bluuebunny/crossref_metadata_2025_split huggingface dataset:
Using an ESP8266 Microcontroller to Develop a Learning Game
Authors: Chursin G , Semenov M 
Published: August 2020

Abstract
This article discusses the possibility of connecting an ESP8266 microcontroller to a learning game developed on the Unity3D game engine. The game is aimed at teaching and developing the skills of students, such as mathematics, physics, programming and the basics of robotics. The paper presents a diagram of the interaction of the microcontroller and the game, as well as a comparative analysis of the Arduino and ESP8266 platforms. Comparison of the Arduino firmware process via USB and ESP8266 over Wi-Fi is carried out. It was possible to establish that the firmware by air takes an average of 2 seconds more, which does not affect the gameplay.
Robust signal processing in nonparametric autoregression
Authors: Pchelintsev Evgeny , Povzun Maria 
Published: August 2020

Abstract
In this paper we consider the problem of a robust adaptive estimation of a periodic signal modeled by the nonparametric autoregression. We develop a new sequential model selection method, using improved estimation approach and the efficient sequential kernel estimators. This procedure is based on the sequential estimators. For robust quadratic risk of proposed estimate we obtain sharp oracle inequality that allows us to establish the efficiency property of this model selection procedure. We give the Monte Carlo simulation results for numerical comparing of the risks of proposed improved procedure and ordinary least squares estimates.
Applying a Principle Component Analysis to Search for Objects on Historical Territories by the Spectral Brightness of Vegetation
Authors: Zlobina A G , Zhurbin I V 
Published: August 2020

Abstract
An approach is proposed for analyzing multispectral aerial photography data to identify traces of human activity; this approach is based on the application of a principal component analysis. Its efficiency is illustrated by a case study of historical territory – the Pudemsky Ironworks (Udmurt Republic, Russia). About 67% of the arable land area abandoned in the latter half of the 19th century is revealed. Nowadays, this site is covered with forest vegetation and no longer has any striking visual or spectral differences from the environment.
Simulation of multiple supersonic jets impinging with obstacle
Authors: Kagenov A M , Kostyushin K V , Aligasanova K L , Kotonogov V A 
Published: August 2020

Abstract
In this paper the mathematical modeling of the interaction of a twin supersonic jet with a flat obstacle in Mars environment and Mach numbers at the nozzle exit 4.5 are considered. The effect of the distance between the nozzles on the shock-wave structure of the jets and their force on obstacle are investigated. The distance ranged from 0.1 to 4 nozzle exit diameters (L/D). It is found that the self-oscillating mode is observed only for the case L/D = 1.5, for the rest of the cases the stationary mode are implemented. With an increase in the distance between the nozzles, the maximum pressure on obstacle decreases from 33 kPa to 13 kPa. In the case when the plumes do not close L/D = 4, the force effect of the jets is 2.5 times less compared to L/D = 0.1.
Features of propagation of slow deformation perturbations in geomedia with faults
Authors: Kazakbaeva A A , Smolin I Yu , Makarov P V 
Published: August 2020

Abstract
Our work is performed in the framework of the mathematical model suggested by P.V. Makarov to describe the joint generation and propagation of ordinary stress waves (propagating with the sound speed) and slow deformation waves of the inelastic nature in loaded elastoplastic media. Structural models of block media with weakened regions (faults or deformation sites) are constructed for test calculations on generation and propagation of slow deformation waves in nonlinear dissipative media. The features of propagation of deformation waves are investigated for different fault orientations and implementations of the algorithm of cellular automata for transmitting slow perturbation. The zigzag and diamond nature of propagating fronts of slow deformation disturbances is revealed.
Algorithms for ranking and unranking the combinatorial set of closed questionnaire answers
Authors: Shcheglov P P , Filippov G A , Shablya Y V , Kruchinin D V 
Published: August 2020

Abstract
In this paper, we study the combinatorial set of closed questionnaire answers with a specified minimum number of correct answers. For this combinatorial set, we obtain an explicit formula for its cardinality function. Using the obtained cardinality function, we construct the corresponding AND/OR tree structure and determine the bijecion rules for this AND/OR tree and the considered combinatorial set. In addition, we develop ranking and unranking algorithms for the combinatorial set.
Designing high performance, power-efficient, reconfigurable compute structures for specialized applications
Authors: Shatravin V , Shashev D V 
Published: August 2020

Abstract
In this paper, a new approach to design high-performance and power-efficient computing structures are proposed for machine learning tasks. Such structures can be very useful in some specialized applications such as autonomous robots, mobile devices, smart sensors for the Internet of Things (IoT) and so on. This approach is based on the concept of reconfigurable homogeneous computing environments. Major advantages of this approach are discussed. The process of designing a set of elementary operations for such structures is described in detail using an example with a typical Feed-Forward Neural Network (FFNN) and its training module.
Reducing dimensions of the histogram of oriented gradients (HOG) feature vector
Authors: Shidlovskiy S V , Bondarchuk A S , Poslavsky S , Shikhman M V 
Published: August 2020

Abstract
The article discusses the basic principles of a HOG-descriptor. A method is proposed for reducing dimensions of the feature vector obtained using the HOG-descriptor by applying the convolution operation and converting the resulting values of gradients and their directions. After using new feature vectors for training, the Support Vector Machine (SVM) classifier showed a 37-fold increase in performance when processing an image with a resolution of 282×159 pixels.
VLSI architecture with a configurable data processing path based on serial distributed arithmetic
Authors: Tarasov I E , Potekhin D S 
Published: August 2020

Abstract
The article considers the VLSI architecture intended for streaming data processing using a configurable path based on sequential distributed arithmetic. The development of specialized VLSIs is advisable if there is a need for serial production of devices, and the domain of application of such VLSIs decreases as the capabilities of the CPU, GPU and FPGA expand. The alternative approach is to build configurable VLSIs that can efficiently perform computations in related applications. The solution under consideration is focused on performing stream operations, including FIR filters and other tasks based on the operation “multiplication and accumulation”, for example, neural networks and deep learning algorithms. The architecture provides for compliance with the GALS (Globally Asynchronous, Locally Synchronous) approach, which allows placing many independent computing nodes in the VLSI. The characteristics of VLSI were evaluated and the areas of preferred application of the considered architecture were considered.
Improving the performance of processing special computing tasks using an asynchronous actor model
Authors: Andrianova E G , Sachkov V E , Zhukov D O 
Published: August 2020

Abstract
The use of third-party ready-made solutions to perform special computing tasks (SCT): processing text arrays in a natural language, the use of cryptography methods in data transmission networks, machine learning in solving applied problems, scientific computations and many other ones is a common practice in modern development of software systems. However, the use of third-party ready-made software solutions often leads to their poor optimization for parallel and distributed operation and to the difficulty in vertical and horizontal scaling, impossibility of modifying a solution, e.g., due to its legal protection, etc. In order to solve these problems, an asynchronous actor model can be used that facilitates developing software for processing special computing tasks. In addition, an asynchronous actor model allows parallel processing of input tasks, provides asynchronous operation, and extends its functionality to third-party ready-made solutions, that it is impossible or forbidden to modify to meet user’s or optimization requirements. Methods, algorithms, and the entire logic of asynchronous actor operation involve transitions of actors from one state to another under the effect of input messages and returning the results of processed tasks to the frame system. This allows setting an automatic grammar and for using methods and approaches for developing software systems based on finite-state automata. The asynchronous actor operation scheme is represented by a directional state graph, which allows using the mathematical apparatus of the finite-state automaton theory (the Mealy model) to describe the same. The developed model is convenient for designing and organizing the SCT processing using computer systems. The use of the asynchronous actor model makes it possible, in special operation modes, to increase the performance of poorly optimized SCT without modifying the same, as compared to task processing without using the asynchronous actor model.
Diagnostics of aluminum alloys with friction stir welded joints based on multivariate analysis of acoustic emission signals
Authors: Dmitriev A A , Polyakov V V , Kolubaev E A 
Published: August 2020

Abstract
The acoustic emission method was applied to study aluminum-magnesium alloys produced by friction stir welding. Alloy specimens were tested under static tension with simultaneous recording of acoustic emission, applied load, and elongation. The recorded acoustic emission signals were processed using projection methods of multivariate data analysis; the informative features used were the coefficients of wavelet discrete decomposition which characterize the low-frequency form of the signal. It is shown that the proposed approach allows the partition of signals formed on different stages of plastic deformation and fracture. Strong differences in acoustic emission signals were revealed and described to be due to the formation of highly defect structure in the weld zones in different welding modes. The obtained results can be useful for acoustic emission diagnostics of welded joints in structures of metal alloys exposed to external loads.
Computations of cross-correlation functions on a single board Raspberry Pi computer
Authors: Faerman V A , Shvetsov M P , Tsavnin A V 
Published: August 2020

Abstract
The paper discusses the implementation of correlation algorithm for time delay estimation on a Raspberry Pi single-board computer. The implemented correlation algorithm is based on Fourier transform. In the course of the study, we applied two alternative solutions for the software implementation of discrete Fourier transform. The first solution stands on FFTW library and uses general-purpose quad-core ARM Cortex A53 processing unit. The alternative method uses VideoCore IV graphic processing unit and is implemented via firmware GPU_FFT library. We have performed a computational experiment on a Raspberry Pi 3B to determine which solution is more preferable for the implementation of correlator. After a comparative study we figured out that estimated processing time is highly dependent on computations parameters and input signals. For small FFT window sizes CPU is proved to be a preferable option. However, for large FFT windows GPU allows significantly accelerating the computations. At some point, you can achieve even better performance by using batching and GPU for direct FFT and CPU for inverse FFT. According with the results, we have concluded that both alternatives have their own potential advantages and particular drawback. We also establish, that Raspberry Pi 3 B computer with HiFiberry extension can be used as a real-time correlator for audio signals.
Use of trainable wavelet transform with adaptive threshold filtering for noise reduction of speech signals
Authors: Lependin A A , Ilyashenko I D , Nasretdinov R S 
Published: August 2020

Abstract
A method of noise reduction based on an adaptive threshold filtering over trainable wavelet transform was proposed. The restrictions on the trainable transform filter parameters were provided by a set of quadratic regularization terms. As an analog of the “hard” and “soft” threshold functions we used their smooth infinitely differentiable versions. The parameters of trainable wavelet transform, and threshold values were estimated by backpropagation and gradient-based optimization algorithm with an adaptive momentum estimation. Results of the proposed method were compared with an approach based on fixed discrete wavelet transform and non-adaptive global and level-depended threshold algorithms on the model problem. We used signal-to-noise ratio between suppressed and clean signals to numerically estimate the efficiency of noise reduction. We showed that the best results were obtained when the proposed trainable method with Daubechies 4 wavelet filters fine-tuning and adaptive level-dependent thresholding were applied.
Optimization method for non-cooperative iris recognition task using Daugman integro-differential operator
Authors: Petrov I , Minakova N 
Published: August 2020

Abstract
We considered the issues arising when Daugman’s integrodifferential operator is used to localize iris edges. Daugman’s integro-differential operator is a widely common method to localize the iris, however, problems of the operator optimization poorly presented in the literature. The article considers existing methods of optimization of the operator applied at the iris recognition step. Based on the provided research we proposed using Nelder-Mead and Differential Evolution methods to optimize the integro-differential operator. The problems of the iris localization and their solving methods were considered. The article focuses attention on non-cooperative iris recognition. The very general boundary conditions based on iris’s anatomy which are not dependent on captured image properties were defined. The results of the comparative analysis of the accuracy and performance of selected optimization methods of Daugman’s integrodifferential operator were presented at the experimental results chapter. It was found that the Differential evolution optimization method gives fine performance and correctness. It was concluded that the Differential evolution is expedient as Daugman’s integro-differential operator optimization method.
Automated analysis of x-ray images using transparency masks
Authors: Dabagov A R , Tomakova R A , Alekseev V A , Kondrashov D S 
Published: August 2020

Abstract
The method for classification of x-ray images, which is implemented in this article, improves the differential diagnosis of socially considerable diseases. The novelty of this method is that the input digital x-ray image is supplemented by a transparency mask which is found by image segmentation, and the classified sign vector is formed by pixels which aren’t masked by the transparency mask. The original x-ray image is divided into segments to classify morphological structures with pathological formings. The classification of the selected segment is implemented by a modified convolutional neural network which uses pooling layers and layers of a fully-connected neural network. The proposed classifier allows dividing patients into two groups: patients without found pathology and patients with disorders of health condition. The found morphological pathological formings help clinicians to find the most promising way for prevention of the disease development at the patients, saving money and time.
Use of the university’s enrolment campaign database for the development of a computer model to predict student expulsion
Authors: Zharikov A V , Zhuravlev E V , Zhurenkov O V , Kozlov D Yu 
Published: August 2020

Abstract
The article discusses the construction of a computer model to predict the problems occurrence in students in the educational process at the university. The following data sources of Altai State University were used for this purpose: “Admissions Office” (enrollees database) and “Dean’s office” (database of students) for 2013-2018. These data were combined using developed SQL scripts. While analyzing the obtained combined data set, we had to face the difficulties typical for solving data analysis problems. Thus, it turned out that there are incomplete and inconsistent data or cases when one and the same entity is named differently, etc. In order to solve these problems, we wrote a script in the R programming language using regular expressions, the data were unified and standardized, and the missing data were restored using the information from other fields of the data set. Then we discarded the variables with the near-zero dispersion, which could not make a significant contribution to the developed predictive model. After that, the data set under study was divided into 2 parts: the 2013-2017 data were taken to build a predictive model with the use of the logistic regression algorithm, while the data for 2018 were used, in fact, to predict whether a particular student would be expelled. It should be mentioned that the 2013-2017 data were divided into the training and test samples in the proportion of 90% and 10% correspondingly. The test result of the computer model built in the R programming language showed satisfactory accuracy; the most significant factors affecting student expulsion were also identified. The paper substantiates the economic feasibility of using the developed computer model at the university.
Diagnostics of diabetic polyneuropathy in children and adolescents using data mining methods
Authors: Krotova O S , Moskalev I V , Nazarkina O M , Khvorova L A 
Published: August 2020

Abstract
The article considers the application of data mining methods to develop a diagnostic model for one of the most common and dangerous diabetes mellitus complications – diabetic polyneuropathy characterized by damage to peripheral nerve fibers. The article explores the possibility of diagnosing diabetic polyneuropathy with the use of machine learning methods. The data base of the study includes 3204 anonymized medical records of children and adolescents with type 1 diabetes residing in the territory of Altai krai. 1100 records hold data on diabetic polyneuropathy. Medical records contain different information: patient complaints, medical case history, test results. The attribute space is represented by 40 different indicators. In the course of the study, we considered the differences between the attribute values in two groups, built the attribute space ensuring the best classification quality. The article presents the results of applying various methods to transform the source data, evaluates the quality of the resulting model. The implementation of all the study phases was carried out with the use of the Python high-level programming language.
Numerical methodology for the assessment of asymmetry coefficient of aerosol scattering in near IR region of spectrum
Authors: Pashnev V V , Pavlov V E , Matyuschenko Yu Ya 
Published: August 2020

Abstract
Based on the aerosol model of IAP RAS, a methodology has been developed for determining the aerosol coefficient of asymmetry of scattered radiation for natural particles. The source of information is the calculation data for the brightness of the cloudless sky in solar almucantarat with different spectral transparency of the atmosphere and the albedo of the underlying surface. By solving the radiation transfer equation by the Monte Carlo method, the asymmetry coefficients of diffuse fluxes were calculated, including the effects of single and multiple scattering, as well as light reflection from the earth’s surface. Tables of average values of the asymmetry coefficients are presented that assume the use of interpolation methods for calculating atmospheric parameters. A technique is proposed for determining the aerosol asymmetry coefficient from the results of observations of a similar integral coefficient, optical depth, and terrain albedo without using an apparatus of solving inverse problems. In this case, the brightness can be observed in any units, including directly the readings of the photometer. Software has been developed that implements this technique.
Reliability assessment of automatic process control systems for the production of concentrates of MD1 and MD2 nanostructures in terms of providing thermal vortex enrichment
Authors: Kolosov A D , Nebogin S A , Gorovoy V O , Govorkov A S , Kononenko R V 
Published: August 2020

Abstract
As part of the project to create a comprehensive resource-saving technology for the associated production of nanostructure concentrates, the reliability of the software package “Automated control system for the technological process of obtaining MD1 and MD2 nanostructure concentrates” was evaluated in terms of providing thermal vortex enrichment. The calculation of the reliability of the system was performed to assess the level of reliability and quantitatively determine the reliability indicators for the implementation of the functions of ACS TO 2.12. The object to be calculated was identified, the goals and objectives of the calculation at this stage, the nomenclature and the required values of the calculated reliability indicators were determined, the calculation method was selected that is adequate to the features of the object, the goals of the calculation, the availability of the necessary information about the object and the source data for the calculation. The initial data for the calculation were obtained and pre-processed; the values of the reliability indicators of the object were calculated. From a comparison of the calculation results with the reference values of the integral safety level (SIL) in accordance with IEC 61508, it follows that the maximum safety level of the ACS TO 2.12 SIL 2, the system availability is 98.97%, and the system uptime is 2 years. The probability of a dangerous failure of the PAZ system is 1.31·10−4, which corresponds to a high value of the integral safety level (SIL 4).
Computational technologies for the synthesis of decentralized control systems for multistage technological processes
Authors: Oskorbin N M 
Published: August 2020

Abstract
This article discusses the development of two-level hierarchical control systems for complex technological processes. This area of research relates to the problems of creating robotic complexes as part of automation systems for managing continuous multi-stage processes according to economic criteria. For the synthesis of decentralized systems of optimal control of processes with a sequential structure, it is recommended to use the dynamic programming method, which allows providing optimal coordinating solutions and justify the setting of local tasks for managing the stages of the technological process, provided that the current states of the input products of these stages are accurately or approximately observed. For an n-stage technological process in conditions of complete information, the problem of a coordinating subsystem is written, the solutions of which do not change for a set of values of input variables of the process and the time period of constancy of external and internal perturbations. It is shown that the implementation of algorithms for controlling individual stages of the process can be performed on the basis of microprocessor technology. Estimates of control error were obtained using a simulation model of a process control system with specified parameters. The article considers the possibility of applying the dynamic programming method in the management of multi-stage technological processes in conditions of incomplete information.