Pub Date : 2024-03-20DOI: 10.1134/s1054661823040363
A. L. Reznik, A. A. Soloviev
Abstract
The most important results obtained in recent years by the research institutes of the Siberian Branch of the Russian Academy of Sciences in the field of development of mathematical methods and the construction of effective information and computing systems for solving fundamental and applied problems of digital image processing are presented. Examples are given of the development of specific high-performance software and hardware systems intended to effectively solve important theoretical and scientific-applied problems, the solutions to which is based on the use of advanced methods of digital image processing.
{"title":"Solving Fundamental and Applied Problems of Digital Image Processing at the Institute of Automation and Electrometry and Other Scientific Schools of the Siberian Branch of the Russian Academy of Sciences","authors":"A. L. Reznik, A. A. Soloviev","doi":"10.1134/s1054661823040363","DOIUrl":"https://doi.org/10.1134/s1054661823040363","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>The most important results obtained in recent years by the research institutes of the Siberian Branch of the Russian Academy of Sciences in the field of development of mathematical methods and the construction of effective information and computing systems for solving fundamental and applied problems of digital image processing are presented. Examples are given of the development of specific high-performance software and hardware systems intended to effectively solve important theoretical and scientific-applied problems, the solutions to which is based on the use of advanced methods of digital image processing.</p>","PeriodicalId":35400,"journal":{"name":"PATTERN RECOGNITION AND IMAGE ANALYSIS","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140885118","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 : 2024-03-20DOI: 10.1134/s1054661823040168
S. V. Gerus, V. V. Dementienko, V. I. Mirgorodskiy
Abstract
Based on an analysis of statistical data on railway and road traffic, as well as laboratory studies, mathematical models are developed that describe the “human–monitoring system–vehicle–traffic system” system. The issues of classifying operators according to their tendency to fall asleep and create emergency situations are explored. A statistical analysis of the accident rate of vehicle drivers was carried out based on their susceptibility to accidents. The degree of effectiveness and safety of monitoring systems is taken into account, as well as the influence of psychological factors caused by drivers excessive trust in the monitoring system. The risks associated with system malfunctions and insufficient efficiency of its operation are calculated. The use of an ineffective driver monitoring system does not reduce, but increases the likelihood of an accident. The design and principles of operation of a driver vigilance telemechanical control system (DVTCS) are described. The device is designed for continuous monitoring of the drivers vigilance and attentiveness while driving rolling stock. The work of DVTCS is based on scientific results according to which episodic changes in skin resistance reflect the level of alertness and wakefulness. It has been shown that due to more reliable, continuous, and nondistracting monitoring of the drivers physiological state the DVTCS provides a higher level of traffic safety than its “Safety Handle” counterpart. Statistical data from operational and laboratory data have been analyzed, indicating a high level of operational safety of the DVTCS. A comparison of Russian and international requirements for the safety level of DVTCS has been carried out. Methods for further improvement of the device are noted.
{"title":"The Physical Principles of the Construction of Systems for Safe Monitoring of the State of a Human Operator","authors":"S. V. Gerus, V. V. Dementienko, V. I. Mirgorodskiy","doi":"10.1134/s1054661823040168","DOIUrl":"https://doi.org/10.1134/s1054661823040168","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>Based on an analysis of statistical data on railway and road traffic, as well as laboratory studies, mathematical models are developed that describe the “human–monitoring system–vehicle–traffic system” system. The issues of classifying operators according to their tendency to fall asleep and create emergency situations are explored. A statistical analysis of the accident rate of vehicle drivers was carried out based on their susceptibility to accidents. The degree of effectiveness and safety of monitoring systems is taken into account, as well as the influence of psychological factors caused by drivers excessive trust in the monitoring system. The risks associated with system malfunctions and insufficient efficiency of its operation are calculated. The use of an ineffective driver monitoring system does not reduce, but increases the likelihood of an accident. The design and principles of operation of a driver vigilance telemechanical control system (DVTCS) are described. The device is designed for continuous monitoring of the drivers vigilance and attentiveness while driving rolling stock. The work of DVTCS is based on scientific results according to which episodic changes in skin resistance reflect the level of alertness and wakefulness. It has been shown that due to more reliable, continuous, and nondistracting monitoring of the drivers physiological state the DVTCS provides a higher level of traffic safety than its “Safety Handle” counterpart. Statistical data from operational and laboratory data have been analyzed, indicating a high level of operational safety of the DVTCS. A comparison of Russian and international requirements for the safety level of DVTCS has been carried out. Methods for further improvement of the device are noted.</p>","PeriodicalId":35400,"journal":{"name":"PATTERN RECOGNITION AND IMAGE ANALYSIS","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140885026","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 : 2024-03-20DOI: 10.1134/s1054661823040107
Y. S. Chernyshova, A. V. Sheshkus, K. B. Bulatov, V. V. Arlazarov
Abstract
This paper considers a scientific school of synthesis of samples and creation of datasets, which is a part of the family of scientific schools associated with image processing and analysis, originating from the work of a team led by Prof. V.L. Arlazarov in the 1970s. As part of the work of the school, the researchers have obtained important fundamental and applied results as well as set new research tasks. Over the years of the school’s existence the scientific team has developed several algorithms and systems for the synthesis and augmentation of image samples. Moreover, they have created and published more than ten open annotated image datasets, including the unique MIDV dataset family that contains synthesized images of identity documents and is the first in the world to allow a full open comparison of recognition systems for such documents.
{"title":"Advances of the Scientific School of V.L. Arlazarov in Dataset Creation and Training Sample Synthesis for Solving Modern Computer Vision Problems","authors":"Y. S. Chernyshova, A. V. Sheshkus, K. B. Bulatov, V. V. Arlazarov","doi":"10.1134/s1054661823040107","DOIUrl":"https://doi.org/10.1134/s1054661823040107","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>This paper considers a scientific school of synthesis of samples and creation of datasets, which is a part of the family of scientific schools associated with image processing and analysis, originating from the work of a team led by Prof. V.L. Arlazarov in the 1970s. As part of the work of the school, the researchers have obtained important fundamental and applied results as well as set new research tasks. Over the years of the school’s existence the scientific team has developed several algorithms and systems for the synthesis and augmentation of image samples. Moreover, they have created and published more than ten open annotated image datasets, including the unique MIDV dataset family that contains synthesized images of identity documents and is the first in the world to allow a full open comparison of recognition systems for such documents.</p>","PeriodicalId":35400,"journal":{"name":"PATTERN RECOGNITION AND IMAGE ANALYSIS","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140201654","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 : 2024-03-20DOI: 10.1134/s1054661823040247
L. I. Kulikova, S. A. Makhortykh, A. N. Pankratov, S. D. Rykunov, M. N. Ustinin
Abstract
The work of the Pushchino school of pattern recognition and data analysis is presented. Basic information and theoretical research are provided, as well as a number of areas of application of the developed mathematical, information, and computer methods. The main focus is on work in the fields of biology, biophysics, biomedicine, bioinformatics, and image analysis and recognition.
{"title":"Spectral Methods in Data Analysis and Pattern Recognition Problems: Works of the Pushchino School","authors":"L. I. Kulikova, S. A. Makhortykh, A. N. Pankratov, S. D. Rykunov, M. N. Ustinin","doi":"10.1134/s1054661823040247","DOIUrl":"https://doi.org/10.1134/s1054661823040247","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>The work of the Pushchino school of pattern recognition and data analysis is presented. Basic information and theoretical research are provided, as well as a number of areas of application of the developed mathematical, information, and computer methods. The main focus is on work in the fields of biology, biophysics, biomedicine, bioinformatics, and image analysis and recognition.</p>","PeriodicalId":35400,"journal":{"name":"PATTERN RECOGNITION AND IMAGE ANALYSIS","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140201655","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 : 2024-03-20DOI: 10.1134/s1054661823040077
L. Aslanyan
Abstract
Herein, a historical analytical survey of work of “Discrete Modelling of Pattern Recognition” (DM-Lab) research group in Armenia is presented. The group is since 1973, supervised by worldwide recognized scientist Yurii Ivanovich Zhuravlev and lead by his former student Levon Aslanyan. The general start of attention to computational mathematics and computational systems in Armenia is concerned with the times of cybernetics as a research direction, and the names of great scientists and policy makers, such as Andranik Iosifyan and Sergei Mergelyan. In early 1950’s a large number of HighTech and defense related organizations were established in area, and their theoretical, scientific cluster was formed around the Yerevan Research Institute of Mathematical Machines, and Computer Center of Academy of Sciences and Yerevan State University. This was the time for intensive stuff and student exchanges inside the larger country USSR. Rimma Podlovchenko, Rafik Tonoyan, Igor’ Zaslavski, Yuri Shoukourian started teaching at Yerevan State University in 70’s, a number of students were delegated to the recognized cybernetical centers, in Moscow, Kiev, Novosibirsk. And one of the results of these developments was appearance of DM-Lab in Armenia, composed by alumnus of Novosibirsk and Moscow State Universities, led by Levon Aslanyan, and supervised globally by RF Academician Yuri Ivanovich Zhuravlev. Further research and education activities lead to defenses of candidate and doctoral dissertations, in Armenia, and at the council of Computer Center of Academy of Sciences of Russian Federation. The initial stuff of DM-Lab group included Gevorg Tonoyan, Levon Asatryan, Vilik Karakhanyan. Local members of the group were Hasmik Sahakyan, Vladimir Sahakyan, Irina Arsenyan, Levon Kazaryan and large number of young PhD students. Research directions at the DM-Lab were and are related to the pattern recognition theory – to mathematical models of forming and analyzing learning sets, studying their properties such as the class compactness hypothesis, in terms of isoperimetry; to forming the logic of interrelations of classes, in terms of logic separation; setting up new approaches in data mining area, etc. All these studies involve intensive research over the years, addressing topics related to the geometry of n-dimensional unite cube and lattices in general, Boolean function minimization, discrete optimization problems, and algorithmic studies coming from data science and artificial intelligence. International relations and activities of the group includes: long term representation of Armenia in the ISO technical groups, representation of Armenia in ICT research programmes of European Council, membership at the ITHEA virtual research institute with its conferences and publishing house. 10’s of research projects were implemented during these years. Projects were funded by UNDP, NATO Research, INTAS, EC Esprit, IST and
{"title":"Logic Separation: Discrete Modelling of Pattern Recognition","authors":"L. Aslanyan","doi":"10.1134/s1054661823040077","DOIUrl":"https://doi.org/10.1134/s1054661823040077","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>Herein, a historical analytical survey of work of “Discrete Modelling of Pattern Recognition” (DM-Lab) research group in Armenia is presented. The group is since 1973, supervised by worldwide recognized scientist Yurii Ivanovich Zhuravlev and lead by his former student Levon Aslanyan. The general start of attention to computational mathematics and computational systems in Armenia is concerned with the times of cybernetics as a research direction, and the names of great scientists and policy makers, such as Andranik Iosifyan and Sergei Mergelyan. In early 1950’s a large number of HighTech and defense related organizations were established in area, and their theoretical, scientific cluster was formed around the Yerevan Research Institute of Mathematical Machines, and Computer Center of Academy of Sciences and Yerevan State University. This was the time for intensive stuff and student exchanges inside the larger country USSR. Rimma Podlovchenko, Rafik Tonoyan, Igor’ Zaslavski, Yuri Shoukourian started teaching at Yerevan State University in 70’s, a number of students were delegated to the recognized cybernetical centers, in Moscow, Kiev, Novosibirsk. And one of the results of these developments was appearance of DM-Lab in Armenia, composed by alumnus of Novosibirsk and Moscow State Universities, led by Levon Aslanyan, and supervised globally by RF Academician Yuri Ivanovich Zhuravlev. Further research and education activities lead to defenses of candidate and doctoral dissertations, in Armenia, and at the council of Computer Center of Academy of Sciences of Russian Federation. The initial stuff of DM-Lab group included Gevorg Tonoyan, Levon Asatryan, Vilik Karakhanyan. Local members of the group were Hasmik Sahakyan, Vladimir Sahakyan, Irina Arsenyan, Levon Kazaryan and large number of young PhD students. Research directions at the DM-Lab were and are related to the pattern recognition theory – to mathematical models of forming and analyzing learning sets, studying their properties such as the class compactness hypothesis, in terms of isoperimetry; to forming the logic of interrelations of classes, in terms of logic separation; setting up new approaches in data mining area, etc. All these studies involve intensive research over the years, addressing topics related to the geometry of <i>n</i>-dimensional unite cube and lattices in general, Boolean function minimization, discrete optimization problems, and algorithmic studies coming from data science and artificial intelligence. International relations and activities of the group includes: long term representation of Armenia in the ISO technical groups, representation of Armenia in ICT research programmes of European Council, membership at the ITHEA virtual research institute with its conferences and publishing house. 10’s of research projects were implemented during these years. Projects were funded by UNDP, NATO Research, INTAS, EC Esprit, IST and","PeriodicalId":35400,"journal":{"name":"PATTERN RECOGNITION AND IMAGE ANALYSIS","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140205494","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 : 2024-03-20DOI: 10.1134/s1054661823040351
Yu. P. Pyt’ev, A. I. Chulichkov, O. V. Falomkina, D. A. Balakin
Abstract
This article provides an overview of the fundamental research directions being pursued at the Faculty of Physics of Lomonosov Moscow State University under the guidance of Professor Yuri Petrovich Pyt’ev. These research directions can be categorized into three primary areas: methods of morphological analysis of images and signals, theory of computer-aided measuring systems, and methods related to the theory of possibilities and subjective mathematical modeling. The article elucidates the foundational ideas and concepts of these directions, contemplates alternative approaches to address similar challenges, and offers both model-based and application-driven examples utilizing the methods corresponding to these directions and their combinations.
{"title":"Data Analysis and Interpretation: Methods of Computer-Aided Measuring Transducer Theory, Morphological Analysis, Possibility Theory, and Subjective Mathematical Modeling","authors":"Yu. P. Pyt’ev, A. I. Chulichkov, O. V. Falomkina, D. A. Balakin","doi":"10.1134/s1054661823040351","DOIUrl":"https://doi.org/10.1134/s1054661823040351","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>This article provides an overview of the fundamental research directions being pursued at the Faculty of Physics of Lomonosov Moscow State University under the guidance of Professor Yuri Petrovich Pyt’ev. These research directions can be categorized into three primary areas: methods of morphological analysis of images and signals, theory of computer-aided measuring systems, and methods related to the theory of possibilities and subjective mathematical modeling. The article elucidates the foundational ideas and concepts of these directions, contemplates alternative approaches to address similar challenges, and offers both model-based and application-driven examples utilizing the methods corresponding to these directions and their combinations.</p>","PeriodicalId":35400,"journal":{"name":"PATTERN RECOGNITION AND IMAGE ANALYSIS","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140884906","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 : 2024-03-20DOI: 10.1134/s1054661823040284
A. S. Mandel, A. I. Mikhalsky
Abstract
Brief historical background on the establishment and activities of the Institute of Control Sciences (IPU RAS). The paper presents key findings of the Institute (obtained mainly in the 20th century) in the field of pattern recognition and of related analysis of complex data. It focuses on four areas of research including (a) the method of potential functions, (b) the theory of learning and self-learning systems, (c) the generalized portrait method and recovery of dependences based on empirical data, and (d) automatic classification methods and expert classification analysis. Relations between these areas are studied. The pioneers in the field are named (M.A. Aizerman, E.M. Braverman, L.I. Rozonoer, Ya.Z. Tsypkin, V.N. Vapnik, A.Ya. Chervonenkis, I.B. Muchnik, and A.A. Dorofeyuk among others) and brief biographical notes on the life and scientific work of these scientists are presented. The follow-ups of the results thus obtained are shown. The bibliography of publications by the Institute’s researchers in leading journals of Russia on pattern recognition problems and related complex data analysis tasks is provided.
{"title":"On the Work of the Institute of Control Sciences of the Russian Academy of Sciences in the Field of Pattern Recognition Theory and Applications in the 20th Century","authors":"A. S. Mandel, A. I. Mikhalsky","doi":"10.1134/s1054661823040284","DOIUrl":"https://doi.org/10.1134/s1054661823040284","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>Brief historical background on the establishment and activities of the Institute of Control Sciences (IPU RAS). The paper presents key findings of the Institute (obtained mainly in the 20th century) in the field of pattern recognition and of related analysis of complex data. It focuses on four areas of research including (a) the method of potential functions, (b) the theory of learning and self-learning systems, (c) the generalized portrait method and recovery of dependences based on empirical data, and (d) automatic classification methods and expert classification analysis. Relations between these areas are studied. The pioneers in the field are named (M.A. Aizerman, E.M. Braverman, L.I. Rozonoer, Ya.Z. Tsypkin, V.N. Vapnik, A.Ya. Chervonenkis, I.B. Muchnik, and A.A. Dorofeyuk among others) and brief biographical notes on the life and scientific work of these scientists are presented. The follow-ups of the results thus obtained are shown. The bibliography of publications by the Institute’s researchers in leading journals of Russia on pattern recognition problems and related complex data analysis tasks is provided.</p>","PeriodicalId":35400,"journal":{"name":"PATTERN RECOGNITION AND IMAGE ANALYSIS","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140884986","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 : 2024-03-20DOI: 10.1134/s1054661823040491
M. N. Ustinin, A. I. Boyko, S. D. Rykunov
Abstract
A new method has been proposed for determining the structure of complex biological and physical systems from their electromagnetic fields. The method is based on spectral analysis of multichannel time series. Optimization of the Fourier transform is achieved by integrating long-term time series. Fine tuning to a given frequency is also possible to increase the signal-to-noise ratio. When analyzing a detailed multichannel spectrum, the signal is reconstructed at each frequency and the inverse problem is solved for the resulting field map. Using the model of one elementary source allows one to correctly solve the inverse problem by exhaustive search. The set of found elementary sources for all frequencies represents the functional structure of the complex system being studied. The method was verified on computer and physical models, after which it was successfully applied in various biological problems. The separation of the encephalogram into a signal from the brain and physiological noise was obtained.
{"title":"Functional Tomography of Complex Systems Using Spectral Analysis of Multichannel Measurement Data","authors":"M. N. Ustinin, A. I. Boyko, S. D. Rykunov","doi":"10.1134/s1054661823040491","DOIUrl":"https://doi.org/10.1134/s1054661823040491","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>A new method has been proposed for determining the structure of complex biological and physical systems from their electromagnetic fields. The method is based on spectral analysis of multichannel time series. Optimization of the Fourier transform is achieved by integrating long-term time series. Fine tuning to a given frequency is also possible to increase the signal-to-noise ratio. When analyzing a detailed multichannel spectrum, the signal is reconstructed at each frequency and the inverse problem is solved for the resulting field map. Using the model of one elementary source allows one to correctly solve the inverse problem by exhaustive search. The set of found elementary sources for all frequencies represents the functional structure of the complex system being studied. The method was verified on computer and physical models, after which it was successfully applied in various biological problems. The separation of the encephalogram into a signal from the brain and physiological noise was obtained.</p>","PeriodicalId":35400,"journal":{"name":"PATTERN RECOGNITION AND IMAGE ANALYSIS","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140884955","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 : 2024-03-20DOI: 10.1134/s1054661823040387
S. D. Rykunov, A. I. Boyko, M. N. Ustinin
Abstract
The functional tomography method, based on the spectral analysis of multichannel time series of long duration, has been used to study the distribution of electrical sources in the human body. The spontaneous activity of various organs and tissues has been studied. The spatial distribution and directions of elementary sources of alpha rhythm in the brain have been examined. Spontaneous brain activity has been studied in mental disorders. Using a cardiogram, the functional structure of the heart has been found, and using myography data, working skeletal muscles have been reconstructed. The spatial distribution of moving magnetic nanoparticles was also found. The coincidence of the results with the anatomical and physical structure of the complex systems being studied confirms the high promise of the proposed method in various fundamental and applied problems.
{"title":"Reconstruction of the Electrical Structure of the Human Body Using Spectral Functional Tomography","authors":"S. D. Rykunov, A. I. Boyko, M. N. Ustinin","doi":"10.1134/s1054661823040387","DOIUrl":"https://doi.org/10.1134/s1054661823040387","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>The functional tomography method, based on the spectral analysis of multichannel time series of long duration, has been used to study the distribution of electrical sources in the human body. The spontaneous activity of various organs and tissues has been studied. The spatial distribution and directions of elementary sources of alpha rhythm in the brain have been examined. Spontaneous brain activity has been studied in mental disorders. Using a cardiogram, the functional structure of the heart has been found, and using myography data, working skeletal muscles have been reconstructed. The spatial distribution of moving magnetic nanoparticles was also found. The coincidence of the results with the anatomical and physical structure of the complex systems being studied confirms the high promise of the proposed method in various fundamental and applied problems.</p>","PeriodicalId":35400,"journal":{"name":"PATTERN RECOGNITION AND IMAGE ANALYSIS","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140889981","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 : 2024-03-20DOI: 10.1134/s1054661823040193
N. Yu. Ilyasova, V. V. Sergeyev, N. S. Demin
Abstract
This article is the first in a series of publications dedicated to the leading scientific school of Academician V.A. Soifer in the field of processing, analysis, and recognition of images and optical signals. The article briefly describes the creation and development of the Samara scientific school of computer image processing. Examples of obtained fundamental results and solved applied problems are given. The most significant publications of the scientific school are listed and analyzed.
{"title":"Scientific School of Academician V.A. Soifer in the Field of Processing, Analysis, and Recognition of Images and Optical Signals","authors":"N. Yu. Ilyasova, V. V. Sergeyev, N. S. Demin","doi":"10.1134/s1054661823040193","DOIUrl":"https://doi.org/10.1134/s1054661823040193","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>This article is the first in a series of publications dedicated to the leading scientific school of Academician V.A. Soifer in the field of processing, analysis, and recognition of images and optical signals. The article briefly describes the creation and development of the Samara scientific school of computer image processing. Examples of obtained fundamental results and solved applied problems are given. The most significant publications of the scientific school are listed and analyzed.</p>","PeriodicalId":35400,"journal":{"name":"PATTERN RECOGNITION AND IMAGE ANALYSIS","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140198900","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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