Open Access Peer-reviewed Commentary

Creating humanoid intelligent digital twin on spectral and holographic approaches

Article Sidebar

Semantic principle of mental lexicon organization
Download PDF
Submitted   Oct 30, 2022
Published   Dec 6, 2022
Issue    Vol 1 No 1 (2022)
DOI   10.25082/RIMA.2022.01.004

Views

1191

Downloads

597

Citations

PlumX Metrics

Main Article Content

Evgeniy Bryndin corresponding author
Research Centre "NATURAL INFORMATIC", National Supercomputer Technological Platform, Novosibirsk, Russia

Abstract

A person perceives his environment through the influence of various complexes of conjugated vibrations on the eyes, ears and other sensitive components of the body. The psyche and neural systems of a person form the impression of vibration impact. The mind creates a language equivalent and connects it with the impression that has formed. Communication links are formed between impressions and language equivalents. Live vibrational information involves a person in a communicative creative process. In the creative communicative process, human intelligence develops. The combination of modern interdisciplinary technologies can contribute to the creation of an intelligent digital twin, similar to humans on spectral and holographic principles, relying on achievements in the field of artificial intelligence. The digital twin fixes the effects of the environment in the form of a spectrogram, and stores the effect result in the form of a hologram. Multilayer neural network systems with additional training work with spectra and holograms and their communications. Language communications are associated with spectra, holograms and their communications. A humanoid digital twin with an ensemble of intellectual agents will be able to form and develop intelligence in interaction with similar digital twins and people.

Keywords
spectrogram, hologram, multilayer neural networks, ensembles of intellectual agents, intellectual digital twin

Article Details

How to Cite
Bryndin, E. (2022). Creating humanoid intelligent digital twin on spectral and holographic approaches. Research on Intelligent Manufacturing and Assembly, 1(1), 28-34. https://doi.org/10.25082/RIMA.2022.01.004
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

References

  1. Poon TC and Liu JP. Introduction to Modern Digital Holography. Publisher: Cambridge University Press, 2014. https://doi.org/10.1017/CBO9781139061346
  2. Picart P and Lebrun D. New Techniques in Digital Holography. Publisher: iSTE Press, 2015. https://doi.org/10.1002/9781119091745
  3. Carpio A. Seeing the invisible: Digital holography, 2022. https://doi.org/10.4171/mag/99
  4. Tahara T, Zhang Y, Rosen J, et al. Roadmap of incoherent digital holography. Applied Physics B, 2022, 128, 193. https://doi.org/10.1007/s00340-022-07911-x
  5. Liu JH and Meng H. A review of underwater digital holography systems. Conference: Ocean Optics2021, 2022.
  6. Petrov V, Pogoda A, Sementin V, et al. Advances in Digital Holographic Interferometry. Journal of Imaging, 2022, 8(7): 196. https://doi.org/10.3390/jimaging8070196
  7. Huang Z, Memmolo P, Ferraro P, et al. Dual-plane coupled phase retrieval for non-prior holographic imaging. PhotoniX, 2022, 3(1): 1-16. https://doi.org/10.1186/s43074-021-00046-w
  8. Tsang PWM, Poon TC, Zhang Y, et al. Digital holography: Applications and emerging technologies. Frontiers in Photonics, 2022, 3: 1-4. https://doi.org/10.3389/fphot.2022.1073297
  9. Terbe D, Orzó L and Zarándy Á. Classification of Holograms with 3D-CNN. Sensors, 2022, 22(21): 8366. https://doi.org/10.3390/s22218366
  10. Li Z, Chen Y, Sun J, et al. High Bandwidth-Utilization Digital Holographic Reconstruction Using an Untrained Neural Network. Applied Sciences, 2022, 12(20): 10656. https://doi.org/10.3390/app122010656
  11. Jaferzadeh K, Son S, Rehman A, et al. Automated Stain-Free Holographic Image-Based Phenotypic Classification of Elliptical Cancer Cells. Advanced Photonics Research, 2022, 2200043. https://doi.org/10.1002/adpr.202200043
  12. Bate T, O’Keefe D, Spencer M, et al. Experimental validation of model-based digital holographic imaging using multi-shot data, Proc. of SPIE Vol. 2022, 12239: 122390D-1. https://doi.org/10.1117/12.2633499
  13. Zhao J, Wang Y, Huang X, et al. Spectroscopic localization of atomic sample plane for precise digital holography. arXiv preprint arXiv:2210.02721, 2022. https://doi.org/10.48550/arXiv.2210.02721
  14. Hassad S, Ferria K, Bouamama L, et al. Multi-view acoustic field imaging with digital color holography. Frontiers in Photonics, 2022, 22. https://doi.org/10.3389/fphot.2022.929031
  15. Liao M, Feng Y, Lu D, et al. Scattering imaging as a noise removal in digital holography by using deep learning. New Journal of Physics, 2022, 24(8): 083014.
  16. Bryndin E. Identification of Natural Novelty and Disasters by Ensembles of Intelligent Agents Based on Spectral Measurement. International Journal Of Innovative Research In Multidisciplinary Education, 2022, 1(2): 55-59.
  17. Pribram KH. Recollections. Neuroquanthology, 2011, 9(3): 370-374. https://doi.org/10.14704/nq.2011.9.3.447
  18. Bryndin E. Ensembles of Intellectual Agents with Decision-Making. Acta Scientific Computer Sciences, 2022, 4(6): 3-8. https://doi.org/10.33258/bioex.v4i3.752
  19. Bryndin E. Intellectual Agent Ensemble with Professional Competencies, Pattern Recognition and Decision Making. Applied Science and Innovative Research, 20022, 6(4): 1-10. https://doi.org/10.22158/asir.v6n4p1
  20. Bryndin E. Unambiguous Identification of Objects in Different Environments and Conditions Based on Holographic Machine Learning Algorithms. Britain International of Exact Sciences Journal (BIoEx-Journal), 2022, 4(2): 72-78. https://doi.org/10.33258/bioex.v4i2.726
  21. Bryndin E. Human Digital Doubles with Technological Cognitive Thinking and Adaptive Behaviour. Software Engineering, 2019, 7(1): 1-9.
  22. Bryndin E. Technology Self-organizing Ensembles of Intelligent Agents with Collective Synergetic Interaction. Automation, Control and Intelligent Systems, 20200, 8(4): 29-37. https://doi.org/10.11648/j.acis.20200804.11
  23. Bryndin E. Functional and Harmonious Self-Organization of Large Intellectual Agent Ensembles with Smart Hybrid Competencies. COJ Robotics & Artificial Intelligence, 20021, 1(4): 1-11 https://doi.org/10.11648/j.ajsea.20211001.11
  24. Bryndin E. Formation of Digital Economy of Necessary Needs Based on Energy Economic Equivalent. Resources and Environmental Economics, 2021, 3(2): 297-304. https://doi.org/10.25082/REE.2021.02.005
  25. Bryndin, E. Transition to international energy economic equivalent. Resources and Environmental Economics, 20021, 3(2): 280-285. https://doi.org/10.25082/REE.2021.02.003
  26. Bryndin, E. Financial turnover of cyclical economy by reinvesting in ecological production of its savings. Resources and Environmental Economics, 2020, 2(1): 96-101. https://doi.org/10.25082/REE.2020.01.001