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The purpose of this study is to develop threat models specific to the object of protection-automated process control systems. When developing the threat models, we propose a method of constructing graphical notations of events (Event-driven Process Chain), the main elements of which are events and functions associated with logical operations. We present the results of the analysis of threats of violation of information security of automated control systems of technological processes. The EPC-models of threats of information security violation of modern information and control systems of industrial enterprises are presented. The EPC-models allow to describe in detail ways of realization of the threats that, in turn, gives the chance to the experts working in the field of information security to design systems of information protection matched to potential threats and taking into account specifics of production. Modeling threats of violation of information security allows the specialist for data protection obtaining sufficient arguments about the feasibility of a violator of the threats to specific automated control systems of technological processes, which contributes to the financing of the project. This paper is not limited to the consideration of only business process models as the only means of creating threat models in the field of information security, and offer EPC-modeling as one of the methods of building information security threats for automated control systems. The EPC models allow to describe ways of realization of the threats actual for modern automated control systems and to estimate probabilities of their realization.

Investigation of the influence of parameters of silver, aluminum, gold, and chromium spiral zone plates on the longitudinal component of Umov-Pointing vector in produced optical vortices by using the frequency-dependent finite-difference time-domain method is presented. It is shown that the aluminum spiral zone plate with a relief height of 50 nm gives an optical vortex with the smallest longitudinal component of Umov-Pointing vector on the optical axis. The gold spiral zone plate is the least effective for the formation of vortex beams with a reverse energy flow.

The article deals with a problem of calculating two reflecting surfaces that form a given irradiance distribution with a flat wavefront, provided that a point source of light is used. A notion of a weak solution for the said problem is formulated and the equivalence of this problem and the Monge–Kantorovich mass transfer is proven.

The paper describes a DF-OPTICS device that the present authors designed for the experimental study of changes in the optical properties of a glass whose surface is exposed to high-speed flows of micron-sized dust particles. The device allows the scattering indicatrix and the spectral transmittance to be automatically measured at each point of the sample with a 0.5-mm increment. Advantages of the developed device include small dimensions and the ability to work in vacuum, allowing it to be used in an accelerator chamber to simulate outer space factors. Experimental results for the K-8 glass put in a microparticle accelerator and bombarded by an aluminum powder PAP-1 with a characteristic size of 1-3 µm and speeds of 2-8 km / s are presented. The device makes it possible to measure the change of the spectral transmittance of transparent materials in the UV and RGB regions with an accuracy of 0.005%, which exceeds the sensitivity of some known spectrophotometers.

A lot of research has been devoted to identity documents analysis and recognition on mobile devices. However, no publicly available datasets designed for this particular problem currently exist. There are a few datasets which are useful for associated subtasks but in order to facilitate a more comprehensive scientific and technical approach to identity document recognition more specialized datasets are required. In this paper we present a Mobile Identity Document Video dataset (MIDV-500) consisting of 500 video clips for 50 different identity document types with ground truth which allows to perform research in a wide scope of document analysis problems. The paper presents characteristics of the dataset and evaluation results for existing methods of face detection, text line recognition, and document fields data extraction. Since an important feature of identity documents is their sensitiveness as they contain personal data, all source document images used in MIDV-500 are either in public domain or distributed under public copyright licenses. The main goal of this paper is to present a dataset. However, in addition and as a baseline, we present evaluation results for existing methods for face detection, text line recognition, and document data extraction, using the presented dataset. (The dataset is available for download at ftp://smartengines.com/midv-500/.), Comment: 7 pages, 6 figures, 5 tables

A method of feature matching in images using descriptor structures is considered in the work. The descriptors in the developed method can be any known solutions in the field of computer vision. However, inaccuracies can occur when matching image pairs. It is proposed that descriptor structures should be compared to eliminate the “outliers”. Descriptor structures are described using graphs. An Umeyama method is used to find matching features using descriptor structures. The method is based on the decomposition of matrices into eigenvalues and eigenvectors for weighted graph matching problems. Thus, matches are based on the descriptor at the initial stage. Two graphs are then constructed for each image based on the resulting sets of mapped features. The weights of the graph are distances between all image features, calculated using the Gauss function. Weight matrices are built for each graph. Matrix decomposition is carried out into eigenvalues and eigenvectors. The resulting matrix is calculated based on the Umeyama method and correct matches are found. Thus, false matches are excluded from the set of matches obtained using descriptors by comparing structures. The method is invariant to zoom and in-plane image rotation. The method leads to correct results only if the number of correct matches is greater than the number of false matches. The complexity of the developed algorithm is proportional to the number of matches found.

In present paper we propose easy way to implement method of interfering vortices with opposite topological charge for the real time determination of the thickness and information about the surface of studied samples with the resolution up to 7 nanometers. The determination of the characteristics of the medial cross-section of submicron-objects becomes possible due to phase sensitivity of interfering singular beams to the slightest changes in the optical path difference between them. The dependence of rotational angle of resulting interference pattern in case of different sample thickness for two singular beams superposition is considered in detail.

Situation awareness is the first and most important step in emergency management. It is a dynamic step involving evolving conditions and environments. It is an area of active research. This study presents a Markov Logic Network to model SA focusing on fire accidents and emergency evacuation. The model has been trained using empirical data obtained from case studies. The case studies involved human participants who were trained for responding to emergencies involving fire and smoke using a virtual environment. The simulated (queried) and empirical findings are reasonably consistent. The proposed model enables implementing an agent that exploits environmental cues and cognitive states to determine the type of emergency currently being faced. Considering each emergency type as a situation, the model can be used to develop a repertoire of situations for agents so that the repertoire can act as an agent’s experience for later decision-making.

Transformations of the vortex beams structure subjected to sectorial perturbation were theoretically and experimentally studied. The analysis was based on computing (measuring) the vortex spectrum that enables us to find the orbital angular momentum (OAM) and Shannon entropy (informational entropy). We have revealed that, in the general case, the number of vortices caused by an external perturbation is not related to the topological charge. For arbitrary perturbation, the topological charge remains equal to the initial topological charge of the unperturbed vortex beam. Growth of the vortex number induced by perturbations is associated with the optical uncertainty principle between the sectorial angle and the OAM. The computer simulation has shown that OAM does not depend on the number of vortices induced by perturbations. Moreover, two maxima are formed both in the positive and negative regions of the vortex spectrum. As a result, the OAM does not practically change in a wide range of perturbation angles from 0 to 90 °. However, at large perturbation angles, when the energy is almost equally redistributed between the vortex modes with opposite signs of the topological charge, the OAM rapidly decreases. At the same time, the Shannon entropy monotonically increases with growing perturbation angle. This is due to the fact that the entropy depends only on the number of vortex states caused by external perturbations.

Multitemporal remote sensing images of a particular territory might include accidental scene distortions. Scene distortion is a significant local brightness change caused by the scene overlap with some opaque object or a natural phenomenon coincident with the moment of image capture, for example, clouds and shadows. The fact that different images of the scene are obtained at different instants of time makes the appearance, location and shape of scene distortions accidental. In this article we propose an algorithm for detecting accidental scene distortions using a dataset of multitemporal remote sensing images. The algorithm applies superpixel segmentation and anomaly detection methods to get binary images of scene distortion location for each image in the dataset. The algorithm is adapted to handle images with different spectral and spatial sampling parameters, which makes it more multipurpose than the existing solutions. The algorithm's quality was assessed using model images with scene distortions for two remote sensing systems. The experiments showed that the proposed algorithm with the optimal settings can reach a detection accuracy of about 90% and a false detection error of about 10%.

Image binarization is still a challenging task in a variety of applications. In particular, Document Image Binarization Contest (DIBCO) is organized regularly to track the state-of-the-art techniques for the historical document binarization. In this work we present a binarization method that was ranked first in the DIBCO`17 contest. It is a convolutional neural network (CNN) based method which uses U-Net architecture, originally designed for biomedical image segmentation. We describe our approach to training data preparation and contest ground truth examination and provide multiple insights on its construction (so called hacking). It led to more accurate historical document binarization problem statement with respect to the challenges one could face in the open access datasets. A docker container with the final network along with all the supplementary data we used in the training process has been published on Github.

This paper discusses a self-mixing interferometry method for measuring distances upon sawtooth modulation of the wavelength of laser radiation. Conditions under which the distance obtained from the spectrum of the modeled autodyne signal coincides with the distance specified in computer simulation are determined. The limiting capabilities of the method are theoretically substantiated for the increased range of deviations of the laser wavelength. The estimation of the limiting capabilities of the distance measurement method on the spectrum of the autodyne signal gives 10 microns at a wavelength of 650 nm at a 5-nm deviation of laser radiation wavelength. We also discuss difficulties of attaining the limiting accuracy of distance measurements associated with the nonlinear dependence of the wavelength emitted by a laser diode on its supply current and the need to analyze the self-mixing signal at high frequencies.

We consider the possibility of using generalized lenses in imaging optical systems. We propose using two generalized lenses in a system similar to a Fourier-correlator to obtain a point-to-point mapping, where the second lens is set so that a plane wave is also formed at the output of the system when the incident wave is plane. We show that for generalized lenses with a small deviation of their degree from 2, such a system forms a fairly high-quality image. We use a standard radial photographic chart to test the system, which allows us to determine its key optical characteristics.

We discuss requirements for the multispectral remote sensing (RS) data utilized in the author's technique for estimating plant species concentration to detect arable land colonization by tree and shrubbery vegetation. The study is carried out using available high-resolution remote sensing data of two arable land plots. The paper considers the influence of resolution, combinations of spectral channels of RS data, as well as the season RS data is acquired on the quality of identification of elementary vegetation classes that form the basis of the plant community – a fallow land. A fallow land represents a piece of arable land that has not been cultivated for a long time. The study was conducted using a technology that is based on image superpixel segmentation. We found out that for determining tree and shrub vegetation, it is preferable to use RS data acquired in autumn, namely, in late September. The combination of red and blue spectral channels turned out to be the best for the analysis of tree-shrub vegetation against the background of grassy plant communities, and the presence of a near-infrared channel is necessary to range the various grassy plant communities in different classes. RS data with a spatial resolution of 2.5 m can be used to define tree-shrub plant communities with a high closeness of crowns (90 % or more), but cannot be used to classify isolated trees. Trees and shrubs (with a height of 8 m) can be classified in images with a spatial resolution of 0.8 m. An increase in spatial resolution does not improve the quality of the classification. The highest accuracies achieved for the land areas studied are 90 % and 83 %. Therefore, the suggested technology can be used in arable land expertise.

The paper considers paraxial Gaussian laser beams with different polarization states and their characteristics, such as the Poynting vector, gradient force, and force density, which are important for evaluating the effects of radiation on matter. An analysis of the considered characteristics of laser radiation from the point of view of the formation of photoinduced microrelief in an azopolymer film is carried out.

Wireless sensor networks (WSNs) comprise tiny devices known as sensors. These devices are frequently employed in short-range communications and can perform various operations such as monitoring, collecting, analyzing, and processing data. WSNs do not require any infrastructure, are reliable, and can withstand adverse conditions. Sensor networks are autonomous structures in which the sensor nodes can enter or leave the network at any time instant. If the entering node is attacker node it will monitor the network operation and can cause security issues in the network that can affect communication. Existing literature presents security improvements in such networks in the form of cryptography, asymmetric techniques, key distribution, and various protocols. However, these techniques may not be effective in the case of autonomous structures and can increase computational complexity. In this paper, a convolutional technique (CT) is proposed that generates security bits using convolutional codes to prevent malicious node attacks on WSNs. Different security codes are generated at different hops and the simulation results demonstrate that the proposed technique enhances network security and reduces computational complexity compared to existing approaches.

A general approach to a structure-functional analysis and synthesis (SFAS) of deep neural networks (CNN). The new approach allows to define regularly: from which structure-functional elements (SFE) CNNs can be constructed; what are required mathematical properties of an SFE; which combinations of SFEs are valid; what are the possible ways of development and training of deep networks for analysis and recognition of an irregular, heterogeneous data or a data with a complex structure (such as irregular arrays, data of various shapes of various origin, trees, skeletons, graph structures, 2D, 3D, and ND point clouds, triangulated surfaces, analytical data descriptions, etc.) The required set of SFE was defined. Techniques were proposed that solve the problem of structure-functional analysis and synthesis of a CNN using SFEs and rules for their combination.

A silicon subwavelengh terahertz axicon has been designed, fabricated, and investigated by methods of numerical and optical experiments. The research has been performed on a free-electron laser workstation NOVOFEL (Budker Institute of Nuclear Physics of SB RAS, Novosibirsk). Diffractive and polarization features of realized element have been investigated.

It was theoretically shown that in the interference pattern of four plane waves with specially selected directions of linear polarization it is formed a reverse flow of energy. The areas of direct and reverse flow alternate in a staggered order in the cross section of the interference pattern. The absolute value of the reverse flow directly depends on the angle of convergence of the plane waves (on the angle between the wave vector and the optical axis) and reach the maximum at an angle of convergence close to 90 degrees. The right-handed triples of the vectors of four plane waves (the wave vector with positive values of projection to optical axis and the vector of electric and magnetic fields) when added in certain areas of the interference pattern form an electromagnetic field described by the left-handed triple of vectors; however, the projection of wave vector to optical axis has negative values. In these areas, the light propagates in the opposite direction. A similar explanation of the mechanism of the formation of a reverse flow can be applied to the case of a sharp focusing of a laser beam with a second-order polarization singularity. It is also shown that if a spherical dielectric Rayleigh nanoparticle is placed in the backflow region, then a force directed in the opposite direction will act on it (the scattering force will be more than the gradient force).

In modern conditions, it can be stated that the classical methods of information and technological security related to the formulation of security policy for the developed and implemented information system lose value and efficiency, because they lag behind the development of information technology itself, do not fit into the speed of software updates and changing user needs. To solve these problems, the concept of the business process as a target function implemented by the information (computer) system is considered, the concept of correctness ("health") of the business process is introduced. Based on the subject-object model of a computer system, a mathematical model of a healthy business process as a trajectory or a family of desirable trajectories in the state of an information system is proposed. Based on this model, we propose consistent practical implementation of the platform to ensure the health of the business process as a set of interface that displays indicators of the health of the business process, reports, alerts, predicates (predictors or extrapolators in the form of formal expressions), the correlator – the core of the platform that controls the logic and contains information models, patterns (sample trajectories), the rules of response to events, the database storage of the business process and the connector that normalizes the data of the business process. From the point of view of the system-analytical approach, this platform is an information-analytical system that forms a new property – the health of the business process, aimed at reducing resource losses by identifying and preventing violations (in particular, cybercrime and fraud) at the process level, as well as the formation of the control environment of the business process. There is also a practical example of ensuring the health of the business process for the logistics system. Thus, a new universal methodological approach to assessing and ensuring the correct functioning of business processes on the basis of a new paradigm of trust (correctness) of the subject-object model of the business process and the concept of health of the business process is proposed, the concept of a platform for ensuring the health of the business process is proposed. The results of the work can be widely used for the design and evaluation of systems of technological and financial profile.

The article considers the program of professional development that allows to gain the necessary knowledge and abilities in the field of safety of the critical information infrastructure (CII). This program explains actions of public authorities, institutions and other organizations for fulfilling the requirements of the Federal law of July 26, 2017 No. 187 “About safety of critical information infrastructure of the Russian Federation”. The purpose of the article is to describe the features of implementation of the offered program for advanced training. The structure of the program, the form and types of advanced training, structure of the final assessment, types of qualification documents are given. The article also describes the educational requirements for students entering and features of teaching the program and interaction with students within a course. Special attention is paid to the practical issues contained in the program related to the organization of secure network communication between elements of the critical information infrastructure using “m-TrusT” microcomputers. As a result of successful mastering of the program, students receive standard certificates and with information obtained on the course could organize works on categorization of the objects of the CII’s and fulfill the requirements of Federal Law No. 187 for them.

The paper analyzes the applicability of methods for estimating the parameters of the VLSI sensitivity by single radiation effects (SEE) using focused laser radiation of picosecond duration in order to expand their application for submicron VLSI. A comparison of ionization track structure from a heavy charged particle and the ionization region of a semiconductor structure under focused laser radiation is made. It is shown that the comparison of geometric dimensions should be carried out during the ionization reaction, when the ionization region of the track expands due to ambipolar diffusion. Limitations due to the influence of optical inhomogeneities located on the surface of the VLSI chip, as well as under irradiation from the bottom side of the chip are determined. It is shown that laser methods can be applied to estimate the dependences of cross sections of single radiation effects as a function of linear energy transfer (LET) for semiconductor structures with sizes significantly smaller than the diameter of the focused laser radiation. Additional features of laser methods are presented to determine the SEE location on chip surface and to study the influence of the electrical mode and the effectiveness of SEE parry methods. The presented results allow the use of laser technique to estimate SEE sensitivity parameters estimation for deep submicron VLSI.

The results of the systematization of measures to protect information resources from attacks on the supply chain of software and computer systems are presented. The phenomena, relevance and popular topics of protecting the supply chains of IT products are noted. Statistics on borrowed components of software products and software systems are presented. Examples of computer attacks on resources and processes of the software supply chain are given. The analysis of the existing terminological base in the field of security of supply chains of software is carried out. The features of the terms for supply chain and supply chain attack are formulated. The analysis of existing models of information security threats associated with computer attacks on the supply chain of software products is done. Limitations of models of threats to information security of the software supply chain are revealed. A review and systematization of measures to protect information from threats in the information sphere related to computer attacks on the software supply chain has been carried out. Known regulatory and methodological documents in the field of the supply chain of IT products are considered. It is concluded that it is necessary to develop the Russian legislative and regulatory framework for information security on the subject of software supply chains. A version of the systematics of information security measures in the life cycle of software delivery of information systems is proposed. Classification signs such as the used controls, information security methods, phases of the software development process are proposed. Possible directions of improving measures to protect information from computer attacks on the supply chain of software in the national and international information security are formulated.

The paper is exploring the concept of implementing multi-currency secure cold wallet on the basis of the microcomputer with dynamically variable architecture MKT. This device is based on the new Harvard architecture, a modification of the classic Harvard architecture, using immutable memory (to ensure the integrity and immutability of the operating system) and session memory blocks (to ensure the correct operation of the software). The paper describes the work mechanism of blockchain and blockchain transactions, the basic principles of cryptocurrency storage. The implementation of the system algorithm for offline signing of transactions and protocols that provide a hierarchical key generation based on HD Wallet technology are reviewed. It is proposed to divide the functionality of the multi-currency wallet into two groups intended for execution either in the operating system that is read-only or available both for reading and writing. The scenario of execution of the working cycle of the designed system is described.

В статье рассматриваются популярные в настоящее время графовые системы управления базами данных (СУБД), способные работать с большими данными, с помощью которых можно реализовать хранение информации, полученной в ходе генерации преступных дел по отмыванию доходов, полученных преступным путем, и финансированию терроризма (ОД/ФТ). Цель работы заключается в выборе защищенной графовой СУБД, подходящей для работы с большими данными финансовых расследований. Для этого решаются следующие задачи: рассматриваются имеющиеся графовые СУБД, проводится их анализ и сравнение друг с другом с особым акцентом на методы защиты, используемые для обеспечения безопасности хранимых данных. Были изучены достоинства и недостатки программных продуктов, а также было проведено сравнение по ряду параметров, характеризующих защиту информации в них. Каждый критерий сравнения имеет развёрнутые комментарии, на основе которых был выбран наиболее удобный, гибкий и современный вариант СУБД для использования при поиске случаев ОД/ФТ. По полученным результатам было установлено, что графовые СУБД подходят для работы с большими данными, а также по ряду параметров была выбрана одна из рассмотренных СУБД, а именно Janus Graph.

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Based on the developed network ontology of the subject area "Preliminary investigation" a conceptual model of the information and analytical decision support system is proposed as a response to the challenge of the increasing need for the analysis of "big data" in the course of a crime investigation. It is demonstrated the importance of automated collection and processing of a variety of information obtained during the individual investigations based on the analysis of network interaction of persons involved in 12 criminal cases.

Modern biometric systems based on face recognition demonstrate high recognition quality, but they are vulnerable to face presentation attacks, such as photo or replay attack. Existing face anti-spoofing methods are mostly based on texture analysis and due to lack of training data either use hand-crafted features or fine-tuned pretrained deep models. In this paper we present a novel CNN-based approach for face anti-spoofing, based on joint analysis of the presence of a spoofing medium and eye blinking. For training our classifiers we propose the procedure of synthetic data generation which allows us to train powerful deep models from scratch. Experimental analysis on the challenging datasets (CASIA-FASD, NUUA Imposter) shows that our method can obtain state-of-the-art results.

The paper is devoted to the investigation of the implementation features of a block algorithm for the FDTD-method on GPU. The block algorithm in general and in the context of the FDTD-method in particular is discussed. The main attention is paid to specifics of determining the shape and volume of blocks, which stem from the use of MATLAB and its Parallel Computing Toolbox. The practical efficiency of the proposed techniques is shown. Possible applications and prospects are discussed.

This research looks into methods for searching of designs of nonpolarizing interference systems, which provide a small discrepancy between spectral characteristics of the energy reflection/transmission coefficient for s- and p- polarizations. The layer thickness of these systems is a multiple of a quarter wavelength. Depending on the method used, the number of film-forming materials can vary from two to four. Analysis of spectral characteristics shows that in the spectral range of interest the discrepancy between the integral characteristics for s- and p-polarizations is less than 2%.

A regular circular weakly guiding double clad optical fiber is considered. For a single-mode regime of the waveguide, an expression for estimating the proportion of the mode field power penetrating into the outer continuous cladding is obtained using a standard approach and a Gaussian model. It is shown that a simpler and more transparent result is obtained in the Gaussian model, which can be used in practical applications, in particular, when designing this type of waveguides with a minimal proportion of power penetrating into the outer shell.

In the recent era, artificial intelligence (AI) is being used to support numerous solutions for human beings, such as healthcare, autonomous transportation, and so on. Cognitive computing is represented as a next-generation application AI-based solutions which provide human–machine interaction with personalized interactions and services that imitate human behavior. On the other hand, a large volume of data is generated from smart city applications such as healthcare, smart transportation, retail industry, and firefighting. There is always a concern on how to efficiently manage the large volume of generated data. Recently many existing researches discussed the analysis of the large quantity of data using cognitive computing; however, these researches are failed to handle the certain problems, namely scalability, and flexibility of data gathered in a smart city environment. Data captured from millions of sensors can be cross implemented across various cognitive computing applications to ensure real-time responses. In this paper, we study the cognitive internet of things (CIoT) and propose a CIoT-based smart city network (CIoT-Net) architecture which describes how data gathered from smart city applications can be analyzed using cognitive computing and handle the scalability and flexibility problems. We discuss various technologies such as AI and big data analysis to implement the proposed architecture. Finally, we describe the possible research challenges and opportunities while implementing the proposed architecture.

This study is focused on enhancing the informativity of optical measurement techniques for particulate matter. The problem is that the description of particulate matter with bimodal and multimodal distributions by an a priori defined analytical function of particle size distribution (for example, a log-normal distribution) is not accurate enough. Here, we explore if experimental data can be approximated by a multivariable function of particle size distribution instead of using the a priori defined log-normal distribution. For the comparison of the approximation results, experiments are conducted on standard samples with granulometric compositions OGS-01LM and OGS-08LM separately and jointly in a mix. The experimental data are recorded by a high-selectivity turbidimetric technique in water suspensions of these samples. The purpose of this study is to present the measurement results as a distribution function that enables one to identify more accurately the particle-size distribution profile and the corresponding disperse characteristics of the aerosol in question when measuring parameters of disperse media by optical techniques. The main objective of this work is to develop, implement and verify a search algorithm for the particle-size distribution function by way of a multi-parameter function. We show that the solution to the problem proposed herein is more universal because it allows slow and fast processes in suspensions and aerosols to be examined with a lower error. The algorithm can be applied to the problems which are based on solving first-kind Fredholm equations.

The article examines physical mechanisms responsible for shaping the vortex avalanche induced by a weak perturbation of the holographic lattice of a combined vortex beam. For this, we have developed a new technique for measuring the degenerate spectra of optical vortices and orbital angular momentum of combined singular beams. The technique is based on measuring the intensity moments of higher orders of a beam containing vortices with both positive and negative topological charges. The appropriate choice of the mode amplitudes in the combined beam enables us to form orbital angular momentum anomalous spectral regions in the form of resonance dips and bursts. Since the intensity moments of a vortex mode with positive and negative topological charges are the same (the moments are degenerate) for an axially symmetric beam, the measurements are carried out in the plane of the double focus of a cylindrical lens. The calibration measurements show that the experimental error is not higher than 4.5 %. We also reveal that the dips and bursts in the orbital angular momentum spectrum are caused by the vortex avalanche induced by weak perturbations of the holographic grating relief responsible for the beam shaping. The appearance of the orbital angular momentum dips or bursts is controlled by the relation between the energy fluxes in the vortex avalanche with positive or negative topological charges.

The architecture of an artificial neural network that solves the problem of constructing interpolation formulas for doubling the size of images is proposed. The trained model receives a 4×4 matrix as an argument. The result is an interpolation formula represented as a weight vector for 4 points. A comparison of the main quality assessments of the proposed method with some well-known adaptive approaches is made. The results of the comparative analysis show that the proposed approach has a better interpolation quality than NEDI and DCCI methods.

With the increasing advance of computer and information technologies, numerous research papers have been published online as well as offline, and as new research fields have been continuingly created, users have a lot of trouble in finding and categorizing their interesting research papers. In order to overcome the limitations, this paper proposes a research paper classification system that can cluster research papers into the meaningful class in which papers are very likely to have similar subjects. The proposed system extracts representative keywords from the abstracts of each paper and topics by Latent Dirichlet allocation (LDA) scheme. Then, the K-means clustering algorithm is applied to classify the whole papers into research papers with similar subjects, based on the Term frequency-inverse document frequency (TF-IDF) values of each paper.

A three-level spiral metalens in an amorphous silicon film is designed. The metalens relief consists of two subwavelength gratings with a 220-nm period (for 633 nm wavelength) and depths of 90 and 170 nm. The metalens forms a left-hand circular polarized optical vortex with topological charge 2 when illuminated by a linearly polarized plane wave. The intensity distribution at a distance of 633 nm is in the form of a subwavelength circle, whereas the longitudinal projection of the Pointing vector has negative values on the optical axis, meaning that a backward energy flow occurs. Two subwavelength gratings with different depth act as quarter-wave plates, transforming linearly polarized light into circularly polarized light with a phase delay of (lambda)/2. This metalens combines functionalities of three optical elements: a quarter-wave plate, a spiral phase plate, and a high-NA diffraction metalens (NA close to unity).

We performed a comparative numerical study of the conservation of properties of individual Laguerre-Gauss beams and their superpositions in a random environment. The simulation is based on the extended Huygens-Fresnel principle. Studies have shown that increasing the number of rings of Laguerre-Gauss modes with the same vortex order allows one to increase the ratio of the energy of the minimum informative coefficient to the energy of the maximum parasitic coefficient, which is important when detecting information encoded in the coefficients. In addition, an increase in the number of rings partially compensates for the stronger effect of random fluctuations on beams with high topological charges. Such a positive impact can be explained by the structural redundancy of multi-ring distributions (the vortex phase structure of the beam is repeated in each ring). A similar result was obtained for beams corresponding to a two-mode superposition. The best result on information preservation was obtained for the superposition with duplication of information in complex conjugate coefficients, and the best ratio of informative energy to parasitic one was obtained for beams with the largest area.

Identification and classification of extremist-related tweets is a hot issue. Extremist gangs have been involved in using social media sites like Facebook and Twitter for propagating their ideology and recruitment of individuals. This work aims at proposing a terrorism-related content analysis framework with the focus on classifying tweets into extremist and non-extremist classes. Based on user-generated social media posts on Twitter, we develop a tweet classification system using deep learning-based sentiment analysis techniques to classify the tweets as extremist or non-extremist. The experimental results are encouraging and provide a gateway for future researchers.

Group key agreement protocol permits a set of users to create a common key to make sure security of information exchange among members of the group. It is extensively used in secure multiparty computation, resource security sharing, and distributed collaborative computing etc. For large wireless ad-hoc network, there is no authentication center, the computing power and communication distance of terminals are constrained, and nodes frequently join and exit the network. For these reasons, Group Key Management for securing multicast communications in an energy-constrained large wireless ad-hoc network environment is still remains a critical and challenging issue. In this direction, we propose a cluster-based hybrid hierarchical-group key agreement (CHH-GKA) framework to provide a scalable solution for Secure Group Communication (SGC) in large wireless ad hoc networks (WANETs). This technique is based on splitting a large group into a certain number of clusters in which the last member of each of the clusters is designated as a cluster head (CH) and the last member of the group is designated as the group controller (GC). First we apply on hand Naresh–Murthy-group key agreement (NM-GKA) protocol locally in every cluster in parallel in level-I to generate CKs and then in level-II, the CHs’ use these CKs and implement NM-GKA protocol again among them to form the complete group key. Finally each CH distributes the group key to all its members through their respective CK encrypted links. In this process, first we survey several cluster-based hierarchical GKA protocols and compare the proposed one with them and show that it provides optimal performance with regard to computation and communication expenses. Further, it also handles dynamic events and is provably secure in formal security model under the cryptographic suppositions.

The spectrum sensing performance depends on the accuracy of the detection about whether primary users are busy or idle. Previous studies on cognitive radio spectrum sensing have shown that the cooperation between secondary users can improve their spectrum detection performance in real cognitive networks. Aiming at the problem of threshold mismatch of energy detectors under noise power uncertainty, a cooperative spectrum sensing method with dynamic dual threshold is proposed. Firstly, the utility function is defined with the objective of minimizing the error probability of spectrum sensing, and the optimum threshold of energy detector is derived. Secondly, in order to mitigate the influence derived from noise uncertainty, an effective dynamic dual threshold adjustment mechanism is presented, and the optimizing combinative fusion rule is discussed with the prerequisite of the minimum global error probability. In addition, in view of insufficient number of cognitive users whose sensing results lie in decision zones, the parameter of credibility is defined to choose the secondary users with reliable local detection for final fusion. Simulation results show that our proposed method can mitigate the influence of noise uncertainty and increase the spectrum sensing accuracy compared with other existing methods.

The article is devoted to the review and analysis of binary descriptors of feature points of objects in digital images under distortion conditions. An overview of the BRIEF, ORB, BRISK, FREAK, AKAZE, LATCH methods is given. The evaluation of properties of the descriptors on sample images is performed. The paper addresses problems of using these methods for real time image processing.

The aim of the study is to provide a test of various well-known gradient boosted decision trees libraries, which are used here in relation to the software identification problem with limited set of executable files belonged to different versions of the same program in the training sample. The importance of software audit for business processes is substantiated. The paper considers the control means of installed software on personal computers of automated systems users. The disadvantages of such software solutions are substantiated with crawling examples for algorithms of program identification and the developed approach to the identification of executable files using the machine learning algorithm – gradient boosting of decision trees, based on the libraries XGBoost, LightGBM, CatBoost is presented. An experiment to identify executable files with the help of XGBoost, LightGBM is performed. On the basis of bicubic measure of clustering quality, a comparative analysis of the results between previously proposed program identification approach based on the CatBoost library, and the results presented in other studies, is performed. The results show that the developed approach allows identifying violations of the established security policy in automated systems information processing.

A mathematical description of a coherent optical correlator for the multi-raster input of long signals is given. It is shown that such an input makes it possible to reduce the value of false correlation maxima that are generally found at the output of a correlator with a single-raster input. It is shown that false maxima do not appear when processing signals with a thumbtack ambiguity function, allowing one to do without a multi-raster input. The results of the theoretical analysis are confirmed by experiments with chirp signals and M-sequence type signals.

Monocular Simultaneous Localization and Mapping (SLAM) is one of the most complex and well-known problems, affecting several scientific fields: robotics, computer vision, virtual reality. This paper aims to study the SLAM problem for the mobile device with a monocular camera and sensors: accelerometer, gyroscope and digital compass. The latter allow to obtain an additional estimation of a mobile device position and orientation. The aim is to assess the potential suitability and efficiency of using extra information from inertial sensors to improve the solution quality and to reduce the time to obtain the solution. The experimental part of the study, including both model and field experiments, allowed to determine the requirements for permissible errors introduced by the sensors of the mobile device. For a specific model of a mobile device, it is shown that the electronic compass meets these requirements, while the errors of the inertial sensors used to determine the movements are unacceptably large.

Accurate detection of air bubbles boundaries is of crucial importance in determining the performance and in the study of various gas/liquid two-phase flow systems. The main goal of this work is edge extraction of air bubbles rising in two-phase flow in real-time. To accomplish this, a fast algorithm based on local variance is improved and accelerated on the GPU to detect bubble contour. The proposed method is robust against changes of intensity contrast of edges and capable of giving high detection responses on low contrast edges. This algorithm is performed in two steps: in the first step, the local variance of each pixel is computed based on integral image, and then the resulting contours are thinned to generate the final edge map. We have implemented our algorithm on an NVIDIA GTX 780 GPU. The parallel implementation of our algorithm gives a speedup factor equal to 17x for high resolution images (1024×1024 pixels) compared to the serial implementation. Also, quantitative and qualitative assessments of our algorithm versus the most common edge detection algorithms from the literature were performed. A remarkable performance in terms of results accuracy and computation time is achieved with our algorithm.

The article is devoted to the effectiveness research of methods of controlled spectral and spectral-spatial classification of hyperspectral data. In particular, minimum distance, support vector machine, mahalanobis distance and maximum likelihood methods are considered on the example of vegetative cover types differentiation. Significant attention is paid to studying the dependence of the accuracy of data classification with listed methods on the spectral features number and their selection method. The perspectivity of complex processing of spectral and spatial features, considering the correlation of close pixels, is demonstrated. The experimental results obtained with various methods of forming training sets are presented.

The program of digitalization of the economies of developed countries highlights a relatively new direction of development of the so-called cyberphysical control systems, in particular, industrial technological processes, which motivates the formulation of non-traditional tasks to ensure the security of critical information infrastructure. The obvious threat of loss of stability of technological processes is the use of counterfeit components in a view of the observed overall increase in a volume of counterfeit products, including electronic components. Therefore, there is a clear request for a systematic solution to the problem of detecting counterfeit not only from the standpoint of the protection of intellectual property, but also in the aspect of industrial safety during the certification of appropriate technical solutions. Considering that for testing centers and laboratories this statement implies the organization of rather new type of activity under conditions of insufficiency of the corresponding regulatory base. The paper reviews the foreign normative documents defining the methods and means of detecting counterfeit in radio electronics, which can be used as the initial basis for the formation of the domestic regulatory framework

The paper gives an overview of the algorithms developed to obtain reference lines and angles on X-ray images. These geometrical characteristics are used in the medical analysis of human joints. We propose the algorithm’s modifications based on the analysis of numerous X-ray images. These modifications allowed obtaining a great increase in calculation speed and the improvement of final results quality given by the corresponding application. They also lead to a significant reduction of manual tuning of the program, arising only in the rare cases when the properties of given images differ significantly from the mean ones.

The article is devoted to the problem of improving the quality of images recorded using low-resolution optical instruments, including diffraction-based cameras. A two-stage image correction technology is proposed. At the first stage, the correction is carried out using a linear FIR filter with a centrally symmetric frequency response in the form of quadratic and exponential functions. The resulting image is then processed with a non-linear filter that performs computer retouching of image areas characterized by a noticeable brightness difference. This procedure is only performed on those pixels in which the absolute value of gradients in different directions is sufficiently high, that is, they are located on the borders of areas with different intensity levels. This allows us to avoid noise amplification in the background, which is typical of traditional filters. The examples of the implementation are provided, showing the possibility of achieving high sharpness and illustrating how the filter can be adjusted by visual perception.