Pub Date : 2023-04-08DOI: 10.32362/2500-316x-2023-11-2-50-57
N. Zenchenko, D. Lavrukhin, I. Glinskiy, D. Ponomarev
Objectives. The study aims to improve the efficiency of a large-area photoconductive terahertz (THz) emitter based on an optical-to-terahertz converter (OTC) having a radiating area of 0.3 × 0.3 mm2 for generating high-power THz radiation by using an array of close-packed profiled sapphire fibers having a diameter in the range of 100–300 μm as focusing optics.Methods. As a photoconductive substrate, we used a semi-infinite LT-GaAs layer (low-temperature grown GaAs; GaAs layer grown by molecular beam epitaxy at a low growth temperature). Additional Si3N4 and Al2O3 layers are intended for reducing leakage currents in the OTC and reducing the reflection of the laser pump pulse from the air/semiconductor interface (Fresnel losses), respectively, at a gap width of 10 μm. For forming the antenna electrodes and feed strips, the Ti/Au metal system was used. The simulation was carried out by the finite element method in the COMSOL Multiphysics environment.Results. The use of a profiled sapphire fiber whose diameter has been optimized with respect to the gap parameters to significantly increase the concentration of charge carriers in the immediate vicinity of the electrodes of an OTC is demonstrated. The integrated efficiency of a large-area photoconductive THz emitter was determined taking into account the microstrip topology of the array with a characteristic size of feed strips proportional to the gap width in the OTC and with the upper (masking) metal layer. The maximum localization of the electromagnetic field in close proximity to the edges of electrodes at the “fiber–semiconductor” interface is achieved with a profiled sapphire fiber diameter of 220 μm.Conclusions. By optimizing the diameter of the sapphire fiber, the possibility of improving the localization of incident electromagnetic waves in close proximity to the edges of the OTC electrodes by ~40 times compared to the case without fiber, as well as increasing the overall efficiency of a large-area emitter by up to ~7–10 times, was demonstrated.
{"title":"Improving the efficiency of an optical-to-terahertz converter using sapphire fibers","authors":"N. Zenchenko, D. Lavrukhin, I. Glinskiy, D. Ponomarev","doi":"10.32362/2500-316x-2023-11-2-50-57","DOIUrl":"https://doi.org/10.32362/2500-316x-2023-11-2-50-57","url":null,"abstract":"Objectives. The study aims to improve the efficiency of a large-area photoconductive terahertz (THz) emitter based on an optical-to-terahertz converter (OTC) having a radiating area of 0.3 × 0.3 mm2 for generating high-power THz radiation by using an array of close-packed profiled sapphire fibers having a diameter in the range of 100–300 μm as focusing optics.Methods. As a photoconductive substrate, we used a semi-infinite LT-GaAs layer (low-temperature grown GaAs; GaAs layer grown by molecular beam epitaxy at a low growth temperature). Additional Si3N4 and Al2O3 layers are intended for reducing leakage currents in the OTC and reducing the reflection of the laser pump pulse from the air/semiconductor interface (Fresnel losses), respectively, at a gap width of 10 μm. For forming the antenna electrodes and feed strips, the Ti/Au metal system was used. The simulation was carried out by the finite element method in the COMSOL Multiphysics environment.Results. The use of a profiled sapphire fiber whose diameter has been optimized with respect to the gap parameters to significantly increase the concentration of charge carriers in the immediate vicinity of the electrodes of an OTC is demonstrated. The integrated efficiency of a large-area photoconductive THz emitter was determined taking into account the microstrip topology of the array with a characteristic size of feed strips proportional to the gap width in the OTC and with the upper (masking) metal layer. The maximum localization of the electromagnetic field in close proximity to the edges of electrodes at the “fiber–semiconductor” interface is achieved with a profiled sapphire fiber diameter of 220 μm.Conclusions. By optimizing the diameter of the sapphire fiber, the possibility of improving the localization of incident electromagnetic waves in close proximity to the edges of the OTC electrodes by ~40 times compared to the case without fiber, as well as increasing the overall efficiency of a large-area emitter by up to ~7–10 times, was demonstrated.","PeriodicalId":282368,"journal":{"name":"Russian Technological Journal","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126518443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-07DOI: 10.32362/2500-316x-2023-11-2-33-49
Juю P. Perova, V. P. Grigoriev, D. Zhukov
Objectives. The study aimed to investigate contemporary models, methods, and tools used for analyzing complex social network structures, both on the basis of ready-made solutions in the form of services and software, as well as proprietary applications developed using the Python programming language. Such studies make it possible not only to predict the dynamics of social processes (changes in social attitudes), but also to identify trends in socioeconomic development by monitoring users’ opinions on important economic and social issues, both at the level of individual territorial entities (for example, districts, settlements of small towns, etc.) and wider regions.Methods. Dynamic models and stochastic dynamics analysis methods, which take into account the possibility of self-organization and the presence of memory, are used along with user deanonymization methods and recommendation systems, as well as statistical methods for analyzing profiles in social networks. Numerical modeling methods for analyzing complex networks and processes occurring in them are considered and described in detail. Special attention is paid to data processing in complex network structures using the Python language and its various available libraries.Results. The specifics of the tasks to be solved in the study of complex network structures and their interdisciplinarity associated with the use of methods of system analysis are described in terms of the theory of complex networks, text analytics, and computational linguistics. In particular, the dynamic models of processes observed in complex social network systems, as well as the structural characteristics of such networks and their relationship with the observed dynamic processes including using the theory of constructing dynamic graphs are studied. The use of neural networks to predict the evolution of dynamic processes and structure of complex social systems is investigated. When creating models describing the observed processes, attention is focused on the use of computational linguistics methods to extract knowledge from text messages of users of social networks.Conclusions. Network analysis can be used to structure models of interaction between social units: people, collectives, organizations, etc. Compared with other methods, the network approach has the undeniable advantage of operating with data at different levels of research to ensure its continuity. Since communication in social networks almost entirely consists of text messages and various publications, almost all relevant studies use textual analysis methods in conjunction with machine learning and artificial intelligence technologies. Of these, convolutional neural networks demonstrated the best results. However, the use of support vector and decision tree methods should also be mentioned, since these contributed considerably to accuracy. In addition, statistical methods are used to compile data samples and analyze obtained results.
{"title":"Models and methods for analyzing complex networks and social network structures","authors":"Juю P. Perova, V. P. Grigoriev, D. Zhukov","doi":"10.32362/2500-316x-2023-11-2-33-49","DOIUrl":"https://doi.org/10.32362/2500-316x-2023-11-2-33-49","url":null,"abstract":"Objectives. The study aimed to investigate contemporary models, methods, and tools used for analyzing complex social network structures, both on the basis of ready-made solutions in the form of services and software, as well as proprietary applications developed using the Python programming language. Such studies make it possible not only to predict the dynamics of social processes (changes in social attitudes), but also to identify trends in socioeconomic development by monitoring users’ opinions on important economic and social issues, both at the level of individual territorial entities (for example, districts, settlements of small towns, etc.) and wider regions.Methods. Dynamic models and stochastic dynamics analysis methods, which take into account the possibility of self-organization and the presence of memory, are used along with user deanonymization methods and recommendation systems, as well as statistical methods for analyzing profiles in social networks. Numerical modeling methods for analyzing complex networks and processes occurring in them are considered and described in detail. Special attention is paid to data processing in complex network structures using the Python language and its various available libraries.Results. The specifics of the tasks to be solved in the study of complex network structures and their interdisciplinarity associated with the use of methods of system analysis are described in terms of the theory of complex networks, text analytics, and computational linguistics. In particular, the dynamic models of processes observed in complex social network systems, as well as the structural characteristics of such networks and their relationship with the observed dynamic processes including using the theory of constructing dynamic graphs are studied. The use of neural networks to predict the evolution of dynamic processes and structure of complex social systems is investigated. When creating models describing the observed processes, attention is focused on the use of computational linguistics methods to extract knowledge from text messages of users of social networks.Conclusions. Network analysis can be used to structure models of interaction between social units: people, collectives, organizations, etc. Compared with other methods, the network approach has the undeniable advantage of operating with data at different levels of research to ensure its continuity. Since communication in social networks almost entirely consists of text messages and various publications, almost all relevant studies use textual analysis methods in conjunction with machine learning and artificial intelligence technologies. Of these, convolutional neural networks demonstrated the best results. However, the use of support vector and decision tree methods should also be mentioned, since these contributed considerably to accuracy. In addition, statistical methods are used to compile data samples and analyze obtained results.","PeriodicalId":282368,"journal":{"name":"Russian Technological Journal","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131777277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-06DOI: 10.32362/2500-316x-2023-11-2-20-32
K. Kumar, R. Parameswaran
Objectives. Snapshots of data can be stored in a holographic medium at varying depths. Data can be written via a spiral data channel in spinning holographic media in the form of circular disks like CDs or DVDs. This data is then read by shining a reference beam through the refraction following writing. However, holographic storage is distinct from CD/DVD media in the sense that information is encoded in all three dimensions. Two-dimensional data is written using a single laser beam that spirals around the material. Prototype holographic storage solutions use minuscule cones formed by individual snapshots or pages to store one million pixels. As compared with magnetic disks and tapes, which have a finite lifespan of 50 years at most, the longevity and dependability of optical media storage is advantageous for long-term archiving. Holographic technology allows for the portability of data-intensive media such as broadcast or high-definition video. However, the shelf life of holographic media remains low due to its sensitivity to light. The primary goals of most storage devices are more storage space and faster data transport. Holographic storage devices have the potential to outperform traditional optical storage devices both in terms of capacity and performance. The present paper aims to evaluate the current international research trends in Holographic Data Storage (HDS) and produce a graphical mapping of co-authorship and countries.Methods. The major outputs of the dataset were authors, document type, publication, institution, nation, and citations. After exporting 1052 data sources, HistCite software was used to analyze the citations; visualization mapping was carried out using VOSviewer software and R programming language for the analysis of the authorcountry-title association on Holographic Storage Devices.Results. The most prominent authors, papers, journals, organizations, and nations in the field of HDS were identified in HistCite. Then, four clusters were investigated using VOSviewer based on author keywords, citation collaboration networks among different organizations, countries, and the HDS co-authorship network.Conclusions. During the study period from 2000–2020 (21 years), 4636 authors contributed to 1052 publications. The highest number of publications was in 2009, with a linear adjustment of R2 = 0.0136. The most prolific author, Lee J., published 3.14% of the articles on this subject. In terms of country distribution, Japan took first-place ranking, claiming 16.54% of the total number of articles. The “holographic” keyword was used in 62.55% of the articles.
{"title":"Bibliometric analysis of holographic data storage literature","authors":"K. Kumar, R. Parameswaran","doi":"10.32362/2500-316x-2023-11-2-20-32","DOIUrl":"https://doi.org/10.32362/2500-316x-2023-11-2-20-32","url":null,"abstract":"Objectives. Snapshots of data can be stored in a holographic medium at varying depths. Data can be written via a spiral data channel in spinning holographic media in the form of circular disks like CDs or DVDs. This data is then read by shining a reference beam through the refraction following writing. However, holographic storage is distinct from CD/DVD media in the sense that information is encoded in all three dimensions. Two-dimensional data is written using a single laser beam that spirals around the material. Prototype holographic storage solutions use minuscule cones formed by individual snapshots or pages to store one million pixels. As compared with magnetic disks and tapes, which have a finite lifespan of 50 years at most, the longevity and dependability of optical media storage is advantageous for long-term archiving. Holographic technology allows for the portability of data-intensive media such as broadcast or high-definition video. However, the shelf life of holographic media remains low due to its sensitivity to light. The primary goals of most storage devices are more storage space and faster data transport. Holographic storage devices have the potential to outperform traditional optical storage devices both in terms of capacity and performance. The present paper aims to evaluate the current international research trends in Holographic Data Storage (HDS) and produce a graphical mapping of co-authorship and countries.Methods. The major outputs of the dataset were authors, document type, publication, institution, nation, and citations. After exporting 1052 data sources, HistCite software was used to analyze the citations; visualization mapping was carried out using VOSviewer software and R programming language for the analysis of the authorcountry-title association on Holographic Storage Devices.Results. The most prominent authors, papers, journals, organizations, and nations in the field of HDS were identified in HistCite. Then, four clusters were investigated using VOSviewer based on author keywords, citation collaboration networks among different organizations, countries, and the HDS co-authorship network.Conclusions. During the study period from 2000–2020 (21 years), 4636 authors contributed to 1052 publications. The highest number of publications was in 2009, with a linear adjustment of R2 = 0.0136. The most prolific author, Lee J., published 3.14% of the articles on this subject. In terms of country distribution, Japan took first-place ranking, claiming 16.54% of the total number of articles. The “holographic” keyword was used in 62.55% of the articles.","PeriodicalId":282368,"journal":{"name":"Russian Technological Journal","volume":"296 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133607829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-06DOI: 10.32362/2500-316x-2023-11-2-7-19
A. Vasiliev, A. Melnikov, S. Lesko
Objectives. In recent years, there has been growing scientific interest in the creation of intelligent interfaces for computer control based on biometric data, such as electromyography signals (EMGs), which can be used to classify human hand gestures to form the basis for organizing an intuitive human-computer interface. However, problems arising when using EMG signals for this purpose include the presence of nonlinear noise in the signal and the significant influence of individual human characteristics. The aim of the present study is to investigate the possibility of using neural networks to filter individual components of the EMG signal.Methods. Mathematical signal processing techniques are used along with machine learning methods.Results. The overview of the literature on the topic of EMG signal processing is carried out. The concept of intelligent processing of biological signals is proposed. The signal filtering model using a convolutional neural network structure based on Python 3, TensorFlow and Keras technologies was developed. Results of an experiment carried out on an EMG data set to filter individual signal components are presented and discussed.Conclusions. The possibility of using artificial neural networks to identify and suppress individual human characteristics in biological signals is demonstrated. When training the network, the main emphasis was placed on individual features by testing the network on data received from subjects not involved in the learning process. The achieved average 5% reduction in individual noise will help to avoid retraining of the network when classifying EMG signals, as well as improving the accuracy of gesture classification for new users.
{"title":"Robust neural network filtering in the tasks of building intelligent interfaces","authors":"A. Vasiliev, A. Melnikov, S. Lesko","doi":"10.32362/2500-316x-2023-11-2-7-19","DOIUrl":"https://doi.org/10.32362/2500-316x-2023-11-2-7-19","url":null,"abstract":"Objectives. In recent years, there has been growing scientific interest in the creation of intelligent interfaces for computer control based on biometric data, such as electromyography signals (EMGs), which can be used to classify human hand gestures to form the basis for organizing an intuitive human-computer interface. However, problems arising when using EMG signals for this purpose include the presence of nonlinear noise in the signal and the significant influence of individual human characteristics. The aim of the present study is to investigate the possibility of using neural networks to filter individual components of the EMG signal.Methods. Mathematical signal processing techniques are used along with machine learning methods.Results. The overview of the literature on the topic of EMG signal processing is carried out. The concept of intelligent processing of biological signals is proposed. The signal filtering model using a convolutional neural network structure based on Python 3, TensorFlow and Keras technologies was developed. Results of an experiment carried out on an EMG data set to filter individual signal components are presented and discussed.Conclusions. The possibility of using artificial neural networks to identify and suppress individual human characteristics in biological signals is demonstrated. When training the network, the main emphasis was placed on individual features by testing the network on data received from subjects not involved in the learning process. The achieved average 5% reduction in individual noise will help to avoid retraining of the network when classifying EMG signals, as well as improving the accuracy of gesture classification for new users.","PeriodicalId":282368,"journal":{"name":"Russian Technological Journal","volume":"169 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128228087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-03DOI: 10.32362/2500-316x-2023-11-1-60-69
M. A. Аnfyorov
{"title":"Algorithm for finding subcritical paths on network diagrams","authors":"M. A. Аnfyorov","doi":"10.32362/2500-316x-2023-11-1-60-69","DOIUrl":"https://doi.org/10.32362/2500-316x-2023-11-1-60-69","url":null,"abstract":"","PeriodicalId":282368,"journal":{"name":"Russian Technological Journal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126827139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-03DOI: 10.32362/2500-316x-2023-11-1-70-80
A. Rechkalov, A. V. Artyukhov, G. Kulikov
{"title":"Logical-semantic definition of a production process digital twin","authors":"A. Rechkalov, A. V. Artyukhov, G. Kulikov","doi":"10.32362/2500-316x-2023-11-1-70-80","DOIUrl":"https://doi.org/10.32362/2500-316x-2023-11-1-70-80","url":null,"abstract":"","PeriodicalId":282368,"journal":{"name":"Russian Technological Journal","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115943778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-03DOI: 10.32362/2500-316x-2023-11-1-41-50
G. V. Kulikov, T. T. Do, A. A. Lelyukh, V. D. Nguyen
{"title":"Optimal reception of multiple phase shift keying and quadrature amplitude modulation signals with non-coherent processing of harmonic interference","authors":"G. V. Kulikov, T. T. Do, A. A. Lelyukh, V. D. Nguyen","doi":"10.32362/2500-316x-2023-11-1-41-50","DOIUrl":"https://doi.org/10.32362/2500-316x-2023-11-1-41-50","url":null,"abstract":"","PeriodicalId":282368,"journal":{"name":"Russian Technological Journal","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125949091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-03DOI: 10.32362/2500-316x-2023-11-1-51-59
T. E. Gelfman, A. P. Pirkhavka, V. Skripachev
{"title":"Analysis of the effectiveness of methods for ensuring the reliability of a communication satellite transponder","authors":"T. E. Gelfman, A. P. Pirkhavka, V. Skripachev","doi":"10.32362/2500-316x-2023-11-1-51-59","DOIUrl":"https://doi.org/10.32362/2500-316x-2023-11-1-51-59","url":null,"abstract":"","PeriodicalId":282368,"journal":{"name":"Russian Technological Journal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128122515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-02DOI: 10.32362/2500-316x-2023-11-1-18-30
A. Voronkov, S. Diane
{"title":"Continuous genetic algorithm for grasping an object of a priori unknown shape by a robotic manipulator","authors":"A. Voronkov, S. Diane","doi":"10.32362/2500-316x-2023-11-1-18-30","DOIUrl":"https://doi.org/10.32362/2500-316x-2023-11-1-18-30","url":null,"abstract":"","PeriodicalId":282368,"journal":{"name":"Russian Technological Journal","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131307301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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