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Optical Assessment of Dentin Materials Opt. Mem. Neural Networks Pub Date : 2020-12-23 P. E. Timchenko, E. V. Timchenko, L. T. Volova, M. A. Zybin, O. O. Frolov, G. G. Dolgushov
Abstract The results of comparative spectral assessment of circumpulpal and mantle dentin and possibility of their further use for making bone substitute materials in dental surgery and implantology are presented in the work. The research was made using the Raman spectroscopy method. It is shown that the researched biological subjects have similar spectral composition and tooth dentin can be eventually
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Influence of Long-Range Interaction on Eigenvalues of Connection Matrix in One-Dimensional Ising Model Opt. Mem. Neural Networks Pub Date : 2020-12-23 B. V. Kryzhanovsky, L. B. Litinskii
Abstract We analyze a finite one-dimensional Ising system with periodic boundary conditions taking into account an arbitrary long-range interaction. We examine a discrete spectrum of eigenvalues of the spin connection matrix and a spectrum density of a continuous distribution obtained in the limit \(L \to \infty \) (L is the linear size of the system). We apply our results to particular cases of long-range
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Information Technology for Decision-making Support for Personalized Parameter Selection in Retinal Laser Treatment and Photocoagulation Outcome Prognostication Opt. Mem. Neural Networks Pub Date : 2020-12-23 N. Yu. Ilyasova, A. S. Shirokanev, N. S. Demin, R. A. Paringer, E. A. Zamytskiy
Abstract We propose a new information technology for mapping macular photocoagulation patterns in the laser treatment of diabetic retinopathy and evaluating the treatment strategy efficacy. The technology relies on processing optical coherence tomography (OCT) data, fundus image segmentation, mapping photocoagulation plans, and development of prognostic indicators of the laser photocoagulation treatment
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Neural Attention Mechanism and Linear Squeezing of Descriptors in Image Classification for Visual Recommender Systems Opt. Mem. Neural Networks Pub Date : 2020-12-23 A. V. Savchenko, K. V. Demochkin, L. V. Savchenko
Abstract In this paper, we analyze effective methods of multi-label classification of image sets in development of visual recommender systems. We propose a two-step algorithm, which at the first step performs fine-tuning of a convolutional neural network for extraction of visual features. At the second stage, the algorithm concatenates the obtained feature vectors of each image from the input set into
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Identification of Street Trees’ Main Nonphotosynthetic Components from Mobile Laser Scanning Data Opt. Mem. Neural Networks Pub Date : 2020-12-23 Shanshan Xu, Sheng Xu
Abstract Laser scanning technique is an important area of the optical and laser technology, which makes the access of 3D individual tree information becomes available. In order to deal with the biomass and structure estimation of the urban forest, many algorithms have been developed for 3D point clouds to extract individual tree information, including tree counts, tree locations, branching structure
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Current Trends in Development of Optical Metrology Opt. Mem. Neural Networks Pub Date : 2020-12-23 O. V. Angelsky, P. P. Maksymyak, C. Yu. Zenkova, S. G. Hanson, Jun Zheng
Abstract This review offers the reader some of the achievements of modern optical metrology. Over the past decades, it has become possible to make a leap in the basic approaches of metrology from the nano to the femto, approaching the pico level of measurements. Control of nano (micro) particle motion by an optical field and their use for testing complex optical fields; ultra-precise determination
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An Effective and Secure Data Sharing in P2P Network Using Biased Contribution Index Based Rumour Riding Protocol (BCIRR) Opt. Mem. Neural Networks Pub Date : 2020-12-23 Dharmendra Kumar, Mayank Pandey
Abstract Data sharing in the Peer to Peer (P2P) networks became an important function in the trustworthy computing. Secure and load balancing control in file sharing is vital to enhance the overall performance of P2P file sharing system. In literature many methods of load balancing control and security control have been used but it is not able to attain the best results in P2P networks. Hence, in this
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Determination of Geometrical Parameters in Blood Serum Films Using an Image Segmentation Algorithm Opt. Mem. Neural Networks Pub Date : 2020-12-23 Maksim Baranov, Elena Velichko, Faridoddin Shariaty
Abstract Observation of films of biological liquids allows finding markers of diseases such as diabetes or pneumonia. This may allow switching to more cost-effective diagnostic methods based on the use of relatively cheap commodity hardware required for optical microscopy. For this propose cuneiform dehydration of biological fluids is a promising method of medical diagnosis based on the study of structures
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Choosing Hyperparameter Values of the Convolution Neural Network When Solving the Problem of Semantic Segmentation of Images Obtained by Remote Sensing of the Earth’s Surface Opt. Mem. Neural Networks Pub Date : 2020-12-23 D. M. Igonin, P. A. Kolganov, Yu. V. Tiumentsev
Abstract Among the tasks solved by artificial neural networks are the tasks of analyzing objects on the images of the underlying Earth’s surface, obtained by the on-board equipment of unmanned aerial vehicle (UAV). For the solution of such problems, the convolutional neural networks (CNN), operating semantic segmentation of the received image, are widely used. In this case, the designer of such networks
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Evaluation of Flat Gain with RAMAN-Thulium Doped Silica Glass Fiber Hybrid Optical Amplifier in Some Band Spectrum for Super Dense Wavelength Division Multiplexing System Opt. Mem. Neural Networks Pub Date : 2020-10-08 Ghanendra Kumar, Chakresh Kumar
Abstract— The performance of RAMAN-TDSGF hybrid optical amplifier (HOA) has been examined in the context of gain and noise figure (NF) for 400 × 10 Gbps super dense wavelength division multiplexing (SD-WDM) system. Characteristics of this novel HOA has also compared with RAMAN-EDFA-RAMAN, RAMAN-EDFA and RAMAN-RAMAN HOA for the range of 1530 nm to 1665, and 1530 nm to 1586 nm respectively. Best rating
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Exponential Discretization of Weights of Neural Network Connections in Pre-Trained Neural Network. Part II: Correlation Maximization Opt. Mem. Neural Networks Pub Date : 2020-10-08 M. M. Pushkareva, I. M. Karandashev
Abstract— In this article, we develop method of linear and exponential quantization of neural network weights. We improve it by means of maximizing correlations between the initial and quantized weights taking into account the weight density distribution in each layer. We perform the quantization after the neural network training without a subsequent post-training and compare our algorithm with linear
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Nonlinear Optical Method of Determination the Chirp of Broadband Femtosecond Laser Pulse in IR-Range Opt. Mem. Neural Networks Pub Date : 2020-10-08 D. L. Hovhannisyan, A. H. Hovhannisyan, A. H. Vardanyan, G. D. Hovhannisyan
Abstract A new method for determining the chirp of a femtosecond broadband IR laser pulse at a central wavelength of 2.5 μm, based on the generation of a spectral supercontinuum in the field of a spectrally limited femtosecond laser pulse, propagating in a single mode fiber based on chalcogenide glass, and subsequent noncollinear generation of radiation at summary frequency by spectrally limited and
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2019 Land Cover Map of Southeast Asia at 30 m Spatial Resolution with Changes Since 2010 Opt. Mem. Neural Networks Pub Date : 2020-10-08 Mukesh Singh Boori, Komal Choudhary, Alexander Kupriyanov
Abstract— Last few decades there are lots of changes in Southeast Asia land cover due to development, industrialization, increasing population, socio-economic activities, and food demands. This research work analysis the maximum likelihood supervised classification approach for Southeast Asia land cover mapping and changes at 30m resolution from 2010 to 2019 using Landsat 8 OLI (Operational Land Imager)
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Spin Glass Energy Minimization through Learning and Evolution Opt. Mem. Neural Networks Pub Date : 2020-10-08 V. G. Red’ko
Abstract The research considers the minimization of spin glass energy via learning and evolution. The Sherrington-Kirkpatrick spin-glass model is used. A population of autonomous agents is considered. The genotype and phenotype of each agent are chains consisting of a great number of spins. The energy of spin glasses is minimized through learning and evolution of agents. The genotypes of agents are
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Roof Material Classification from Aerial Imagery Opt. Mem. Neural Networks Pub Date : 2020-10-08 R. A. Solovyev
Abstract— this paper describes an algorithm for classification of roof materials using aerial photographs. Main advantages of the algorithm are proposed methods to improve prediction accuracy. Proposed methods includes: method of converting ImageNet weights of neural networks for using multi-channel images; special set of features of second level models that are used in addition to specific predictions
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Method for Calculating Detection Probability of Objects Images by a Human Opt. Mem. Neural Networks Pub Date : 2020-10-08 Y. S. Gulina, V. Ya. Kolyuchkin
Abstract The article presents the results of research on the development of a method for calculating detection probability of noisy objects images by a human. The proposed calculation method are based on the visual system models, which take into account the features of images pre-processing carried out in the human eyes, as well as at the stages of primary and secondary processing performed in the
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Molecular Dynamics of Rhodamine 6G Solutions as Revealed by the Computer Processing of Fluorescence Microscopy Images Opt. Mem. Neural Networks Pub Date : 2020-10-08 E. A. Savchenko, A. A. Andryakov, E. N. Velichko
Abstract In this article the image processing of workflow from raw camera frames is detailed. The results of the visualization and quantitative analysis of the images from the rhodamine 6 G solution obtained using total internal reflection fluorescence microscopy are presented. The main stages for image processing of fluorescent molecules are considered. The following actions are described: acquisition
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Supervised Machine Learning Classification Algorithmic Approach for Finding Anomaly Type of Intrusion Detection in Wireless Sensor Network Opt. Mem. Neural Networks Pub Date : 2020-10-08 Ashwini B. Abhale, S. S. Manivannan
Abstract— From the last decade, the use of internet and its growth is continuously increasing. Similarly, numbers of services are coming out along with the internet and it is being used for providing facilities to human beings. Wireless sensor have been used for various application such as fire safety, military application, petroleum industry, security system, monitoring and environmental condition
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Use of Generative Adversarial Networks to Altering Remote Sensing Data Opt. Mem. Neural Networks Pub Date : 2020-10-08 M. V. Gashnikov, A. V. Kuznetsov
Abstract— The paper investigates the use of generative adversarial networks (GAN) for intentional modification of Earth remote sensing data. A generative neural network that includes a special subnet for object boundary inpainting is considered. The network comprises two GAN: the first completes the object boundary, and the second repaints blank areas. Actual remote sensing data are used to test the
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High Quality Factor and Dispersion Compensation Based on Fiber Bragg Grating in Dense Wavelength Division Multiplexing Opt. Mem. Neural Networks Pub Date : 2020-10-08 Bedir Yousif, Ahmed Sh. Samrah, Rawan Waheed
Abstract Chromatic Dispersion Compensation (CDC) using Fiber Bragg Grating (FBG) techniques in Dense Wavelength Division Multiplexing (DWDM) system is presented in this article. Pulse broadening effects on the transmitted signal can be limited by Dispersion Compensation (DC) mechanisms. To overcome this problem and improve the system performance, a suggested system for 10 Gbps using Chirped Fiber Bragg
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Study of Laser Ablative Destruction of Composites with Nanoscale Coatings of Hafnium and Zirconium Dioxides Opt. Mem. Neural Networks Pub Date : 2020-07-07 O. V. Mkrtychev; V. E. Privalov; V. G. Shemanin; Yu. V. Shevtsov
AbstractAn experimental study of laser ablation on the samples that comprise nanofilms of hafnium and zirconium dioxides on the surface of glass and silicon, and obtaining parameters of pulsed laser ablation in this work, provide new data describing the ablation mechanism. The samples were exposed to pulsed radiation from an Nd3+:YAG laser in order to measure the threshold energy density of laser ablation
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Implementation Features of Invariant Optical Correlator Based on Amplitude LC SLM Opt. Mem. Neural Networks Pub Date : 2020-07-07 D. S. Goncharov; E. K. Petrova; N. M. Ponomarev; R. S. Starikov; E. Yu. Zlokazov
AbstractMathematical simulations of invariant optical-digital correlator operation are performed with the LC SLM used for the input images display and for display of the correlation filter holograms. Different phase dependences on amplitude are considered, particularly the measured dependence of the LC SLM HoloEye LC 2002. A method for optimization of correlation filters to eliminate the object recognition
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Simulation of Fiber-Optic Buffer Loop Memory with All-Optical 2R Regeneration Opt. Mem. Neural Networks Pub Date : 2020-07-07 A. V. Polyakov
AbstractStructure of the fiber-optic recirculating loop memory with periodic 2R-regeneration of information flow in the optical range is designed. The modified optical fiber loop mirror for 2R-regeneration of wavelength division multiplexed return-to-zero signals is proposed. On the basis of the proposed mathematical model, using numerical modeling, the efficiency of the restoration of optical information
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Visualization of Volumetric Defects in a ZnGeP 2 Single-Crystal by Digital Holography Method Using Strontium Vapor Laser Radiation Opt. Mem. Neural Networks Pub Date : 2020-07-07 A. I. Gribenyukov; N. N. Yudin; S. N. Podzyvalov; M. M. Zinoviev; A. S. Olshukov; A. S. Shumeiko; A. N. Soldatov; N. A. Yudin
AbstractA method for visualization of volumetric defects in a ZnGeP2 single-crystal by digital holography method using strontium vapor laser radiation is proposed. The possibility of obtaining a volume distribution of defects with dimensions of ≥15–20 μm and their identification in a crystal is shown. The identification in a ZnGeP2 single-crystal of such volumetric defects as growth bands and needle
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Optimal Quantization and Adaptive Interpolation in Compression of Multidimensional Signals Opt. Mem. Neural Networks Pub Date : 2020-07-07 M. V. Gashnikov
AbstractThe paper deals with the algorithms of optimal quantization and adaptive interpolation in interpolative and hierarchical compression of multidimensional signals. An uneven quantization scale optimization algorithm is invented to tackle the unknown number of quantization levels given soft requirements for the optimization criterion. An important instance of applying the quantizer to the compression
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Global Finite-time Stability for Fractional-order Neural Networks Opt. Mem. Neural Networks Pub Date : 2020-07-07 Xiaolong Hu
AbstractThis paper is concerned with the global Mittag-Leffler stability (GMLS) and global finite-time stability (GFTS) for fractional Hopfield neural networks (FHNNs) with Hölder neuron activation functions subject to nonlinear growth. Firstly, four functions possessing convexity are proposed, which can guarantee that four formulas with respect to the fractional derivative are established. Correspondingly
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A Method of Small Object Detection Based on Improved Deep Learning Opt. Mem. Neural Networks Pub Date : 2020-07-07 Changgeng Yu; Kai Liu; Wei Zou
AbstractIn this paper, a parallel SSD (Single Shot MultiBox Detector) fusion network based on inverted residual structure (IR-PSN) is proposed to solve the problems of the lack of extracted feature information and the unsatisfactory effect of small object detection by deep learning. Firstly, the Inverted Residual Structure (IR) is adopted into the SSD network to replace the pooling layer. The improved
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Method for Whale Re-identification Based on Siamese Nets and Adversarial Training Opt. Mem. Neural Networks Pub Date : 2020-07-07 W. Wang; R. A. Solovyev; A. L. Stempkovsky; D. V. Telpukhov; A. A. Volkov
AbstractTraining Convolutional Neural Networks that do well in one-shot learning settings can have wide range of impacts on real-world datasets. In this paper, we explore an adversarial training method that learns a Siamese neural network in an end-to-end fashion for two models—ConvNets model that learns image embeddings from input image pair, and head model that further learns the distance between
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Image Processing for Analysis of Bio-Liquid Films Opt. Mem. Neural Networks Pub Date : 2020-04-02 M. A. Baranov; E. N. Velichko; A. A. Andryakov
AbstractIn this paper we discuss image processing algorithms for analysis of structures in the films of biological liquids obtained by the cuneiform dehydration method. In medical diagnostics the cuneiform dehydration may be considered as a promising method of biological liquids studies. Main types of structures in the biological liquid films are presented. Today the methods of interpretation and algorithms
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Radial-Basis Function Neural Network Synthesis on the Basis of Decision Tree Opt. Mem. Neural Networks Pub Date : 2020-04-02 Sergey Subbotin
AbstractThe problem of neural network synthesis on the precedents is addressed. The aim of the study is to create methods for radial-basis neural network model constructing having high levels of generalization and accuracy, which do not require user participation in the process of model building. The method of decision tree transforming into a neural network model is proposed. For a given sample, a
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Computation-Efficient Face Recognition Algorithm Using a Sequential Analysis of High Dimensional Neural-Net Features Opt. Mem. Neural Networks Pub Date : 2020-04-02 A. D. Sokolova; A. V. Savchenko
AbstractThe goal of the study is to increase the computation efficiency of the face recognition that uses feature vectors to describe facial images on photos and videos. These high-dimensional feature vectors are nowadays produced by convolutional neural networks. The methods to aggregate the features generated for each video frame are used to process the video sequences. A novel hierarchical recognition
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The Research about Recurrent Model-Agnostic Meta Learning Opt. Mem. Neural Networks Pub Date : 2020-04-02 Shaodong Chen; Ziyu Niu
AbstractAlthough Deep Neural Networks (DNNs) have performed great success in machine learning domain, they usually show poorly on few-shot learning tasks, where a classifier has to quickly generalize after getting very few samples from each class. A Model-Agnostic Meta Learning (MAML) model, which is able to solve new learning tasks, only using a small number of training data. A MAML model with a Convolutional
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Score Prediction Model Based on Neural Network Opt. Mem. Neural Networks Pub Date : 2020-04-02 Yijun Chen; Lan Guo; Cong Zhang
AbstractWith the rapid development of Internet technology, the data information on the network has also increased in an unprecedented amount. However, the emergence of this phenomenon makes it difficult for network users to find information of great value to themselves in the massive data. Therefore, score prediction is very important. It is an urgent task to establish an effective and practical scoring
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Optimized Extreme Learning Clustering and Orthogonally Projected User Grouping for Online Social Networks Opt. Mem. Neural Networks Pub Date : 2020-04-02 B. Gayathri Devi; V. Pattabiraman
AbstractIn social media, organizing friendship relationships is difficult since the more number of increasing users in Online Social Networks (OSN). To overcome this challenge, users in OSN heavily depend on grouping which is considered to be advantageous but at the same time found to be more cumbersome. More recently, recommender system and novel data clustering algorithm have been presented in OSN
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Multi-Start Method with Cutting for Solving Problems of Unconditional Optimization Opt. Mem. Neural Networks Pub Date : 2020-04-02 V. A. Kostenko
AbstractIn the present paper, we present a multi-start method with dynamic cutting of “unpromising” starts of locally optimal algorithms for solving continuous unconditional optimization problems. We also describe our results of testing of the proposed method for solving problems of feed-forward neural network training.
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Object Detection with Deep Neural Networks for Reinforcement Learning in the Task of Autonomous Vehicles Path Planning at the Intersection Opt. Mem. Neural Networks Pub Date : 2020-02-10 D. A. Yudin; A. Skrynnik; A. Krishtopik; I. Belkin; A. I. Panov
AbstractAmong a number of problems in the behavior planning of an unmanned vehicle the central one is movement in difficult areas. In particular, such areas are intersections at which direct interaction with other road agents takes place. In our work, we offer a new approach to train of the intelligent agent that simulates the behavior of an unmanned vehicle, based on the integration of reinforcement
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Designing a New Radial Basis Function Neural Network by Harmony Search for Diabetes Diagnosis Opt. Mem. Neural Networks Pub Date : 2020-02-10 Davar Giveki; Homayoun Rastegar
AbstractRadial Basis Function Neural Networks (RBFNNs) have been widely used for classification and function approximation tasks. So, it is worthy to try improving and developing new learning algorithms for RBFNNs in order to get better results. This paper presents a new learning method for RBFNNs. Hence, an improved learning algorithm for center adjustment of RBFNNs using Harmony search (HS) algorithm
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Modeling and Characterization of Resistor Elements for Neuromorphic Systems Opt. Mem. Neural Networks Pub Date : 2020-02-10 V. B. Kotov; F. A. Yudkin
AbstractPhysical structures changing their resistance in operation can serve as a basis for making elements of neural networks (synapses, neurons, etc.). The processes inducing changes of resistance are rather complicated and cannot be described readily. To demonstrate the potential of this sort of variable resistors it is possible to substitute a complex physical system by a simple mathematical model
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On the Possibilities of Encoding Digital Images Using Fractional Fourier Transform Opt. Mem. Neural Networks Pub Date : 2020-02-10 P. A. Ruchka; M. L. Galkin; M. S. Kovalev; G. K. Krasin; N. G. Stsepuro; S. B. Odinokov
AbstractData encryption is becoming increasingly relevant with the development of digital technologies. A particularly promising direction is the development of encryption methods based on optical transformations. Fractional Fourier Transform is a well-known method of encoding data, especially graphic. However, an assessment of the method’s resistance to unauthorized data decryption has not been carried
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Global Mittag-Leffler Stability of Fractional Hopfield Neural Networks with δ-Inverse Hölder Neuron Activations Opt. Mem. Neural Networks Pub Date : 2020-02-10 Xiaohong Wang; Huaiqin Wu
AbstractIn this paper, the global Mittag-Leffler stability of fractional Hopfield neural networks (FHNNs) with \(\delta \)-inverse hölder neuron activation functions are considered. By applying the Brouwer topological degree theory and inequality analysis techniques, the proof of the existence and uniqueness of equilibrium point is addressed. By constructing the Lure’s Postnikov-type Lyapunov functions
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Investigation on Hollow Beam Propagation through Turbulence Conditions in Free Space Optical Communication Opt. Mem. Neural Networks Pub Date : 2020-02-10 Y. P. Arul Teen; Nimmy Lazer; T. Nathiyaa; K. B. Rajesh
AbstractIn Free Space Optical Communication (FSO), the optical signal from the laser source severely affects while travelling through free space atmospheric channel due to scattering, absorption and other effects of atmospheric turbulence conditions. This degrades the performance of FSO communication. In this article, we have generated the hollow beam from the laser output by the inverse axicon lens
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Exponential Discretization of Weights of Neural Network Connections in Pre-Trained Neural Networks Opt. Mem. Neural Networks Pub Date : 2020-02-10 M. Yu. Malsagov; E. M. Khayrov; M. M. Pushkareva; I. M. Karandashev
AbstractTo reduce random access memory (RAM) requirements and to increase speed of recognition algorithms we consider a weight discretization problem for trained neural networks. We show that an exponential discretization is preferable to a linear discretization since it allows one to achieve the same accuracy when the number of bits is 1 or 2 less. The quality of the neural network VGG-16 is already
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Comparative Efficiency Analysis for Various Neuroarchitectures for Semantic Segmentation of Images in Remote Sensing Applications Opt. Mem. Neural Networks Pub Date : 2020-02-10 D. M. Igonin; Yu. V. Tiumentsev
AbstractThe problem of image understanding currently attracts considerable attention of researchers, since its solution is critically important for a significant number of applied problems. Among the most critical components of this problem is the semantic segmentation of images, which provides a classification of objects on the image at the pixel level. One of the applied problems in which semantic
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Multidimensional Signal Interpolation Based on Factorization and Dimension Reduction of Decision Rules Opt. Mem. Neural Networks Pub Date : 2020-02-10 M. V. Gashnikov
AbstractWe research adaptive multidimensional signal interpolators based on switching between several interpolating functions at each signal sample. We perform the switching by decision rule, which is optimized for each signal in the parameter space of this decision rule. Algorithms for factorization and dimension reduction of decision rules are proposed. We investigate new classes of interpolating
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Bus Arrival Time Prediction Using Recurrent Neural Network with LSTM Architecture Opt. Mem. Neural Networks Pub Date : 2019-09-30 A. A. Agafonov; A. S. Yumaganov
AbstractArrival time of public vehicles to transport stops is a key point of information systems for passengers. Accurate information on the arrival time is important for travel arrangements since it helps to decrease the wait time at a stop and to choose an optimal alternate route. Recently, such information has been included to mobile navigation applications too. In the present paper, we analyze
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Change in Density of States of 2D Ising Model when Next-Neighbor Interaction Is Introduced Opt. Mem. Neural Networks Pub Date : 2019-09-30 I. M. Karandashev; B. V. Kryzhanovsky
AbstractIn the present paper we analyzed a change in the density of states of a two-dimensional Ising model when a next-next-neighbor interaction is introduced. In other words, we examined two-dimensional lattices with diagonal connections. The same as in a three-dimensional model in this case each spin has 6 connections. Since the model is planar, we can calculate the free energy and other characteristics
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Semi-Empirical Continuous Time Neural Network Based Models for Controllable Dynamical Systems Opt. Mem. Neural Networks Pub Date : 2019-09-30 M. V. Egorchev; Yu. V. Tiumentsev
AbstractWe discuss the problem of mathematical and computer modeling of nonlinear controllable dynamical systems with incomplete knowledge about the object of modeling and the conditions of its operation. The suggested approach is based on a merging of theoretical knowledge for the system with training tools of artificial neural network (ANN) field. We present an extension of previously proposed semi-empirical
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Modeling Brain Cognitive Functions by Oscillatory Neural Networks Opt. Mem. Neural Networks Pub Date : 2019-09-30 Yakov Kazanovich
AbstractWe describe an oscillatory neural network designed as a system of generalized phase oscillators with a central element. It is shown that a winner-take-all principle can be realized in this system in terms of the competition of peripheral oscillators for the synchronization with a central oscillator. Several examples illustrate how this network can be used for the simulation of various cognitive
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Agriculture Phenology Monitoring Using NDVI Time Series Based on Remote Sensing Satellites: A Case Study of Guangdong, China Opt. Mem. Neural Networks Pub Date : 2019-09-30 Komal Choudhary; Wenzhong Shi; Mukesh Singh Boori; Samuel Corgne
Abstract—This article presents the use of the Normalized Differences Vegetation Index (NDVI) time series based change detection method for agriculture phenology monitoring. NDVI make use of the multi-spectral remote sensing data band combinations techniques to find out landscape such as agriculture, vegetation, land use/cover, water bodies and forest. Geographic Information System (GIS) technology
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Upgrade the Evaluation of the Contribution of the Active Element Cross Section Geometry to the He-Ne Laser Energy Characteristics Opt. Mem. Neural Networks Pub Date : 2019-09-30 V. A. Kozhevnikov; V. E. Privalov; V. G. Shemanin
AbstractThe models for estimating the contribution of the cross section geometry to the active medium gain of the He-Ne laser have been considered in this work. Expanding the range of studied cross sections it has been found that these models were not analytical and demand the approximate calculations. Modern methods and computing tools should clarify the results of 1960–1970 and it will bring to more
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Spectral Analysis of Spongy Bone Tissue after Ovariectomy and the Efficacy of Recovery Using Allogeneic Hydroxyapatite Opt. Mem. Neural Networks Pub Date : 2019-09-30 E. V. Timchenko; P. E. Timchenko; E. V. Pisareva; M. Y. Vlasov; L. T. Volova; I. V. Fedorova; A. S. Tumchenkova; A. N. Subatovich; M. A. Daniel
AbstractDuring the work, experimental studies of spongy bone tissue of animals after ovariectomy and evaluation of its regeneration by allogeneic hydroxyapatite (HAP) by Raman spectroscopy were carried out. Also deconvolution of Raman spectra of the studied samples was carried out. Coefficients allow to evaluate the efficiency of HAP for the correction of osteoresorption in modeling osteoporosis with
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Efficient Hybrid Descriptor for Face Verification in the Wild Using the Deep Learning Approach Opt. Mem. Neural Networks Pub Date : 2019-09-30 Bilel Ameur; Mebarka Belahcene; Sabeur Masmoudi; Ahmed Ben Hamida
AbstractIn this work, we propose a novel model-based on a new Deep Hybrid Descriptor learning called DeepGLBSIF (Gabor Local Binarized Statistical Image Feature) for effective extraction and over-complete features in multilayer hierarchy. The typology of our methodology is the same as that of Convolutional Neural Network (CNN) which is one of the intensively-applied deep learning architectures. This
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Erratum to: A New Neural Network Classifier Based on Atanassov’s Intuitionistic Fuzzy Set Theory Opt. Mem. Neural Networks Pub Date : 2019-09-30 Davar Giveki, Homayoun Rastegar, Maryam Karami
erratum
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Active Vision: From Theory to Application Opt. Mem. Neural Networks Pub Date : 2019-09-30 A. I. Samarin; L. N. Podladchikova; M. V. Petrushan; D. G. Shaposhnikov
AbstractAn overview of known works in active vision area and our recent results on application of the foveal visual preprocessor to detect the head motion parameters are presented. In overview, the main directions of research and development in the field of artificial foveal active vision have been considered. It is justified that: (i) for a successful solution of complex problems in this area and
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Effect of Alternate Polarization for SD-WDM System Using Hybrid Optical Amplifier Opt. Mem. Neural Networks Pub Date : 2019-07-01 Chakresh Kumar; Ghanendra Kumar; Rakesh Goyal
AbstractThe effect of alternate polarization (alP) has been investigated for 400 × 200 Gbps super dense wavelength division multiplexing (SD-WDM) system using RAMAN-EDFA-RAMAN hybrid optical amplifier (HOA). Further evaluation has carried out for the characteristics of bit error rate (BER) and quality factor (Q-factor) for enhance long haul optical communication. Remarkable performances have recorded
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Study of Fault Tolerance Methods for Hardware Implementations of Convolutional Neural Networks Opt. Mem. Neural Networks Pub Date : 2019-07-01 R. A. Solovyev; A. L. Stempkovsky; D. V. Telpukhov
AbstractThe paper concentrates on methods of fault protection of neural networks implemented as hardware operating in fixed-point mode. We have explored possible variants of error occurrence, as well as ways to eliminate them. For this purpose, networks of identical architecture based on VGG model have been studied. VGG SIMPLE neural network that has been chosen for experiments is a simplified version
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Motor Imagery-based Brain-Computer Interface: Neural Network Approach Opt. Mem. Neural Networks Pub Date : 2019-07-01 D. M. Lazurenko; V. N. Kiroy; I. E. Shepelev; L. N. Podladchikova
AbstractA neural network approach has been developed for detecting EEG patterns accompanying the implementation of motor imagery, which are mental equivalents of real movements. The method is based on Local Approximation of Spectral Power using Radial Basis Functions (LASP-RBF) and the original algorithm for interpreting the time sequence of neural network responses. An asynchronous neural interface
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Comparison of Face Recognition and Detection Models: Using Different Convolution Neural Networks Opt. Mem. Neural Networks Pub Date : 2019-07-01 Kai Kang
AbstractFace detection and recognition plays an important role in many occasions. This study explored the application of convolutional neural network in face detection and recognition. Firstly, convolutional neural network was briefly analyzed, and then a face detection model including three convolution layers, four pooling layers, introduction layers and three fully connected layers was designed.
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Agricultural Vegetation Monitoring Based on Aerial Data Using Convolutional Neural Networks Opt. Mem. Neural Networks Pub Date : 2019-07-01 V. Ganchenko; A. Doudkin
AbstractIn the present paper we discuss a problem of recognition of a state of agricultural vegetation using aerial data of different spatial resolutions. To solve this problem, we develop a classifier allowing us to divide the input images into three classes, which are “healthy vegetation”, “diseased vegetation”, and “soil”. The proposed classifier is based on two convolutional neural networks allowing
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Performance Analysis of Hybrid 2D Codes at Constant Weight Using ( n , w , λ a , λ c ) OOCs for OCDMA Opt. Mem. Neural Networks Pub Date : 2019-07-01 Manisha Bharti
AbstractHybrid optical two-dimensional (2D) codes due to their large cardinality and improved system performance for constant weight have recently been designed and studied for optical CDMA system. In this manuscript, an attempt has been made to analyze the performance of four different 2D codes from a communication point of view; that describe their use to support a system under certain conditions
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