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Image optical processing based on convolutional neural networks in sports video recognition simulation
The demand of sports video recognition simulation is increasing, but the traditional methods have some limitations in dealing with optical problems....
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Quantum convolutional neural networks for multiclass image classification
The quantum convolutional neural networks (QCNNs) are emerging as a promising solution for image classification problems on near-term quantum...
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Scheme of Signal Processing in a Multimode Communication Receiver Based on Convolutional Neural Networks
AbstractA scheme for optical signal processing in a multimode communication receiver based on deep convolutional neural networks and simulating the...
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Flat-Field Correction of X-Ray Tomographic Images Using Deep Convolutional Neural Networks
AbstractIt is proposed that neural networks be used to solve the problem of flat-field correction. A process is described for selecting parameters of...
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Estimating Significant Wave Height from X-Band Navigation Radar Using Convolutional Neural Networks
AbstractMarine radars are vital for safe navigation at sea, detecting vessels and obstacles. Sea clutter, caused by Bragg scattering, is usually...
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Optical image enhancement based on convolutional neural networks for key point detection in swimming posture analysis
Based on optical image enhancement, this paper uses convolutional neural network to detect key points of swimming posture. In swimming posture...
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Optical Videoscope Image Super-Resolution Based on Convolutional Neural Networks
Image super-resolution is the process performed to improve the resolution of the images from Low Resolution (LR) to High Resolution (HR). Videoscope...
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Application of Convolutional Neural Networks for Data Analysis in TAIGA-HiSCORE Experiment
AbstractThe Tunka Advanced Instrument for gamma-ray and cosmic ray Astrophysics (TAIGA) is a hybrid observatory for the detection of extensive air...
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Reactor field reconstruction from sparse and movable sensors using Voronoi tessellation-assisted convolutional neural networks
The aging of operational reactors leads to increased mechanical vibrations in the reactor interior. The vibration of the in-core sensors near their...
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A proposed video super-resolution reconstruction strategy using wavelet multi-scale convolutional neural networks
High-resolution (HR) images are often required for most applications, as they incorporate complementary information. However, the optimal utilization...
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Multiplexed orbital angular momentum beams demultiplexing using hybrid optical-electronic convolutional neural network
Advancements in optical communications have increasingly focused on leveraging spatial-structured beams such as orbital angular momentum (OAM) beams...
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Convolutional Neural Networks for Predicting Morphological and Nonlinear Optical Properties of Thin Films of Quasi-Two-Dimensional Materials
Two-dimensional materials are promising candidates for the creation of flat photonics devices. The main problem of using such materials for applied...
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Correlated optical convolutional neural network with “quantum speedup”
Compared with electrical neural networks, optical neural networks (ONNs) have the potentials to break the limit of the bandwidth and reduce the...
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Convolutional neural network based decoders for surface codes
The decoding of error syndromes of surface codes with classical algorithms may slow down quantum computation. To overcome this problem it is possible...
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Efficient quantum state tomography with convolutional neural networks
Modern day quantum simulators can prepare a wide variety of quantum states but the accurate estimation of observables from tomographic measurement...
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Identifying Discrete Breathers Using Convolutional Neural Networks
Artificial intelligence in the form of deep learning is now very popular and directly implemented in many areas of science and technology. In the...
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Decoding Fluorescence Excitation-Emission Matrices of Carbon Dots Aqueous Solutions with Convolutional Neural Networks to Create Multimodal Nanosensor of Metal Ions
AbstractIn this study, to create a carbon dots-based multimodal nanosensor of metal ions, a new approach to solving the inverse problem of...
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Simulation of UML graph classification model by using data preprocessing and convolutional neural network
With the gradual strengthening of science and technology and the continuous progress of the times, various fields of technology have been improved,...
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Hybrid quantum-classical convolutional neural networks
Deep learning has been shown to be able to recognize data patterns better than humans in specific circumstances or contexts. In parallel, quantum...
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Shallow hybrid quantum-classical convolutional neural network model for image classification
Currently, quantum neural networks (QNNs) have achieved some success in image classification due to their strong computational capabilities. However,...
