Recently, data mining and neural networks are increasingly used for wavefront recognition from interferograms. In this case, there is considerable freedom in choosing the structure of the reference beam. In this work, a comparative study of the effectiveness of using neural networks for solving the problem of recognizing wavefront aberrations based on linear (flat reference beam) and conical (conical reference wavefront) interferograms is carried out. The effectiveness of recognition of types and levels of aberrations by conical interferograms based on the use of neural networks is shown: the average absolute error is reduced by 3 times, compared with linear interferograms. This effect is related to the rotational invariance of the introduced aberrations.
Synthetic Aperture Radar (SAR) interferometry is an active remote sensing technology that uses microwaves to characterize the earth's surface. SAR interferometry allows to measure the 3D profile of the earth's surface, recover surface topography, and determine topographic displacements over time. The microwave SAR signal is usually highly distorted. Distortions can be caused by, for example, atmospheric disturbances and various characteristics of earth's surface scatterers reflectance. Compensation for these distortions is performed by filtering the phase and evaluating the degree of coherence of the original images. This is an important step to improve the accuracy of the subsequent pphase-unwrapping operation. In this paper, we investigate the use of U-net neural networks for preprocessing the SAR interferogram at various parameters of the distortion of the SAR signal. Two neural networks filter the SAR interferogram and determine the degree of coherence, respectively.
In this work, training and recognition of the types of aberrations corresponding to individual Zernike functions have been carried out by the pattern of the intensity of the point scattering function (PSF) using convolutional neural networks. The PSF intensity patterns in the focal plane were modeled using the Fast Fourier Transform algorithm. When training a neural network, the learning coefficient and the number of epochs for a dataset of a given size were selected empirically. The average prediction errors of the neural network for each type of aberration were obtained for a set of 15 Zernike functions from a dataset of 15 thousand PSF pictures. As a result of training, for most types of aberrations, averaged absolute errors were obtained in the range 0.012–0.015, however, the determination of the aberration coefficient (magnitude) requires additional research and data, for example, calculating the PSF in the extrafocal plane.
The paper proposes a video surveillance scheme for compact placement of a system for railway rolling stock accounting. This design is based on the use of a tilted diffractive optical element and a tilted lens. Such an optical design makes it possible to significantly increase the depth of focus of the imaging system. This work considers the influence of the tilt of a diffractive lens on the shape and size of the focused area. Analytical relations describing the geometry of the focused region for various spectral channels are given. The possibility of increasing by several times the size of the zone of accurate image classification using a neural network has been demonstrated. The proposed approach has been tested on real-world dataset of images of house number plates.
The paper proposes using a two-zone different level Fresnel lens to increase the depth of field. On the one hand, such a diffractive optical element can reduce the weight of the device compared to, for example, cubic phase and binary axicon apodization. On the other hand, such an element has a simpler structure compared to a harmonic lens or free-form DOE. A neural network is used to restore the image. Optimization of the surface relief of the proposed two-zone lens is performed.
We simulate impulse propagation in all-optical temporal integrator based on a photonic crystal nanobeam cavity. This cavity (6 x 0.5 μm) is tens times smaller in size than Bragg grating or microring resonator proposed as an optical integrator earlier. The demonstrate good correspondence between numerical and analytical results.
This research article contains an experiment with implementation of image filtering task in Apache Storm and IBM InfoSphere Streams stream data processing systems. The aim of presented research is to show that new technologies could be effectively used for sliding window filtering of image sequences. The analysis of execution was focused on two parameters: throughput and memory consumption. Profiling was performed on CentOS operating systems running on two virtual machines for each system. The experiment results showed that IBM InfoSphere Streams has about 1.5 to 13.5 times lower memory footprint than Apache Storm, but could be about 2.0 to 2.5 slower on a real hardware.
We propose and numerically investigate an all-optical temporal integrator based on a photonic crystal nanobeam cavity. We show that an array of photonic crystal cavities enables high-order temporal integration.
We propose and investigate a complex hyperspectral image classification method with regard to the spatial proximity of pixels. Key feature of the method is that it uses common and relatively simple algorithms to attain high accuracy. The method combines the results of pixel-wise support vector machine classification and a set of contours derived from kmeans++ image clustering. To prevent redundant processing of similar data a principal component analysis is used. The method proposed enables the accuracy and speed of hyperspectral image classification to be enhanced.
Potentialities of the computational experiment when studying focusing elements for laser light are analyzed. We substantiate the need for the computational experiment when choosing a method for solving ill-posed inverse problems of the diffraction theory and analyzing new types of elements to focus laser light, the feasibility and efficiency of the optical elements under predetermined physical parameters of an optical system.
The formation of contact holes in semiconductor integrated circuit device fabrication is considered. The contact holes size usually corresponds to the resolution of the stepper. The vortex mask technique is intended to improve resolution in patterning contact holes through exposure. By adopting this technique, an isolated contact hole with a diameter of approximately 80 nm and 250 nm pitch can be formed with KrF, NA=0.63. The conventional vortex mask approach permits the single exposure printing of regular arrays of contact holes. The unwanted contact holes should be erased from resist pattern by exposing the wafer with a second, conventional trim mask. The double exposure process makes the fabrication more expensive. In the present work the vortex mask for single exposure printing of irregular arrays of contact holes is suggested.
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