Scientific School

Full name

Methods and Software Tools for Data Processing in Multimedia Technologies, Digital Twins, and Automatic Object Identification

Field of Knowledge and Specialization:

Scientific direction and field of knowledge: 12 Information Technologies

Scientific specialization: 121 Software Engineering (01.05.02 Mathematical Modeling and Computational Methods, 01.05.03 Mathematical and Software Support of Computing Machines and Systems)

Educational specialization: 121 Software Engineering

Educational and Research Programs for Bachelor’s, Master’s, and PhD:

  • Bachelor’s Degree Program – Software Engineering for Multimedia and Information Retrieval Systems;
  • Master’s and Postgraduate Programs – Software Engineering for Multimedia and Information Retrieval Systems;
  • PhD Program – Software Engineering.

About the founder of the school:

The founder of the scientific school is Professor, D.Sc.  Ivan Dychka, Dean of the Faculty of Applied Mathematics.

Outstanding Graduates of the School:

  • DmytroYarovyi (Master’s graduate in 2018):
    • Position: Group leader at Amazon (Canada)
  • Andrii Datsenko (Master’s graduate in 2017):
    • Position: Group leader at Facebook (United Kingdom)

Research Equipment:

Within the operation of the scientific school in 2017, the Multimedia, Mulsemedia, and Immersive Technologies Educational and Scientific Laboratory  was established. This lab houses equipment for developing cutting-edge multimedia interfaces and software for 3D visualization, creating 3D models of objects and 3D printing, as well as developing applications for augmented and virtual reality.

Key Scientific Achievements of the Scientific School

The history of the development of the scientific school includes two stages.

First Stage (1990 – 2012)

The scientific school was initiated by D.Sc., Prof. Ivan Dychka in 1990. During this period, the main scientific task was the development of theoretical foundations for automatic object identification based on graphical codes. Primarily, the attention of the scientific group members (Ivan  Dychka, Yevgeniya Sulema, Ali Abud) was focused on developing new high-density, durable barcodes. At this time, members of the scientific group actively collaborated with the Ukrainian branch of the International EAN Association, which began implementing barcodes in Ukraine in the 1990s. The members of the scientific school successfully addressed the scientific and technical challenge of machine-coded input of textual information, and they developed methods to increase the information density of data presentation in the form of high-density durable barcodes.

Second Stage (2013 – since today)

During the second stage of the scientific school’s development, the primary focus expanded from machine-coded textual data entry for accounting objects to broader tasks involving the acquisition, formation, and processing of temporal multimodal data. This expanded scope allows for a comprehensive characterization of objects whose states change over time. A new scientific challenge emerged as researchers began processing data from digital twins of objects. The concept of digital twins was further developed through the integration of MULSEMEDIA (MULtiple SEnsorial MEDIA) and immersive technologies, spearheaded by Yevgeniya Sulema. Subsequently, researchers Viktor Legeza and Oleksandr Neshchadym joined the scientific school, focusing on solving mathematical modeling problems essential for constructing digital twins.

Relevant directions pursued by the school members include developing methods and software tools for information retrieval systems (Tetiana Zabolotnia, Yakiv Yusyn); developing methods to guarantee data integrity and security (Mykola Onai, Semen Shyrochyn, Yevgen Radchenko); and developing methods for processing medical graphical data (Yevgeniya Sulema, Oksana Shkurat).

Simultaneously, the development of the graphic coding research direction continues (Ivan Dychka, Olga Sulema). Currently, automatic identification based on machine-readable graphic codes has expanded from black and white barcodes to multicolored matrix codes.

Thus, today the scientific school brings together researchers conducting studies aimed at a comprehensive solution to the global scientific problem – the formation, representation, and processing of multimodal data about research objects, search, and accounting for a wide range of applications.

Most significant results over 5 years

  • Developed theoretical foundations for representation and processing of temporal multimodal data – algebraic system of aggregates (ASA) and the concept of multi-images.
  • Developed a programming paradigm for multi-images and the programming language ASAMPL for processing temporal multimodal data.

International recognition of the scientific school

Membership in international and national scientific organizations:

  • International Organization for Standardization, ISO/IEC JTC 1/SC 34 “Document description and processing languages” Committee (Yevgeniya Sulema)
  • National Technical Committee for Standardization “Information Technologies,” TC 20 (Yevgeniya Sulema)
  • International Institute of Informatics and Systemics (Yevgeniya Sulema)
  • Editorial board of the international scientific journal “Journal of Systemics, Cybernetics and Informatics” (Yevgeniya Sulema)
  • Editorial board of the international scientific journal “International Journal of Interactive Mobile Technologies” (Mykola Onai)

Lectures Conducted at the Invitation of International Partners (Yevgeniya Sulema):

  • Keynote Speaker at the 29th IEEE International Conference on Systems, Signals and Image Processing IWSSIP2016, topic of keynote speech is “Digital Twins Technology: What Is the Future?”, http://iwssip.stuba.sk, Sofia, Bulgaria (online), June 1, 2022.
  • Invited Lecturer, British University in Egypt (https://www.bue.edu.eg/), lecture topic is “Synergy of Digital Twins, Mulsemedia, and Neural Networks: is there any future for it?”, Cairo, Egypt (online), December 5, 2020.
  • Keynote Speaker at the 9th International Conference on Interactive Collaborative and Blended Learning ICBL2020, topic of keynote speech is “Digital Twins Technology for Education 4.0“, McMaster University, Hamilton, Canada (online), October 14, 2020.
  • Visiting Professor at University of Malta, lecture topic is “Multimedia, Mulsemedia and Immersive Technologies”, Msida, Malta, May 7-11, 2018.
  • Visiting Professor at Lappeenranta University of Technology, lecture topic is “Medical Image Processing and Protection”, Lappeenranta, Finland, April 13-20, 2018.
  • Visiting Professor at Cyprus University of Technology, course title is “Multimedia, Mulsemedia and Immersive Technologies”, Limassol, Cyprus, January 22-28, 2017.

Work within the framework of permanent and ad hoc specialized academic councils for the defense of doctoral and candidate dissertations, as well as philosophy doctorates.

Specialized Academic Council D 26.002.02 (Ivan Dychka).

Defenses of dissertations for the degree of Doctor and Candidate of Sciences, as well as Philosophy Doctorates, 2017-2021.

Doctoral dissertations:

  • Sulema Yevgeniya, “Methods, Models, and Tools for Processing Multimodal Data of Digital Twins of Investigated Objects,” 2020.

Candidate dissertations:

  • Mykola Onai, “Methods and Tools for Improving the Efficiency of Computational Operations in Finite Fields,” 2017.
  • Oksana Shkurat, “Methods and Information Technology for Processing Archival Medical Images,” 2020.

Philosophy doctors dissertations:

  • Yevgen Radchenko, “Algorithmic support and software of Internet user’s multimedia data protection systems”, 2020.
  • Olga Sulema, “Algorithmic and Software Solutions for Automatic Identification Processes of Logistics Objects Based on Barcodes with Three Color Gradations,” 2021.
  • Yakiv Yusyn, “Methods and software tools for metamorphic testing of software systems for automatic clustering of natural language text data”, 2023.
  • Andrii Dychka, “Algorithms and software for automatic identification processes based on multicolor interference-resistant barcodes in medical information systems”, 2024.

Scientific Monographs and Textbooks, 2017-2023

Scientific Monographs:

  • Yevgeniya Sulema, Andreas Pester, Bertrand Laforge, Frederic Andres. Augmented Reality User’s Experience: AI-Based Data Collection, Processing and Analysis. Chapter 2 in book “Augmented Reality and Artificial Intelligence: The Fusion of Advanced Technologies”, pp. 31–46. Springer, 2023
  • Liubov Oleshchenko. Cartographic social network software development for Іnternet user events searching. Global and national development trends digital economy. Collective Monograph. Praha: OKTAN PRINT, 2023, 353 р. (рр. 151-163) еBook ISBN 978-80-88618-10-2. DOI: https://doi.org/10.46489/gandtde-23-29
  • Yevgeniya Sulema, Etienne Kerre, “Multimodal Data Representation and Processing Based on Algebraic System of Aggregates,” Chapter 5 in the book “Mathematical Methods in Interdisciplinary Sciences” (editor Chakraverty S.), Wiley, USA, 2020, 2 authors.

Textbooks:

  • I. Dychka, V. Tarasenko., M. Onai. “Fundamentals of Applied Theory of Digital Automata.” Kyiv: Publishing House “Politekhnika”, 2019. 508 p.
  • V. Legeza, “Mathematical Analysis. Volume 1.” Kyiv: Publishing House “Politekhnika”, 2019. 336 p.
  • V. Legeza., “Mathematical Analysis. Volume 2.” Kyiv: Publishing House “Politekhnika”, 2020. 396 p.

Scientific Articles

Number of scientific articles in publications indexed by the bibliographic databases Scopus and/or Web of Science: 188.

  1. Andreas Pester, Yevgeniya Sulema, Ivan Dychka, Olga Sulema. Temporal Multimodal Data Processing Algorithms Based on Algebraic System of Aggregates. Journal “Algorithms”. 2023, 16(4), 186. https://doi.org/10.3390/a16040186 (Scopus)
  2. Yevgeniya Sulema, Andreas Pester, Bertrand Laforge, Frederic Andres. Augmented Reality User’s Experience: AI- Based Data Collection, Processing and Analysis. Chapter 2 in book “Augmented Reality and Artificial Intelligence: The Fusion of Advanced Technologies”. Springer, 2023. (Scopus)
  3. Liubov Oleshchenko. Software Testing Errors Classification Method Using Clustering Algorithms. “Lecture Notes in Networks and Systems”, vol. 703, pp. 553–566. Springer, Singapore, 2023. https://doi.org/10.1007/978-981-99-3315-0_42. (Scopus)
  4. Legeza, V., Neshchadym, O., Drozdenko, L. Determination of Brachistochronous Trajectories of Movement of a Material Point in a One-Dimensional Vector Field. “Lecture Notes on Data Engineering and Communications Technologies”, vol 181, pp. 700–708. Springer, Cham, 2023. https://doi.org/10.1007/978-3-031-36118-0_63 (Scopus)
  5. Danyil Peschanskyi, Pavlo Budonnyi, Yevgeniya Sulema, Frederic Andres, Andreas Pester. “Temporal Data Processing with ASAMPL Programming Language in Mulsemedia Applications”, Lecture Notes in Networks and Systems, Vol. 524, pp. 473-485, Springer, Cham, 2023, ISSN 978-3-031-17091-1. (Scopus)
  6. Liubov Oleshchenko. Machine Learning Algorithms Comparison for Software Testing Errors Classification Automation. “Lecture Notes on Data Engineering and Communications Technologies”, vol 181, pp. 615–625. Springer, Cham, 2023. https://doi.org/10.1007/978-3-031-36118-0_55 (Scopus)
  7. Inna Saiapina, Halyna Holub, Ivan Kulbovskyi. Improving Noise Immunity of Audio Frequency Track Circuits Using Neural Networks and Data Classification. “Lecture Notes in Intelligent Transportation and Infrastructure”, Part F1379, pp. 696–705. Springer, Cham, 2023. https://doi.org/10.1007/978-3-031-25863-3_67 (Scopus)
  8. Ivan Dychka, Yevgeniya Sulema, Dmytro Rvach, Liubov Drozdenko. Programming Language ASAMPL 2.0 for Mulsemedia Applications Development, “Lecture Notes on Data Engineering and Communications Technol.”, Vol. 134, pp. 107–116. Springer, 2022. (Scopus)
  9. Yusyn Y., Zabolotnia T. Metamorphic Testing and Serverless Computing: A Basic Architecture. Journal “Informatica”, 2022, vol. 46(6), pp. 95–104. (Scopus)
  10. Dychka, I., Legeza, V., Oleshchenko, L., Bohutskyi, D. Method Simultaneous Using GAN and RNN for Generating Web Page Program Code from Input Image. “Advances in Intelligent Systems and Computing”, vol 1247, pp. 338–349. Springer, Cham, 2021. https://doi.org/10.1007/978-3-030-55506-1_31
  11. Yevgeniya Sulema, Etienne Kerre. “On Fuzziness in Algebraic System of Aggregates”, in “New Mathematics and Natural Computation”, Vol. 17, No. 1, 2021, pp. 145–152. https://doi.org/10.1142/S1793005721500071. (Scopus)
  12. Ivan Dychka, Olga Sulema, Anton Salenko, Yevgeniya Sulema, “Augmented Reality Application Based on Information Barcoding”, Advances in Intelligent Systems and Computing, Vol. 1192, Springer Nature Switzerland AG, 2021, ISSN 2194-5357. (Scopus)
  13. Dychka I., Onai M., Sulema O. Data Compression in Black-Gray-White Barcoding. Radio Electronics, Computer Science, Control, 2020. No 1, P. 125–134. (Web of Science)
  14. Dychka, I.A., Legeza V.P., Onai M.V., Severin A.I. Modified Change-of-basis Conversion Method in GF (2m) // Radio Electronics, Computer Science, Control. 2020. # 2, P. 117-128. (Web of Science)
  15. Sulema, Y., Dychka, I., Sulema O. Multimodal Data Representation Models for Virtual, Remote, and Mixed Laboratories Development, Springer, Lecture Notes in Networks and Systems, 2019, Volume 47, pp. 559-569. DOI: 10.1007/978-3-319-95678-7_62. (Scopus)
  16. Yevgeniya Sulema, Etienne Kerre. On Fuzziness in Algebraic System of Aggregates, Journal “New Mathematics and Natural Computation”, Vol. 17, No. 1, 2021, pp. 145–152. (Scopus)
  17. Yevgeniya Sulema, Abhishek Bhattacharya, Niall Murray, Mulsemedia Data Representation Based on Multi-Image Concept, Advances in Intelligent Systems and Computing, Vol. 1192, Springer Nature Switzerland AG, 2021, pp. 480–491. (Scopus)
  18. Sulema Ye. Multimodal Data Processing Based on Algebraic System of Aggregates Relations, Scientific Journal “Radio Electronics, Computer Science, Control”, No 1, 2020, pp. 169-180. (Web of Science)
  19. Radchenko, Y., Dychka, I., Sulema, Y., Suschuk-Sliusarenko, V., Shkurat, O. Steganographic Protection Method Based on Huffman Tree. Advances in Intelligent Systems and Computing, 2020, 902, pp. 283–292. (Scopus)
  20. Oksana Shkurat, Yevgeniya Sulema, Viktoriya Suschuk-Sliusarenko, Andrii Dychka, Image Segmentation Method Based on Statistical Parameters of Homogeneous Data Set, Advances in Artificial Systems for Medicine and Education, Springer, Vol. 902, 2019, pp 271-281. (Scopus)

Scientific articles in publications included in the list of scientific professional publications of Ukraine: 354.

  1.  I. Dychka,Y.Sulema Model of Multimodal Data Representation for Comprehensive Description of Observation Objects. Bulletin of Vinnytsia Polytechnic Institute, 2020. No. 1, pp. 53–60.
  2. Y.Sulema, D. Rvach  Models of Computation for Digital Twins Data Processing. Scientific News of KPI, 2020. No. 2, pp. 74–81.
  3. Y. Sulema, V. Peschanskii Timewise Data Processing with the Programming Language ASAMPL. Scientific Notes of V.I. Vernadsky Taurida National University. Technical Sciences, 2020. Vol. 31(70), Part 1. No. 1, pp. 132–137.
  4. Y. Sulema, V, Glinskii Semantics and Pragmatics of the Programming Language ASAMPL. Programming Problems, 2020. No. 1, pp. 74–83.
  5. V. Legeza  Mathematical Pendulum Model with a Mobile Suspension Point. Scientific News of KPI, 2020. No. 4, pp. 35–41.
  6. I. Dychka, Y. Sulema, Bukhtiiarov Iu. Digital Twin Information Technology for Biomedical Data Complex Representation and Processing. Bulletin of Kherson National Technical University, 2019. No. 3 (70), pp. 112–119.
  7. I. Dychka,Y. Sulema Ordering Operations in Algebraic System of Aggregates for Multi-Image Data Processing. Scientific News of KPI, 2019. No. 1, pp. 15–23.
  8. M.Onai, O. Sulema,  A. Dychka Data Encoding Based On Tricolor Matrix Barcodes. KPI Science News, 2019. Vol. 2, pp. 37–45.
  9. I. Dychka, Y. Sulema  Logical Operations in Algebraic System of Aggregates for Multimodal Data Representation and Processing. Scientific News of KPI, 2018. No. 6, pp. 44–52.
  10. O. Shkurat, Y. Sulema, A. Dychka Complicated Shapes Estimation Method for Objects Analysis in Video Surveillance Systems. Scientific News of KPI, 2018. No. 3, pp. 53–62.

Patents for Inventions, Licensing Agreements

  1. V. Legeza, I. Dychka, L. Khomichak Method of selecting an energy-saving profile of the upper structure of the road pavement – LDK / Patent of Ukraine for utility model No. 119867, published on 10.10.2017, Bulletin No. 19/2017; description – 8 pages.
  2. V. Legeza, I. Dychka  Method of placing and fixing long-length loads on the coupling of railway platforms // Patent of Ukraine for utility model No. 122786, published on 25.01.2018, Bulletin No. 2, description 5 pages.

Implementation of research results in the economy and education:

The research results have been integrated into the educational process in teaching disciplines such as “Software for Automatic Identification Systems,” “Multimedia Interfaces and 3D Visualization,” and “Technologies for Processing Digital Images and Signals” for students specializing in 121 Software Engineering.

Participation in exhibitions, contests, and innovation projects, and hackathons:

  • The project of our students from the Phoggers team (Nikita Gorobets, Mariia Nikolina, Glib Skopyk, Aliko Tektov) won a silver medal at the National Computer Project Competition INFOMATRIX UKRAINE 2024. The goal of the project was to create an information and communication system that would improve the efficiency of communication between residents and city services to improve the overall urban environment.
  • Project “Poket KPI”, developed by KP-01 students Danil Trofimov and Kyrylo Shevchenko, won first place in the Sikorsky Challenge Junior 2023 startup competition.
  • Project “Colorizer” (2023), developed by student Kateryna Tarelkina, won third place in the final of Boot Camp for International Cooperation in Entrepreneurship for Sustainable Development. The competition was held as part of the international project ‘Resilience of Education: Sustainability and Cooperation for Ukrainian Universities’.
  • Project “Shot analyzer” (2018): The project executors, graduate students Ivashchenko Mykhailo and Okhrimchuk Denis, received the Leonid Kuchma Presidential Fund Prize “Ukraine.”
    • The “Shot analyzer” automated system aims to enhance the efficiency of sniper training on open ranges. The real-time analysis includes target identification and positioning on the overall scene, as well as the identification and positioning of hits on the targets. The system responds to changes in the scene, such as changes in lighting, target orientation, camera orientation, etc. Additionally, during system operation, there is interaction with the user, and the processed data is transmitted to a device that connects to the system.
    • The software complex has competitive advantages, utilizing a developed analytical module that incorporates artificial intelligence and computer vision technologies.
    • Project status: A business model has been developed, and a demo version of the system has been created, and presented at the final of the Sikorsky Challenge 2019 Defense Technology Competition.

Scientific seminars and conferences organized jointly with international partners:

  • Workshop “Data retrieval, presentation and analysis of taste and flavour data” at the Experiment@ International Conference – expatWS’21, Portugal (online), November 24, 2021;
  • International Workshop on Cross Reality, Artificial Intelligence, and Online Learning within the 18th International Conference on Remote Engineering and Virtual Instrumentation (REV2021), Hong Kong, China (online), February 24, 2021;
  • Mixed Reality Applications for Industry and Education (MIRINDE) Special Session of the International Conference on Interactive Mobile Communication, Technologies and Learning IMCL2019, 31 October – 1 November, 2019, Thessaloniki, Greece;
  • Augmented Reality and Immersive Applications (ARIA) Special Session of the International Conference on Interactive Mobile Communication, Technologies and Learning IMCL2017, 30 November – 1 December, 2017, Thessaloniki, Greece.