Issue 4 (210), article 5

DOI:https://doi.org/10.15407/kvt210.04.080

Cybernetics and Computer Engineering, 2022, 4(210)

KUTSIAK O.A., PhD (Engineering),
Senior Researcher of the Bioelectrical Control & Medical Cybernetics Department
https://orcid.org/0000-0003-2277-7411
e-mail: spirotech85@ukr.net

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

MOBILE SYSTEM FOR THE PATIENT’S MOTOR FUNCTIONS STATE DIAGNOSTICS

Introduction. The diagnostics of motor functions state plays an important role both as a result of the central nervous system impairments (stroke etc.) and as a result of injuries, traumas, etc. As mobile devices expand the possibilities of modern medicine, the actual task is the synthesis of an effective mobile system for the motor functions state diagnosing at various stages of rehabilitation.

The purpose of the paper is to develop a mobile system for personalized motor functions diagnostics for their and speech motility rehabilitation, which functional capabilities contribute to the rehabilitation effectiveness increasing and usability both in clinical and home conditions, as well as in the fields conditions.

The results. The recovery of motor and speech functions, in particular for persons after injuries and traumas, as well as usage by the patient at home, put forward requirements for personalization, mobility, ease of perception and usability of information given to the user.

According to the requirements, the interface of mobile system for the motor function diagnostics was developed: set of user tasks was defined, scenario was developed for the patient to test own motor functions within the mobile system. The relation database’s infologic model has been developed for the storage and accumulation of patients’ motor functions data and following analysis by a physician.

The algorithm for personalized motor functions diagnosing has been developed. It is based on expanded range of evidence criteria are not taken into account by known analogues. The algorithm is implemented in the MovementTestStroke 1.2 mobile system with taking into account the interface and relation database. Such a system provides objectification of assessment, reduction of the probability of a physician’s error and urgency in diagnostic and treatment decisions-making, provides necessary and sufficient information to the user in a convenient digital and graphical forms, simplifies for the physician the motor functions state analyzing and the personalized treatment strategy creating.

Conclusions. The mobile system for motor function diagnostics can be used in clinical, home and field conditions, not only to assess the motor functions state affected by central nervous system pathologies, but also by injuries and traumas, etc., which creates the basis for personalized, mobile, urgent diagnostic and treatment decisions-making by the physician.

Keywords: diagnostics, motor functions, quantitative assessment, criteria, algorithm, software system, mobile system, stroke, injuries

Download full text!

REFERENCES

1. Ginex V. et al. Motor recovery in post-stroke patients with aphasia: the role of specific linguistic abilities, Topics in Stroke Rehabilitation, 2017. Vol. 24. Issue 6. pp. 428–434. URL: https://doi.org/ HYPERLINK “https://doi.org/10.1080/10749357.2017.1305654” 10.1080/10749357.2017.1305654 (Last access: 1.10.2022).

2. Vovk M.I., Kutsiak O.A. Information technologies for muscle functions control. Retrospective analysis and development prospects. Cybernetics and Computer Engineering. 2022. № 1 (207). pp. 87–100. (in Ukrainian).

3. Vovk, M.I., Halian, Ye.B., Kutsiak, O.A. Computer Software & Hardware Complex for Personal Oral Speech Restoration after a Stroke. Sci. innov. 2020. Vol. 16, № 1(91). pp. 54–68. https://doi.org/10.15407/scine15.05.054 (Last access: 1.10.2022).

4. Maceira-Elvira P. et al. Wearable technology in stroke rehabilitation: towards improved diagnosis and treatment of upper-limb motor impairment. Journal of NeuroEngineering and Rehabilitation (2019) 16:142. https://doi.org/10.1186/s12984-019-0612 (Last access: 1.10.2022).

5. Adams J.L. et al. Digital Technology in Movement Disorders: Updates, Applications, and Challenges. Current Neurology and Neuroscience Reports. 2021. Vol. 21. URL: https://doi.org/ 10.1007/s11910-021-01101-6 (Last access: 1.10.2022).

6. Kostiuk M. Trauma Assessment.  https://www.statpearls.com/articlelibrary/viewarticle/30531 (Last access: 1.10.2022).

7. Corona F. Quantitative assessment of upper limb motor impairments in people with neurological diseases. https://iris.unica.it/bitstream/11584/255954/2/tesi%20di%20dottorato_Federica%20Corona.pdf

8. Hassen D.B. Mobile-aided diagnosis systems are the future of health care. EMHJ. Vol. 26. No. 9. 2020. pp. 1135–1140

9. Rahimi S.A. et al. Are mobile health applications useful for supporting shared decision making in diagnostic and treatment decisions? Global Health Action. 2017. Vol. 10. URL: https://doi.org/10.1080/16549716.2017.1332259 (Last access: 1.10.2022).

10. Motor function diagnosis apparatus and method, and program: patent JP6433805B2. Tottori University; publ. date 05.12.2018

11. Bingyu Pan et al. Motor Function Assessment of Upper Limb in Stroke Patients. Journal of Healthcare Engineering. 2021. Vol. 2021. URL: https://doi.org/10.1155/ 2021/6621950 (Last access: 1.10.2022).

12. Maceira-Elvira P. et al. Wearable technology in stroke rehabilitation: towards improved diagnosis and treatment of upper-limb motor impairment. Journal of NeuroEngineering and Rehabilitation. 2019. Vol. 16. URL: https://jneuroengrehab.biomedcentral.com/articles/10.1186/s12984-019-0612-y (Last access: 1.10.2022).

13. Jalloul N. Wearable sensors for the monitoring of movement disorders. Biomedical journal. 2018. Vol. 41. pp. 249–253. URL: https://doi.org/10.1016/j.bj.2018.06.003 (Last access: 1.10.2022).

14. Movement monitoring system and apparatus for objective assessment of movement disorders: patent US 2011/0213278 A1. Fay Horak et al. publ. date 01.09.2011.

15. Vovk M.I., Kutsiak O.A., Lauta A.D., Ovcharenko M.А. Information Assistance of Researches on the Dynamics of Movement Restoration After the Stroke. Kibernetika i vyčislitel`naâ tehnika.. 2017. № 3 (189). pp. 61–78. (in Ukrainian).

16. Reliability and validity of the Medical Research Council (MRC) scale and a modified scale for testing muscle strength in patients with radial palsy. Tatjana Paternostro-Sluga et al. J Rehabil Med. Vol 40. Issue 8 URL: https://www.medicaljournals.se/jrm/content/abstract/ 10.2340/16501977-0235 (Last access: 1.10.2022).

17. Collin C., Wade D. Assessing motor impairment after stroke: a pilot reliability study. J Neurol Neurosurg Psychiatry. 1990. Vol. 53(7). pp. 576–579.

18. Schaechter J.D. et al. Motor Recovery and Cortical Reorganization after Constraint-Induced Movement Therapy in Stroke Patients: A Preliminary Study. Neurorehabilitation and Neural Repair. 2002. Vol. 16(4). URL: https://doi.org/10.1177/154596830201600403(Last access: 1.10.2022).

19. Chino N. et al. Stroke Impairment Assessment Set (SIAS). Jpn J Rehabil Med. 1994. Vol. 31. No. 2. URL: https://doi.org/10.2490/jjrm1963.31.119 (Last access: 1.10.2022).

20. Jarm T., Kramar P., Županič A. Rating Stroke Patients Based on Movement Analysis. IFMBE Proceedings 16. 2007. pp. 266–269, URL: URL: https://doi.org/10.1007/978-3-540-73044-6_66 (Last access: 1.10.2022).

21. Olesh E.V. et al. Automated Assessment of Upper Extremity Movement Impairment due to Stroke. Plos One. 2014. Vol. 9. Issue 8. URL: https://doi.org/10.1371/journal.pone.0104487 (Last access: 1.10.2022).

22. A motor function test system: patent WO2005/039412 A1. Panella L. et al. publ. date 06.05.2005.

23. Vovk М.І., Kutsyak O.A. Software module for personal diagnostics of motor functions after stroke. Cybernetics and Computer Engineering. 2019. № 4 (198). рр. 62–77

24. Vovk М.І., Kutsyak О.А. AI-technology of motor functions diagnostics after a stroke. Cybernetics and Computer Engineering. 2021. № 2 (204). pp. 84-100.

25. Booch G., Rumbaugh J., Jacobson I. The Unified Modeling Language User Guide. Boston, 1999. 482 p.

Received 30.09.2022

Issue 4 (210), article 4

DOI:https://doi.org/10.15407/kvt210.04.060

Cybernetics and Computer Engineering, 2022, 4(210)

FAINZILBERG L.S., DSc (Engineering), Professor,
Chief Researcher of the Intelligent Automatic Systems Department,
https://orcid.org/0000-0002-3092-0794
e-mail: fainzilberg@gmail.com

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

MOBILE INFORMATION TECHNOLOGY FOR ASSESSING THE ADAPTATION CAPABILITIES OF THE HUMAN BODY UNDER CONDITIONS OF INCREASED LOADS

Introduction. An important role in assessing the body’s adaptive reserves under conditions of physical and emotional stress is played by information obtained with the help of special tests. Such tests should be convenient enough to quickly obtain the result, including at home and in the field.

The purpose of the paper is to develop the principles of building mobile IT for the operational assessment of the adaptive capabilities of the human body in the field and at home and the implementation of IT on a smartphone.

Methods. To assess tolerance to physical and emotional stress, a cognitive graphical image is constructed that integrally characterizes the regulatory patterns of changes in the physiological parameters of the heart rate, calculated in three states: at rest, at the height of the load and during restitution.

Results. It is shown that reliable information about the pulse wave (finger photoplethysmogram) during testing can be obtained using the built-in camera of a smartphone without additional technical means based on the developed original computational procedures that provide for the selection of reliable and unreliable cycles. To manage the physical load on the internal processor of the smartphone, a virtual teacher animation procedure is implemented, which demonstrates the correct technique and sets the required pace of the load. The emotional load management module is based on the Stroop effect and boils down to doing mental work under time pressure. The experiments confirmed that the cognitive graphic image makes it possible almost instantly to identify physiological indicators that demonstrate an inadequate response of the body to the load and rest after it.

Conclusions. The developed technology for assessing the adaptive capabilities of the human body under conditions of increased physical and emotional stress provides reliable testing in the field and at home, and the test results can be interpreted by a person without special medical education.

Keywords: information technology on a smartphone, regulatory patterns, body tolerance to physical and emotional stress.

Download full text!

REFERENCES

  1. Lazurenko S. I., Biloshitskyi S. V., Semenov A. M. Adaptation and adaptive capabilities of man. Actual problems of education and upbringing of people with special needs. Collection of scientific works. 2013, No. 11(13). P.194207. http://ap.uu.edu.ua/article/32 (In Ukrainian).
  2. Korkushko O. V., Pisaruk A. V., Shatilo V. G., Lishnevskaya V. Yu., Chebotarev N. D., Analysis of heart rate variability in clinical practiceю. Age aspects. K: Institute of Gerontology of the Academy of Medical Sciences, 2002. 189 p. (In Russian).
  3. Corr P.B., Yamada K.A.,Witkowski F.V. Mechanisms controlling cardiac autonomic function and their relation to arihythmogenesis. The Heart and Cardiovascular System.  1986. N-Y: Raven Press. 13431403.
  4. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Heart Rate Variability. Standards of Measurement, Physiological Interpretation and Clinical Use. Circulation. 1996. Vol. 93. P. 10431065.
  5. Prokopiev N.Ya., Kolunin E.T., Gurtovaya M.N., Mitasov D.I. Physiological approaches to the evaluation of functional stress tests. Basic research. 2014. No. 2. P. 146–150. (In Russian).
  6. Sidorov S.P., Perkhurov A.M., Stefan O.S. The significance of the correct implementation of the functional test methodology with 20 squats in assessing the state of the cardiovascular system of young athletes. Physical education in prevention, treatment and rehabilitation. 2009. No. 2 (29). pp. 3944. (In Russian).
  7. Minina E.N., Fainzilberg L.S., Orikhovskaya K.B. Qualitative assessment of the adaptive reserves of the cardiovascular system based on the regulatory patterns of the reference cycle of a single-channel ECG // Journal “Modern Science. Topical issues of theory and practice. Series natural and technical sciences. – 2016. – No. 8. P. 103113. (In Russian).
  8. Baevsky R.M., Ivanov G.G., Chireikin L.V. Analysis of heart rate variability using various electrocardiographic systems (guidelines). Bulletin of arrhythmology. 2001.
    No. 24. S. 6587. (In Russian).
  9. Alian A.A., Shelley K.H. Photoplethysmography. Best Practice & Research. Clinical Anaesthesiology. 2014. Vol. 28, No. 4. P. 395–406. https://doi.org/10.1016/j.bpa.2014.08.006.
  10. Papon M.T.I., Ahmad I., Saquib N., Rahman A. Non-invasive heart rate measuring smartphone applications using on-board cameras: A short survey.  Proceeding of 2015 International Conference on Networking Systems and Security.  Dhaka, 2015. P. 16.
  11. Laure D., I. Paramonov I. Improved Algorithm for Heart Rate Measurement Using Mobile Phone Camera. Proceedings of the 13th Conference of Open Innovations Association FRUCT and 2nd Seminar on e-Tourism for Karelia and Oulu Region. 2013. P. 8593.  HYPERLINK “https://doi.org/10.23919/FRUCT.2013.8124232” https://doi.org/10.23919/FRUCT.2013.8124232
  12. Boland P. The emerging role of cell phone technology in ambulatory care. Journal of Ambulatory Care Managemen. 2007. Vol. 30. No. 2. P. 126133. doi: 10.1097/ 01.JAC.0000264602.19629.84.
  13. Jonathan E., Leahy M. Investigating a smartphone imaging unit for photoplethysmography. Physiol Measurements. 2010. Vol. 31. No. 11. P. 7983. doi: 10.1088/0967-3334/ 31/11/N01. Epub 2010 Sep 24.
  14. Rong-Chao Peng et al. Investigation of Five Algorithms for Selection of the Optimal Region of  Interest in Smartphone Photoplethysmography.  Journal of Sensors. Volume 2016. Article ID 6830152.  HYPERLINK “https://doi.org/10.1155/2016/6830152” \t “_blank” https://doi.org/10.1155/2016/6830152
  15. Trofimov P.A., Purtov K.S., Kublanov V.S. Measuring human heart rate variability using a smartphone camera. Computer Image Analysis: Intelligent Solutions in Industrial Networks (CAI-2016): Collection of scientific papers based on the materials of the International Conference May 5-6, 2016. Ekaterinburg: UMC UPI, 2016. P. 134137.
  16. Zenkin A.A. Cognitive computer graphics. M.: Nauka, 1991. 192 p. (In Russian).
  17. Pospelov D.A. Cognitive graphics are a window to a new world. Software products and systems. 1992. No. 2. P. 4–6. (In Russian).
  18. Fainzilberg L., Potapova T. Computer Analysis and Recognition of Cognitive Phase Spase Electro-Cardio Graphic Image // Proc. of the 6th Int. Conf. on Computer Analysis of Images and Patterns (CAIPS’95). Prague (Czech Republic). 1995. P. 668–673.
  19. Fainzilberg L.S., Orikhovskaya K.B. Information technology for assessing the adaptive reserves of the body in the field. Cybernetics and computer technology. 2015. Issue. 181. S. 422.
  20. Fainzilberg L.S. A method of assessing the adequacy of the body’s response to stress. Patent of Ukraine for the invention No. 116548. Bull. No. 27, 2018. (In Ukrainian).
  21. Fainzilberg L.S. The method of obtaining a dynamic series of cardio intervals based on the pulse wave. Patent of Ukraine for the invention No. 126520. Bull. No. 43, 2022. (In Ukrainian).
  22. Fainzilberg L.S. Intelligent digital medicine tools for home use. Clinical informatics and telemedicine. 2020. Vol. 15. Issue. 16. S. 4556. (In Russian). http://doi.org/10.31071/ kit2020.16.03.
  23. Lupanov VP, Nuraliev EYu, Sergienko IV. Funkcionalnye nagruzochnye proby
    v diagnostike ishemicheskoj bolezni serdcza, ocenke riska oslozhnenij i prognoza. 2016. M.: Izd-vo OOO «PatiSS». (In Russian)
  24. Aronov D.M., Lupanov V.P. Functional tests in cardiology. M.: Medpress-inform, 2002. 296 p. (In Russian)
  25. Halson S.L., Jeukendrup A.E. Does Overtraining Exist?An Analysis of Overreaching and Overtraining Research. Sports Med. 2004. Vol. 34, No. 14. P. 967–981.
    doi: 10.2165/00007256-200434140-00003.
  26. Prokopiev N.Ya., Kolunin E.T., Gurtovaya M.N. et al. Physiological approaches to the assessment of functional stress tests. Basic research. 2014. No. 2. P. 146–150.
    (In Russian)
  27. Sidorov S.P., Perkhurov A.M., Stefan O.S. The value of the correct implementation of the functional test technique with 20 squats in assessing the state of the cardiovascular system of young athletes. Physical education in prevention, treatment and rehabilitation. 2009. No. 2. P. 3944. (In Russian)
  28. Trigranyan R.A. Stress and its importance for the body. M.: Nauka, 1988. 176 p. (In Russian)
  29. Kornatsky V.M. Tretyak I.V. Influx of psychoemotional disorders on the development and overcoming of cardiovascular pathology. Ukrainian Cardiology Journal “Ukrcardio”.2008. No. 6. P. 94100. (In Russian)
  30. Fainzilberg L.S., Kondratyuk T.V., Semergey N.A. ANTISTRESS is a new information technology for managing the regulatory systems of the human body based on biofeedback. Control systems and machines. 2011. No. 3. P. 6272. (In Russian)
  31. Williams, J.M.G., Mathews, A., MacLeod, C. The emotional Stroop task and psychopathology. Psychol. Bull. 1996. No. 120. P. 3–24.. HYPERLINK “https://psycnet.apa.org/doi/10.1037/0033-2909.120.1.3” \t “_blank”https://doi.org/10.1037/0033-2909.120.1.3.
  32. Esgalhado G, Pereira H., Silva P.Adaptation of an Emotional Stroop Test for Screening of Suicidal Ideation in Portugal. Behav. Sci. 2022, No. 12, P. 281292. https://doi.org/ 10.3390/bs12080281
  33. Lamers M.J.M., Roelofs A., Rabeling-Keus I.M. Selective attention and response set in the Stroop task. Memory & Cognition 2010, No. 38, P. 893–904. https://doi.org/10.3758/ MC.38.7.893
  34. Gritsenko V.I., Fainzilberg L.S., Kravchenko A.N., Korchinskaya Z.A., Orikhovskaya K.B., Pasko V.S., Stanislavskaya S.S. Cognitive graphic images in the task of evaluating the body’s response to stress by the phasegraphy method. Control systems and machines. 2016. No. 6, pp. 2433.  HYPERLINK “http://dspace.nbuv.gov.ua/handle/123456789/117310” http://dspace.nbuv.gov.ua/handle/123456789/117310. (In Russian).

Received 16.09.2022

Issue 4 (210), article 3

DOI:https://doi.org/10.15407/kvt210.04.038

Cybernetics and Computer Engineering, 2022, 4(210)

VOLKOV O.Ye.1, PhD (Engineering), Leading Researcher,
Director
https://orcid.org/0000-0002-5418-6723
e-mail: alexvolk@ukr.net

VOLOSHENYUK D.O.1, PhD (Engineering),
Senior Researcher of the Intelligent Control Department
https://orcid.org/0000-0003-3793-7801
e-mail: p-h-o-e-n-i-x@ukr.net

ODARCHENKO R.S.2, PhD (Engineering), Assistant Professor,
Professor of the Telecommunication and Radio-electronic Systems Department
https://orcid.org/0000-0002-7130-1375
e-mail: odarchenko.r.s@ukr.net

BONDAR S.O.1, PhD student,
Researcher of the Intelligent Control Department
https://orcid.org/0000-0003-4140-7985
e-mail: orangearrows@bigmir.net

SEMENOH R.V.1, PhD student,
Junior Researcher of the Intelligent Control Department
https://orcid.org/0000-0002-6714-0644
e-mail: ruslansemenog20@icloud.com

SHCHERBINA O.A.2, DSc (Engineering),
Associate Professor of the Department of Electronics,
Robotics and Monitoring Technologies and Internet of Things
https://orcid.org/0000-0002-6058-2749
e-mail: kafre@ukr.net

1International Research and Training Center for Information Technologies and Systems of the National Academy of Sciences of Ukraine and the Ministry of Education and Science of Ukraine,
40, Akad. Hlushkov av., Kyiv, 03680, Ukraine

2National Aviation University,
1, Lubomyr Husar av., Kyiv, 03058, Ukraine.

ANALYSIS OF MULTIPLE INPUT MULTIPLE OUTPUT SYSTEM DESIGNS FOR BASE STATIONS AND 5G WIRELESS NETWORK MOBILE APPS

Introduction. Because of the fast technological development, cellular connection networks are becoming such type of the network domain that could support several frequency ranges of different cellular generations and it needs to have an optimal antenna design structure to have the most efficient signal receiving. So the multiple input multiple output (MIMO) antennas were chosen as the most appropriate instrument to operate at 5G mobile networks. According to purpose, all 5G cellular connection antenna systems could be relatively divided into two types: base station antenna systems and antennas for mobile apps. In one’s turn, dependently from the frequency range, each of defined types include two subgroups, such as: lower than 6 GHz and higher than 6 GHz. 5G base station MIMO antenna systems for the range that is lower than 6 GHz are often integrating to the 4G antenna systems that simplifies its accomplishment and its placing on the cell tower.

Purpose of the paper is to discover good decoupling and carrying capacity securing in moderate dimensions of the antenna elements during the antenna designing for the 5G mobile apps.  5G system architecture depends on universal antenna design for the millimeter range tasks solving. One of such tasks is large losses overcome on the way of millimeter wave spreading at the free space that weaken signal power significantly.

Results of the research is in definition of the most efficient decoupling and carrying capacity support of the MIMO antenna system. Total dimensions, compact location and optimal work parameters are also reasons for the best MIMO antenna system design definition for its usage for the 5G wireless network mobile applications.

Conclusion. The most optimal structure design for MIMO antenna system could be a real step forward at cellular technologies. Using advantages of all previous network generations, the brand new MIMO wireless antenna system have abilities to work with minimal losses and in the most flexible and frequency-optimal way ever. Development also demonstrates influence of the dimensions on the base station block location and universality of its usage complexly with antennas of, practically, any possible design.

Keywords: cellular network, base stations, multiple input multiple output, 5G.

Download full text!

REFERENCES

1. T.S. Rappaport et al., “Millimeter Wave Mobile Communications for 5G Cellular: It Will Work!,” in IEEE Access, vol. 1, pp. 335-349, 2013, doi: 10.1109/ACCESS.2013.2260813.

2. H.T. Chattha, “4-Port 2-Element MIMO Antenna for 5G Portable Applications,” in IEEE Access, vol. 7, pp. 96516-96520, 2019, doi: 10.1109/ACCESS.2019.2925351.

3. J. Guo, L. Cui, C. Li and B. Sun, “Side-Edge Frame Printed Eight-Port Dual-Band Antenna Array for 5G Smartphone Applications,” in IEEE Transactions on Antennas and Propagation, vol. 66, no. 12, pp. 7412-7417, Dec. 2018, doi: 10.1109/TAP.2018.2872130.

4. N.O. Parchin et al., “Eight-Element Dual-Polarized MIMO Slot Antenna System for 5G Smartphone Applications,” in IEEE Access, vol. 7, pp. 15612-15622, 2019, doi: 10.1109/ACCESS.2019.2893112.

5. M. Ikram, N. Nguyen-Trong and A. Abbosh, “Multiband MIMO Microwave and Millimeter Antenna System Employing Dual-Function Tapered Slot Structure,” in IEEE Transactions on Antennas and Propagation, vol. 67, no. 8, pp. 5705-5710, Aug. 2019, doi: 10.1109/TAP.2019.2922547.

6. Y. -L. Ban, C. Li, C. -Y. -D. Sim, G. Wu and K. -L. Wong, “4G/5G Multiple Antennas for Future Multi-Mode Smartphone Applications,” in IEEE Access, vol. 4, pp. 2981-2988, 2016, doi: 10.1109/ACCESS.2016.2582786.

7. WRC-15 Press Release. (2019). World Radiocommunication Conference Allocates Spectrum for Future Innovation. Accessed: 27, 2015. Online. . Available: http://www.itu.int/net/pressof-ce/press-releases/2015/56.aspx.

8. M. Matinmikko-Blue, S. Yrjölä, V. Seppänen, P. Ahokangas, H. Hämmäinen and M. Latva-Aho, “Analysis of Spectrum Valuation Elements for Local 5G Networks: Case Study of 3.5-GHz Band,” in IEEE Transactions on Cognitive Communications and Networking, vol. 5, no. 3, pp. 741-753, Sept. 2019, doi: 10.1109/TCCN.2019.2916309.

9. E. Lagunas, C. G. Tsinos, S. K. Sharma and S. Chatzinotas, “5G Cellular and Fixed Satellite Service Spectrum Coexistence in C-Band,” in IEEE Access, vol. 8, pp. 72078-72094, 2020, doi: 10.1109/ACCESS.2020.2985012.

10. N. Hussain, M. Jeong, A. Abbas, T. Kim and N. Kim, “A Metasurface-Based Low-Profile Wideband Circularly Polarized Patch Antenna for 5G Millimeter-Wave Systems,” in IEEE Access, vol. 8, pp. 22127-22135, 2020, doi: 10.1109/ACCESS.2020.2969964.

11. R. Ullah, S. Ullah, R. Ullah, F. Faisal, I. B. Mabrouk and M. J. A. Hasan, “A 10-Ports MIMO Antenna System for 5G Smart-Phone Applications,” in IEEE Access, vol. 8, pp. 218477-218488, 2020, doi: 10.1109/ACCESS.2020.3042750.

12. Z. Wu, B. Wu, Z. Su and X. Zhang, “Development challenges for 5G base station antennas,” 2018 International Workshop on Antenna Technology (iWAT), 2018, pp. 1-3, doi: 10.1109/IWAT.2018.8379163.

13. E.G. Larsson, O. Edfors, F. Tufvesson and T.L. Marzetta, “Massive MIMO for next generation wireless systems,” in IEEE Communications Magazine, vol. 52, no. 2, pp. 186-195, February 2014, doi: 10.1109/MCOM.2014.6736761.

14. H.T. Chattha, M.K. Ishfaq, B.A. Khawaja, A. Sharif and N. Sheriff, “Compact Multiport MIMO Antenna System for 5G IoT and Cellular Handheld Applications,” in IEEE Antennas and Wireless Propagation Letters, vol. 20, no. 11, pp. 2136-2140, Nov. 2021, doi: 10.1109/LAWP.2021.3059419.

15. L.Yang and T.Li, ‘‘Box-folded four-element MIMO antenna system for LTE handsets,’’ Electron. Lett., vol. 51, no. 6, pp. 440–441, Mar. 2015.  HYPERLINK “https://doi.org/10.1049/el.2014.3757”  doi: 10.1049/el.2014.3757.

16. M. Abdullah et al., “Future Smartphone: MIMO Antenna System for 5G Mobile Terminals,” in IEEE Access, vol. 9, pp. 91593-91603, 2021, doi: 10.1109/ACCESS.2021.3091304.

17. Y. Wang and Z. Du, “A Wideband Printed Dual-Antenna System With a Novel Neutralization Line for Mobile Terminals,” in IEEE Antennas and Wireless Propagation Letters, vol. 12, pp. 1428-1431, 2013, doi: 10.1109/LAWP.2013.2287199.

18. C. Gao, X.-Q. Li, W.-J. Lu, and K.-L. Wong, ‘‘Conceptual design and implementation of a four-element MIMO antenna system packaged within a metallic handset,’’ Microw. Opt. Technol. Lett., vol. 60, no. 2, pp. 436–444, Feb. 2018. doi: HYPERLINK “https://doi.org/10.1002/mop.30978” 10.1002/mop.30978.

19. P. Xingdong, H. Wei, Y. Tianyang and L. Linsheng, “Design and implementation of an active multibeam antenna system with 64 RF channels and 256 antenna elements for massive MIMO application in 5G wireless communications,” in China Communications, vol. 11, no. 11, pp. 16-23, Nov. 2014, doi: 10.1109/CC.2014.7004520.

20. Y. Gao, R. Ma, Y. Wang, Q. Zhang and C. Parini, “Stacked Patch Antenna With Dual-Polarization and Low Mutual Coupling for Massive MIMO,” in IEEE Transactions on Antennas and Propagation, vol. 64, no. 10, pp. 4544-4549, Oct. 2016, doi: 10.1109/TAP.2016.2593869.

21. M.V. Komandla, G. Mishra and S.K. Sharma, “Investigations on Dual Slant Polarized Cavity-Backed Massive MIMO Antenna Panel With Beamforming,” in IEEE Transactions on Antennas and Propagation, vol. 65, no. 12, pp. 6794-6799, Dec. 2017, doi: 10.1109/TAP.2017.2748239.

22. A. Alieldin, Y. Huang, M. Stanley, S.D. Joseph and D. Lei, “A 5G MIMO Antenna for Broadcast and Traffic Communication Topologies Based on Pseudo Inverse Synthesis,” in IEEE Access, vol. 6, pp. 65935-65944, 2018, doi: 10.1109/ACCESS.2018.2878639.

23. M. Kaboli, M.S. Abrishamian, S.A. Mirtaheri and S.M. Aboutorab, “High-Isolation
XX-Polar Antenna,” in IEEE Transactions on Antennas and Propagation, vol. 60, no. 9, pp. 4046-4055, Sept. 2012, doi: 10.1109/TAP.2012.2207059.

24. Y.He, Z. Pan, X. Cheng, Y.He, J. Qiao and M.M. Tentzeris, “A Novel Dual-Band, Dual-Polarized, Miniaturized and Low-Profile Base Station Antenna,” in IEEE Transactions on Antennas and Propagation, vol. 63, no. 12, pp. 5399-5408, Dec. 2015, doi: 10.1109/TAP.2015.2481488.

25. Y. Cui, R. Li and P. Wang, “Novel Dual-Broadband Planar Antenna and Its Array for 2G/3G/LTE Base Stations,” in IEEE Transactions on Antennas and Propagation, vol. 61, no. 3, pp. 1132-1139, March 2013, doi: 10.1109/TAP.2012.2229377.

26. H. Huang, Y. Liu and S. Gong, “A Novel Dual-Broadband and Dual-Polarized Antenna for 2G/3G/LTE Base Stations,” in IEEE Transactions on Antennas and Propagation, vol. 64, no. 9, pp. 4113-4118, Sept. 2016, doi: 10.1109/TAP.2016.2589966.

27. R. Wu and Q. -X. Chu, “A Compact, Dual-Polarized Multiband Array for 2G/3G/4G Base Stations,” in IEEE Transactions on Antennas and Propagation, vol. 67, no. 4, pp. 2298-2304, April 2019, doi: 10.1109/TAP.2019.2902652.

28. W. Wu, H. Peng and J. Mao, “A new compact dual-polarized co-axial full-band antenna for 2G/3G/LTE base station applications,” 2017 IEEE Electrical Design of Advanced Packaging and Systems Symposium (EDAPS), 2017, pp. 1-3, doi: 10.1109/EDAPS.2017.8276911.

29. H. Huang, X. Li and Y. Liu, “A Novel Vector Synthetic Dipole Antenna and Its Common Aperture Array,” in IEEE Transactions on Antennas and Propagation, vol. 66, no. 6, pp. 3183-3188, June 2018, doi: 10.1109/TAP.2018.2819894.

30. Y. Liu, S. Wang, N. Li, J.-B. Wang and J. Zhao, “A Compact Dual-Band Dual-Polarized Antenna With Filtering Structures for Sub-6 GHz Base Station Applications,” in IEEE Antennas and Wireless Propagation Letters, vol. 17, no. 10, pp. 1764-1768, Oct. 2018, doi: 10.1109/LAWP.2018.2864604.

31. A. Alieldin et al., “A Triple-Band Dual-Polarized Indoor Base Station Antenna for 2G, 3G, 4G and Sub-6 GHz 5G Applications,” in IEEE Access, vol. 6, pp. 49209-49216, 2018, doi: 10.1109/ACCESS.2018.2868414.

32. Y. Zhu, Y. Chen and S. Yang, “Integration of 5G Rectangular MIMO Antenna Array and GSM Antenna for Dual-Band Base Station Applications,” in IEEE Access, vol. 8, pp. 63175-63187, 2020, doi: 10.1109/ACCESS.2020.2984246.

33. Y. Zhu, Y. Chen and S. Yang, “Decoupling and Low-Profile Design of Dual-Band Dual-Polarized Base Station Antennas Using Frequency-Selective Surface,” in IEEE Transactions on Antennas and Propagation, vol. 67, no. 8, pp. 5272-5281, Aug. 2019, doi: 10.1109/TAP.2019.2916730.

34. A.I. Sulyman, A. Alwarafy, G.R. MacCartney, T.S. Rappaport and A. Alsanie, “Directional Radio Propagation Path Loss Models for Millimeter-Wave Wireless Networks in the 28-, 60-, and 73-GHz Bands,” in IEEE Transactions on Wireless Communications, vol. 15, no. 10, pp. 6939-6947, Oct. 2016, doi: 10.1109/TWC.2016.2594067.

35. L. Wei, R. Q. Hu, Y. Qian and G. Wu, “Key elements to enable millimeter wave communications for 5G wireless systems,” in IEEE Wireless Communications, vol. 21, no. 6, pp. 136-143, December 2014, doi: 10.1109/MWC.2014.7000981.

36. T.S. Rappaport, J.N. Murdock and F. Gutierrez, “State of the Art in 60-GHz Integrated Circuits and Systems for Wireless Communications,” in Proceedings of the IEEE, vol. 99, no. 8, pp. 1390-1436, Aug. 2011, doi: 10.1109/JPROC.2011.2143650.

37. S.F. Jilani and A. Alomainy, “Millimetre-wave T-shaped MIMO antenna with defected ground structures for 5G cellular networks,” IET Microwaves, Antennas Propag., vol. 12, no. 5, pp. 672-677, 2018, doi: 10.1049/iet-map.2017.0467

38. S. Li, T. Chi, Y. Wang and H. Wang, “A Millimeter-Wave Dual-Feed Square Loop Antenna for 5G Communications,” in IEEE Transactions on Antennas and Propagation, vol. 65, no. 12, pp. 6317-6328, Dec. 2017, doi: 10.1109/TAP.2017.2723920.

39. H.A. Diawuo and Y. -B. Jung, “Broadband Proximity-Coupled Microstrip Planar Antenna Array for 5G Cellular Applications,” in IEEE Antennas and Wireless Propagation Letters, vol. 17, no. 7, pp. 1286-1290, July 2018, doi: 10.1109/LAWP.2018.2842242.

40. S.F. Jilani and A. Alomainy, “A Multiband Millimeter-Wave 2-D Array Based on Enhanced Franklin Antenna for 5G Wireless Systems,” in IEEE Antennas and Wireless Propagation Letters, vol. 16, pp. 2983-2986, 2017, doi: 10.1109/LAWP.2017.2756560.

41. Z. Chen and Y.P. Zhang, “FR4 PCB grid array antenna for millimeter-wave 5G mobile communications,” 2013 IEEE MTT-S International Microwave Workshop Series on RF and Wireless Technologies for Biomedical and Healthcare Applications (IMWS-BIO), 2013, pp. 1-3, doi: 10.1109/IMWS-BIO.2013.6756214.

42. S.F. Jilani, M.O. Munoz, Q.H. Abbasi and A. Alomainy, “Millimeter-Wave Liquid Crystal Polymer Based Conformal Antenna Array for 5G Applications,” in IEEE Antennas and Wireless Propagation Letters, vol. 18, no. 1, pp. 84-88, Jan. 2019, doi: 10.1109/LAWP.2018.2881303.

43. S.F. Jilani, Q.H. Abassi and A. Alomainy, “Millimeter-Wave Compact and High-Performance Two-Dimensional Grid Array for 5G Applications,” 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, 2019, pp. 25-26, doi: 10.1109/APUSNCURSINRSM.2019.8889123.

44. S.F. Jilani, Q.H. Abassi and A. Alomainy, “Millimetre-Wave MIMO Array of a Compact Grid Antenna for 5G Wireless Networks and Beyond,” 2020 International Conference on UK-China Emerging Technologies (UCET), 2020, pp. 1-4, doi: 10.1109/UCET51115.2020.9205326.

45. N.K. Sahu, G. Das and R.K. Gangwar, “Dielectric Resonator Based MIMO Antenna with Circular Polarization Diversity for WiMAX Applications,” 2019 PhotonIcs & Electromagnetics Research Symposium – Spring (PIERS-Spring), 2019, pp. 604-612, doi: 10.1109/PIERS-Spring46901.2019.9017508.

46. I. Dioum, A. Diallo, S.M. Farssi and C. Luxey, “A Novel Compact Dual-Band LTE Antenna-System for MIMO Operation,” in IEEE Transactions on Antennas and Propagation, vol. 62, no. 4, pp. 2291-2296, April 2014, doi: 10.1109/TAP.2014.2301151.

47. W. Han, X. Zhou, J. Ouyang, Y. Li, R. Long and F. Yang, “A Six-Port MIMO Antenna System With High Isolation for 5-GHz WLAN Access Points,” in IEEE Antennas and Wireless Propagation Letters, vol. 13, pp. 880-883, 2014, doi: 10.1109/LAWP.2014.2310739.

48. J. Deng, J. Li, L. Zhao and L. Guo, “A Dual-Band Inverted-F MIMO Antenna With Enhanced Isolation for WLAN Applications,” in IEEE Antennas and Wireless Propagation Letters, vol. 16, pp. 2270-2273, 2017, doi: 10.1109/LAWP.2017.2713986.

49. Y. Ding, Z. Du, K. Gong and Z. Feng, “A Novel Dual-Band Printed Diversity Antenna for Mobile Terminals,” in IEEE Transactions on Antennas and Propagation, vol. 55, no. 7, pp. 2088-2096, July 2007, doi: 10.1109/TAP.2007.900249.

50. S. Khan, H. Ali, R. Khan, R. Harry and C. Tanougast, “A cross-shaped MIMO reconfigurable dielectric resonator antenna for GSM and LTE/UMTS applications,” 2018 29th Irish Signals and Systems Conference (ISSC), 2018, pp. 1-4, doi: 10.1109/ISSC.2018.8585348.

51. L. Alex and S. Amma, “Compact Inverted U Shaped Slot Triple Band MIMO Antenna for WLAN and WiMAX Applications,” 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), 2018, pp. 1034-1036, doi: 10.1109/ICICCT.2018.8472992.

52. C.F. Ding, X.Y. Zhang, C. Xue and C. Sim, “Novel Pattern-Diversity-Based Decoupling Method and Its Application to Multielement MIMO Antenna,” in IEEE Transactions on Antennas and Propagation, vol. 66, no. 10, pp. 4976-4985, Oct. 2018, doi: 10.1109/TAP.2018.2851380.

53. L. Chang, Y. Yu, K. Wei and H. Wang, “Orthogonally Polarized Dual Antenna Pair With High Isolation and Balanced High Performance for 5G MIMO Smartphone,” in IEEE Transactions on Antennas and Propagation, vol. 68, no. 5, pp. 3487-3495, May 2020, doi: 10.1109/TAP.2020.2963918.

54. L. Sun, Y. Li, Z. Zhang and Z. Feng, “Wideband 5G MIMO Antenna With Integrated Orthogonal-Mode Dual-Antenna Pairs for Metal-Rimmed Smartphones,” in IEEE Transactions on Antennas and Propagation, vol. 68, no. 4, pp. 2494-2503, April 2020, doi: 10.1109/TAP.2019.2948707.

55. W. Jiang, B. Liu, Y. Cui and W. Hu, “High-Isolation Eight-Element MIMO Array for 5G Smartphone Applications,” in IEEE Access, vol. 7, pp. 34104-34112, 2019, doi: 10.1109/ACCESS.2019.2904647.

56. X. Zhang, Y. Li, W. Wang and W. Shen, “Ultra-Wideband 8-Port MIMO Antenna Array for 5G Metal-Frame Smartphones,” in IEEE Access, vol. 7, pp. 72273-72282, 2019, doi: 10.1109/ACCESS.2019.2919622.

57. R. Ullah, S. Ullah, B. Kamal and R. Ullah, “A Four-Port Multiple Input Multiple Output (MIMO) Antenna for Future 5G Smartphone Applications,” 2019 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), 2019, pp. 1-5, doi: 10.1109/ICECCE47252.2019.8940779.

58. Z. Ren and A. Zhao, “Dual-Band MIMO Antenna With Compact Self-Decoupled Antenna Pairs for 5G Mobile Applications,” in IEEE Access, vol. 7, pp. 82288-82296, 2019, doi: 10.1109/ACCESS.2019.2923666.

59. J. Li, X. Zhang, Z. Wang, X. Chen, J. Chen, Y. Li, and A. Zhang,, “Dual-Band Eight-Antenna Array Design for MIMO Applications in 5G Mobile Terminals,” in IEEE Access, vol. 7, pp. 71636-71644, 2019, doi: 10.1109/ACCESS.2019.2908969.

60. J.D. Park, M. Rahman and H. N. Chen, “Isolation Enhancement of Wide-Band MIMO Array Antennas Utilizing Resistive Loading,” in IEEE Access, vol. 7, pp. 81020-81026, 2019, doi: 10.1109/ACCESS.2019.2923330.

61. Y. Li, C. -Y. -D. Sim, Y. Luo and G. Yang, “Multiband 10-Antenna Array for Sub-6 GHz MIMO Applications in 5-G Smartphones,” in IEEE Access, vol. 6, pp. 28041-28053, 2018, doi: 10.1109/ACCESS.2018.2838337.

62. Y. Li, C. -Y. -D. Sim, Y. Luo and G. Yang, “12-Port 5G Massive MIMO Antenna Array in Sub-6GHz Mobile Handset for LTE Bands 42/43/46 Applications,” in IEEE Access, vol. 6, pp. 344-354, 2018, doi: 10.1109/ACCESS.2017.2763161.

63. Y. Liu, A. Ren, H. Liu, H. Wang and C. -Y. -D. Sim, “Eight-Port MIMO Array Using Characteristic Mode Theory for 5G Smartphone Applications,” in IEEE Access, vol. 7, pp. 45679-45692, 2019, doi: 10.1109/ACCESS.2019.2909070.

64. W. Hong, “Solving the 5G Mobile Antenna Puzzle: Assessing Future Directions for the 5G Mobile Antenna Paradigm Shift,” in IEEE Microwave Magazine, vol. 18, no. 7, pp. 86-102, Nov.-Dec. 2017, doi: 10.1109/MMM.2017.2740538.

65. M. S. Sharawi, M. Ikram and A. Shamim, “A Two Concentric Slot Loop Based Connected Array MIMO Antenna System for 4G/5G Terminals,” in IEEE Transactions on Antennas and Propagation, vol. 65, no. 12, pp. 6679-6686, Dec. 2017, doi: 10.1109/TAP.2017.2671028.

66. Y. Li, C. -Y. -D. Sim, Y. Luo and G. Yang, “Multiband 10-Antenna Array for Sub-6 GHz MIMO Applications in 5-G Smartphones,” in IEEE Access, vol. 6, pp. 28041-28053, 2018, doi: 10.1109/ACCESS.2018.2838337.

67. S. Chen, P. Wu, C.G. Hsu and J. Sze, “Integrated MIMO Slot Antenna on Laptop Computer for Eight-Band LTE/WWAN Operation,” in IEEE Transactions on Antennas and Propagation, vol. 66, no. 1, pp. 105-114, Jan. 2018, doi: 10.1109/TAP.2017.2775284.

68. M. Ikram, R. Hussain, and M. S. Sharawi, ‘‘4G/5G antenna system with dual function planar connected array,’’ IET Microw., Antennas Propag., vol. 11, no. 12, pp. 1760–1764, 2017, doi: 10.1049/iet-map.2017.0148.

69. R. Hussain, A.T. Alreshaid, S.K. Podilchak, and M.S. Sharawi, ‘‘Compact 4G MIMO antenna integrated with a 5G array for current and future mobile handsets,’’ IET Microw., Antennas Propag., vol. 11, no. 2, pp. 271–279, 2017, doi: 10.1049/iet-map.2016.0738.

70. E. Al Abbas, M. Ikram, A. T. Mobashsher and A. Abbosh, “MIMO Antenna System for Multi-Band Millimeter-Wave 5G and Wideband 4G Mobile Communications,” in IEEE Access, vol. 7, pp. 181916-181923, 2019, doi: 10.1109/ACCESS.2019.2958897.

Received 16.08.2022

Issue 4 (210), article 2

DOI:https://doi.org/10.15407/kvt210.04.026

Cybernetics and Computer Engineering, 2022, 4(210)

E.G. REVUNOVA1, DSc (Engineering),
Leading Researcher, Department of Neural Information Processing Technologies
e-mail: egrevunova@gmail.com

O.V. TYSHCHUK2,
Senior Software Engineer,
e-mail: avtyshcuk@gmail.com

O.O. DESIATERYK3, PhD (Phys&Math),
Assistant Professor, Faculty of Mechanics and Mathematics,
e-mail: sasha.desyaterik@gmail.com

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

2Roku Inc., Kyiv, Ukraine,

3Taras Shevchenko National University of Kyiv, 4e, Ave Glushkov, Kyiv, 03127, Ukraine

THE TECHNOLOGY OF THE STABLE SOLUTION FOR DISCRETE ILL-POSED PROBLEMS BY MODIFIED RANDOM PROJECTION METHOD

Introduction. Ill-posed problems solution is actual for many areas of science and technology. For example, discrete ill-posed problems (DIP) appears after discretization of the integral equations in the spectrometry, gravimetry, magnitometry, electrical prospecting and others.

In the case of linear DIP the matrix, which model some measuring system, makes a linear transformation of input vector to the output vector. Usually DIP output vector contains noise and singular values series of the matrix smoothly decrease to zero. In this case, the solution (input vector estimation) using the inversion of the transformation matrix is unstable and inaccurate. To overcome instability and increase accuracy we use regularization methods.

We develop an approach which uses regularizing properties of random projection to obtain a stable solution of DIP. However, the development of effective sustainable methods for solving DIP continues to be a problem of current interest.

The purpose of the paper is to increase the accuracy of DIP solution by the random projection method.

Results. In this paper we developed the method of stable solution of DIP by the modified method of random projection. For this modification the regularization by random projection is complemented by the regularization in the ridge regression style.

For the our method we obtained expressions which connect in the direct way the solution error components with the matrix specter and the regularization parameter. For the developed method the experimental research of the accuracy is conducted on the test problems.

Conclusions. The modified method of random projecting is characterized by stability and increased accuracy of the solution. This achieved by simultaneous ridge regression style regularization and random projecting. The representation of the solution error in the form where error components are related to the matrix specter and regularization parameter is important for further study of the error.

Keywords: random projection method, simultaneous ridge regression, regularization, stable solution, discrete ill-posed problems.

Download full text!

REFERENCES

  1. 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.
  2. Durrant R.J., Kaban A. Random projections as regularizers: learning a linear discriminant from fewer observations than dimensions. Machine Learning, vol. 99, N 2, 2015, P. 257-286.
  3. R.J. Durrant and A. Kaban. Compressed Fisher Linear Discriminant Analysis: Classification of Randomly Projected Data. In Proceedings16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2010), 2010.
  4. Xiang H., Zou J. Regularization with randomized SVD for large-scale discrete inverse problems. Inverse Problems. 29(8):085008, 2013.
  5. 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.
  6. Wei Y., Xie P., Zhang L. Tikhonov regularization and randomized GSVD. SIAM J. Matrix Anal. Appl. 2016. Vol. 37, N 2. P. 649–675.
  7. Hansen, P. Rank-deficient and discrete ill-posed problems. Numerical aspects of linear inversion. Philadelphia: SIAM. 1998. 247 p.
  8. Tikhonov A., Arsenin, V. Solution of ill-posed problems. Washington: V.H. Winston. 1977. 231 p.
  9. Hansen, P.C. The truncated SVD as a method for regularization. BIT 27, (1987), 534–553.
  10. 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.
  11. Revunova EG, Rachkovskij DA, Stable transformation of a linear system output to the output of system with a given basis by random projections, The 5th Int. Workshop on Inductive Modelling (IWIM’2012), Kyiv, 2012, p. 37-41 (in Russian).
  12. Revunova EG, Randomization approach to the reconstruction of signals resulted from indirect measurements, Proc. 4th International Conference on Inductive Modelling (ICIM’2013), Kyiv, 2013, p. 203-208 (in Russian).
  13. Revunova E.G., Tyshchuk A.V. A model selection criterion for solution of discrete ill-posed problems based on the singular value decomposition,  The 7th International Workshop on Inductive Modelling (IWIM’2015), Kyiv-Zhukyn, 2015, p.43-47(in Russian).
  14. 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.
  15. 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.
  16. Revunova E.G. Averaging over matrices in solving discrete ill-posed problems on the basis of random projection. Proc. CSIT’17. 2017. Vol. 1. P. 473 – 478.
  17. 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.
  18. Revunova E.G. Improving the accuracy of the solution of discrete ill-posed problem by random projection. Cybernetics and Systems Analysis. 2018. Vol. 54, N 5. P. 842–852 (in Russian).
  19. Revunova E.G., Tyshcuk O.V., Desiateryk О.О. On the generalization of the random projection method for problems of the recovery of object signal described by models of convolution type. Control Systems and Computers. 2021. N 5–6. P. 25–34.
  20. Tyshchuk O.V., Desiateryk O.O., Volkov O.E., Revunova E.G., Rachkovskij D.A., A linear system output transformation for sparse approximation. Cybernetics and Systems Analysis. 2022. Vol. 58, N. 5. P. 840–850.
  21. Marzetta T., Tucci G., Simon S. A random matrix-theoretic approach to handling singular covariance estimates. IEEE Trans. Information Theory. 2011. Vol. 57, N 9. P. 6256–6271.
  22. Hansen P. C. Regularization Tools: A Matlab package for analysis and solution of discrete ill-posed problems. Numer. Algorithms. 1994. Vol. 6, N 1. P. 1–35.
  23. Rachkovskij D.A, Revunova E.G. Intelligent gamma-ray data processing for environmental monitoring. In: Intelligent data analysis in global monitoring for environment and security. Kiev-Sofia: ITHEA. 2009. P. 124–145.

Received 04.10.2022

Issue 4 (210), article 1

DOI:https://doi.org/10.15407/kvt210.04.003

Cybernetics and Computer Engineering, 2022, 4(210)

SUROVTSEV I.V., DSc (Engineering), Senior Researcher,
Head of the Digital Ecological Monitoring Systems Department
https://orcid.org/0000-0003-1133-6207
e-mail: igorsur52@gmail.com

BOGACHUK Yu.P., PhD (Engineering),
Senior Researcher, Intelligent Control Department
https://orcid.org/0000-0002-3663-350X
e-mail: dep185@irtc.org.ua

KOMAR M.M., PhD (Engineering),
Deputy Director for Scientific and Organizational Work,
https://orcid.org/0000-0001-9194-2850
e-mail: nickkomar08@gmail.com

SIERIEBRIAKOV A.K., PhD Student,
Researcher of Intelligent Control Department
https://orcid.org/0000-0003-3189-7968
e-mail: sier.artem1002@outlook.com

BABAK O.V., PhD (Engineering),
Senior Researcher of the Digital Ecological Monitoring Systems Department,
https://orcid.org/0000-0002-7451-3314
e-mail: dep115@irtc.org.ua

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

RECOGNITION OF THE TYPE OF MARINE SHIP BASED ON COMPARISON WITH NORMALIZED REFERENCE PARAMETERS OF RADIOLOCATION SIGNALS

Introduction. The problem of marine ship types recognition remains relevant because it primarily focuses on the safety of sea and inland navigation. The basis of the identification of the type of marine ship is the use of training samples – a set of reference normalized parameters of mathematical models of radar portraits of reflected signals, recorded in the database, for which the type of ship is reliably known.

The purpose of the paper is to develop a method for recognizing the type of surface marine ship by comparing the parameters of the radar portrait of the reflected signal of the radar object with the reference parameters of the signals of mathematical models of known types of marine ships.

Methods. The recognition method is based on comparison of the normalized parameters of the radar signal of the object with the normalized parameters of the mathematical models of the database references through a full search, after which a decision is made in favor of the type of marine ship for which the overall measure of inconsistency or the identification criterion is minimal. The identification criterion is the sum of dimensionless features, which are a measure of similarity in the chosen metric of the parameters regarding reference object.

Results. Testing of the developed recognition method on examination samples made it possible to identify the type and real orientation angle of the ship at the level of 83%, as well as to identify the types and recognize the orientation angles of marine ships at the level of 96%.

Conclusions. The new method of recognition of the type of marine ship is characterized by the use of insignificant computing power, high speed of analysis, compactness of the reference database, high reliability and accuracy of recognition. Determination of auxiliary alternative values of the identification of the type and orientation angle of the ship helps in the dynamic mode of observation to statistically specify the characteristics of the recognition of the ship. The developed method of recognizing the type of ship can be used in the military sphere, its use in radar systems will improve the safety of sea and inland navigation.

Keywords: recognition method, identification, type of marine ship, radar portrait of reflected signal.

Download full text!

REFERENCES

  1. Vasiliev V.I. Recognizing systems. Directory. K.: Naukova Dumka. 1983, 422 p. (in Russian).
  2. Vasilyev V.I., Surovtsev I.V. Inductive methods for pattern detection based on reduction theory. Control System and Computers. 1998, N 5, pp. 3–13 (in Russian).
  3. Xinglong Liu, Yicheng  Li, Yong Wu, Zhiyuan Wang, Wei He and Zhixiong Li. A Hybrid Method for Inland Ship Recognition Using Marine Radar and Closed-Circuit Television. J. Mar. Sci. Eng. 2021, 9, 1199. https://doi.org/10.3390/jmse9111199
  4. Ma F., Chen Y.W., Yan X.P., Chu X.M., Wang J. A novel marine radar targets extraction approach based on sequential images and Bayesian Network. Ocean. Eng. 2016, 120, 64–77.
  5. Misovi´c D.S., Mili´c S.D., Ðurovi´c Ž.M. Vessel detection algorithm used in a laser monitoring system of the lock gate zone. IEEE Trans. Intell. Transp.Syst. 2015, 17, 430–440.
  6. Liu, Yan-sen, Wang Yang, and Xue-Meng Yang. Acoustic spectrum and signature analysis on underwater radiated noise of a passenger ship target based on the measured data. International Conference on Signal Processing Systems, 2019, Chengdu, China.
  7. https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11384/113840H/Acoustic- spectrum-and-signature-analysis-on-underwater-radiated-noise-of/10.1117/12.2559664.pdf
  8. Zhu C., Seri S.G., Mohebbi-Kalkhoran H. et al. Long-range automatic detection, acoustic signature characterization and bearing-time estimation of multiple ships with coherent hydrophone array. Remote Sensing, 2020. 12(22), 3731. https://www.mdpi.com/2072-4292/12/22/3731/pdf.
  9. Scarafoni Daniel et al. Automatic target recognition and geo-location for side scan sonar imagery.” The Journal of the Acoustical Society of America 141, 2017. № 5. 3925–3925.
  10. Volkov O.Ye., Taranukha V.Yu., Linder Ya.M. et al. Acoustic monitoring technology, detection and localization of objects in a controlled space. Control Systems and Computers. 2020. № 4. P. 35–43 (in Ukrainian).
  11. Volkov O.Ye., Taranukha V.Yu., Linder Ya.M., Komar M.M., Volosheniuk D.O. Devising an acoustic method for investigation of a complex form object parameters. Cyb. and Comp. Eng. 2021.  N 4 (206). 39-53. DOI: 10.15407/ kvt206.04.039
  12. Shirman Y.D., Gorshkov S.A., Leshchenko S.P., Orlenko V.M., Sedyshev S.Y., Sukharevskiy O.I. Computer Simulation of Aerial Target Radar Scattering, Recognition, Detection, and Tracking. Boston – London: Artech house, 2002, 294 p.
  13. Molchanov P., Totsky A., Egiazarian K., Leshchenko S., Jarabo-Amores Pilar M. Classification of Aerial Targets by Using Bicoherence-Based Features Extracted from Micro-Doppler Contributions. IEEE Transaction on aerospace and electronic systems. 2014. № 2(50). 1455-1467.
  14. Leshchenko S. The recognition quality effect of speed and aspect angle measurement errors using high range resolution profiles for aerial objects. Science and Technology of the Air Force of Ukraine. 2019. no 4(60). P. 23-30.  HYPERLINK “https://doi.org/10.30748/soivt.2019.60.03” DOI 10.30748/soivt.2019.60.03 (in Ukrainian).
  15. Voinov, S.; Krause, D.; Schwarz, E. Towards automated vessel detection and type recognition from VHR optical satellite images. In Proceedings of the 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 22–27 July 2018; 4823–4826.
  16. Solmaz, B.; Gundogdu, E.; Yucesoy, V.; Koç, A.; Alatan, A.A. Fine-grained recognition of maritime vessels and land vehicles by deep feature embedding. IET Comput. Vis. 2018, 12, 1121–1132.
  17. Youssef N.N. Radar cross section of complex targets. Proceedings of the IEEE. 1989. Vol. 77, Issue 5. 722–734.
  18. Ting, C., Wei, G., & Bing, S. (2011, July). A new radar emitter recognition method based on pulse sample figure. In Fuzzy Systems and Knowledge Discovery (FSKD), 2011 Eighth International Conference on (Vol. 3, pp. 1902-1905). IEEE
  19. Petrova N., Jordanova I., Roeb J. Radar emitter signals recognition and classification with feedforward networks. Procedia Computer Science. N 22 (2013), 1192–1200.
  20. Khrychov V.S., Legenky M.M. Facet model of an object of complex shape for the calculation of electromagnetic scattering. Bulletin of V.N. Karazin Kharkiv National University. Radiophysics and Electronics Series, 2019. (28), P. 44–52 (in Ukrainian).
  21. French A. Target recognition techniques for multifunction phased array radar. Computer Science. 2010. Doctoral thesis, UCL (University College London), 308 p.
  22. Jiansheng F., Xiaohong D., Wanlin Y. Radar HRRP Recognition Based on Discriminant Information Analysis. Wseas Transactions on Information Science and Aapplications. – 2011.  N 4(8), 185
  23. Method for histogram digital filtration of chrono-potentiometric data: patent 96367, Ukraine: IPC (2006) G01N 27/48. Surovtsev I.V., Galimova V.M., Babak O.V.: a201005608; claimed 11.05.10; published 25.10.11, Bull. 20 (in Ukrainian).-201.

Received 25.08.2022

Issue 4 (210)

DOI:https://doi.org/10.15407/kvt210.04

View web version

TABLE OF CONTENTS:

Informatics and Information Technologies:

Surovtsev I.V., Bogachuk Yu.P., Komar M.M., Sieriebriakov A.K., Babak O.V.
Recognition of the Type of Marine Ship Based on Comparison with Normalized Reference Parameters of Radiolocation Signals

Revunova E.G., Tyshchuk O.V., Desiateryk O.O.
The Technology of the Stable Solution for Discrete Ill-posed Problems by Modified Random Projection Method

Intelligent Control and Systems:

Volkov O.Ye., Voloshenyuk D.O., Odarchenko R.S., Bondar S.O., Semenoh R.V., Shcherbina O.A.
Analysis of Multiple Input Multiple Output System Designs for Base Stations and 5g Wireless Network Mobile Apps

Medical and Biological Cybernetics:

Fainzilberg L.S.
Mobile Information Technology for Assessing the Adaptation Capabilities of the Human Body under Conditions of Increased Loads

Kutsiak O.A.
Mobile System for the Patient’s Motor Functions State Diagnostics

Issue 3 (209), article 5

DOI:https://doi.org/10.15407/kvt208.03.063

Cybernetics and Computer Engineering, 2022, 3(209)

KIFORENKO S.I., DSc (Biology), Senior Researcher,
Leading Researcher, the Department of Mathematical and Technical
Methods in Biology and Medicine
https://orcid.org/00000000-0001-2345-6789
e-mail: skifor@ukr.net

BELOV V.M., DSc (Medicine), Professor,
Head of the Department of Mathematical and Technical
Methods in Biology and Medicine
https://orcid.org/0000-80120001-9717
e-mail: motj@ukr.net

HONTAR T.M., PhD (Biology), Senior Researcher,
the Department of Mathematical and Technical
Methods in Biology and Medicine
https://orcid.org/0000-0002-9239-0709
e-mail: gtm_kiev@ukr.net

KOZLOVSKA V.O,
Researcher, the Department of Mathematical and Technical
Methods in Biology and Medicine
https://orcid.org/0000-0001-5898-1639
e-mail: vittoria13apr@gmail.com

OBELETS T.A., PhD student,
Junior Researcher, the Department of Mathematical and Technical
Methods in Biology and Medicine
https://orcid.org/0000-0002-9425-1470
e-mail: obel.tet@gmail.com

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

METHODOLOGICAL ASPECTS OF USING NORMOMETRICAL SCALING FOR MULTIDIMENSIONAL ASSESSMENT OF HEALTH RESERVES

Introduction. One of the directions of modern research in the field of digital medicine is the development of a methodological base for assessing, supporting and managing personal health. The use of the methodology of a systemic approach to solving biomedical problems is fundamental for the rational organization of scientific research at the stages of diagnosis, forecasting and correction of the health state of individuals and population groups.

Scientific research, which is aimed at the development of information technology for assessing personal health reserves of a practically healthy person based on indicators of physical and psychosocial status is relevant and oriented for use at the stages of pre-hospital diagnosis.

The purpose of the paperis to show the expediency of using methods of multidimensional hierarchical normometric scaling for quantitative assessment of the body’s health and its reserve capabilities for pre-clinical diagnosis and activation of adaptation in changing conditions of the external environment.

Results. An algorithm for calculating the norm index of various health indicators using normometric scaling  has been developed for multidimensional assessment of health reserves.

Information support for algorithms for calculating the range of the norm-index for natural and heuristic indicators of physical and psychosocial health status  has been developed for the needs of digital medicine.

Modules of the software-algorithmic complex “Health-Reserve” have been developed for multidimensional quantitative assessment of reserve capabilities of the human body and personality based on the norm-index scale system for information mobile technologies.

Conclusions. The algorithm for calculating the norm-index range for natural and heuristic indicators of physical and psycho-social health status makes it possible to increase the resolution of the indicators` reference zone that are taken into account in the human health assessment.

The development of computer modules for multidimensional quantitative assessment of the health and personality of a person based on norm-index scales makes it possible to automate and quickly collect data based on the results of examinations, analyze the diagnosed conditions dynamics and can be an effective tool for screening and monitoring the health of the population.  The use of mobile Android applications implemented by the developed technologies increases the quality of personal decision-making by the user due to the expansion of accessibility and increased efficiency in providing the necessary information for the organization of one’s life.

Keywords: normometric scaling, health reserves, health quantitative assessment, indicators norm-index, mobile applications.

Download full text!

REFERENCES

1. Antomonov Yu.G., Belov V.M., Gritsenko V.I. et al. Open concept of health. Kyiv: Preprint, Institute of Cybernetics V.M. Glushkova, 1993. 27 p. (in Russian)

2. Amosov N.M. My health system. Kyiv: Health, 1997. 56 p. (in Russian)

3. Apanasenko G.L. Individual health: theory and practice of management, informational aspects. Medical informatics and engineering. 2009, no. 4, pp. 61-64. (in Russian)

4. Menatti L., Bich L., Saborido C. Health and environment from adaptation to adaptivity: a situated relational account. Biology and Philosophy. 2022, 31(2), pp. 237-265.
https://doi.org/10.1007/s40656-022-00515-w

5. Marakushin D.I., Chernobai L.V., Isayeva I.M. et al.. Functional reserves of the organism as an indicator of the effectiveness of regulatory processes that ensure the adaptation of the organism to the action of environmental factors. Ukrainian Journal of Medicine. Biology and Sports. 2020. Vol. 5, no. 1 (23), pp. 21-29. (in Ukranian)
https://doi.org/10.26693/jmbs05.01.021

6. Cullati S., Kliegel M., Widmer E. Development of reserves over the life course and onset of vulnerability in later life. Nature Human Behaviour. 2018. 2(8), pp. 551-558.
https://doi.org/10.1038/s41562-018-0395-3

7. Folke K., Colding J., Berkes, F. Synthesis: formation of stability and adaptability in socio-ecological systems. Navigation of socio-ecological systems: building resilience to complexity and change. 2003. 9 (1), pp. 352-387.
https://doi.org/10.1017/CBO9780511541957.020

8. Kooijman S. A. Quantitative aspects of metabolic organization: a discussion of concepts. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences. 2001. 356(1407), pp. 331-349.
https://doi.org/10.1098/rstb.2000.0771

9. Sadikova A.B.Q. Valeology And Philosophy Of Life. The American Journal of Social Science and Education Innovations. 2021. 3(12), pp. 82-86.

10. Baevsky R.M. Evaluation of the adaptive capabilities of the body and the problem of restorative medicine. Bulletin of restorative medicine. 2004, no. 2, pp. 18-22. (in Russian)

11. Apanasenko G.L. Popova L.A., Maglovany A.V. Sanology. Fundamentals of health management. LAMBERT Academic Publishing, 2012. 404 p. (in Russian)

12. Lazurenko S.I., Biloshitsky S.V., Semenov A.M. Adaptation and adaptive capacity of people. Actual problems of training and development of people with special needs. 2014, no. 11, pp.194-207. (in Ukranian)

13. Kravchenko V.V. Information technology aspects of control and evaluation of physical health. Bulletin of the Kiev National University. Series Cybernetics. 2014, no. 1(14), pp. 27-32. (in Russian)

14. Istomin A.G., Tkachenko A.V. Modern methods and hardware-software complexes for assessing the adaptive capabilities and the level of health of the human body. Kharkov: Kharkov National Medical University, 2010. (in Russian)

15. Wasson C.S., Charles S. System engineering analysis, design, and development: Concepts, principles, and practices. JohnWiley&Sons, 2015.

16. Rebizant W., Janusz Sz.,Wiszniewski A. Fundamental sof System Analysis and Synthesis. Digital Signal Processing in Power System Protection and Control. Springer, London, 2011. 29-52.
https://doi.org/10.1007/978-0-85729-802-7_4

17. Shultz K.S., Whitney D.J., Zickar M.J. Measurement theory in action: Case studies and exercises. Routledge, 2020. 434 p.
https://doi.org/10.4324/9781003127536

18. Argotti Y., Baron C., Esteban P. Quality quantification in systems engineering from the qualimetry eye. In IEEE International Systems Conference. 2019, April, pp. 1-8.
https://doi.org/10.1109/SYSCON.2019.8836756

19. Makaricheva V.V. Information-computer system for assessing physical warehouse health. Control systems and machines. 2016, no. 1, pp. 81-91. (in Ukranian)

20. Davison M.L. Multidimensional scaling. Minnesota: University of Minnesota, 1987. 254 p.

21. Khazeeva N.M. The use of multidimensional scaling in sociological research: a brief review. URL: HYPERLINK “https://studfile.net/preview/594480/” https://studfile.net/preview/594480/ (in Russian)

22. Antomonov M.Yu., Voloshchuk E.V. Construction of integral indicators of quantitative traits using one-dimensional and multidimensional statistical methods. Kibernetika i vycislitel’naa tehnika. 2012. Iss. 167, pp. 61-68. (in Russian)

23. Kiforenko S.I., Kotova A.B. Multidimensionality as a basis for systematic health assessment. Kibernetika i vycislitel’naa tehnika. 2006. Iss. 150, pp. 60-69. (in Russian)

24. Lutsyk B.D. Lapovets L.E., Swan G.B. Clinical laboratory diagnostics: training. Manual. Kyiv: Medicine, 2011. 208 p.

25. Belov V.M., Kotova A.B., Kiforenko S.I. The principle of the golden section in the context of the quantitative assessment of health. Normo index. Control systems and machines. 2016,no. 1, pp.73-80. (in Russian)
https://doi.org/10.15407/usim.2016.01.073

26. Karabayev M., Gasanova N., Batirov M., Kosimova G. Principles and constants of the golden proportion as a criterion in donosological diagnostics of the functional states of the body and in the assessment of the probability of their changes. Norwegian Journal of Development of the International Science. 2022. Iss. (77-1), pp. 19-27.

Received 03.06.2022

HOW TO CITE:
Kiforenko S.I., Belov V.M., Hontar T.M., Kozlovska V.O, Obelets T.A. Methodological Aspects of Using Normometrical Scaling for Multidimensional Assessment of Health Reserves. Cybernetics and Computer Engineering, 2022, no 3(209), pp. 63-80.
https://doi.org/10.15407/kvt208.03.063

Issue 3 (209), article 4

DOI:https://doi.org/10.15407/kvt208.03.045

Cybernetics and Computer Engineering, 2022, 3(209)

KOVALENKO O.S.1, DSc (Medicine), Professor,
Head of the Medical Information Technologies Department
https://orcid.org/0000-0001-6635-0124
e-mail: askov49@gmail.com

AVERYANOVA O.A.2,
Senior Lecturer, Faculty of Biomedical Engineering,
https://orcid.org/0000-0002-4536-2174
e-mail: olgaaveryanova@ukr.net

MARESOVA T.A.1,
Junior Researcher of the Medical Information Technologies Department
https://orcid.org/0000-0002-4210-7426
e-mail: tamaresova@gmail.com

NENASHEVA L.V.1,
Junior Researcher of the Medical Information Technologies Department
https://orcid.org/0000-0003-1760-2801
e-mail: larnen@ukr.net

KUPMAN L.O.2,
Student, Faculty of Biomedical Engineering,
e-mail: linakumpanbsn@gmail.com

DVORNITSKA D.O.2,
Student, Faculty of Biomedical Engineering,
e-mail: dvornitska.olena@lll.kpi.ua

1International Scientific and Educational Center
of Information Technologies and Systems
NAS of Ukraine and MES of Ukraine,
40, Akad. Hlushkova av., Kyiv, 03187, Ukraine

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

THE USE OF TELEMEDICINE TECHNOLOGIES TO CREATE A MEDICAL INFORMATION SYSTEM FOR MEDICAL AND SOCIAL CARE

Introduction. The use of digital medicine methods is becoming significantly urgent due to the COVID-19 pandemic, the current martial law in Ukraine, and the lack of medical equipment in some rural areas.

The same applies to providing medical care to the chronically ill. Such assistance includes social measures, which include care for the sick and disabled, provision of food etc. in addition to therapeutic and diagnostic measures. These measures are referred to as “medical and social care”.

The purpose of the paper is to apply the methods of digital medicine, which include telemedicine technologies in the construction of a medical information system model (MIS) to help chronic patients with telemedicine modules for the implementation of appropriate medical services in the hospital home settings.

Results. The types and methods of telemedicine technologies were analyzed, the diagram of business processes of the “Telemedicine” module was designed. The modules of the system were described with the specification of their realization, and the technical realization of the MIS for chronic care was carried out.  The technical requirements of the “Electronic prescription” module were described, and the diagrams for the tasks that are frequently used in practice were provided.

Conclusions. Based on the results of the analysis of capabilities and experience of using modern telemedicine systems, the architecture of medical information system for the medical and social care of patients was developed which covers the doctor and patient modules, united functionally by defined business processes with the performance of specific functions ( (online interaction between doctor and patient, issuing an electronic prescription etc.).

The use of the proposed MIS, which is made using a modern REST API platform for downloading files directly from clients, and an application implemented on the basis of the Waterfal method and the Python programming language, ensures the organization of the interaction of the medical staff with patients, in particular, the implementation of remote consultation and the provision of electronic prescriptions on based on entries in the patient’s electronic card.

Keywords: medical and social care, telemedicine technologies, medical information systems, electronic prescriptions

Download full text!

REFERENCES

1. Vitacca M., Scalvini S., Spanevello A., Balbi B. Telemedicine and home care: controversies and opportunities. Breathe. 2006. V. 3, no 2, pp.149-158.
https://doi.org/10.1183/18106838.0302.148

2. Wootton R, Bonnardot L. Telemedicine in low-resource settings. Front Public Health. 2015;3:3.
https://doi.org/10.3389/fpubh.2015.00003

3. Gutierrez M, Moreno R, Rebelo M. Information and communication technologies and global health challenges. Global Health Informatics. 2017, pp.50-93;
https://doi.org/10.1016/B978-0-12-804591-6.00004-5

4. Smith AC, et al. Telehealth for global emergencies: Implications for coronavirus disease 2019 (COVID-19). J Telemed Telecare. 2020; 26(5):309-13.
https://doi.org/10.1177/1357633X20916567

5. Ohannessian R. Telemedicine: potential applications in epidemic situations. European Research in Telemedicine. 2015;4(3):95-8.
https://doi.org/10.1016/j.eurtel.2015.08.002

6. World Health Organization. Telemedicine: Opportunities and developments in member states: Report on the second Global survey on eHealth [Internet]. 2010. Geneva, Switzerland: World Health Organization. 93 p. (Global Observatory for eHealth Series). Available at: http://www.who.int/goe/publications/goe_telemedicine_2010

7. Goldberg E.M., Lin M.P., Burke L.G., Jimenez F.N., Davoodi N.M., Merchant R.C. Perspectives on Telehealth for older adults during the COVID-19 pandemic using the quadruple aim: interviews with 48 physicians. BMC Geriatrics. 2022. V. 22, 188.
https://doi.org/10.1186/s12877-022-02860-8

8. How Telemedicine is Affecting Home Health Care. Available at: https://www.theseniorlist.com/blog/how-telemedicine-is-affecting-home-health-care/

9. Ikram U., Gallani S., Figueroa J.F., Feeley Th.W. 4 Strategies to Make Telehealth Work for Elderly Patients. Available at: https://hbr.org/2020/11/4-strategies-to-make-telehealth-work-for-elderly-patients.

10. Weiquan Wang, Li Sun, Tao Liu, Tian Lai. The use of E-health during the COVID-19 pandemic: a case study in China’s Hubei province. Available at: https://doi.org/10.1080/14461242.2021.1941184

11. Cimperman M., Makovec Brencic M., Trkman P. Analyzing older users’ home telehealth services acceptance behavior. International Journal of Medical Informatics, March 2016, 90:22-31.
https://doi.org/10.1016/j.ijmedinf.2016.03.002

12. Danko D. V., Povidaichyk O. S. Medical and social work with different categories of clients. Scientific bulletin of the Uzhgorod National University. 2009. Iss. 33, pp.51-54

13. Thomas L. What is Telemedicine? Available at: https://www.news-medical.net/health/What-is-Telemedicine.aspx

14. Aliouche H. What is Remote Surgery/Telesurgery? Available at: https://www.news-medical.net/health/What-is-Remote-SurgeryTelesurgery.aspx

15. Telemedicine was implemented in cardiology at the Center for Emergency Medical Care Available at: https://bukoda.gov.ua/news/u-centri-ekstrenoyi-medichnoyi-dopomogi-vprovadili-telemedicinu-u-kardiologiyi (in Ukrainian)

16. Organization of medical and social assistance. Available at: https://stud.com.ua/ 27525/meditsina/organizatsiya_mediko_sotsialnoyi_dopomogi (in Ukrainian)

17. Yang Xiao, Hui Chen Mobile Telemedicine: A Computing and Networking Perspective.- Available at: https://www.routledge.com/Mobile-Telemedicine-A-Computing-and-Networking-Perspective/Xiao-Chen/p/book/9781420060461

18. 5 Most Important Ways Telehealth Helps Home Care Patients. Available at: https://www.chaptershealth.org/chapters-of-life-blog/patients/home-care-and-telehealth-the-five-most-important-benefits-to-patients/

19. Romanyuk O.A., Kovalenko A.S., Kozak L.M. Information support for interaction of instrumental research systems and long-term storage of digital medical images in health care institutions. Kibernetika i vycislitel’naa tehnika. 2016, no. 184. pp. 56-71. (in Russian)
https://doi.org/10.15407/kvt184.02.056

20. Resolution of the Cabinet of Ministers of Ukraine dated April 25, 2018 No. 411 “Some issues of the electronic health care system”. Available at: HYPERLINK “https://zakon.rada.gov.ua/laws/show/411-2018-%D0%BF#Text” https://zakon.rada.gov.ua/laws/show/411-2018-%D0%BF#Text (in Ukrainian)

21. Order of the Ministry of Health of Ukraine dated February 28, 2020 No. 587 “Some issues of maintaining the Register of medical records, referral records and prescriptions in the electronic health care system”. Available at: https://zakon.rada.gov.ua/laws/ show/z0237-20#Text (in Ukrainian)

22. Kovalenko A.S., Kozak L.M., Romanyuk O.A. Information technologies of digital medicine. Kibernetika i vycislitel’naa tehnika. 2017, no. 1(187). pp.67-79 (in Russian).
https://doi.org/10.15407/kvt187.01.067

23. Kovalenko O.S., Mishchenko R.F., Kozak L.M. Transformation of Clinical Decision Support Systems into FHIR Structures to Ensure Quality of Medical Care. Cybernetics and Computer Engineering, 2019. 4(198), pp. 78-94.
https://doi.org/10.15407/kvt198.04.078

24. Benson, T. & Grieve, G. Principles of Health Interoperability: SNOMED CT, HL7 and FHIR. London: Springer-Verlag, 2016.
https://doi.org/10.1007/978-3-319-30370-3

25. Oemig, F., Snelick, R. Healthcare Interoperability Standards Compliance Handbook: Conformance and Testing of Healthcare Data Exchange Standards. Switzerland: Springer International Publishing, 2016.
https://doi.org/10.1007/978-3-319-44839-8

26. Bender, D. & Sartipi, K. HL7 FHIR: an Agile and RESTful approach to healthcare information exchange. in Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems p. 326-331 (2013) Available at: https://doi.org/
10.1109/CBMS.2013.6627810. (last accessed 04.04. 2022).
https://doi.org/10.1109/CBMS.2013.6627810

27. Offit K. Personalized medicine: New genomics, old lessons. Hum Genet. 2011;130:3-14.
https://doi.org/10.1007/s00439-011-1028-3

28. Health information exchange. Available at: https://www.healthit.gov/topic/health-it-and-health-information-exchange-basics/health-information-exchange (last accessed 10.04.2022).

29. Health information exchange definition – Defined by experts. Available at: https://www.pdnseek.com/health-information-exchange-definition-defined-by-experts/ (last accessed September 04.05. 2022).

30. Detailed clinical model. Available at: www.detailedclinicalmodels.nl/dcm-en (last accessed 14.09. 2012)

31. Prime PACS. Available at: https://play.google.com/store/apps/details?id= com.rosenfield.PrimePACS&hl=uk&gl=US (last accessed 14.05. 2022).

Received 16.05.2022

HOW TO CITE:
Kovalenko O.S., Averyanova O.A., Maresova T.A., Nenasheva L.V., Kupman L.O., Dvornitska D.O. The use of telemedicine technologies to create a medical information system for medical and social care. Cybernetics and Computer Engineering, 2022, no 3(209), pp. 45-63.
https://doi.org/10.15407/kvt208.03.045

Issue 3 (209), article 3

DOI:https://doi.org/10.15407/kvt208.03.033

Cybernetics and Computer Engineering, 2022, 3(209)

GRITCENKO V.I., Corresponding Member of the NAS of Ukraine,
Directorate Advisor
https://orcid.org/0000-0002-6250-3987
e-mail: vig@irtc.org.ua

SUKHORUCHKINA O.N., Senior Researcher,
System Information Technologies Department,
https://orcid.org//0000-0002-7441-6661
e-mail: sukhoru@irtc.org.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.
40, Akad. Glushkov av., Kyiv, 03187, Ukraine

FROM COMMAND CONTROL TO THE AUTONOMY OF MOBILE ROBOTS

Introduction.The urgent needs of the modern technological order and the development of intelligent information technologies, covering a wide range of scientific areas, have led to the emergence of new principles for the organization of robot control systems. The main goal of modern robotics is to minimize direct human involvement in the control loop when the robot performs tasks in a weakly deterministic non-stationary environment. Historically, robotics for such operating conditions has progressed from remote command control to autonomous systems with the possibility of supervision by human. The influence of intelligent control on increasing the degree of autonomy of service mobile robots is considered. The important subsystems in the organization of intelligent control systems for autonomous mobile robots and the objective difficulties of their practical implementation are shown.

The purpose of the paper is to discuss the influence of intelligent control on the level of autonomous capabilities of robots in dynamic and incompletely defined conditions and the objective difficulties of creating universal approaches to the implementation of autonomous service robots control systems.

Results. The ways of increasing the autonomous capabilities of mobile robots are considered. The role of the supervisory control principle on the way to reducing human participation in the processes of remote control of service robots is given.

Conclusions. The use of the proposed structural solutions of the service mobile robot intelligent control system and the methodology for organizing its activating subsystem made it possible to significantly increase the autonomous resources of the robot when performing complex tasks in a weakly deterministic nonstationary environment.

Keywords:autonomous mobile robot, intelligent control system, supervisory control

Download full text!

REFERENCES

1. Fu K.S. Learning control systems and intelligent control systems: An intersection of artificial intelligence and automatic control. IEEE Trans. Automatic Control. 1971. pp. 70-72.
https://doi.org/10.1109/TAC.1971.1099633

2. Saridis G.N. Toward the realization of intelligent controls. Proc. IEEE. 1979, vol. 67. Iss. 8, pp. 1115-1133.
https://doi.org/10.1109/PROC.1979.11407

3. Meystel A. Intelligent control: Issues and perspectives. Proc. IEEE Workshop Intelligent Control. 1985. pp. 1-15.

4. Antsaklis P.J., Passino K.M., Wang S.J. Towards intelligent autonomous control systems: Architecture and fundamental issues. HYPERLINK “https://link.springer.com/journal/10846” Journal of Intelligent and Robotic Systems. 1989, vol. 1. pp. 315-342.
https://doi.org/10.1007/BF00126465

5. Simmons R., et al. Autonomous task control for mobile robots. Proc. of the Fifth International Symposium on Intelligent Control. IEEE Computer Society Press, Los Alamitos, CA. 1990. P. 663-668.

6. DARPA Robotics Challenge. https://www.darpa.mil/program/darpa-robotics-challenge [Last accessed 24.04.2022]

7. World Robotics – Service Robot Report. International Federation on Robotics, 2018. https://ifr.org/downloads/press2018/Executive_Summary_WR_Service_Robots_2018.pdf

8. Guha A., Dudziak M. Knowledge based controllers for autonomous system. Proc. IEEE Workshop Intelligent Control. 1985, pp. 134-138.

9. Saridis G.N. Intelligent control-operating systems in uncertain environments. In book: Uncertainty and Control. 2006, pp. 215-236.
https://doi.org/10.1007/BFb0007285

10. Rapoport G.N., Gertz A.G. Artificial and biological intelligence. Generality of structure, evolution and processes of cognition. Moscow: KomKniga, 2005. 312 p. (in Russian)

11. Makarov I.M., Lokhin V.M., Manko S.V., Romanov M.P. Artificial intelligence and intelligent control systems. Moscow: Nauka, 2006. 333 p. (in Russian)

12. Zhdanov A.A. Autonomous artificial intelligence. Moscow: BINOM, Laboratoriya znaniy, 2008. 359 p. (in Russian)

13. Gonzalez A.G.C., Alves M.V.S., Viana G.S., Carvalho L.K., Basilio J.C. Supervisory control-based navigation architecture: A new framework for autonomous robots in industry 4.0 Environments. IEEE Trans. on Industrial Informatics. 2018. 14(4). P. 1732-1743.
https://doi.org/10.1109/TII.2017.2788079

14. Sheridan Thomas B. Telerobotics, automation, and human supervisory control. MIT Press, Cambridge, 1992.

15. Cheng G., Zelinsky A. Supervised autonomy. A Framework for Human-Robot Systems Development, Autonomous Robots. 2001. 10(3). P. 251-266.
https://doi.org/10.1023/A:1011231725361

16. Ferrell W.R., Sheridan T.B. Supervisory control of remote manipulation. IEEE Spectrum. 1967. 4(10). P. 81-88.
https://doi.org/10.1109/MSPEC.1967.5217126

17. Jones P.M., Jasek C.A. Intelligent support for activity management (ISAM): An architecture to support distributed supervisory control. IEEE Transactions on Systems, Man, and Cybernetics, Special issue on Human Interaction in Complex Systems. Vol. 27. No. 3. May 1997. P. 274-288.
https://doi.org/10.1109/3468.568737

18. Chechkin A.V. Activating subsystem is the main feature of the intelligent system. Intellektualnye sistemy. Moscow: Izd-vo MGU. 2001, 6. Iss. 1-4., pp. 91-110. (in Russian)

19. Sukhoruchkina O.N. The structures and information processes of mobile robot intelligent control. Zbirnyk naukovykh prats Instytutu problem modelyuvannya v energetytsi im. G.Ye. Pukhova NAN Ukrainy. Kyiv, 2012. No. 62, pp. 93-101. (in Russian)

20. Anokhin P.K. Key questions of the theory of functional systems. Moscow: Nauka, 1980. (in Russian)

21. Sukhoruchkina O.N. Activating subsystem of mobile robot intelligent control, Sbornik dokladov Vserossiyskoe nauchno-tekhnic. konferentsii “Ekstremalnaya robototekhnika” (Rossiya, Sankt-Peterburg, 25-26 sentyabrya), Izdatelstvo “Politekhnika-servis”, Sankt-Peterburg, 2012, pp. 101-105. (in Russian)

22. Sukhoruchkina O.N., Progonnyi N.V. Intelligent Control of Mobile Robot when Tracking a Moving Object. Journal of Automation and Information Sciences. 2019. 51(11), pp. 50-62.
https://doi.org/10.1615/JAutomatInfScien.v51.i11.50

23. Sukhoruchkina O.N. On parallel information processes of intelligent control of a mobile robot. Trudy XXI Mezhdunar. nauchno-tekhnic. konferentsii “Ekstremalnaya robototekhnika”. Sankt-Peterburg: Izdatelstvo “Politekhnika-servis”. 2010, pp. 338-340. (in Russian)

Received 06.06.2022

HOW TO CITE:
Gritcenko V.I., Sukhoruchkina O.N. From Command Control to the Autonomy of Mobile Robots. Cybernetics and Computer Engineering, 2022, no 3(209), pp. 33-45.
https://doi.org/10.15407/kvt208.03.033

Issue 3 (209), article 2

DOI:https://doi.org/10.15407/kvt208.03.021

Cybernetics and Computer Engineering, 2022, 3(209)

STEPASHKO V.S., DSc (Engineering), Prof.,
Head of the Department of
Information Technologies of Inductive Modeling
https://orcid.org/0000-0001-7882-3208
e-mail: stepashko@irtc.org.ua

SAVCHENKO-SYNIAKOVA Ye.А., PhD (Engineering),
Senior Researcher,
the Department of Information Technologies of Inductive Modeling,
https://orcid.org/0000-0003-4851-9664
e-mail: savchenko_e@meta.ua

PIDNEBESNA H.А., PhD (Engineering), Researcher,
the Department of Information Technologies of Inductive Modeling
https://orcid.org/0000-0002-5735-9861
e-mail: pidnebesna@ukr.net

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, Akad. Hlushkov av., Kyiv, 03187, Ukraine

PROBLEM OF CONSTRUCTING AN ONTOLOGICAL METAMODEL OF ITERATIVE ITIRATIVE GROUP METHOD OF DATA HANDLING ALGORITHMS

Introduction. Data volumes are permanently increasing and some new approaches are needed for storage and processing them considering the development and improvement of modern computers. This puts forward new requirements to automatic data processing tools and intelligent systems for analyzing information with taking into account its semantics.

The advantage of iterative GMDH algorithms is that they are able to work with a large number of arguments. The generalized iterative GMDH algorithm includes various former modifications of these algorithms. For example, algorithms of multilayer and relaxation types as well as varieties of iterative-combinatorial (hybrid) algorithms are diverse particular cases of the generalized one.

Metamodeling is the construction of generalized models of a certain group of objects (software tools, mathematical models, information systems). An ontological metamodel of the iterative GMDH algorithms was built using the Protege tools in order to structure knowledge in this subject area.

The purpose of the paper is to analyze the developed iterative GMDH algorithms and propose an approach to structuring knowledge оn iterative GMDH algorithms by building an ontological metamodel of this subject area.

Results. A retrospective analysis of the developed iterative GMDH algorithms іs carried out in the paper, their advantages and disadvantages are indicated. It is shown that the generalized iterative algorithm, whose special cases are both known and new varieties of multilayer, relaxation and iterative-combinatorial GMDH algorithms, makes it possible to compare the effectiveness of various algorithms and solve real modeling problems. Based on the results of this study, an ontological metamodel of iterative GMDH algorithms has been developed.

Conclusions. The advantage of iterative GMDH algorithms is that they allow processing big data sets. The generalized iterative algorithm allows forming typical architectures of previously developed modifications of these algorithms when setting up various operating modes of this algorithm. The construction of an ontological metamodel based on this one allows structuring knowledge on the available iterative algorithms making it possible to automate the design and use of specialized software tools for specific applied tasks.

Keywords: inductive modeling, GMDH, iterative algorithms, mathematical model, metamodeling, subject area, ontology

Download full text!

REFERENCES

1 Ivakhnenko A.G., Stepashko V.S. Noise-immunity of modeling. Kiev: Naukova dumka, 1985. 216 p. (In Russian).

2 Ivakhnenko, A.G. Group method of data handling as competitor for the method of stochastic approximation. Soviet Automatic Control, 1968, no 3, pp. 58-72 (In Russian).

3 Spravochnik po tipovym programmam modelirovaniya / Red. Ivakhnenko A.G. Kiev: Tekhnika, 1980. 184 p. (In Russian).

4 Chandrasekaran B., Josephson J.R., Benjamins, R.V., Ontologies. What are ontologies, and why do we need them?” IEEE Intelligent Systems and their Applications. 1999, V. 14. Iss. 1. pp. 20-26.
https://doi.org/10.1109/5254.747902

5 Stepashko V.S., Bulgakova A.S., “http://usim.org.ua/arch/2013/2/3.pdf” The Generalized Iterative Algorithm of the Group Method of Data Handling. Upravlyayushchie Sistemy i Mashiny, 2013, no 2, pp. 5-17 (In Russian).

6 Stepashko V., Bulgakova O., Zosimov V. Construction and Research of the Generalized Iterative GMDH Algorithm with Active Neurons. In: Advances in Intelligent Systems and Computing II. AISC book series, Volume 689. Berlin: Springer, 2017, pp. 474-491.
https://doi.org/10.1007/978-3-319-70581-1_35

7 Ruy F.B., Guizzardi G., Falbo, R.A., Reginato, C.C., Santos, V.A. From reference ontologies to ontology patterns and back. Data & Knowledge Engineering, 2017, 109, pp. 41-69.
https://doi.org/10.1016/j.datak.2017.03.004

8 Savchenko Ye., Stepashko V. Metamodeling and metalearning approaches in inductive modeling tools. Preprint, [online]. Available at: https://easychair.org/publications/ preprint/6L1W [Accessed 23 Apr. 2018].

9 Flach P. Machine learning: the art and science of algorithms that make sense of data, Cambridge University Press, 2012. 396 p.
https://doi.org/10.1017/CBO9780511973000

10 Savchenko Ye.A, Stepashko V.S.,. “Analysis of approaches to metalearning and metamodeling”. Inductive modeling of complex systems, Coll. sciences works. Kyiv: IRTCITS, 2017, Iss. 9, pp. 86-94 (In Ukrainian).

11 Pidnebesna H.A. Conceptual development of ontology for the design of inductive modeling. Inductive modeling of complex systems. Coll. sciences works. Kyiv: IRTCITS, 2013, 5, pp. 248-256 (In Ukrainian).

12 Valkman Yu.R. Ontologies: formal and informal. Report at the seminar “Pattern computer”. 08.11.2011, [online]. Available at: http://www.irtc.org.ua/image/seminars/archive/int [Accessed 18 Dec. 2017] (In Russian).

13 Stepashko V., Bulgakova O., Zosimov V. Construction and Research of the Generalized Iterative GMDH Algorithm with Active Neurons. In: Advances in Intelligent Systems and Computing II. AISC book series, 2017, V. 689, Berlin: Springer, pp. 474-491.
https://doi.org/10.1007/978-3-319-70581-1_35

14 Pavlov A.V., Kondrashova N.V. On the Convergence of the Generalized Relaxation Iterative Algorithm for the Method of Group Consideration of Arguments. Upravlyayushchie Sistemy i Mashiny, 2012, no 3 (239), pp. 24-29, 38 (In Russian).

15 Pavlov A.V. “Generalized Relaxation Iterative GMDH Algorithm”. Inductive modeling of complex systems, Coll. sciences works. Kyiv: IRTCITS, 2011, Iss. 4, pp. 121-134 (In Ukrainian).

16 Ivakhnenko N.A., Marchev A.A. Self-organization of a mathematical model for long-term planning of construction and installation works. Automation. 1978, no. 3, pp. 12-18 (In Russian).

17 Sheludko O.I. GMDH algorithm with orthogonalized complete description for model synthesis based on the results of the planned experiment. 1974, no 5, pp. 32-42 (In Russian).

18 Yefimenko S., Stepashko V. Technologies of Numerical Investigation and Applying of Data-Based Modeling Methods”. Proceedings of the II International Conference on Inductive Modelling ICIM-2008, 15-19 September 2008, Kyiv, Ukraine. Kyiv: IRTCITS, pp. 236-240.

19 Yefimenko, S.M. Stepashko, V.S. Computer tests as an instrument for effectiveness investigation of modeling algorithms. Proceedings of International Workshop on Inductive Modelling (IWIM 2007), Prague: Czech Technical University, pp. 123-127.

20 Tyryshkin A.V., Andrakhanov A.A., Orlov A.A. GMDH-based Modified Polynomial Neural Network Algorithm”, Chapter 6 in Book GMDH-methodology and implementation in C (With CD-ROM). London: Imperial College Press, World Scientific, 2015, pp. 107-155.
https://doi.org/10.1142/9781848166110_0006

21 Kordik P. Why Meta-learning is Crucial for Further Advances of Artificial Intelligence? [online]. Available at: [Accessed 18 Dec. 2020].

22 Stepashko V., Bulgakova O., Zosimov V.,. “Hybrid Algorithms for Self-Organizing Models for Predicting Complex Processes”. Inductive modeling of complex systems. Coll. sciences works. Kyiv: IRTCITS, 2010, pp. 236-246 (In Ukrainian).

23 Ivakhnenko A.G., Ivakhnenko G.A., Muller J.-A. Self-Organization of Neuronets with Active Neurons. Patt. Recognition and Image Analysis. 1994, 4 (4), pp. 177-188.

24 Bulgakova O.S., Stepashko V.S. Comparative Analysis of the Efficiency of Iterative GMDH Algorithms Using Computational Experiments. Visnyk CHDTU. 2011, no 1, pp. 41-44 (In Ukrainian).

Received 23.06.2022

HOW TO CITE:
Stepashko V.S., Savchenko-Syniakova Ye.А., Pidnebesna H.А. Problem of Constructing an Ontological Metamodel of Itirative Group Method of Data Handling Algorithms. Cybernetics and Computer Engineering, 2022, no 3(209), pp. 21-33.
https://doi.org/10.15407/kvt208.03.021