationship between filter parameters, the central frequency and the corresponding attenuation, and the control voltages. According to the obtained results, in such a way, the optimization time is shortened significantly.

Hybrid ANN- ECP Approach for Design of Capacitive RF MEMS Switches
PRESENTER: Tomislav Ciric

ABSTRACT. RF MEMS switches have been efficiently applied in various applications in communication systems. As the dimensions of the switch bridge influence the switch behaviour during the design of a switch it is necessary to perform inverse modeling, i.e. to determine the bridge dimensions to ensure the desired switch characteristics, such as the resonant frequency. In this paper an inverse modeling approach based on combination of artificial neural networks and lumped element circuit model has been considered. This approach allows determination of the bridge fingered part length for the given resonant frequency and the bridge solid part length, generating at the same time values of the elements of the switch lumped element model. Validity of the model is demonstrated by an appropriate example.

Design of well-matched UHF Planar Bowtie Dipole Antenna using Neural Model
PRESENTER: Maja Sarevska

ABSTRACT. In this paper we present neural model for planar bowtie dipole antenna based on multilayer perceptron network (MLP). This model is part of realized software under the name “BT ANN Design ZM” for design of a well- matched above mentioned antenna in UHF band. For desired central frequency of the bowtie antenna, this software provides fast estimation of the length and flare angle in order antenna to be well- matched on the feed line. Current software version provides antenna design matched on 50 OHM feed line in the frequency band 500 - 3500 MHz.

Study of Loop Probe Dimensions Influence on a Probe Calibration Factor in a Near-Field Measurement

ABSTRACT. Near-field measurements of either electromagnetic fields demands removing the influence of the probes, transmission lines and measurement circuits on the measurement results. This paper is focused on investigation of the magnetic loop probe influence in the procedure of obtaining the probe calibration factor. The additional correction of the probe calibration factor is presented in order to take into account possibility of usage of probes with different dimensions.

Linearization of Doherty Amplifier by Injection of Digitally Processed Baseband Signals at the Output of the Main and Auxiliary Cell

ABSTRACT. The linearization of a broadband two-way microstrip Doherty amplifier is performed by modified linearization approach that exploits the baseband nonlinear signals of the second-order. The required linearization signals adequately tuned in magnitude and phase in the digital domain modulate the second harmonics of fundamental carrier, which are then inserted at the outputs of the main and auxiliary amplifier transistors in the Doherty topology. The estimation of the proposed linearization method influence on a Doherty power amplifier is evaluated for two-tone baseband signal characterized by tone separation up to 30 MHz for several input signal power levels, and for OFDM digitally modulated signal.

Point charge located inside a bi-isotropic sphere made of the material of Tellegen

ABSTRACT. In this paper we perform calculation of electric and magnetic scalar potential of a point charge q, located inside a bi-isotropic sphere made of a material of the Tellegen type and placed in the air. The Poisson’s and Laplace’s equations are solved in spherical coordinate system by satisfying boundary conditions on the boundary between air and bi-isotropic medium.

17:00-19:00 Session 4B: Poster Session (PO2) - Information and Communications Technologies
Location: Room C
Analysis of semilogarithmic companding quantization

ABSTRACT. In this paper, an analysis of semilogarithmic companding quantization of amplitudes having the Laplacian probability density function is performed. Particular attention is focused on studying quantizer robustness on changes in amplitude variance. In order to achieve better robustness of the quantizer in question, a method for its design is proposed, which provides the approximately equal quality of the quantized signal at the boundaries of the predefined variance range. The presented analysis and the obtained results can be useful in digital processing of speech and other non-stationary signals having wide amplitude dynamics.

Noisy Reference Loss for PSK Signal Detection over Fisher-Snedecor F Fading Channel

ABSTRACT. In this paper, analysis of the average bit error rate (ABER) of coherent receiver influenced by imperfect carrier phase over Fisher Snedecor F fading channel is presented. Binary and quaternary phase-shift keying signal detection is assumed. Approximation expressions for the ABER evaluation are derived recalling Maclaurian series expansion of the complementary error function. In order to illustrate the degradation effect of the phase noise on ABER in the presence of shadowing, the expression of noisy reference loss is also derived. The effect of phase-locked loop parameters on ABER and noisy reference loss is examined when multipath fading and shadowing appear simultaneously.

A Comparative Performance Analysis of Extreme Learning Machine and Echo State Network for Wireless Channel Prediction

ABSTRACT. In this work, a comparative performance analysis of an extreme learning machine (ELM) and an echo state network (ESN) for forecasting of wireless channel conditions is carried out. These two algorithms are applied to predict signal-to-noise ratio (SNR) for single-input single-output (SISO) system in both picocellular and microcellular environments. Performance indicators used to gain insight into accuracy and effectiveness of ELM and ESN techniques are normalized mean squared error (NMSE) and time consumption. The experimental results performed on measured SNR values show that the ESN algorithm is characterized by shorter test time and higher prediction accuracy in picocellular environment, while the ELM model is recommended for channel prediction in environment which is less frequency selective.

Overall Model Normalization towards Adequate Prediction and Presentation of QoE in Overall Telecommunication Systems

ABSTRACT. A method for QoE parameters prediction in an overall telecommunication system, including users and telecommunication network, based on QoS indicators prediction, is overviewed. Four normalization techniques are discussed. An indicators’ Scale Normalization is proposed. Numerical illustrations are presented.

Quantum Resource Distribution Management in Multi-Task Environment

ABSTRACT. Resource distribution management model based on quantum optimization approach is introduced in this paper. The proposed quantum strategy can guarantee a low computational complexity and high accuracy. moreover, we focused on setting up of the parameters of the quantum algorithm with respect to the constraint’s variables of the proposed model

Development of an Education Information Portal with Microservices
PRESENTER: Jozef Kostolny

ABSTRACT. Many existing web portals make the learning of children and preparations of teachers easier, however, only few of them combine all desired functionalities. For this reason, an education information portal is being developed with the application of modern architectural patterns, the utilization of a microservices architecture and the ASP .NET Core framework.

SiPM based Laser Imaging Detection and Ranging VCII Interface

ABSTRACT. We here propose an interface circuit for Silicon photomultipliers sensors for Laser Imaging Detection and Ranging (LIDAR) applications. The proposed interface has been designed at transistor level by means of a 150 nm technology process from LFoundry. Simulation results are presented in the paper demonstrating the feasibility of the proposed design to be used for such applications where short current pulses need to be detected as in LIDAR systems.

Feature Extraction for Drone Classification

ABSTRACT. In this research an approach for extracting features from image dataset is proposed for classification vertical take-off and landing unmanned aerial vehicles. These vehicles are highly movable targets in various backgrounds while they occupy a small portion of the camera field of view. For this propose, histogram of oriented gradient is used for as a feature for classification is analyzed. Obtained results show that features from unmanned aerial vehicles are well separated from the other objects in image.

Novel algorithm for segmentation of renal cyst from CT image sequences

ABSTRACT. In this paper a novel hybrid segmentation method for optimization of the renal cyst diagnosis from CT image sequences is proposed. The method is based on several segmentation techniques. A locally optimized front propagation algorithm according to the level set paradigm with variable term is in the core of the segmentation of the kidney from CT image sequences. For the cyst segmentation are used the split & merge and color based k-mean clustering algorithms. The accuracy evaluation of the renal segmentation with Dice similarity index represents high results for the whole experimental data set – 90, 97%.

Detection and Boundary Extraction of Martian Impact Craters by a Pyramidal Approach

ABSTRACT. During the last ten years, Mars has been extensively explored and mapped by several NASA and ESA orbital missions, generating large datasets of high-resolution images. This type of information helps the understanding of impact processes occurring on the surface of celestial bodies. In this work, we introduced a novel automated approach for detection and boundary extraction of Martian impact craters. We implemented a pyramidal image representation and classical morphological operations, involving Hough transform (HT), which identified regions with a specific circular form. This was tested on 3D mesh data of Mars, provided by Mars Orbiter Laser Altimeter (MOLA). We will demonstrate the potential and usefulness of such automated approach in planetary science.

Descriptive Statistical Analysis in the Process of Educational Data Mining

ABSTRACT. The paper describes the process of descriptive statistical analysis of a training set of educational data extracted from distributed sources of blended learning environments. The determined variability in values has indicated the use of the histogram discretization method at the preprocessing phase. Estimation of classification models created over a discretized educational data set has confirmed the importance of descriptive statistical analysis in the case of creating a training set of data from distributed sources.

Evaluation of Development and Application of Big Data

ABSTRACT. The development of the global economic system is accompanied by the massive introduction of innovative digital technologies in all sectors of the economy. The widespread introduction and use of information and communication technologies have led to the formation of a new direction in the development of the economy - digital, based on the use of the most advanced digital technologies. The most promising technologies of recent years are cloud computing, blockchain, neural network technologies, big data, and many others. The basic concepts and types of big data were analyzed in this paper; a comparative analysis was conducted between traditional databases and information storage in the field of big data. The global big data market is analyzed in this paper: financial volumes are determined, the dynamics of its development are assessed, and research is conducted on individual market sectors, in particular, infrastructure, software and services provided, the main market players are identified. Particular attention is paid to the development of the direction of cloud computing in the field of big data. The areas of application of big data in the industry context are analyzed, the main consumers are identified. Based on a comprehensive analysis in the field of big data, their further prospects for development and application are determined.

Big Data: Evaluation of the Basic Trends of the Russian Market

ABSTRACT. The modern world is at a new stage in its development, which is due to the large-scale and massive introduction of innovative digital technologies. The introduction of information and communication technologies led to the digital transformation of the entire world economic system and the formation of a new type of economy - a digital one based on the use of the most advanced digital technologies. Under digital transformation within traditional sectors, new directions began to emerge that became the drivers of the development of an innovative economy. This is Industry 4.0 in industry and Fintech in the financial sector, which allows the formation of new approaches to management and organization within their areas. The introduction of innovative technologies in today's world is happening with great acceleration: something that used to take years, today take a few months. Technologies that used to be in the field of research and experimentation have moved into practical implementation; big data processing technologies have become one of these technologies in recent years. The basic concepts and principles of big data are analyzed in the paper; their participation and integration into the third technology platform are considered. The development of big data is proceeding rapidly. Many countries joined the race in this area, in connection with which the development of the Russian big data market was evaluated: a financial analysis of the market was carried out, growth rates were determined, and the main players were identified. As part of the study, an industry analysis of the Russian big data market was made. Particular attention within the analysis was paid to the reasons and barriers that stand in the way of the development of big data in Russia. Based on a comprehensive analysis, the main trends were identified for the further development and application of these technologies in the Russian economy.

Parameter estimation of the IPDT model using the Lambert W function

ABSTRACT. This paper considers the identification of integral processes with dead time. In order to complete the identification process, the following tests were used: the closed-loop under proportional controller step test and the relay feedback test. Based on the received data from the closed-loop response, parameters of the integrator plus dead time model were estimated using the Lambert W function. The result was illustrated with examples.

Stability analysis of Linear, Time-Invariant Systems with Time Delay

ABSTRACT. The paper considers relative stability of the linear, time-invariant systems with time delay and multiple poles. The method in question is based on the usage of the Lambert W function. The suggested algorithm determines the Gain margin, Phase margin, Delay margin, as well as the gain and phase crossover frequencies. This method can be used with the system without time delay as well. The results are illustrated with examples.

Fault Location and Isolation in Power Distribution Network Using Markov Decision Process

ABSTRACT. Precise fault location in distribution network is one of the most important applications among intelligent monitoring and outage management tasks used for realization of self-healing networks. The data gathered from various intelligent sensors installed throughout the power system could be utilized for smart approaches to locating faults, helping the system restoration, reducing outage time and improving system reliability. Since the distribution network is radial, with multiple laterals connected to the main feeder, faults at various locations may lead to the same voltages and currents observed at the substation. In other words, using the substation measurements to calculate the fault location, multiple failure states are possible. In this paper, Markov Decision Process is used as a tool for the determination of the faulted feeder section and its isolation from the grid. The algorithm is based on transition probabilities among states obtained from intelligent sensors and tested on a radial distribution network with 3 sectionalizers.