Optical Memory and Neural Networks最新文献

A metrological device—Hartmannometer—based on a Shack-Hartmann wavefront sensor for measuring the flatness of optical surfaces was developed and researched, and the results were compared with the results obtained from measurements using a Fizeau interferometer. The paper presents a method for calibrating a Hartmannometer, and also compares the results of measuring a test optical surface using the developed device and a classical Fizeau interferometer. The total amplitude of wavefront distortions measured using a Fizeau interferometer was 0.127 μm (standard deviation 0.022 μm). The Hartmannometer showed distortions amplitude of 0.131 µm (standard deviation 0.024 µm).

In this work, we studied the photoinduced polarimetric and optical properties of the photosensitive carbazole-based azopolymer prepared in the form of thin films. Poly-N-(2,3-epoxypropyl)carbazole was used as a polymer matrix, which was copolymerized with the commercially available azo dye Solvent Yellow 3. Thin films of µm thicknesses were obtained by home-made rod-coating techniques. Polarization holographic recording was applied for direct diffraction gratings patterning. The polarization states of the recording beams were P–P, S–S, ±45° and left-right circular. The optical path of the probe beam passing through investigating media is defined by the summary changes in surface topography and volume anisotropy. The periodically modulated polarization/amplitude interference patterns produced by the gratings were investigated by in situ measurements of the diffraction efficiency (DE) kinetics in the first diffraction order at the DE saturation value. The surface relief was measured by AFM. A comparison of the behavior of azopolymer films during the recording of diffraction gratings with different polarization configurations of recording beams was carried out. The presented results confirm the possibility of recording not only the amplitude and phase of light, as in scalar holography, but also the polarization states of interfering beams. The angular dependences of the probe beam azimuth and ellipticity were analyzed, and some peculiarities of azopolymer photoinduced changes are discussed.

Determining the order of a vortex beam is an important problem in optics. One way is to insert an astigmatic distortion. However, other types of aberrations, such as coma, can also be useful in determining the topological charge of a vortex beam. The influence of the type and magnitude of aberrations on the distortion of the intensity pattern of vortex beams is investigated using Gauss-Laguerre modes of different orders.

This paper presents a theoretical work of a new device concept for frequency division demultiplexing with excellent performance based on waveguides system containing segments and loops in the presence of two geometrics defects. This system permits the separation of two frequency, based on 1D photonic waveguides loops structures. The system under consideration possesses a Y‑shaped demultiplexer configuration, consisting of a single input and two output channels (transmission lines). Each output channel contains an alternating unit cell consisting of a segment and a loop. The creation of a geometrical defect at the segment level in the middle of each output line allows the creation of two defect modes inside the bandgaps. The numerical results show that this demultiplexer system is able to separate two signals (electromagnetic waves) of different frequencies and guide each signal through an output channel. We perform the analytical calculation of the transmission rates T₁, T₂, and reflection R using the interface response theory, which is based on Green’s function method for the proposed demultiplexer system. The proposed device offers high transmission efficiency, high quality factor and a large frequency difference between defect modes, hence, it is highly desirable for frequency division demultiplexing applications.

The paper investigates algorithms using long intensity gradients for georeferencing of Earth remote sensing data. The case is considered in which one “reliable” referenced set of remote sensing data is already known for a particular area. New input data are referenced to this “reliable” set by detecting resemblant fragments in the “relible” data set and new remote sensing data. A set of pairs of resemblant fragments makes it possible to calculate the transformation parameters of new data. To increase the efficiency of resemblant fragments detection, we go to the space of long intensity gradients, which makes the georeferencing method more stable to admissible differences between resemblant fragments. The paper considers a few algorithms of going to the long gradient space and compares them. The computaional experiment provides grounds for recommending the best way of going to the long gradient space.

The 1/t Wang-Landau algorithm is analyzed from the viewpoint of execution time and accuracy when it is used in computations of the density of states of a two-dimensional Ising model. We find that the simulation results have a systematic error, the magnitude of which decreases with increasing the lattice size. The relative error has two maxima: the first one is located near the energy of the ground state, and the second maximum corresponds to the value of the internal energy at the critical point. We demonstrate that it is impossible to estimate the execution time of the 1/t Wang-Landau algorithm in advance when simulating large lattices. The reason is that when the final value of the modification factor was reached, the criterion for transition to mode 1/t was not met. The simultaneous calculations of the density of states for energy and magnetization are shown to lead to higher accuracy in estimating statistical moments of internal energy.

Recently, there has been an increased interest in NeRF methods which reconstruct differentiable representation of three-dimensional scenes. One of the main limitations of such methods is their inability to assess the confidence of the model in its predictions. In this paper, we propose a new neural network model for the formation of extended vector representations, called uSF, which allows the model to predict not only color and semantic label of each point, but also estimate the corresponding values of uncertainty. We show that with a small number of images available for training, a model that quantifies uncertainty performs better than a model without such functionality. Code of the uSF approach is publicly available at https://github.com/sevashasla/usf/.

Analyzing hyperspectral images poses a non-trivial challenge due to various challenges. To overcome most of these challenges one of the widely employed approach involves utilizing indices, such as the Normalized Difference Vegetation Index (NDVI). Indices provide a powerful means to distill complex spectral information into meaningful metrics, facilitating the interpretation of specific features within the hyperspectral domain. Moreover, the indices are usually easy to compute. However, creating indices for discerning arbitrary data classes within an image proves to be a challenging task. In this paper, we present an algorithm designed to automatically generate lightweight descriptors, suited for discerning between arbitrary classes in hyperspectral images. These lightweight descriptors within the algorithm are characterized by indices derived from selected informative layers. Our proposed algorithm streamlines the descriptor generation process through a multi-step approach. Firstly, it employs Principal Component Analysis (PCA) to transform the hyperspectral image into a three-channel representation. This transformed image serves as input for a Segment Anything Model (SAM). The neural network outputs a labeled map, delineating different classes within the hyperspectral image. Subsequently, our Informative Index Formation algorithm (INDI) utilizes this labeled map to systematically generate a set of lightweight descriptors. Each descriptor within the set is adept at distinguishing a specific class from the remaining classes in the hyperspectral image. The paper demonstrates the practical application of the developed algorithm for hyperspectral image segmentation.

Reducing the amount of car accidents and the deaths that result from them requires close monitoring of drivers’ health and alertness. Identifying driver weariness has been a major practical concern and problem in recent years. A number of machine learning algorithms have been used for monitoring the driver’s health system, even though accurate and early identification is more challenging. In order to overcome this issues, vehicle driver health is monitored using wearable ECG based on an optimized Deep Belief Network (DBN) is proposed. The collected ECG raw signal is pre-processed using a notch filter and high pass filter and an adaptive sliding window to improve the signal quality. After that, Wavelet Packet Decomposition (WPD) and the Short Time Fourier Transform (SIFT) are used to extract features from the pre-processed signal. It enables for the extraction of both time and frequency domain data. In order to classify whether a driver is fit to drive, is under stress, or has a heart condition, the extracted statistical features are sent for further classification using an optimized Deep Belief Neural Network (DBN). The walrus optimization technique is utilized to set the learning rate of the DBN classifier in an optimal manner. To prevent collisions between vehicles, the driver will be alerted via a buzzer system in the event of stress or heart problems. According to the results of the experimental research, the proposed technique achieves 95.1% accuracy, 92.5% precision, 96.5% specificity, 93% of recall, and 92.7% of the f1-score. Thus, the driver health monitoring system can be accurately detected using this automated model.

Plant diseases can harm crops and reduce the amount of food that can be cultivated, which is problematic for farmers. Technology is being utilized to develop computer-based programs that can recognize plant diseases and assist farmers in making better decisions after identifying plant leaf diseases. In most of these models, machine learning algorithms are applied, to make predictions about potential plant diseases using mathematical models and neural networks. Many researchers discussed the variants of DNN and CNN algorithms to solve the discussed problems and gave better results. In this paper, the novel approach is discussed and implemented where the plant disease is identified whether the plant leaf captured image has a noisy background or not; or whether the leaf image is segmented or not. The authors developed an adaptive algorithm which gives the results in two phases: the classification of the plant disease based on the original input leaf image and secondly, the identification of plant leaf disease after applying the segmentation process. The result of this two-phase proposed model is analyzed and compared with existing popular models like AlexNet, ResNet-50, and the EffNet the results are convincing. The proposed model has 97.39% accuracy when the noiseless image is taken; while the 90.26% accuracy is there, in case of noisy background image as an input; and the results are outstanding, if the authors are applying their segmentation-based AH-CNN model on the noisy real-time image, the accuracy is 95.27%.