The laboratory is a part of the Institute for Automation and Control Processes.
Field of research
Our laboratory works out the distributed fiber-optic measuring systems for monitoring of the physical field distributions. This system consists of the set of integrating fiber-optical measuring lines. Relations between characteristic space frequencies of both the measuring network and the function of the investigated physical field distribution which determine a quality of the reconstruction has been found. We experimentally and theoretically study adaptive processing of non-stationary speckled waves by a dynamic reflection holographic grating formed in photorefractive crystals. This effect is proposed for adaptive processing of optical signals derived from single-fiber multi-mode interferometers. Tomographic methods of reconstruction of the distributions of both scalar and vector physical fields from incomplete data obtained by the distributed fiber-optic measuring system are developed. Neural network algorithms for the tomographic reconstruction of the data of the distributed fiber-optical measuring networks are elaborated. The methods of optimization the the artificial neural networks training process were proposed and studied. Both physical principles and architecture of multilayer optical neural network construction based on the both holographic and waveguide elements and measuring lines with phase sensitivity to parameters of both vector and scalar physical fields are developed. Staff
Our photo From left to right in upper row: In bottom row: |
Romashko R.V., Kulchin Yu.N., Shandarov S.M., Kargin Yu.F., Volkov V.V., Adaptive correlation filter based on dynamic reflection hologram formed in photorefractive Bi12TiO20 crystal. — Optical Review, 12 (1) pp. 58-60, 2005.
Kulchin Yu.N., Denisov I.V., Kamenev O.T., Kim A.Yu., Panov A.V., Neural data processing method for fiber-optical distributed measuring systems. — Optical Memory & Neural Networks, 12 (3), pp. 165-172, 2003.
Yu.N. Kulchin, E.V. Denisova, I.V. Denisov, Application of algebraic and neural-like methods for reconstruction of distribution functions of physical fields. — Optical Memory & Neural Networks, 12 (4), pp. 283–297, 2003.
Yu.N. Kulchin, I.V. Denisov, E.V. Denisova, E.N. Piskunov, Prismatic neural chip for distributed measuring networks. — Optical Memory & Neural Networks, 12 (3), pp. 237–242, 2003.
Yu.N. Kul'chin, Adaptive distributed optoelectronic information-measuring systems. — Phys. Usp., 46 (8), 867-872, 2003.
Kulchin Yu.N., Vasil’ev V.V., Nikonov Yu.Yu., Optoelectronic method of noncontacting reconstruction of the profile of a surface of three dimensional objects of large dimensions. — Measurement Techniques, 46 (3), pp. 224-229, 2003.
Kulchin Yu.N., Kamenev O.T., Petrov Yu.S., Kantur M., Fiber optical distributed network for dynamic deformation measuring. — Proc. SPIE, 5129, pp. 68-73, 2003.