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DOI: 10.17586/1023-5086-ХХХХ-ХХ-ХХ-ХХ-ХХ

UDC 621.373:535

All-optical shaping of a 3D self-induced transparency soliton in ⁸⁷Rb vapours

Sergey N. Bagaev¹, Igor B. Mekhov², Igor A. Chekhonin^3*, Mikhail A. Chekhonin⁴

¹Institute of Laser Physics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia

^{2, 3, 4}St. Petersburg State University, St. Petersburg, Russia

¹baev@laser.nsc.ru https://orcid.org/0000-0003-4470-2779

²meov@yahoo.com https://orcid.org/0000-0001-9699-8335

³chonin@mail.ru https://orcid.org/0000-0001-6862-6737

⁴chonin@bk.ru https://orcid.org/0000-0001-6862-9267

Corresponding author: Igor A. Chekhonin, chonin@mail.ru, +7(111) 111-11-11

Abstract

Subject of study. Three-dimensional solitons of the theory of self-induced transparency of laser pulses with a converging cylindrical wave front and different transverse spatial profiles of the pulse field in ⁸⁷Rb vapor. Aim of study. Experimental study of three-dimensional solitons of the theory of self-induced transparency of laser pulses and development of prototypes of new devices for resonant quantum microwave photonics using laser signal processing methods in the microwave region of the spectrum. Method. In the caustic of a focused beam of a laser pump pulse with a cylindrical wave front, a transverse spatial profile of the electric field strength of a special shape is created. The computer generated holograms developed by us can be used to create an arbitrary profile. Main results. The properties of a three-dimensional self-induced transparency soliton are studied for various detuning frequencies of the input pulse field with respect to atomic resonance. The maximum laser pulse power was 8.5 mW; the pulse duration was 4–5 ns. The time resolution of the recording system is 27 ps. It is shown that the all-optical control of the carrier frequency of the input pulse determines the properties of the output pulse – compression of the pulse duration (generation of a strobe pulse), the value of the soliton delay in time, the time shift of the carrier frequency of the soliton. Practical significance. The results obtained in the study of the properties of three-dimensional self-induced transparency solitons will serve as the basis for the development of prototypes of signal processing devices using low-power laser diodes.

Keywords: self-induced transparency, soliton, resonant medium, computer generated hologram

Acknowledgment: this work was supported by the Russian Science Foundation, project № 17–19–01097.

For citation: Bagaev S.N., Mekhov I.B., Chekhonin I.A., Chekhonin M.A. All-optical shaping of a 3D self-induced transparency soliton in ⁸⁷Rb vapours [in Russian] // Opticheskii Zhurnal. 2023. V. 90. № 5. P. 00–00. http://doi.org/10.17586/1023-5086-ХХХХ-ХХ-ХХ-ХХ-ХХ

OCIS codes: 060.5530, 050.1590, 060.5625

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DOI: 10.17586/1023-5086-ХХХХ-ХХ-ХХ-ХХ-ХХ

UDC 621.373:535

Categorization of objects and scenes by a neural network whose input modules are pre-trained to decode spatial texture inhomogeneities

D. V. Yavna^*, V. V. Babenko, O. A. Gorbenkova, I. V. Plavelsky, V. D. Voronaya, A. S. Stoletniy

Southern Federal University, Rostov-on-Don, Russia

Abstract

Scope of research. Investigation of the possibility of using neural network models of second­order visual mechanisms as input data for neural network classifiers. Second­order visual mechanisms make it possible to detect spatial inhomogeneities in contrast, orientation, and spatial frequency in an image. These mechanisms are traditionally considered by visual researchers as one of the stages of early visual processing; their role in the perception of textures has been well studied. The purpose of the work is to study whether the use of classifier input modules previously trained to demodulate spatial modulations of brightness gradients will contribute to the categorization of objects and scenes. Method. Neural network modeling was used as the main method. At the first stage of the study, a set of texture images was generated, which is used to train neural network models of second­order visual mechanisms, and these models were trained. At the second stage, samples of objects and scenes were prepared, on which classifier networks were trained. Previously trained models of second­order visual mechanisms with frozen weights were placed at the input of these networks. Main results. The second order information, presented as a map of instantaneous values of the modulation function of contrast, orientation and spatial frequency in the image, may be sufficient to identify only some classes of scenes. In general, within the framework of the proposed neural network architectures, the use of modulation function values for solving the problem of object classification turned out to be ineffective. Thus, the hypothesis that second­order visual filters encode features that allow identifying an object was not confirmed. This result makes it necessary to test an alternative hypothesis that the role of second­order filters is limited to participation in the construction of saliency maps, and the filters themselves are windows through which information comes from the outputs of first­order filters. Practical significance. The possibility of using second­order models of visual mechanisms in computer vision systems was assessed.

Keywords: visual processing mechanisms, texture, convolutional neural network, classifier neural network, machine vision

Acknowledgment: the study was financially supported by the Russian Foundation for Basic Research, project № 18­29­22001 MK "An investigation of neurocognitive technologies of attentional control and formation of mental representations of visual web content".

For citation: Yavna D.V., Babenko V.V., Gorbenkova O.A., Plavelsky I.V., Voronaya V.D., Stoletniy A.S. Categorization of objects and scenes by a neural network whose input modules are pre­trained to decode spatial texture inhomogeneities [in Russian] // Opticheskii Zhurnal. 2024. V. 91. № ХХ. P. ХХ–ХХ. http://doi.org/10.17586/1023-­5086­-ХХХХ­-ХХ-­ХХ-­ХХ-­ХХ

OCIS сodes: 100.4996, 330.5370