A neural network-based method for trainable spatial-spectral sensitivity correction of a push-broom hyperspectral sensor is proposed. Unlike traditional calibration approaches that equalize the recorded signal from a uniformly illuminated Lambertian reference target along the slit using precomputed correction coefficients, the proposed calibration is implemented as neural network layers whose parameters are jointly optimized with the classification model during training. Three types of trainable calibration layers have been developed, based on a learnable matrix of correction coefficients, its partial polynomial approximation, and vector factorization. Experimental evaluation was conducted on hyperspectral images acquired under similar but not identical capture conditions. The calibration layers were integrated into a 3D convolutional neural network and a spatial-spectral transformer. The results demonstrate a consistent improvement in classification quality compared to baseline models without calibration: the accuracy increased by 0.59% to 14.27%, and the F1-score increased by 0.17% to 10.98%, which confirms the effectiveness of the proposed layers.

This study explores the use of transformer architecture as an image encoder model for the task of 3D mesh generation from a single image. Traditionally, models based on autoencoder architecture perform such tasks, where an encoder produces a latent representation that a decoder subsequently converts into a 3D model. When processing image-based input, the ResNet18 convolutional network is a commonly used encoder. In this paper, we investigate replacing the convolutional network with a transformer-based approach while using binary space partitioning (BSP) for 3D object generation. Our experiments demonstrate that a transformer-based architecture, specifically the Compact Convolutional Transformer (CCT), can achieve performance comparable to its convolutional counterpart and exceed it both in quantitative metrics and visual quality. The best CCT-based model achieves a Chamfer Distance (CD) of 1.59 and a Light Field Distance (LFD) of 3907, whereas the convolutional variant attains a CD of 1.64 and an LFD of 3981. The CCT-based model also demonstrates superior 3D reconstruction quality on test samples. Additionally, the transformer model requires four times fewer parameters to achieve these results, though computational resources are two times higher in terms of Multiply-Accumulate operations (MACs). These findings indicate that the transformer-based model is more parameter-efficient and can achieve superior results compared to traditional convolutional networks in single-view reconstruction tasks.

Alongside scalar optical vortex beams, which have a topological charge (TC), a spiral wave front and carry an orbital angular momentum (OAM) that can be transferred to microparticles and rotate them along circular trajectories, polarization optical vortices are also known, in which the polarization state in the beam cross section changes with a change in the azimuthal angle. Such vortices have points of polarization singularity, which are described by indices similar to TZ. However, polarization OAMs for polarization vortices have not yet been considered. Meanwhile, laser beams with inhomogeneous polarization can perform spiral transfer of matter in polarization-sensitive media. In this paper, two possible definitions of polarization OAMs are considered. One OAM is proportional to the azimuthal rate of change of the linear polarization vector direction, and the second (hybrid OAM) is proportional to the azimuthal rate of change of the degree of ellipticity of the polarization ellipse. For example, the normalized polarization OAM is equal to the order of a cylindrical vector beam and is also equal to the order of a Poincaré beam.

The purpose of this work is software implementation of the temporal frame interpolation, the formation of selection criteria and the choice of a suitable neural network model based on the obtained practical data. And also, evaluation of its efficiency for eliminating the interframe shift effect of dynamic objects on the depth maps of multi-area active-pulse television measuring systems in order to improve the accuracy of map building. As initial data for the experiments, static frames were recorded while moving the test rig along the X and Z axes. The static frames are images of the test rig, averaged 100 times, at a distance of 13 meters, which moved along an automated linear guide with a step of 1 mm. As a result of the work, an assessment of the interframe shift effect influence on space depth maps of multi-area active-pulse television measuring systems containing dynamic objects was made. The implementation and testing of the temporal frame interpolation algorithm for suppressing the interframe shift effect of dynamic objects on depth maps was also performed. The algorithm was implemented using Python and the PyCharm IDE with SciPy, NumPy, OpenCV, PyTorch, Threading and other libraries. Numerical values of the RMSE, PSNR, and SSIM metrics were obtained before and after eliminating the effect of interframe shift of dynamic objects on depth maps. The use of the temporal frame interpolation algorithm allows more accurate measurement of distance to moving object in the field of view of multi-area active-pulse television measuring systems.

This article studies acousto-optical transformations of fiber modes in a circular single-mode anisotropic optical fiber with a traveling torsional acoustic wave of the fundamental order. For the first time, analytical expressions for resonant optical modes and the spectrum of propagation constants are obtained by applying a resonant perturbation theory to the scalar wave equation. This made it possible to consistently describe the experimentally demonstrated mutual transformation of linearly polarized LP0 fiber modes, while taking into account an optical frequency shift. A new effect of acoustically controlled sign inversion of the spin angular momentum of the fundamental mode is predicted. A possibility of generating entangled optical states in which the polarization direction and frequency of the optical beam are entangled has been established.

Metasurfaces represent a revolutionary paradigm shift in optics that has opened the era of multifunctional light transformations using ultra-thin elements. In contrast to the traditional approach based on refractive and diffractive optical elements, metasurfaces use complex arrays of subwavelength structures to control the phase, amplitude, and polarization of incident light waves. This revolutionary technology offers many advantages, including the creation of ultra-compact optical systems, providing polarization transformations and sharp focusing beyond the diffraction limit, as well as compensation for optical aberrations in a significantly thinner and lighter format. Metasurfaces have been successfully applied in a variety of applications: from high-quality imaging to augmented reality, spectroscopy, environmental monitoring, and improving technology and medical equipment. Due to their remarkable adaptability and exceptional properties, metasurfaces have enormous potential to break the seemingly insurmountable boundaries of light technology. This review presents main applications of metasurfaces in terms of expanding the capabilities of computer optics, discusses challenges and prospects for development, including based on reconfigurable devices and using artificial intelligence methods.

We study tight focusing of coaxial superposition of two cylindrical vector beams (CVB) of different orders. In the initial plane, the polarization singularity index of such superposition equals the half-sum of the orders of the two constituent CVBs. Such superposition has neither spin angular momentum (SAM) nor transverse energy flow in the initial plane. We demonstrate that if two constituent CVBs are of different-parity orders, then, in the focal plane, there occur regions with nonzero longitudinal SAM components of alternating sign, alongside regions where opposite-handed transverse energy flows are rotating along closed paths (clockwise and counterclockwise). This means that the longitudinal spin and orbital Hall effects arise in the focal plane. On the contrary, if the two CVBs are of same-parity orders, polarization in the focal plane is inhomogeneous linear and the energy flow (Umov-Poynting vector) only has an on-axis component.

The paper proposes an algorithm for controlling the flight of an autonomous unmanned aerial vehicle (UAV) along the railway without operator participation and the use of satellite navigation systems such as GPS and GLONASS. With no operator and satellite navigation systems being involved, it becomes possible to inspect tracks over long distances, as the communication range with the UAV and external electromagnetic interference are of no concern. The algorithm is based on the use of computer vision: determining the location of rails in the video image and generating control signals to control the pitch, yaw and roll of the UAV in such a way as to keep the image of the rails in the middle of the video frame. Experimental UAV flights showed that the algorithm reliably determines the rails position and keeps the UAV flying along the railway.

We investigate the topological charge of superposition of parallel identical vortex beams of arbitrary shape – either Laguerre-Gaussian beams or Bessel-Gaussian beams or some other vortex beams with a rotationally symmetric intensity distribution. It is known that if all beams in the superposition are in-phase then the topological charge of the whole superposition is equal to the topological charge of each constituent beam, n. We show that if the beams are arranged on a circle with their phases linearly increasing on this circle, so that the phase difference between the neighboring beams equals 2πp/N, where N is the number of beams and р is integer, then the superposition has the topological charge n+p.

Beam divergence is one of the instrument resolution parameters in neutron computed tomography. In pinhole geometry, due to the finite size of the source, geometric unsharpness affects the transmission images and therefore influences the reconstructed data. In this paper, we propose an approach for deterministic simulation of this effect for a voxelized 3D object. The idea behind the proposed approach is to use multiple point sources at a pinhole position and collect transmission images from each of them. The implementation was done using the ASTRA toolbox by calculating cone beam projections from each point source. This approach was applied to a porous phantom. Artifacts associated with beam divergence were identified in the reconstructed data. The influence of beam divergence on the segmentation of pores by binarization of the reconstructed data has been considered.