Issue 2 (204), article 5


Cybernetics and Computer Engineering, 2021, 2(204)

VOVK M.I., PhD (Biology), Senior Researcher,
Head of Bioelectrical Control & Medical Cybernetics Department

KUTSIAK O.A., PhD (Engineering),
Senior Researcher of Bioelectrical Control & Medical Cybernetics Department

International Research and Training Center for Information Technologies and Systems of the NAS of Ukraine and of MES of Ukraine,
40, Acad. Glushkov av. Kyiv, 03187, Ukraine


Introduction. Diagnostics of motor functions plays an important role in the motor functions restoration after stroke. Synthesis of effective technologies for personalized assessment of motor functions disorders at different rehabilitation stages is an urgent scientific and applied task.

The purpose of the paper is to develop information technology for diagnostics of motor functions deficit after stroke, that uses artificial intelligence tools to increase the effectiveness of the diagnostic process.

Results. The theoretical and practical foundations to synthesize AI-technology for personal diagnostics of motor functions deficit, and the assessment of their restoration as a result of rehabilitation measures after stroke have been developed. For informational assistance to the physician in the diagnostic process, artificial intelligence is used. A new class of mobile digital medicine tools – the specialized software modules for motor functions diagnostics “MovementTestStroke 1.1 (PC)” installed in the PC-structure, and “MovementTestStroke 1.1 (MD)” installed in mobile platforms running under Android operation system have been developed. Software implementation — Visual Studio 2019, C# programming language. Structural and functional models of user – software modules interaction, algorithms for motor function deficit diagnostics, and UML-diagrams of these modules are presented.

Functional features of the technology: an expanded range of evidence criteria for personalized quantitative assessment of limb movements deficit, storage in the Database and display on the interface the results of deficit assessment, as well as the deficit dynamics during the rehabilitation course in a convenient form (tables, graphs) make it possible to reduce the physician’s error, prevent complications, identify the disorders specifics, compare the rehabilitation effectiveness of the upper and lower limbs, their distal and proximal parts, including fine motor skills of the hand, restoration of which helps to restore speech in motor or motor and sensory aphasia.

Conclusions. The usage of artificial intelligence tools to diagnose motor deficit will increase the diagnostic effectiveness, and, as a consequence, rehabilitation services for patients after stroke.

Keywords: diagnostics, motor functions, stroke, personal quantitative assessment, criteria, technology, artificial intelligence, software module, structural-functional model, algorithm, activity diagram.

Download full text!


1. Norrving Bo. Action Plan for Stroke in Europe 2018-2030. European Stroke Journal. 2018. Vol. 3(4). pp. 309-336.

2. Vovk M.I., Kutsiak O.A., Lauta A.D., Ovcharenko M.A. Information Assistance of Researches on the Dynamics of Movement Restoration After the Stroke. Cybernetics and Computer Engineering. 2017, No 3 (189), pp. 61-78. (in Ukrainian)

3. Gritsenko V.I., Vovk M.I. Trenar – Innovative Technology of Restoration of Movements. Science and Business – the basis of economic development: materials of the International Scientific and Practical Forum. Ukraine, Dnipropetrovsk, 2012, pp. 204-206. (in Russian)

4. Varun H Buch, Irfan Ahmed, Mahiben Maruthappu. Artificial intelligence in medicine: current trends and future possibilities. Br J Gen Pract. 2018. No 68(668). pp. 143-144.

5. 5. Bernard Marr. The 9 Biggest Technology Trends That Will Transform Medicine And Healthcare In 2020. URL: 6db7334072cd (Last accessed: 1.05.2021)

6. Ahuja A.S. The impact of artificial intelligence in medicine on the future role of the physician. PeerJ. 2019. URL:

7. Artificial intelligence in medicine: the main trends in the world. URL: (Last accessed: 05.05.2021) (in Russian).

8. Vovk M.I., Kutsyak O.A. Software module for personal diagnostics of motor functions after stroke. Cybernetics and Computer Engineering. 2019, No 4 (198), pp. 62-77.

9. Belova A., Shchepetova O. Scales, tests and questionnaires in medical rehabilitation. Moscow: Antidor, 2002. 440 p. (in Russian)

10. Smychek V., Ponomareva E. Craniocerebral trauma (clinic, treatment, examination, rehabilitation). Minsk: Research Institute of ME and R, 2010. 430 p. (in Russian)

11. Certificate of registration the copyright “Computer program “Diagnostics of deficit of general limb movement, fine motor skills of the hand, walking form by the technique for quantitative assessment of movements deficit in patients after stroke “MovementTestStroke 1.0 (PC)””” / M.I. Vovk, O.A. Kutsiak (Ukraine); No. 98161; published dated 16.06.2020 [in Ukrainian].

12. Booch G., Rumbaugh J., Jacobson I. The Unified Modeling Language User Guide. Boston: Addison-Wesley Professional, 1998. 391 p.

13. Fowler M. UML Distilled: A Brief Guide to the Standard Object Modeling Language. Boston: Addison-Wesley Professional, 2004. 175 p.

Received 01.04.2021

Issue 2 (204), article 4


Cybernetics and Computer Engineering, 2021, 2(204)

FAINZILBERG L.S.1, DSc. (Engineering), Professor,
Chief Researcher of the Department of Automatic Systems
ORCID: 0000-0002-3092-0794

SOLOVEY S.R.2, Student Faculty of Biomedical Engineering,

1International 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,
40, Acad. Glushkova av., Kyiv, 03187, Ukraine,

2The National Technical University of Ukraine «Igor Sikorsky Kyiv Polytechnic Institute»
37, Peremohy av., Kyiv, 03056, Ukraine


Introduction. In connection with the COVID-19 pandemic, it is important to start treatment promptly in case of a threat of developing viral pneumonia in a patient. The solution to this problem requires the creation of new means for detecting respiratory disorders with a minimum probability of “missing the target”. At the same time, it is equally important to minimize visits to medical institutions by healthy patients because of the danger of their contact with possible carriers of coronavirus infections, that is, to minimize the likelihood of a «false alarm».

Purpose of the article is to develop a method that allows a patient to signal at home about the advisability of contacting a medical institution for an in-depth examination of the respiratory system, and to assess the possibility of implementing this method on a smartphone using a built-in microphone.

Methods. A distinctive feature of the proposed approach lies in the construction of a personalized standard of normal respiratory respiration for a particular patient based on self-learning from a finite sample of observations at home and in comparison, based on original computational algorithms of phonospirograms of sound signals of the following observations with the standard.

Results. A prototype of information technology has been developed that will provide home alarms about possible respiratory disorders, requiring consultation with a doctor and the need for an in-depth medical examination.

It is shown that the construction of a personalized standard of normal breathing can be carried out based on the use of a set of original computational procedures for a finite sample of realizations, independently registered by the user using a microphone built into a smartphone. The algorithm for constructing a standard is based on digital processing of a matrix of paired distances between phonospirograms of the final training sample of observations.

Findings. A software application that provides the implementation of the proposed computational procedures can be implemented on a smartphone of average performance running the Android operating system.

Keywords: respiratory noises, intelligent IT, computational procedures, smartphone.

Download full text!

1. Piirila P., Sovijarvi A.R. Crackles: recording, analysis and clinical significance. European Respiratory Journal. 1995, no. 8(12), pp. 2139-2148.

2. Forgacs P. The functional basis of pulmonary sounds. Chest Journal. 1978, vol. 73,no 3, pp. 399-405. DOI: 10.1378/chest.73.3.399.

3. Kosovets LI Experience of electronic registration and classification of breathing sounds of children with bronchopulmonary diseases. Collection of works of acoustic symposium “Consonance-2011”. 2011: Institute of Hydromechanics of the National Academy of Sciences of Ukraine, pp. 154-159. (In Russian).

4. Pasterkamp H., Carson C., Daien D., Oh Y. Digital respirosonography. New images of lung sounds. Chest Journal. 1989, vol. 96, no 6, pp. 1405-1412. DOI: 10.1378/chest.96.6.1405.

5. Pasterkamp H., Patel S., Wodicka G.R. Asymmetry of respiratory sounds and thoracic transmission. Medical and Biological Engineering and Computing. 1997, vol. 35, pp. 103-106.

6. Wodichka G.R., Kraman S.S., Zenk G.M., Pasterkamp H. Measurement of respiratory acoustic signals. Chest Journal. 1994. vol. 106, no. 4. pp. 1140-1144.

7. Murphy R.L.H., Vyshedskiy A. et all. Automated Lung Sound Analysis in Patients With Pneumonia. Respiratory Care. 2005, vol. 49, no. 12, pp. 1490-1497. DOI: 10.1378/chest.124.4_MeetingAbstracts.190S-b

8. Vovk I.V., Goncharova I.Yu. An analytical method for assessing the acoustic properties of stethoscopes. Acoustic bulletin. 2000, vol. 3, no. 2, pp. 10-16. (In Russian).

9. Goncharova Yu.O. Prospects for storing phonospirographic computer diagnostics in children with bronchogenic dysplasia. Bulletin of VDNZU “Ukrainian Medical Stomatological Academy”. 2013, vol. 13, issue 2 (42), pp. 85-88. (In Russian).

10. Gritsenko V.I., Fainzilberg L.S. Intelligent information technologies in digital medicine on the example of phase-graphy. Kyiv: Naukova Dumka, 2019. 423 p. (In Russian).

11. Cugell D.W. Lung sound nomenclature. The American Review of Respiratory Disease. 1987, vol. 136, no. 4, pp. 1016.

12. Earis J. Lung sounds. Thorax. 1992, no, 47, pp. 671-672.

13. Loudon R.G., Murphy R.L. 1984. Lung sounds. The American Review of Respiratory. 1984, Vol. 130, pp. 663-673.

14. Paciej R., Vyshedskiy A., Bana D. Squawks in pneumonia. Thorax. 2004, vol. 59, pp. 177-178.

15. Wilkins R.L., Dexter J.R., Murphy R.L., Belbono E.A. Lung sound nomenclature survey. Chest Journal. 1990, no. 98, pp. 886-889. DOI: 10.1378/chest.98.4.88.

16. Sounds in human lungs download and listen online. URL: (Last accessed: 24.12.2020) (In Russian).

17. Makarenkova A.A., Ermakova O.V. Preliminary studies of breathing sounds in patients with chronic obstructive pulmonary disease. Abstracts of the reports of the acoustic symposium “Consonance-2009”. 2009, Institute of Hydromechanics of the National Academy of Sciences of Ukraine, pp. 40-41. (In Russian).

18. Fainzilberg L.S. An approach to diagnostic personification decisions on the example of evaluation of cardiac activity. Kibernetika i vycislitel’naa tehnika. 2014, no. 178, p. 52-65. (In Russian).

19. Frigo M., Johnson S.G. FFTW: An adaptive software architecture for the FFT. Proc. of the IEEE Intern. Conf. on Acoustics, Speech, and Signal Processing, Seattle, 1998: WA, vol. 3, pp. 1381-1384.

20. Sejdic E. Djurovic I. JiangJ. Time-frequency feature representation using energy concentration: An overview of recent advances. Digital Signal Processin. 2009, vol. 19, no 1, pp. 153-183.

21. Bureev A.S. Mathematic model for spectral characteristics of respiratory sounds registered in trachea region. Global Journal of Pure and Applied Mathematics. 2016, vol. 12, no 5. pp. 4569-4578.

22. Ghafarian P., Jamaati H., Hashemian S.M. A Review on human respiratory modeling. Tanaffos. 2016, vol.15, no. 2, pp. 61-69.

23. Harper P., Kraman S.S., Pasterkamp H., Wodicka R. An acoustic model of the respiratory tract. IEEE Transactions on Biomedical Engineering. 2001, vol. 48, no. 5, pp. 543-550.

24. Harper P. Modeling and measurement of flow effects on tracheal sounds. IEEE Transactions on Biomedical Engineering. 2003, vol. 50, no 1, pp. 1-10.

25. Liu Y., So R.M.C., Zhang C.H. Modeling the bifurcating flow in an asymmetric human lung airway. Journal of Biomechanics. 2003, vol. 36, no. 7, pp. 951-959.

26. Venegas J.G. Self-organized patchiness in asthma as a prelude to catastrophic shifts. Nature. 2005, vol. 434, pp. 777-782.

27. Xi J. Numerical study of dynamic glottis and tidal breathing on respiratory sounds in a human upper airway model. Sleep and Breathing. 2017, vol. 22, pp. 463-479.

28. Gurung A. Computerized lung sound analysis as diagnostic aid for the detection of abnormal lung sounds: A systematic review and meta-analysis. Respiratory Medicine. 2011, vol. 105, no. 9, pp. 1396-1403.

29. Schmidt A., Zidowitz S., Kriete A., Denhard T., Krass S., Peitgen H.O. A digital reference model of the human bronchial tree. Computerized Medical Imaging and Graphics. 2004, vol. 28, no. 4, pp. 203-211. DOI: 10.1016/j.compmedimag.2004.01.001.

30. Korenbaum V.I. Acoustic diagnostics of the human respiratory system based on an objective analysis of respiratory sounds. Vestnik FEB RAS. 2004, no. 5, pp. 68-79. (In Russian).

31. Furman E.G., Sokolovsky V.L., Furman G.B. Mathematical model of respiratory noise propagation in the respiratory tract. Russian journal of biomechanics. 2018, vol. 22, no. 2b, pp. 166-177. (In Russian).

32. Dyachenko A.I., Mikhailovskaya A.N. Respiratory acoustics (Review). Proceedings of the Prokhorov General Physics Institute. 2012, vol. 68, pp. 136-181. (In Russian).

Received 02.03.2021

Issue 2 (204), article 3


Cybernetics and Computer Engineering, 2021, 2(204)

SHEPETUKHA Y.M., PhD (Engineering), Senior Researcher,
Leading Researcher of the Intelligent Control Department
ORCID: 0000-0002-6256-5248

Head of the Intelligent Control Department
ORCID: 0000-0002-5418-6723

Senior Researcher of Intelligent Control Department
ORCID: 0000-0002-0119-0964

International Research and Training Center for Information Technologies and Systems of NAS of Ukraine and MES of Ukraine,
40, Acad. Glushkov av., Kyiv, 03187, Ukraine


Introduction. Scientific-technical level of any country in a modern world is mainly determined by a current state and development rate of informational technologies. At the same time, the main avenue of information technologies’ improvement is their intellectualization. Due to intellectualization, it became possible to create advanced systems with principally novel functional capabilities, in particular, high-speed computer systems able to autonomous actions in a complex and dynamic environment. Control means for complex objects and processes play an important role in the operation of autonomous systems. Therefore, the study of theoretical as well as applied issues of such systems’ construction is an important scientific and engineering problem.

The purpose of the paper is to examine both current state and development prospects of a new direction in the area of intelligent information technologies – the elaboration of autonomous control systems for complex objects and processes in a dynamic environment; to formulate a well-grounded approach for the increase in intellectualization level of decision processes in such systems.

Methods. The development of autonomous control systems, as well as the increase in decision making processes’ intellectualization level in such systems, is based on the usage of the following conceptual, theoretical and methodological instruments: the theory of informational technologies’ intellectualization, the methodology of intelligent control, the theoretical fundamentals of artificial intelligence systems’ construction, decision making methods, the methodology of image-based reasoning, methods for simulation of image-based comprehension of environment.

Results. An approach for the consistent usage of methods of artificial intelligence, decision making and intelligent control aimed at the development of autonomous means for the control of complex objects and processes has been examined. Appropriateness of creation of the systems profiled for operations in designated problem domains has been grounded. Both specific features and components of the framework for decision making in intelligent control systems have been determined. Both necessity of the creation of intelligent environment and important role of sensor networks have been stressed. Methodology for the construction of informational images, which represent the most important components of a current situation, has been proposed. Examples of the usage of informational images for performing both dynamic and evolutional re-planning have been considered.

Conclusions. A reasonable way for the development of intelligent control systems is the one that provides a consistent usage of different types of models. Image-based representation of a current situation’s essential interconnections is an efficient instrument for the intellectualization at different stages of decision making processes – alternative generation, understanding of inconsistencies among different data sources, execution of choice procedure, evaluation of results. The application of artificial intelligence elements for decision making in autonomous systems is especially well-grounded in cases of time shortage as well as availability of a great number of existing alternatives.

Keywords: intellectualization of information technologies, intelligent control, decision making, autonomy, artificial intelligence, image, uncertainty, adaptation.

Download full text!


1. Mertoguno J.S. Human decision making model for autonomic cyber systems. International Journal on Artificial Intelligence Tools. 2014, Vol. 23, N. 6. URL: S0218213014600239. – Title from the screen.

2. Gonzales D., Harting S. Designing unmanned systems with greater autonomy. RAND Corporation Research Report, Santa Monica, CA, USA, 2014. URL: RR600/RR626/RAND_RR626.pdf. – Title from the screen.

3. Bradshaw J.M., Hoffman R.R., Johnson M., Woods D.D. The seven deadly myths of “autonomous systems”. IEEE Intelligent Systems. 2013, Vol. 28, N. 3, pp. 54-61.

4. Groumpos P.P. Complex systems and intelligent control: issues and challenges. IFAC Proceedings Volumes. 2001, Vol. 34, N.8, pp. 29-36. URL: – Title from the screen.

5. Artificial Intelligence (AI): What is it and how does it work? URL: – Title from the screen.

6. Schubert J., Brynielsson J., Nilsson M., Svenmarck P. Artificial intelligence for decision support in command and control systems. Proceedings of the 23rd International Command and Control Research & Technology Symposium “Multi-Domain C2”, Pensacola, FL, USA, 2018. URL: – Title from the screen.

7. Cunneen M., Mullins M., Murphy F. Autonomous vehicles and embedded artificial intelligence: the challenges of framing machine driving decisions. Applied Artificial Intelligence. 2019, Vol. 33, N.8, pp. 706-731.

8. Phillips-Wren G. AI tools in decision making support systems: a review. International Journal on Artificial Intelligence Tools. 2012, Vol. 21, N.2. URL: _Ai_Tools_in_Decision_Making_Support_Systems_a_Review. – Title from the screen.

9. Petitti A., Di Paola D. A network of stationary sensors and mobile robots for distributed ambient intelligence. IEEE Intelligent Systems. 2016,Vol. 31. N.6, pp. 28-34.

Received 04.04.2021

Issue 2 (204), article 2


Cybernetics and Computer Engineering, 2021, 2(204)

CHABANIUK V.S.1,2, PhD (Phys.-Math.),
Senior Researcher of the Cartography Department, Institute of Geography,
Director of “Intelligence systems-GEO” LLC,
ORCID: 0000-0002-4731-7895

Head of Production of “Intelligence systems-GEO” LLC,
ORCID: 0000-0002-4927-4200

Junior Researcher of the Cartography Department, Institute of Geography,
ORCID: 0000-0001-5164-6272

1Institute of Geography, National Academy of Sciences of Ukraine
44, Volodymyrska str., 01054, Kyiv, Ukraine

2“Intelligence systems-GEO” LLC,
6/44, Mykilsko-Slobidska str., 02002, Kyiv, Ukraine


Introduction. The revolutionary changes in information technology of the last two decades allow the construction of electronic atlases (EA), the capabilities of which are fundamentally richer than the capabilities of “classic” EA. This is achieved through the use of the systemic properties of the new generation of EA, which are therefore named systemic. Systemic EA remain the simplest and most effective spatial information models of territorial systems allowing applying them for the decision of many practical problems.

The purpose of the paper is to formulate the need for systemic EA and describe methods for studying their systemic properties. These methods will be used to find and describe critical systemic properties without which EA cannot be systemic.The methods are founded on Relational Cartography and Model-Based Engineering.

Results. The evolution of “classic” EA is considered: from paper atlases and their images to analytical atlases. It is shown that on the imaginary border of classic and nonclassic EA there are already new generation EA — systemic EA. Both the theory and practice of such systemic EA have many unresolved problems. Some of them are described in the article. The authors believe that many problems can be solved by implementing the critical systemic properties of EA. Two methods are used to study the problems and to prove the results: Conceptual frameworks and Solutions frameworks. Both the methods themselves and the possibility of their application to find the critical systemic properties of the new generation of EA are described.

Conclusions. The main problems of electronic atlases of the new generation are described and their solution is offered by a method of Conceptual frameworks and a method of Solutions framework.

Keywords: systemic electronic atlas, Conceptual framework, Solutions framework, critical system property.

Download full text!

1. Hurni Lorenz. Atlas Information Systems, pp. 85-92. In Shekhar Shashi, Xiong Hui, Zhou Xun, Eds. Encyclopedia Of GIS.- Springer, 2017, 2nd Ed.- 2507 (2550) p.

2. Large Encyclopedic Dictionary. Ch. editor Prokhorov A.M. Soviet Encyclopedia, 1993. 1628 p. (in Russian)

3. Salichtchev K.A. Cartography. Textbook.- M.: MSU Publishing, 3rd Ed. 1990. 400 p. (in Russian)

4. Vozenilek Vit. Aspects of the Thematic Atlas Compilation, pp. 3-12. In: Brus Jan, Vondrakova Alena, Vozenilek Vit, Eds. Modern Trends in Cartography: Selected Papers of CARTOCON 2014.- Springer, 2015.- 534 p.

5., accessed 2021-may-05.

6. Etymological dictionary of the modern Russian language. Compiled by A.K. Shaposhnikov: in 2 volumes M.: Flinta, 2016, 2nd ed. stereotyped. V. 1.- 584 p. V. 2.- 576 p. (in Russian)

7. Chabaniuk Viktor. Relational Oartography: Theory and practice.- Kyiv: Institute of Geography of the NAS of Ukraine, 2018.- 525 p. (in Ukrainian)

8. Kraak Menno-Jan, Ormeling Ferjan. Cartography: Visualization of Geospatial Data.- Prentice Hall, 2010, 3rd Ed.- 198 (249) p.

9. Cauvin Colette, Escobar Francisco, Serradj Aziz. Thematic Cartography. Volume 3: New Approaches in Thematic Cartography.- ISTE-Wiley, 2010 (Adapted and updated from two volumes Cartographie Thematique 3 et 4.-LAVOISIER, 2008).- 291 (320) p.

10. Chabaniuk V., Dyshlyk O. Atlas Basemaps in Web 2.0 Epoch.- The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B4, 2016 XXIII ISPRS Congress, 12-19 July 2016, Prague, Czech Republic, pp. 611-618.

11. Sui Daniel Z., Holt James B. Visualizing and Analysing Public-Health Data Using Value-by-Area Cartograms: Toward a New Synthetic Framework.- Cartographica, Vol. 43, Iss. 1, 2008, pp. 3-20.

12. Berlyant, A.M. Geoiconics. M .: Astrea, 1996.- 208 p. (in Russian)

13. Roth Robert E. Interacting with Maps: The science and practice of cartographic interaction.- The Pennsylvania State University, Doctor of Philosophy (Geography) Dissertation. 2011. 215 (225) p.

14. Sieber, R. and Losang, E.: Current Challenges in Atlas Cartography, Abstr. Int. Cartogr. Assoc., 2, 32,,

15. Vozenilek, Vit. Atlases and Systems Theory within Systematic Cartography, Abstr. Int. Cartogr. Assoc., 1, 386, 2019.

16. Azocar Fernandez Pablo Ivan, Buchroithner Manfred Ferdinand. Paradigms in Cartography: An Epistemological Review of the 20th and 21st Centuries.-Springer, 2014.- 150 p.

17. Andreessen Marc. 2007. Analyzing the Facebook Platform, three weeks in [Blog post].- Accessed 2021-may-03.

18. Pulsifer Peter L., Taylor D.R. Fraser. The cartographer as mediator: Cartographic representation from shared geographic information, pp. 149-180. In Taylor D.R. Fraser (Ed.). Cybercartography: Theory and Practice (Modern Cartography Series 4).- Elsevier, 2005.- 574 p.

19. Parush A., Pulsifer P.L., Philps K., Dunn G. Understanding Through Structure: The Challenges of Informational and Navigation Architecture in Taylor D.R.F. and Caquard S. (eds.) Cybercartography. Special Issue of Cartographica on Cybercartography, 2006, 41 (1), 21-34.

20. Nunaliit,, accessed 2021-may-05.

21. Hayes Amos, Pulsifer Peter L., Fiset J.P. The Nunaliit Cybercartographic Atlas Framework, pp. 129-140. In Taylor D.R. Fraser, Editor. Developments in the Theory and Practice of Cybercartography: Applications and Indigenous Mapping (Modern Cartography Series 5).- Elsevier, 2014.- 364 p.

22. Reyes Maria del Carmen. Cybercartography from a Modelling Perspective, pp. 63-99. In: Taylor D.R. Fraser (Ed.). Cybercartography: Theory and Practice (Modern Cartography Series 4).- Elsevier, 2005. -574 p.

23. Reyes Carmen, Taylor D.R. Fraser, Martinez Elvia, Lopez Fernando. Geocybernetics: A new Avenue of Research in Geomatics?- Cartographica: The International Journal of Geographic Information and Geovisualization, 41(1), 2006, pp. 7-20.

24. Reyes C., Paras M. Geocybernetics: A pathway from empiricism to cognitive frameworks. En “GEOcibernetica: lnnovating in Geomatics for Society”. 2012.

25. Taylor D.R. Fraser. Some recent developments in the theory and practice of Cybercartography, pp. 55-68. In: Taylor D.R. Fraser, Anonby Erik, Murasugi Kumiko (Eds.). Further Developments in the Theory and Practice of Cybercartography (Modern Cartography Series 9).- Elsevier, 2019.- 525 p.

26. Lopez-Caloca F., Sanchez-Sandoval R., Reyes M., Lopez-Caloca A., 2014. From cybercartography to the paradigm of geocybernetics, pp. 17-32. In: Taylor D.R.F. (Ed.), Developments in the Theory and Practice of Cybercartography: Applications and Indigenous Mapping (Modern Cartography Series 5).- Elsevier, 2014.- 364 p.

27. Kobben Barend. Towards a National Atlas of the Netherlands as Part of the National Spatial Data Infrastructure.- The Cartographic Journal, Vol. 50, No. 3, 2013, pp. 225-231.

28. Bar H.R., Sieber R. Towards high standard interactive atlases. In: Proceedings of the International Cartographic Conference, Beijing, China, 2001, 7 p.

29. Sieber Rene, Serebryakova Marianna, Schnurer Raimund, Hurni Lorenz. Atlas of Switzerland Goes Online and 3D Concept, Architecture and Visualization Methods, pp. 171-184 // Gartner Georg, Jobst Markus, Huang Haosheng, Editors. Progress in Cartography. EuroCarto 2015 (Lecture Notes in Geoinformation and Cartography. Subseries: Publications of the International Cartographic Association (ICA)).- Springer, 2016.- 480 p.

30. Sieber Rene, Hollenstein Livia, Odden Benedicte, Hurni Lorenz. From Classic Atlas Design to Collaborative Platforms The SwissAtlasPlatform Project.- 25th International Cartographic Conference, Paris, 2011, 10 p.

31. The version as of 06.02.2021 is considered.

32. Sieber Rene, Schmid Christoph, Wiesmann Samuel. Smart legend smart atlas!- XXII International Cartographic Conference (ICC2005), 2005, 9 p.

33. Lechthaler Mirjanka. Interactive and Multimedia Atlas Information Systems as a Cartographic Geo-Communication Platform, pp. 382-402 / LNG&C2010, Cartography in Central and Eastern Europe. Selected Papers of the 1st ICA Symposium on Cartography for Central and Eastern Europe. Gartner Georg, Ortag Felix (Eds.).- Springer, 2010.- 570 p.

34. genderATlas (accessed 2021-may-05,

35. genderATlas fur die Schule (accessed 2021-may-05,

36. Riegler M., Wenk M.L., Aufhauser E., Ledermann F., Schmidt M., Gartner G.- genderATlas Osterreich Entwicklung eines zielgruppenorientierten Online-Tools. 2015.

37. Interactive National Atlas of Spain (, accessed 2021-may-05).

38. Interactive Atlas of Belgium (, accessed 2021-may-05).

39. Geoclip framework (, accessed 2021-may-05)

40. Huber S., Schmid C. 2ndatlas of Switzerland: interactive concepts, functionality and techniques.- In: Proceedings of the 21st International Cartographic Conference, Durban, ICA, 2003, pp. 1398-1405.

41. Alexander Christopher. The Timeless Way Of Building.- Oxford University Press, 1979.- 552 p.

42. Chabaniuk V.S., Dyshlyk O.P. Conceptual Framework of the Electronic version of the National Atlas of Ukraine.- Ukrainian Geographical Journal, 2014, 2, p. 58-68. (in Ukrainian)

43. Chabaniuk Viktor, Dyshlyk Oleksandr. GeoSolutions Framework Reinvented: Method, pp. 115-138 // in Analysis, Modeling and Control. Vol. 3, Collection of Scientific Papers of the Department of Applied Nonlinear Analysis. Edited by prof. Makarenko A.S.- Institute for Applied System Analysis at the Igor Sikorsky Kyiv Politechnic Institute, Kyiv, 2018.- 250 p.

44. Favre Jean-Marie. Towards a Basic Theory to Model Model Driven Engineering.- Proc. of the 3rd UML Workshop in Software Model Engineering (WiSME-2004), 2004, 8 p.

45. Bhatt Niraj. MVC vs. MVP vs. MVVM.- July 18, 2009. (accessesed 2021-may-05,

46. Implementing the MVVM Pattern Using the Prism Library 5.0 for WPF, 2014-may-05.-, accessed 2021-may-03.

47. Brambilla Marco, Cabot Jordi, Wimmer Manuel. Model-driven Software Engineering in Practice (Synthesis Lectures on Software Engineering).- Morgan & Claypool Publishers, 2nd, 2017.- 209 p.

48. Cultural heritage in the Atlas geoinformation system of sustainable development of Ukraine: L.G. Rudenko, Watering can, V.S. Chabaniuk and others. / for ed. L.G. Rudenko.- Kyiv: Institute of Geography of the National Academy of Sciences of Ukraine, 2018.- 172 p. (in Ukrainian)

Received 22.03.2021

Issue 2 (204), article 1


Cybernetics and Computer Engineering, 2021, 2(204)

GRITSENKO V.I., Corresponding Member of NAS of Ukraine,
Director of 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
ORCID: 0000-0003-4813-6153

BABAK O.V., PhD (Engineering), Senior Researcher,
Ecological Digital Systems Department
ORCID: 0000-0002-7451-3314

SUROVTSEV I.V., DSc (Engineering), Senior Researcher,
Head of the Ecological Digital Systems Department
ORCID: 0000-0003-1133-6207

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,
40, Acad. Glushkov av., Kyiv, 03187, Ukraine


Introduction. The 5G, 6G mobile technologies, which are actively developing in the world, and the Internet of Things (IoT), Big Data (BD), artificial intelligence (AI) are closely intertwined. It is important to understand the features of the relationship to effectively use them in new intelligent information technologies.

The purpose of the article is to highlight the most important features of the relationship, which are viewed on the basis of experience in implementing 5G and 6G technologies.

Results. the Internet of Things, industrial (IIoT), the Internet in total (IoE) use 5G, 6G technologies, as well as cloud, fog and boundary computing for high-speed communication with devices. Machine learning (ML), Date Mining, neural networks and simulation are used to analyze BD. AI algorithms are an integral part of all technologies, they allow you to intelligently connect and control 5G / 6G + IoT + BD + AI.

Conclusions. 5G and 6G high-speed networks, Internet of Things technology, cloud computing, big data analysis and artificial intelligence are necessary conditions for the further development of the digital economy.

Keywords: communication networks, big data, Internet of Things, artificial intelligence.

Download full text!


1. 5G and the impact it will have on our global economy. URL:

2. Gritsenko V.I., Surovtsev I.V., Babak O.V. 5G wireless communication system. Cybernetics and computer engineering. 2019, N. 3 (197), pp. 5-19. (in Ukrainian).

3. 6G. The Next Hyper-Connected. Experience for All. URL: downloads/researchareas/6G%20Vision.pdf

4. IoT technology stack – from IoT devices, sensors, actuators and gateways to IoT platforms IoT. URL:

5. Business guide to Industrial IoT (Industrial Internet of Things). URL:

6. Cloud, Fog and Edge Computing: Differences and Prospects for Technology Development. URL: (in Russian).

7. Development of 5G networks in the world. URL: Статья:Развитие_сетей_5G_в_мире (in Russian).

8. 6G – the sixth generation of mobile communications. URL: Статья:6G_(шестое_поколение_мобильной_связи) (in Russian).

9. Big Date. URL:

10. W. Saad, M. Bennis and M. Chen, A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems. IEEE Network. Vol. 34, no. 3, pp. 134-142, May/June 2020,

11. Y. Zhao, G. Yu, H. Xu. 6G Mobile Communication Network: Vision, Challenges and Key Technologies SCIENTIA SINICA Information. 2019, vol. 49, issue 8, pp. 963-987 (in Chinese), DOI:

12. Artificial Intelligence. URL: (in Russian).

13. Benjamin Jokela. Merging Artificial Intelligence and the Internet of Things. Control Engineering Russia. 2019, N2 (80), pp. 70-72. URL: (in Russian).

14. Accelerating city transformation using frontier technologies. A U4SSC deliverable. URL:

15. 6G will be 8000 times faster than 5G. URL: 2020/02/03/6g-8000-5g.html (in Russian).

16. Rozenberg I.N. Intelligent control. Modern management technology. ISSN 2226-9339. 2017, N4 (76). URL: Russian).




Received 20.03.2021