Issue 3 (193), article 5

DOI:https://doi.org/10.15407/kvt192.03.083

Kibern. vyčisl. teh., 2018, Issue 3 (193), pp.

Kiforenko S.I.1, DSc (Biology), Senior Researcher,
Leading Researcher of Dep. of Application Mathematical
and Technical Methods in Biology and Medicine
e-mail: skifor@ukr.net

Hontar T.M.1, PhD (Biology), Senior Researcher,
Dep. of Application Mathematical and Technical Methods in Biology and Medicine
e-mail: gtm_kiev@ukr.net

Ivaskiva K.Y.2, PhD (Medicine), Senior Researcher,
Scientific-advisory of Dep. of Ambulatory and Preventive Care for Patients with Endocrine Pathology
e-mail: _k_iva@ukr.net

Obelets T.A.3, Computer Systems Analyst
e-mail: obel.tet@gmail.com

1 International Research and Training Centre for Information Technologies
and Systems of the National Academy of Sciences of Ukraine and Ministry of Education and Science of Ukraine, Glushkov ave., 40, Kyiv, 03187, Ukraine

2 State Institution “V.P. Komisarenko Institute of Endocrinology and Metabolism of NAMS of Ukraine”, 69, Vyshgorodska St., Kyiv, 04114, Ukraine

3 National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Peremogy av. 37, Kyiv, 03056, Ukraine

INFORMATIONAL DECISION SUPPORT SYSTEM FOR MONITORING AND CORRECTING SOMATIC HEALTH

Introduction. Somatic health is the most important component of human health. It is the physical status that has a responsible role in the material and energy provision of the functioning of the physiological systems of the organism and their maintenance within the boundaries of the homeostatic norm. Emphasis on the motivation and self-control of their health, on an adequate orientation in the use of modern health-saving technologies is relevant.

The purpose of the article is to create a decision support system for increasing awareness of the health status and improve the efficiency of correcting the state of somatic health by using modern computer and mobile technologies.

Methods. The paper describes the information technology of quantitative assessment and correction of a person’s physical health, which is based on the information-structural model of a person’s physical health. A software-algorithmic complex for use in personal computers and mobile applications to support decision-making in the selection of recreational activities has been developed.

Results. The information structure of physical health was developed from the viewpoint of management theory, taking into account the subjective-objective aspects of evaluation and the multidimensionality of the research object. To support decision making in assessing health status and selecting preventive measures, a set of computer programs “HEALTH-ENERGY BALANCE ” has been developed. The basis of the complex is the modules “Early Diagnosis” — for assessing the condition and “Energobalance” — to support decision making when choosing a balanced diet, adequate activity and daily energy costs. Given the current trends in the development of the mHealth industry, this software is adapted for use in mobile devices.

Conclusion. The developed technology allows, using non-invasive techniques, to quantify the state of physical health. Depending on the position of the evaluation criterion on the diagnostic scale, the user is given the opportunity to choose the appropriate recreational techniques and minimize the mismatch between the energy value of the food ration and energy expenditure in selecting the activity mode. Implementation of the developed algorithms in mobile Android applications to the smartphone increases the effectiveness of supporting independent decision-making when organizing the life of the user.

Keywords: somatic health, automated information technology, objectively-subjective evaluation, software, mobile applications.

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REFERENCES
1.    Health Ukrainians: frightening statistics. URL: https://comments.ua/society/593551-zdorove-ukraintsev-pugayushchaya-statistika.html. (Last accessed: 08.02.18.) (in Russian)
2.    Nazarova E.N, Zhilov Yu. D. Fundamentals of a healthy lifestyle. Moscow: Academy, 2013. 256 p. (in Russian)
3.    Apanasenko G.L. Individual health: theory and practice of management, information aspects. Meditsinskaya informatika i inzheneriya. 2009. № 4. P. 61–64. (in Russian)
4.    Dartau L.A, Misernitsky Yu.L., Stefanyuk A.R. Human health and quality of life: problems and management features. Moscow: SINTEG, 2009. 400 p. (in Russian)
5.    Chestnov OP, Boytsov S.A., Kulikov A.A., Baturin D. Mobile health: world experience and perspectives. Profilakticheskaya meditsina. 2014. Vol. 17. №. 4. P. 3–9.
6.    Apanasenko G.L. Valeology: first results and immediate prospects. Teoriya i praktika fizicheskoy kul’tury. 2001. № 6. P. 2–8. (in Russian)
7.    Ivanova S.S, Stafeeva A.V. Substantial aspects of physical, mental and social health and the possibility of forming their harmonious correlation. Fundamental’nyye issledovaniya. 2014. No. 11 (part 12). P. 2729–2733. (in Russian)
8.    Gritsenko V.I, Vovk M.I., Kotova A.B. Bioecomedicine. Kiev: Nauk. dumka, 2001. 318 p. (in Russian)
9.    Gritsenko V.I., Kotova A.B., Kiforenko S.I. et al. Information technology in biology and medicine. Course of lectures: the textbook. Kiev: Nauk. dumka, 2007. 382 p. (in Ukrainian)
10.    Pustovoit O.G., Kotova A.B, Kiforenko S.I. Information technology research and management of human physical health. Upravlyayushchiye sistemy i mashiny. 2010. N 3. P. 70–77. (in Russian)
11.    Kotova A.B., Belov V.M. Human Health: Challenges, Methods, Approaches. Kiev: Nauk. dumka, 2017. 132 p. (in Ukrainian)
12.    Kiforenko S.I., Kotova A.B. Multidimensionality as a basis for the systemic evaluation of health. Kibernetika i vyčislitel’naâ tehnika. 2006. Iss. 150. P. 60–69. (in Russian)
13.    Cooper K. Aerobics for well-being [2 nd ed. additional, revised]. Moscow: Fizkultura i sport, 1989. 224 p. (in Russian)
14.    Apanasenko G.L. Diagnosis of individual health.Sovremennyye reabilitatsionnyye tekhnologii. 2012. № 8. P. 64–69. (in Russian)
15.    Landa B.H. Methods for the integrated assessment of physical development and physical fitness: Textbook. allowance. Moscow : Sovetskiy sport, 2011. 348 p. (in Russian).
16.    Nikiforov G.S. Psychology of professional health. St. Petersburg: Rech’, 2006. 408p. (in Russian).
17.    Cooper’s motor tests. URL: http://ggym.ru/kuper.php#sthash.66u3jT5v.dpuf. (Last accessed: 31.01.18.)
18.    Baevsky R.M. Assessment of adaptive capabilities of the body and the problem of restorative medicine. Vestnik vosstanovitel’noy meditsiny. 2004. № 2. P. 18–22. (in Russian).
19.    Belov V.M., Kotova A.B., Dubovenko M. N., Kiforenko S.I. Computer program “System of express diagnostics of a state of health”: a certificate of registration of copyright law on the work №37242, Ukraine. — 04/03/2011.
20.    Kiforenko S.I., Kravchenko V.V. Information and technological aspects of monitoring and evaluation of physical health. Visnyk KNU. Seriya Kibernetyka. 2014. №1(14). P. 27–32. (in Russian).
21.    Makaricheva V.V. Computer decision support system for the integrated assessment of physical health. Visnyk KNU. Seriya Kibernetyka. 2016. №1(164). P. 15–20. (in Russian).
22.    WHO. Diet, nutrition and the prevention of chronic diseases (report of the joint WHO – FAO expert consultation). 2003. 196 p. (in Russian).
23.    WHO. Resolutions and reports. Global Strategy on Diet, Physical Activity and Health (WHA57.17, 2004). 2004. 18 p. (in Russian).
24.    On Approval of the Norms of Physiological Needs of the Population of Ukraine in the Basic Nutrients and Energy. Order of 09.03.2017 № 1073 of the Ministry of Health of Ukraine. URL: http://zakon2.rada.gov.ua/laws/show/z1206-17. (Last accessed: 26.01.18)
25.    Energy costs for various activities. Guidelines. URL: http://docplayer.ru/26767072-Ministerstvo-zdravoohraneniya-ukrainy-gigiena-pitaniya.html. (Last accessed: 01.03.18)
26.    WHO. Innovative methods of care for chronic conditions: the main elements for action (global report 2002). 2002. 92 p. (in Russian).
27.    Kiforenko S.I., Kotova A.B., Lavrenyuk N.V., Ivaskiva E.Yu. Diagostics of diabetes mellitus. Progressive information technology. Upravlyayushchiye sistemy i mashiny. 2015. № 4. P. 67–71. (in Russian).

Recieved 03.06.2018

Issue 3 (193), article 4

DOI:https://doi.org/10.15407/kvt192.03.064

Kibern. vyčisl. teh., 2018, Issue 3 (193), pp.

Shakhlina L.Ya.-G.1, DSc. (Medicine), Professor,
Professor of Sport Medicine cafedra
e-mail: sportmedkafedra@gmail.com

Aralova N.I.2, PhD. (Engineering),Senior Researcher,
Senior Researcher of Dept. of Optimization of Controlled Processes
e-mail: aralova@ukr.net

1 National University of Physical Education and Sport of Ukraine
Fiscultury Street, 1, Kiev, 03150, Ukraine

2 Institute of Cybernetics of National Academy of Science of Ukraine, Acad.Glushkov av., 40, Kiev, 03187, Ukraine

FORECASTING THE ORGANISM REACTION OF THE ATHLETES ON INHIBITING HYPOXIC MIXTURES ON THE MATHEMATICAL MODEL OF THE FUNCTIONAL RESPIRATION SYSTEM

Introduction. In the modern sports of higher achievements, the issues of training and competitive activity of athletes using the hypoxic factor in natural-mountain conditions or with artificial hypoxic training with the use of pressure chambers or hypoxicators continue to attract great interest among specialists in the field of physiology, medicine, sports pedagogy. The influence of reproductive hormones on the functional breathing system responsible for the aerobic capacity of the female body remains insufficiently studied. There are no scientifically substantiated programs for training athletes, mainly developing the quality of endurance, in conditions of hypoxic hypoxia, taking into account the phases of the menstrual cycle.

The purpose of the article is to determine the reaction of the functional breathing system and to reveal the degree of tissue hypoxia in athletes when inhaled hypoxic gas mixture with 11% oxygen in different phases of the menstrual cycle.

Results. On the mathematical model of the functional breathing system, based on physiological examination data, an imitation of a hypoxic mixture with athletes was performed with athletes of 11% oxygen in different phases of the menstrual cycle. The partial pressures and voltages of oxygen in alveolar air, arterial and mixed venous blood, heart, brain and skeletal muscle tissues were calculated. Numerical experiments were also performed with the replacement of the real values of the minute volume of respiration and the minute volume of blood in the corresponding phases of MC for adaptation processes in other phases of the cycle.

Conclusions. The results of the prediction on the mathematical model of the respiratory system of the athlete’s reactions to the inhalation of hypoxic mixtures testify to the specificity of functional self-regulation and, consequently, the adaptive capabilities to the hypoxia of the female body during cyclic changes in the hormonal status in different phases of the menstrual cycle. The results of preliminary studies show that under hypoxic conditions, as a result of inhalation of a gas mixture with 11% oxygen without a compenetration increase in pulmonary ventilation and systemic circulation, the oxygen tension in the body tissues may be below the critical level and with different degrees of expression in different phases of the MC, which is confirmed by the presented results of calculation on a mathematical model of oxygen tension in the studied tissues.

The obtained results testify to the need for further study of the individual reactions of the organism of athletes in conditions of hypoxia for the scientific substantiation of sports training for women taking into account the biological characteristics of their organism.

Keywords: mathematical model of the functional breathing system, training process of athletes, interval hypoxic training, phases of the menstrual cycle, functional self-organization of the organism

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REFERENCES
1.    Olympic sport, by V.N. Platonova (ed.). Kyiv: Olympic literature, 2009. V. 2. P. 641–671. (in Russian).
2.    Shakhlina L.- Ya. G., Chistyakova MA Psychophysiological conditions of athletes of high qualification, who specialize in judo in various phases of the menstrual cycle. Physiotherapy exercises and sports medicine. 2013. No. 8 (116). P. 11–16. (in Russian).
3.    Shakhlina L.Ya.-G., Vovchanytsya Yu.L., Kalitka S.V. Morphological and biochemical composition of red blood of athletes of high qualification, specializing in sports, the predominant development of the quality of endurance. Therapeutic physical training and sports medicine. 2013. No. 9 (117). P. 22–25. (in Russian).
4.    Shakhlina L.Ya.-G. The reaction of the athlete’s body to reduce the oxygen content in the inspired air in different phases of the menstrual cycle. Sports medicine. 2008. No. 1. P. 78–82. (in Russian).
5.    Iordanskaya F.A. Man and woman in the sport of higher achievements. Problems of sexual dimorphism. Moscow: Sov.sport, 2012. 256 p. (in Russian).
6.    Shakhlina L.Ya.-G. Features of functional adaptation of the organism of high-qualified athletes to large physical loads. Sports Medicine. 2012. No. 1. C. 20–30. (in Russian).
7.    Shakhlina L.Ya.-G., Evpak N.A. Interrelation of the psychophysiological state and special working capacity of athletes specializing in water polo. Sports medicine. 2015. N 1–2. P.59–63. (in Russian).
8.    Kolchinskaya AZ, Tsyganova TN, Ostapenko LA Normobaric interval hypoxic training in medicine and sports. Moscow: Medicine, 2003. 408 p. (in Russian).
9.    Shakhlina L.Ya.-G. Medical and biological principles of sports training of women. Kyiv: Nauk. dumka, 2001. 325 p. (in Russian).
10.    Onopchuk Yu.N., Beloshitsky P.V., Aralova N.I. To the question of the reliability of the functional systems of the organism. Kibernetika i vyčislitel’naâ tehnika. 1999. Issue. 122. P. 72–82. (in Russian).
11.    Sport medicine: Textbook for students of higher educational institutions of physical education / editor L.Ya.-G. Shakhlina. Kiev: Naukova dumka, 2016. 452 p. (in Russian).
12.    Onopchuk Yu.N., Gritsenko V.I., Vovk M.I., Kotova A.B. & other. Homeostasis of the functional respiratory system as a result of intrasystemic and systemic-environmental information interaction. Bioecomedicine. Single information space. Kiev. 2001. P. 59–81. (in Russian).
13.    Onopchuk Yu.N., Gritsenko V.I., Vovk M.I., Kotova A.B. & other. Homeostasis of the functional circulatory system as a result of intrasystemic and systemic-environmental information interaction. Bioecomedicine. Single information space. Kiev. 2001. P. 82–104. (in Russian).
14.    Aralova N.I., Shakhlina L. Ya.-H. The mathematical models of functional self-organization of the human respiratory system with a change of the hormonal states of organism. Journal of Automation and Information Sciences. No. 3, pp. 132–141. (in Russian).
15.    Secondary tissues hypoxia ed. A.Z. Kolchinskaya. Kyiv: Nauk.dumka, 1983. 253 p. (in Russian).
16.    K.B. Polinkevich, Y.N. Onopchuk. Conflicts in the regulation of the primary functions of the respiratory system of the body and the mathematical model for their solution. Cybernetic. 1986, No. 3, pp. 100–104 (in Russian).
17.    Onopchuk Yu.N. On one imitation model for the study of complex physiological processes. Cybernetics. 1979. No. 3. P. 66–72. (in Russian).
18.    Onopchuk Yu.N. About a general scheme of regulation of external respiration regimes, minute volume of blood and tissue blood flow by oxygen demand. Cybernetics. 1980. N 3. P. 110–115. (in Russian).
19.    Onopchuk Yu.N., Polinkevich K.B., Bobryakova I.L. The conceptual models of control of the respiratory system and their analysis in mathematical modeling. Cybernetics and system analysis. 1993. No. 6. P. 76–88. (in Russian).
20.    Aralova N. I. Research of role of hypoxia, hypercaphnia and hypometabolism in the regulation of the respiratory ststem in their internal and external disturbances based on the mathematical model. Kibernetika i vyčislitel’naâ tehnika. 2017. No 188, pp. 49–64. (in Russian).
21.    Aralova A.A., Aralova N. I., Kovalchuk – Khimiuk L. A., Onopchuk, Yu. N. Computer – aided information system of functional diagnostics of sportsmen. Control Systems and Computers. 2008. No 3, pp. 73–78. (in Russian).
22.    Aralova N.I. Mathematical model of the mechanism short- and medium-functional adaptation of breath of persons work in extreme conditions high. Kibernetika i vyčisli-tel’naâ tehnika. 2015. Vol. 182. P. 16–21. (in Russian).
23.    Aralova N.I. Information technologies of decision making support for rehabilitation of sportsmen engaged in combat sport. Journal of Automation and Information Sciences. 2016. No. 3. P. 160–170. (in Russian). https://doi.org/10.1615/JAutomatInfScien.v48.i6.70
24.    Kolchinskaya A.Z. Oxygen regimes of an organism of a child and adolescent Kyiv: Nauk. dumka, 1973. 320 p.

Received 04.05.2018

Issue 3 (193), article 3

DOI:https://doi.org/10.15407/kvt192.03.043

Kibern. vyčisl. teh., 2018, Issue 3 (193), pp.

Vovk M.I., PhD (Biology), Senior Researcher,
Head of Bioelectrical Control & Medical Cybernetics Dep.
e-mail: vovk@irtc.org.ua; imvovk3940@gmail.com

Galyan Ye.B., PhD (Engineering), Researcher,
Bioelectrical Control & Medical Cybernetics Dep.
e-mail: galian@irtc.org.ua

Kutsyak A.А., PhD (Engineering), Researcher,
Bioelectrical Control & Medical Cybernetics Dep.
e-mail: spirotech85@ukr.net

Lauta A.D., PhD (Medicine), Senior Researcher,
Bioelectrical Control & Medical Cybernetics Dep.
e-mail: dep140@irtc.org.ua

International Research and Training Center for Information Technologies and Systems of the National Academy of Sciences of Ukraine and of Ministry of Education and Science of Ukraine, Acad. Glushkov av. 40, Kyiv, 03187, Ukraine

FORMATION OF INDIVIDUAL COMPLEX OF CONTROL ACTIONS FOR MOTOR AND SPEECH REHABILITATION AFTER A STROKE

Introduction. At present, one of the leading directions in the healthcare system is an individual approach to treatment. Restoration of movements and oral speech after a stroke suggests the formation of an individual complex of control actions – programs, techniques for general limb movements, fine motor hand training to reduce the deficit of motor and speech functions acquired as a result of pathology.

The purpose of the article is to determine on the basis of which algorithms, the informative criteria for estimating the deficit of motor and speech functions, as well as rules to be solved, an individual set of movements, programs and training schedule to restore motor and speech functions after a stroke are formed.

Results. A general and additional algorithms for the formation of an individual complex of control actions for motor and speech functions rehabilitation after a stroke have been developed. According to the algorithms, the patient is tested for general and specific contraindications to the use of muscle electrical stimulation and / or biofeedback training, quantitative assessment of motor and speech functions, muscle tonus according to new original techniques, verification of limitations to the application of programs and the duration of training. Additional algorithms are designed taking into account both hyper- and hypotonic parameters. A special feature of these algorithms is the introduction of additional restrictions, for which intervals of muscle tone values are formed.

Decision rules have been developed using the algebra of predicates, logical variables corresponding to the specified criteria and indicators. According to these rules, in each stage of rehabilitation, a set of movements and training programs recommended by priority (“Synthesis”, “Donor”, “Threshold”, “Biofeedback” according to TRENAR® technology) and their schedule are determined in binary form.

Conclusions. The considered approach to the formation of an individual complex of control actions for movement and speech rehabilitation after a stroke is the theoretical basis to synthesize the mobile information technology of digital medicine for assistance the physician in diagnosing and carrying out individual rehabilitation of motor and speech functions after a stroke.

Keywords: stroke, movement, speech, rehabilitation, quantitative assessment, algorithm, decision rules, individual control actions, programs, electrical muscle stimulation, biofeedback training.

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REFERENCES1.    

1.    Vovk M.I. New opportunities for movement and speech rehabilitation. Kibernetika i vyčislitel’naâ tehnika. 2016. Iss.186. P. 78–93 (in Russian).
2.    Vilensky B.S. Stroke: … SPb: Foliant, 2002. 397 p. (in Russian).
3.    Vovk М.І., Kutsyak О.А., Lauta A.D., Ovcharenko М.А. Information support of researches on the dynamics of movement restoration after the stroke. Kibernetika i vyčislitel’naâ tehnika. 2017. №3 (189). P. 61–78. (in Ukrainian).
4.    Vovk M.I., Peleshok S.R., Galian E.B., Ovcharenko M.A. Method for assessing motor and sensory speech disorders. Collection of articles of the scientific and information center “Znanie” on the basis of the XI international correspondence scientific-practical conference: “The development of science in the XXI century” 3 part, Kharkov: a collection with articles (standard level, academic level). Donetsk: Scientific and Information Center “Knowledge”, 2016. pp. 70–76. (in Russian).
5.    Belova A.N., Schepetova O.N. Scales, tests and questionnaires in medical rehabilitation. Moscow: Antidor, 2002. 440 p. (in Russian).
6.    Kadykov A.S., Chernikova L.A., Shakhparonova N.V. Rehabilitation of Neurological Patients. Мoscow: MEDpress-inform, 2008. 560 p. (in Russian).

Received 4.06.2018

Issue 3 (193), article 2

DOI:https://doi.org/10.15407/kvt192.03.027

Kibern. vyčisl. teh., 2018, Issue 3 (193), pp.

Antomonov M.Y., DSc (Biology), Professor,
Chief Researcher of the Laboratory of Epidemiological Research and Medical Informatics
e-mail: antomonov@gmail.com
State Institution “O.M. Marzіeiev Institute for Public Health of the National Academy of Medical Sciences of Ukraine”, 50,  Popudrenko str.  Кyiv, 02660

INFORMATION TECHNOLOGY FOR CONSTRUCTING THE COMPOSITE INDICES FOR DATA OF DIFFERENT TYPES USED IN MEDICAL AND ENVIRONMENTAL STUDIES

Introduction. Information technologies used in medical and environmental researches often deal wiht huge amounts of information processing. These technologies allow us to identify and investigate previously hidden dependencies and interactions in complex environmental, medical and biological systems, and on the other hand, it is accompanied by the analysis of large data sets, some of which (sometimes most of them) have an uninformative (noisy) character. One of the ways of solving this problem are the methods of constructing composite indices (CI), i.e. complex indicators, which allow to perform an integral assessment of the state and functioning of ecological, medical and biological systems.

The purpose of the paper is to develop a generalized information technology for constructing composite indices for different types of data used in medical and environmental studies.

Results. Medical and ecological researches include two main components: analysis of the states of both human health and of the environment; in solving such problems it is necessary to evaluate and analyze the state of the bioobject according to the data of different types: quantitative, rank, binary and qualitative variables. The developed general information technology is oriented on supporting the solution of a wide range of medical and hygienic tasks and integrates various approaches to processing and analysing of data of different types. Proposed technology consists of four stages: the formation and initial analysis of an initial indicators set, the calculation and normalization for obtainig unnamed equivalents, the actual design of the composite indices, and their verification. The implementation of this technology makes it possible to compare data of different dimensions, determine the significance of specific characteristics in a general research totality, to evaluate the integral state and to classify the research objects.

Conclusion. The proposed information technology for the construction of composite indices based on data of different types: quantitative, rank, binary and qualitative variables, is an effective tool for determining and comparing the state of bioobjects of different nature, and its use makes it possible to avoid mistakes in the incorrect application of mathematical methods for processing medical and ecological information.

Keywords: information technology, composite indicators, processing medical and ecological quantitative, rank, binary and qualitative variables.

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REFERENCES
1.      Suter, E, et al. Indicators and Measurement Tools for Health Systems Integration: A Knowledge Synthesis. International Journal of Integrated Care, 2017; 17 (6): 4, 1–17. DOI: https://doi.org/10.5334/ijic.3931
2.    T. Hastie, R. Tibshirani, J. Friedman. The elements of statistical learning / data mining, inference, and Prediction. Second edition, 12th printing 2017, 745 p.
3.    Antomonov M.Yu., Voloshchuk E.V. Constructing integral indicators of quantitative characteristics using one-dimensional and multidimensional statistical methods. Kibernetika i vyčislitel’naâ tehnika. 2012. Iss. 167. P. 61–68 (in Russian).
4.    Mikheienko О.І. Integrated method for assessing the health of the human body. Pedahohika, psykholohiya ta medyko-biolohichni problemy fizychnoho vykhovannya i sportu. 2011. Iss. 6. P. 93–101 (in Ukrainian).
5.    Apanasenko G.L. Diagnosis of individual health. Gigiyena i sanitariya. 2004. Iss. 1. P. 55–58 (in Russian).
6.    Merkov A.M., Polyakov L.E. Sanitary statistics (manual for doctors). Moscow: Meditsina, 1974. 384 p. (in Russian).
7.    Bulich E.G., Muravov I.V. Human health: The biological basis of vital activity and motor activity in its stimulation. Kiev: Olimpiyskaya literatura, 2003. 424 p. (in Russian).
8.    Apanasenko G.L. The book is about health. Kiev: Medkniga, 2007. 132 p. (in Russian).
9.    Bezruk V.V. Anthropometry. Assessment of physical development of children. Methods of evaluation: methodical instructions for practical classes for students of the third year of medical faculty (specialty “medical psychology”). Chernivtsi, 2008. 19 p. (in Ukrainian).
10.    Verevina M.L., Rusakov N.V., Zhukova T.V, Gruzdeva O.A. Assessment of the incidence of the population, depending on living conditions. Gigiyena i sanitariya. 2010. Iss. 1. P. 21–25. (in Russian)
11.    Bolshakov A.M., Krutko V.N. Integral health indicators and complex systems for their evaluation. Gigiyena i sanitariya. 2011. Iss. 6. P. 51 52 (in Russian).
12.    Medic V.A., Tokmachev M.S. Manual on Health and Health Statistics. Moscow: Meditsina, 2009. 527 p. (in Russian).
13.    Shekera O.G. Health: Basic terms and indicators. Zdorovʺya suspilʹstva. 2011. Iss. 1. P. 26–31 (in Russian).
14.    Krivova O.A., Kozak L.M. Comprehensive assessment of regional demographic development. Kibernetika i vyčislitel’naâ tehnika. 2015. Iss. 182. pp. 70–84. (in Russian).
15.    Rogozinskaya N.S., Kozak L.M. Mathematical models for the dynamics of statistical indicators for the study of the health status of the population in terms of cancer incidence. Kibernetika i vyčislitel’naâ tehnika. 2011, Iss 166. P. 85–96. (in Russian).
16.    GOST 2874-82 Drinking water. Hygienic requirements and quality control. — Enter. 85-01-01. Moscow: Izdatel’stvo standartov, 1985. 6 p. (in Russian).
17.    Turbinsky V.V., Maslyuk A.I. The risk to the public health of the chemical composition of drinking water. Hygiene and sanitation. 2011. Iss. 2. P. 23–27. (in Russian).
18.    Gnevashev M.V. Statistical methods for assessing the state of water bodies on a set of ecosystem indicators for water protection purposes. Ekaterinburg, 2006. 42 p. (in Russian).
19.    Belogokrov V.P., Lozannsky V.R., Pesina S.A. Application of generalized indicators for assessing the level of contaminated water bodies. Integrated assessment of surface water quality. StPb .: Gidrometeoizdat, 2001. 34 p. (in Russian).
20.    Index of atmospheric pollution (IZA) URL: http://moreprom.ru/article.php?id=56. [Last accessed: 08.06.2018] (in Russian).
21.    Kakareka S.V. Estimation of total air pollution. Geografiya i prirodnyye resursy. 2012. Iss. 2. P. 14–20 (in Russian).
22.    Pinigin M.A. Hygienic basis for assessing the degree of air pollution. Hygiene and sanitation. 1993. Iss. 7. P. 4–8 (in Russian).
23.    Antonomov M.Yu. Mathematical processing and analysis of medico-biological data 2 ed. — Kiev: MEDC “Medinform”, 2018. 579 p. (in Russian)
24.    Saati T.L. Adoption of decisions. The method of analyzing hierarchies. Moscow: Radio i svyaz’, 1989. 316 p. (in Russian).

Resieved 11.06.2018

Issue 3 (193), article 1

DOI:https://doi.org/10.15407/kvt192.03.005

Kibern. vyčisl. teh., 2018, Issue 3 (193), pp.

Revunova E.G., Ph.D. (Engineering),
Senior Researcher Department of Neural Information Processing Technologies
e-mail: egrevunova@gmail.com

Rachkovskij D.A., DSc. (Engineering),
Leading Researcher, Department of Neural Information Processing Technologies
e-mail: dar@infrm.kiev.ua

International Research and Training Center for Information Technologies
and Systems of the National Academy of Sciences of Ukraine
and Ministry of Education and Science of Ukraine,
Acad. Glushkova av., 40, Kiev, 03187, Ukraine

RANDOM PROJECTION AND TRUNCATED SVD FOR ESTIMATING DIRECTION OF ARRIVAL IN ANTENNA ARRAY

Introduction. The need to solve inverse problems arises in many areas of science and technology in connection with the recovery of the object signal based on the results of indirect remote measurements. In the case where the transformation matrix has a high conditional number, the sequence of its singular numbers falls to zero, and the output of the measuring system contains noise, the problem of estimating the input vector is called discrete ill-posed problem (DIP). It is known that the DIP solution using pseudoinverse of the input-output transformation matrix is unstable. To overcome the instability and to improve the accuracy of the solution, regularization methods are used.

Our approaches to ensuring the stability of the DIP solution (truncated singular decomposition (TSVD) and random projection (RP)) use the integer regularization parameter, which is the number of terms of the linear model. Regularization with an integer parameter makes it possible to provide a model close to the best in terms of the accuracy of the input vector recovery, and also to reduce the computational complexity by reducing the dimensionality of the problem.

The purpose of the article is to develop an approach to estimating the direction of arrival of signals in the antenna array using the DIP solution, to compare the results with the well-known MUSIC method, to reveal the advantages and disadvantages of the methods.

Results. Comparison of TSVD and MUSIC (implemented in real numbers) when working with correlated sources and five snapshots showed the advantage of TSVD in terms of the power of the useful signal Pratio by 2.2 times with the number of antenna elements K = 15 and by 4.7 times with K = 90. The advantage of TSVD in Pratio is by 3.7 times for K = 15 and by 4.2 times for K = 90. Comparison of RP and MUSIC (implemented in real numbers), when working with correlated sources and five snapshots, showed the advantage of RP in Pratio by 3 times at K = 15 and by 4.4 times at K = 90. When working with 100 snapshots, the advantage of RP in Pratio is by 3.8 times for K = 15 and by 4.2 times for K = 90.

Conclusions. The approach to determining the direction of arrival based on the l2-regularization methods provides a stable solution in the case of a small number of snapshots, high noise and correlated source signals. Methods of determining the direction of arrival based on l2-regularization, in contrast to l1-regularization, do not impose restrictions on the properties of the input-output transformation matrix, do not require a priori information on the number of signal sources, allow constructing efficient hardware implementations.

Keywords: Direction of arrival estimation, truncated singular value decomposition, random projection, MUSIC.

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REFERENCES

1. Hansen P. Rank-deficient and discrete ill-posed problems. Numerical aspects of linear inversion. Philadelphia: SIAM, 1998. 247 p. https://doi.org/10.1137/1.9780898719697

2. Tikhonov A., Arsenin V. Solution of ill-posed problems. Washington: V.H. Winston, 1977. 231 p.

3. Starkov V. Constructive methods of computational physics in interpretation problems. Kiev: Naukova Dumka, 2002. 263 p. (in Russian)

4. Hansen P.C. The truncated SVD as a method for regularization. BIT. 1987. Vol. 27, N 2. P. 534–553. https://doi.org/10.1007/BF01937276

5. Revunova E.G., Tishchuk A.V. Criterion for choosing a model for solving discrete ill-posed problems on the basis of a singular expansion. Control systems and machines. 2014. N 6. P. 3–11. (in Russian).

6. Revunova E.G., Tyshchuk A.V. A model selection criterion for solution of discrete ill-posed problems based on the singular value decomposition. Proc. IWIM’2015 (20–24th of July, 2015, Kyiv–Zhukyn) . Kyiv–Zhukyn. 2015. P.43–47.

7. Revunova E.G. Model selection criteria for a linear model to solve discrete ill-posed problems on the basis of singular decomposition and random projection. Cybernetics and Systems Analysis. 2016. Vol. 52, N.4. P. 647–664. https://doi.org/10.1007/s10559-016-9868-4

8. Revunova E.G. Study of error components for solution of the inverse problem using random projections. Mathematical Machines and Systems. 2010. N 4. P. 33–42 (in Russian).

9. Revunova E.G. Randomization approach to the reconstruction of signals resulted from indirect measurements. Proc. ICIM’13 (16-20th of September, 2013, Kyiv). Kyiv, 2013. P. 203–208.

10. Revunova E.G. Analytical study of the error components for the solution of discrete ill-posed problems using random projections. Cybernetics and Systems Analysis. 2015. Vol. 51, N. 6. P. 978–991. https://doi.org/10.1007/s10559-015-9791-0

11. Revunova E.G. Averaging over matrices in solving discrete ill-posed problems on the basis of random projection. Proc. CSIT’17(05–08th of September, 2017, Lviv). Lviv, 2017. Vol. 1. P. 473–478. https://doi.org/10.1109/STC-CSIT.2017.8098831

12. Revunova E.G. Solution of the discrete ill-posed problem on the basis of singular value decomposition and random projection. Advances in Intelligent Systems and Computing II. Cham: Springer. 2017. P. 434–449.

13. Revunova E.G. Increasing the accuracy of the solution of discrete ill-posed problems by the method of random projections. Control systems and machines. 2018. N 1. P. 16–27. (in Ukrainian)

14. Revunova E.G., Tishchuk A.V., Desyaterik A.A. Criteria for choosing a model for solving discrete ill-posed problems based on SVD and QR decompositions. Inductive modeling of complex systems. 2015. N 7. P. 232–239. (in Russian).

15. Revunova E.G., Rachkovskij D.A. Using randomized algorithms for solving discrete ill-posed problems. Intern. Journal Information Theories and Applications. 2009. Vol. 2, N. 16. P. 176–192.

16. Rachkovskij D.A., Revunova E.G. Randomized method for solving discrete ill-posed problems. Cybernetics and Systems Analysis. 2012. Vol. 48, N. 4. P. 621–635. https://doi.org/10.1007/s10559-012-9443-6

17. Schmidt R.O. Multiple emitter location and signal parameter estimation. IEEE Trans. Antennas Propagation. 1986. Vol. AP–34. P. 276–280. https://doi.org/10.1109/TAP.1986.1143830

18. Krim H., Viberg M. Two decades of array signal processing research: The parametric approach. IEEE Signal Processing Magazine. 1996. Vol. 13, N 4. P. 67–94. https://doi.org/10.1109/79.526899

19. Schmidt R.O. A signal subspace approach to multiple emitter location spectral estimation. PhD thesis. Stanford University, 1981. 201 p.

20. Bartlett M.S. Smoothing periodograms from time series with continuous spectra. Nature. 1948. Vol. 161. P. 686–687. https://doi.org/10.1038/161686a0

21. Malioutov D.M., Cetin M., Fisher J.W. III, Willsky A.S. Superresolution source localization through data-adaptive regularization. Proc. SAM’02 (6 august, 2002, Rosslyn, Virginia). Rosslyn, Virginia, 2002. P. 194–198. https://doi.org/10.1109/SAM.2002.1191027

22. Malioutov D., Cetin M., Willsky A.S. A sparse signal reconstruction perspective for source localization with sensor arrays. IEEE Transactions on Signal Processing. 2005. Vol. 53, N 8. P. 3010–3022. https://doi.org/10.1109/TSP.2005.850882

23. Panahi A. Viberg M. Fast lasso based DOA tracking. Proc. CAMSAP’11 (13–16th of December, 2011, San Juan, Puerto Rico) . San Juan, Puerto Rico, 2011. P. 397–400.

24. Panahi A. Viberg M. A novel method of DOA tracking by penalized least squares. Proc. CAMSAP’13 (15–18th of December, 2013, St. Martin, France). St. Martin, France, 2013. P. 61–64.

25. Golub G.H., Van Loan C.F. Matrix Computations. Baltimore: The Johns Hopkins University Press, 1996.

26. Ivakhnenko A., Stepashko V. Noise-immunity of modeling. Kiev: Naukova Dumka, 1985. (in Russian)

27. Stepashko V. Theoretical aspects of GMDH as a method of inductive modeling. Control systems and machines 2003. N 2. P. 31–38. (in Russian)

28. Stepashko V. Method of critical variances as analytical tool of theory of inductive modeling. Journal of Automation and Information Sciences. 2008. Vol. 40, N 3. P. 4–22. https://doi.org/10.1615/JAutomatInfScien.v40.i3.20

29. Xiang H., Zou J. Regularization with randomized SVD for large-scale discrete inverse problems. Inverse Problems. 29(8):085008, 2013. https://doi.org/10.1088/0266-5611/29/8/085008

30. Xiang H., Zou J. Randomized algorithms for large-scale inverse problems with general Tikhonov regularizations. Inverse Problems. 2015. Vol. 31, N 8:085008. P. 1–24.

31. Wei Y., Xie P., Zhang L. Tikhonov regularization and randomized GSVD. SIAM J. Matrix Anal. Appl. 2016. Vol. 37, N 2. P. 649–675. https://doi.org/10.1137/15M1030200

32. Zhang L., Wei Y. Randomized core reduction for discrete ill-posed problem. arXiv:1808.02654. 2018.

33. Misuno I.S., Rachkovskij D.A., Slipchenko S.V., Sokolov A.M. Searching for text information with the help of vector representations. Problems of Programming. 2005. N. 4. P. 50–59. (in Russian)

34. Rachkovskij D.A. Formation of similarity-reflecting binary vectors with random binary projections. Cybernetics and Systems Analysis. 2015. Vol. 51, N 2. P. 313–323. https://doi.org/10.1007/s10559-015-9723-z

35. Ferdowsi S., Voloshynovskiy S., Kostadinov D., Holotyak T. Fast content identification in highdimensional feature spaces using sparse ternary codes. Proc. WIFS’16 (4–7th of December, 2016, Abu Dhabi, UAE). Abu Dhabi, UAE, 2016. P. 1–6.

36. Rachkovskij D.A., Slipchenko S.V., Kussul E.M., Baidyk T. N. Properties of numeric codes for the scheme of random subspaces RSC. Cybernetics and Systems Analysis. 2005. Vol. 41, N. 4. P. 509–520. https://doi.org/10.1007/s10559-005-0086-8

37. Rachkovskij D.A., Slipchenko S.V., Kussul E.M., Baidyk T.N. Sparse binary distributed encoding of scalars. 2005. Journal of Automation and Information Sciences. Vol. 37, N 6. P. 12–23. https://doi.org/10.1615/J
Automat Inf Scien.v37.i6.20

38. Rachkovskij D.A., Slipchenko S.V., Misuno I.S., Kussul E.M., Baidyk T. N. Sparse binary distributed encoding of numeric vectors. Journal of Automation and Information Sciences. 2005. Vol. 37, N 11. P. 47–61. https://doi.org/10.1615/J
Automat Inf Scien.v37.i11.60

39. Kleyko D., Osipov E., Rachkovskij D.A. Modification of holographic graph neuron using sparse distributed representations. Procedia Computer Science. 2016. Vol. 88. P. 39–45. https://doi.org/10.1016/j.procs.2016.07.404

40. Kleyko D., Osipov E., Senior A., Khan A.I., Sekercioglu Y.A. Holographic graph neuron: A bioinspired architecture for pattern processing. IEEE Trans. Neural Netw. Learn. Syst. 2017.Vol. 28, N 6. P. 1250–1262. https://doi.org/10.1109/TNNLS.2016.2535338

41. Kleyko D., Rahimi A., Rachkovskij D., Osipov E., Rabaey J. Classification and recall with binary hyperdimensional computing: Tradeoffs in choice of density and mapping characteristics. IEEE Trans. Neural Netw. Learn. Syst. 2018.

42. Kleyko D., Osipov E. On bidirectional transitions between localist and distributed representations: The case of common substrings search using vector symbolic architecture. Procedia Computer Science. 2014. Vol. 41. P. 104–113. https://doi.org/10.1016/j.procs.2014.11.091

43. Recchia G., Sahlgren M., Kanerva P., Jones M. Encoding sequential information in semantic space models: Comparing holographic reduced representation and random permutation. Comput. Intell. Neurosci. 2015. Vol. 2015. Art. no. 58. https://doi.org/10.1155/2015/986574

44. Räsänen O.J., Saarinen J.P. Sequence prediction with sparse distributed hyperdimensional coding applied to the analysis of mobile phone use patterns. IEEE Trans. Neural Netw. Learn. Syst. 2016. Vol. 27, N 9. P. 1878–1889. https://doi.org/10.1109/TNNLS.2015.2462721

45. Slipchenko S. V., Rachkovskij D.A. Analogical mapping using similarity of binary distributed representations. Int. J. Information Theories and Applications. 2009. Vol. 16, N 3. P. 269–290.

46. Kanerva P. Hyperdimensional computing: An introduction to computing in distributed representation with high-dimensional random vectors. Cogn. Comput. 2009. Vol. 1, N 2. P. 139–159. https://doi.org/10.1007/s12559-009-9009-8

47. Gallant S. I., Okaywe T.W. Representing objects, relations, and sequences. Neural Comput. 2013. Vol. 25, N 8. P. 2038–2078. https://doi.org/10.1162/NECO_a_00467

48. Gritsenko V.I., Rachkovskij D.A., Goltsev A.D., Lukovych V.V., Misuno I.S., Revunova E.G., Slipchenko S.V., Sokolov A.M., Talayev S.A. Neural distributed representation for intelligent information technologies and modeling of thinking. Kibernetika i vyčislitel’naâ tehnika. 2013. Vol. 173. P. 7–24. (in Russian).

49. Frolov A.A., Rachkovskij D.A., Husek D. On information characteristics of Willshaw-like auto-associative memory. Neural Network World. 2002. Vol. 12, No 2. P. 141–158.

50. Frolov A.A., Husek D., Rachkovskij D.A. Time of searching for similar binary vectors in associative memory. Cybernetics and Systems Analysis. 2006. Vol. 42, No. 5. P. 615–623. https://doi.org/10.1007/s10559-006-0098-z

51. Frady E. P., Kleyko D., Sommer F. T. A theory of sequence indexing and working memory in recurrent neural networks. Neural Comput. 2018. Vol. 30, N. 6. P. 1449–1513. https://doi.org/10.1162/neco_a_01084

52. Kussul N.N., Sokolov B.V., Zyelyk Y.I., Zelentsov V.A., Skakun S.V., Shelestov A.Y. Disaster risk assessment based on heterogeneous geospatial information. J. of Automation and Information Sciences. 2010. Vol. 42, N 12. P. 32–45. https://doi.org/10.1615/JAutomatInfScien.v42.i12.40

53. Kussul N., Shelestov A., Basarab R., Skakun S., Kussul O., Lavrenyuk M. Geospatial intelligence and data fusion techniques for sustainable development problems. Proc. ICTERI’15. 2015. P. 196–203.

54. Kussul N., Skakun S., Shelestov A., Kravchenko O., Kussul O. Crop classification in Ukraine using satellite optical and SAR images. International Journal Information Models and Analyses. 2013. Vol. 2, N 2. P. 118–122.

55. Kussul N., Lemoine G., Gallego F. J., Skakun S. V, Lavreniuk M., Shelestov A. Y. Parcel-based crop classification in Ukraine using Landsat-8 data and Sentinel-1A data. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. 2016. Vol. 9, N 6. P. 2500–2508. https://doi.org/10.1109/JSTARS.2016.2560141

56. Lavreniuk M., Kussul N., Meretsky M., Lukin V., Abramov S., Rubel O. Impact of SAR data filtering on crop classification accuracy. Proc. UKRCON’17 (29th of May — 02th of June, 2017, Kyiv). Kyiv, 2017.2017. P. 912–917. https://doi.org/10.1109/UKRCON.2017.8100381

57. Kussul N., Lavreniuk M., Shelestov A., Skakun S. Crop inventory at regional scale in Ukraine: developing in season and end of season crop maps with multi-temporal optical and SAR satellite imagery. European Journal of Remote Sensing. 2018. Vol. 51, N 1. P. 627–636. https://doi.org/10.1080/22797254.2018.1454265

58. Moreira A., Prats-Iraola P., Younis M., Krieger G., Hajnsek I., Papathanassiou K. A tutorial on synthetic aperture radar. IEEE Geosci. Remote Sensing Mag. 2013. Vol. 1, N 1. P. 6–43. https://doi.org/10.1109/MGRS.2013.2248301

59. Ramakrishnan S., Demarcus V., Le Ny J., Patwari N., Gussy J. Synthetic aperture radar imaging using spectral estimation techniques. Technical Report. University of Michigan, 2002. 34 p.

Received 15.05.2018