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Journal articles on the topic 'Three-dimensional convolution'

Author: Grafiati

Published: 30 May 2022

Last updated: 31 May 2022

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1

McCutchen, C. W. "Convolution relation within the three-dimensional diffraction image." Journal of the Optical Society of America A 8, no. 6 (1991): 868. http://dx.doi.org/10.1364/josaa.8.000868.

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2

Wells, N. H., C. S. Burrus, G. E. Desobry, and A. L. Boyer. "Three-dimensional Fourier convolution with an array processor." Computers in Physics 4, no. 5 (1990): 507. http://dx.doi.org/10.1063/1.168385.

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3

Feng Bowen, 冯博文, 吕晓琪 Lü Xiaoqi, 谷宇 Gu Yu, 李菁 Li Qing, and 刘阳 Liu Yang. "Three-Dimensional Parallel Convolution Neural Network Brain Tumor Segmentation Based on Dilated Convolution." Laser & Optoelectronics Progress 57, no. 14 (2020): 141009. http://dx.doi.org/10.3788/lop57.141009.

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4

Fan, Wenxian, and Yebing Zou. "Three-dimensional Motion Skeleton Reconstruction Algorithm for Gymnastic Dancing Movements." International Journal of Circuits, Systems and Signal Processing 16 (January 7, 2022): 1–5. http://dx.doi.org/10.46300/9106.2022.16.1.

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Aiming at the problem of inaccurate matching results in the traditional three-dimensional reconstruction algorithm of gymnastic skeleton, a three-dimensional motion skeleton reconstruction algorithm of gymnastic dance action is proposed. Taking the center of gravity of the human body as the origin, the position of other nodes in the camera coordinate system relative to the center point of the human skeleton model is calculated, and the human skeleton data collection is completed through action division and posture feature calculation. Polynomial density is introduced into the integration of co

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Hyeon, Janghun, Weonsuk Lee, Joo Hyung Kim, and Nakju Doh. "NormNet: Point-wise normal estimation network for three-dimensional point cloud data." International Journal of Advanced Robotic Systems 16, no. 4 (2019): 172988141985753. http://dx.doi.org/10.1177/1729881419857532.

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In this article, a point-wise normal estimation network for three-dimensional point cloud data called NormNet is proposed. We propose the multiscale K-nearest neighbor convolution module for strengthened local feature extraction. With the multiscale K-nearest neighbor convolution module and PointNet-like architecture, we achieved a hybrid of three features: a global feature, a semantic feature from the segmentation network, and a local feature from the multiscale K-nearest neighbor convolution module. Those features, by mutually supporting each other, not only increase the normal estimation pe

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Yu Feng, 冯雨, 易本顺 Benshun Yi, 吴晨玥 Chenyue Wu, and 章云港 Yungang Zhang. "Pulmonary Nodule Recognition Based on Three-Dimensional Convolution Neural Network." Acta Optica Sinica 39, no. 6 (2019): 0615006. http://dx.doi.org/10.3788/aos201939.0615006.

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7

Kim, Dongyi, Hyeon Cho, Hochul Shin, Soo-Chul Lim, and Wonjun Hwang. "An Efficient Three-Dimensional Convolutional Neural Network for Inferring Physical Interaction Force from Video." Sensors 19, no. 16 (2019): 3579. http://dx.doi.org/10.3390/s19163579.

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Interaction forces are traditionally predicted by a contact type haptic sensor. In this paper, we propose a novel and practical method for inferring the interaction forces between two objects based only on video data—one of the non-contact type camera sensors—without the use of common haptic sensors. In detail, we could predict the interaction force by observing the texture changes of the target object by an external force. For this purpose, our hypothesis is that a three-dimensional (3D) convolutional neural network (CNN) can be made to predict the physical interaction forces from video image

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8

Kou, Shan Shan, Colin J. R. Sheppard, and Jiao Lin. "Calculation of the volumetric diffracted field with a three-dimensional convolution: the three-dimensional angular spectrum method." Optics Letters 38, no. 24 (2013): 5296. http://dx.doi.org/10.1364/ol.38.005296.

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Li, Qiang, Qi Wang, and Xuelong Li. "Mixed 2D/3D Convolutional Network for Hyperspectral Image Super-Resolution." Remote Sensing 12, no. 10 (2020): 1660. http://dx.doi.org/10.3390/rs12101660.

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Deep learning-based hyperspectral image super-resolution (SR) methods have achieved great success recently. However, there are two main problems in the previous works. One is to use the typical three-dimensional convolution analysis, resulting in more parameters of the network. The other is not to pay more attention to the mining of hyperspectral image spatial information, when the spectral information can be extracted. To address these issues, in this paper, we propose a mixed convolutional network (MCNet) for hyperspectral image super-resolution. We design a novel mixed convolutional module

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10

Igumnov, L. A., I. V. Vorobtsov, and S. Yu Litvinchuk. "Boundary Element Method with Runge-Kutta Convolution Quadrature for Three-Dimensional Dynamic Poroelasticity." Applied Mechanics and Materials 709 (December 2014): 101–4. http://dx.doi.org/10.4028/www.scientific.net/amm.709.101.

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The paper contains a brief introduction to the state of the art in poroelasticity models, in BIE & BEM methods application to solve dynamic problems in Laplace domain. Convolution Quadrature Method is formulated, as well as Runge-Kutta convolution quadrature modification and scheme with a key based on the highly oscillatory quadrature principles. Several approaches to Laplace transform inversion, including based on traditional Euler stepping scheme and Runge-Kutta stepping schemes, are numerically compared. A BIE system of direct approach in Laplace domain is used together with the discret

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Kastengren, Alan L., and J. Craig Dutton. "Aspects of Shear Layer Unsteadiness in a Three-Dimensional Supersonic Wake." Journal of Fluids Engineering 127, no. 6 (2005): 1085–94. http://dx.doi.org/10.1115/1.2062727.

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The near wake of a blunt-base cylinder at 10° angle-of-attack to a Mach 2.46 free-stream flow is visualized at several locations to study unsteady aspects of its structure. In both side-view and end-view images, the shear layer flapping grows monotonically as the shear layer develops, similar to the trends seen in a corresponding axisymmetric supersonic base flow. The interface convolution, a measure of the tortuousness of the shear layer, peaks for side-view and end-view images during recompression. The high convolution for a septum of fluid seen in the middle of the wake indicates that the s

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12

Gupta, P. K., L. A. Bennett, and A. P. Raiche. "Hybrid calculations of the three‐dimensional electromagnetic response of buried conductors." GEOPHYSICS 52, no. 3 (1987): 301–6. http://dx.doi.org/10.1190/1.1442304.

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The hybrid method for computing the electromagnetic response of a three‐dimensional conductor in a layered, conducting half‐space consists of solving a finite‐element problem in a localized region containing the conductor, and using integral‐equation methods to obtain the fields outside that region. The original scheme obtains the boundary values by iterating between the integral‐equation solution and the finite‐element solution, after making an initial guess based on primary values from the field. A two‐dimensional interpolation scheme is then used to speed the evaluation of the [Formula: see

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Hu, Shi-Min, Zheng-Ning Liu, Meng-Hao Guo, et al. "Subdivision-based Mesh Convolution Networks." ACM Transactions on Graphics 41, no. 3 (2022): 1–16. http://dx.doi.org/10.1145/3506694.

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Convolutionalneural networks (CNNs) have made great breakthroughs in two-dimensional (2D) computer vision. However, their irregular structure makes it hard to harness the potential of CNNs directly on meshes. A subdivision surface provides a hierarchical multi-resolution structure in which each face in a closed 2-manifold triangle mesh is exactly adjacent to three faces. Motivated by these two observations, this article presents SubdivNet , an innovative and versatile CNN framework for three-dimensional (3D) triangle meshes with Loop subdivision sequence connectivity. Making an analogy between

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Abdelazeem, Rania M., Doaa Youssef, Jala El-Azab, Salah Hassab-Elnaby, and Mostafa Agour. "Three-Dimensional Holographic Reconstruction of Brain Tissue Based on Convolution Propagation." Journal of Physics: Conference Series 1472 (February 2020): 012008. http://dx.doi.org/10.1088/1742-6596/1472/1/012008.

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15

Lagaris, I. E., and D. G. Papageorgiou. "CONVUS — an efficient package for calculating three-dimensional convolution-type integrals." Computer Physics Communications 76, no. 1 (1993): 80–86. http://dx.doi.org/10.1016/0010-4655(93)90122-s.

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Yang, Zhuqing. "A Novel Brain Image Segmentation Method Using an Improved 3D U-Net Model." Scientific Programming 2021 (August 18, 2021): 1–10. http://dx.doi.org/10.1155/2021/4801077.

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Medical image segmentation (IS) is a research field in image processing. Deep learning methods are used to automatically segment organs, tissues, or tumor regions in medical images, which can assist doctors in diagnosing diseases. Since most IS models based on convolutional neural network (CNN) are two-dimensional models, they are not suitable for three-dimensional medical imaging. On the contrary, the three-dimensional segmentation model has problems such as complex network structure and large amount of calculation. Therefore, this study introduces the self-excited compressed dilated convolut

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17

Zhao, Di. "Mobile GPU Computing Based Filter Bank Convolution for Three-Dimensional Wavelet Transform." International Journal of Mobile Computing and Multimedia Communications 7, no. 2 (2016): 22–35. http://dx.doi.org/10.4018/ijmcmc.2016040102.

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Mobile GPU computing, or System on Chip with embedded GPU (SoC GPU), becomes in great demand recently. Since these SoCs are designed for mobile devices with real-time applications such as image processing and video processing, high-efficient implementations of wavelet transform are essential for these chips. In this paper, the author develops two SoC GPU based DWT: signal based parallelization for discrete wavelet transform (sDWT) and coefficient based parallelization for discrete wavelet transform (cDWT), and the author evaluates the performance of three-dimensional wavelet transform on SoC G

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18

Starkschall, George. "Beam-commissioning methodology for a three-dimensional convolution/superposition photon dose algorithm." Journal of Applied Clinical Medical Physics 1, no. 1 (2000): 8. http://dx.doi.org/10.1120/1.308246.

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Starkschall, George, Roy E. Steadham, Richard A. Popple, Salahuddin Ahmad, and Isaac I. Rosen. "Beam-commissioning methodology for a three-dimensional convolution/superposition photon dose algorithm." Journal of Applied Clinical Medical Physics 1, no. 1 (2000): 8–27. http://dx.doi.org/10.1120/jacmp.v1i1.2651.

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Banjai, Lehel, and Maryna Kachanovska. "Sparsity of Runge–Kutta convolution weights for the three-dimensional wave equation." BIT Numerical Mathematics 54, no. 4 (2014): 901–36. http://dx.doi.org/10.1007/s10543-014-0498-9.

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21

Zha, Wenshu, Wen Zhang, Daolun Li, Yan Xing, Lei He, and Jieqing Tan. "Convolution-Based Model-Solving Method for Three-Dimensional, Unsteady, Partial Differential Equations." Neural Computation 34, no. 2 (2022): 518–40. http://dx.doi.org/10.1162/neco_a_01462.

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Abstract Neural networks are increasingly used widely in the solution of partial differential equations (PDEs). This letter proposes 3D-PDE-Net to solve the three-dimensional PDE. We give a mathematical derivation of a three-dimensional convolution kernel that can approximate any order differential operator within the range of expressing ability and then conduct 3D-PDE-Net based on this theory. An optimum network is obtained by minimizing the normalized mean square error (NMSE) of training data, and L-BFGS is the optimized algorithm of second-order precision. Numerical experimental results sho

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22

Miao Guang, 苗光, and 李朝锋 Li Chaofeng. "Detection of Pulmonary Nodules CT Images Combined with Two-Dimensional and Three-Dimensional Convolution Neural Networks." Laser & Optoelectronics Progress 55, no. 5 (2018): 051006. http://dx.doi.org/10.3788/lop55.051006.

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23

Yu, Dawen, and Shunping Ji. "Grid Based Spherical CNN for Object Detection from Panoramic Images." Sensors 19, no. 11 (2019): 2622. http://dx.doi.org/10.3390/s19112622.

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Recently proposed spherical convolutional neural networks (SCNNs) have shown advantages over conventional planar CNNs on classifying spherical images. However, two factors hamper their application in an objection detection task. First, a convolution in S2 (a two-dimensional sphere in three-dimensional space) or SO(3) (three-dimensional special orthogonal group) space results in the loss of an object’s location. Second, overlarge bandwidth is required to preserve a small object’s information on a sphere because the S2/SO(3) convolution must be performed on the whole sphere, instead of a local i

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Skoglund, Ulf. "Quantitative Image Processing in 3D." Microscopy and Microanalysis 4, S2 (1998): 442–43. http://dx.doi.org/10.1017/s1431927600022339.

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Three-dimensional reconstructions from projections are usually ridden by noise from different sources. A common problem among many three-dimensional reconstruction techniques is the systematic absence of certain projections, but also the accidental absence of spurious projections. In these three-dimensional reconstructions such absences are visible as directional smearing due to convolution. Other convolution effects such as those due to the optics of the instrument used to record the data usually cause severe damping of high frequencies and even contrast reversal (common in images from electr

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Li, N., C. Wang, H. Zhao, X. Gong, and D. Wang. "A NOVEL DEEP CONVOLUTIONAL NEURAL NETWORK FOR SPECTRAL–SPATIAL CLASSIFICATION OF HYPERSPECTRAL DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 897–900. http://dx.doi.org/10.5194/isprs-archives-xlii-3-897-2018.

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Spatial and spectral information are obtained simultaneously by hyperspectral remote sensing. Joint extraction of these information of hyperspectral image is one of most import methods for hyperspectral image classification. In this paper, a novel deep convolutional neural network (CNN) is proposed, which extracts spectral-spatial information of hyperspectral images correctly. The proposed model not only learns sufficient knowledge from the limited number of samples, but also has powerful generalization ability. The proposed framework based on three-dimensional convolution can extract spectral

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26

Baddour, Natalie. "Operational and convolution properties of three-dimensional Fourier transforms in spherical polar coordinates." Journal of the Optical Society of America A 27, no. 10 (2010): 2144. http://dx.doi.org/10.1364/josaa.27.002144.

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27

Karasik, Y. B. "How to compute three-dimensional convolution and/or correlation optically: A mathematical foundation." Journal of Modern Optics 45, no. 4 (1998): 817–23. http://dx.doi.org/10.1080/09500349808230624.

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28

Bai, Xue, Ze Liu, Jie Zhang, et al. "Comparing of two dimensional and three dimensional fully convolutional networks for radiotherapy dose prediction in left-sided breast cancer." Science Progress 104, no. 3 (2021): 003685042110381. http://dx.doi.org/10.1177/00368504211038162.

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Fully convolutional networks were developed for predicting optimal dose distributions for patients with left-sided breast cancer and compared the prediction accuracy between two-dimensional and three-dimensional networks. Sixty cases treated with volumetric modulated arc radiotherapy were analyzed. Among them, 50 cases were randomly chosen to conform the training set, and the remaining 10 were to construct the test set. Two U-Net fully convolutional networks predicted the dose distributions, with two-dimensional and three-dimensional convolution kernels, respectively. Computed tomography image

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29

Zheng, Yang, Jieyu Zhao, Yu Chen, Chen Tang, and Shushi Yu. "3D Mesh Model Classification with a Capsule Network." Algorithms 14, no. 3 (2021): 99. http://dx.doi.org/10.3390/a14030099.

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With the widespread success of deep learning in the two-dimensional field, how to apply deep learning methods from two-dimensional to three-dimensional field has become a current research hotspot. Among them, the polygon mesh structure in the three-dimensional representation as a complex data structure provides an effective shape approximate representation for the three-dimensional object. Although the traditional method can extract the characteristics of the three-dimensional object through the graphical method, it cannot be applied to more complex objects. However, due to the complexity and

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Qing, Yuhao, and Wenyi Liu. "Hyperspectral Image Classification Based on Multi-Scale Residual Network with Attention Mechanism." Remote Sensing 13, no. 3 (2021): 335. http://dx.doi.org/10.3390/rs13030335.

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In recent years, image classification on hyperspectral imagery utilizing deep learning algorithms has attained good results. Thus, spurred by that finding and to further improve the deep learning classification accuracy, we propose a multi-scale residual convolutional neural network model fused with an efficient channel attention network (MRA-NET) that is appropriate for hyperspectral image classification. The suggested technique comprises a multi-staged architecture, where initially the spectral information of the hyperspectral image is reduced into a two-dimensional tensor, utilizing a princ

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31

Haizhong, Qian. "I3D: An Improved Three-Dimensional CNN Model on Hyperspectral Remote Sensing Image Classification." Security and Communication Networks 2021 (November 29, 2021): 1–12. http://dx.doi.org/10.1155/2021/5217578.

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Hyperspectral image data are widely used in real life because it contains rich spectral and spatial information. Hyperspectral image classification is to distinguish different functions based on different features. The computer performs quantitative analysis through the captured image and classifies each pixel in the image. However, the traditional deep learning-based hyperspectral image classification technology, due to insufficient spatial-spectral feature extraction, too many network layers, and complex calculations, leads to large parameters and optimizes hyperspectral images. For this rea

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Hu, Guo X., Zhong Yang, Lei Hu, Li Huang, and Jia M. Han. "Small Object Detection with Multiscale Features." International Journal of Digital Multimedia Broadcasting 2018 (September 30, 2018): 1–10. http://dx.doi.org/10.1155/2018/4546896.

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The existing object detection algorithm based on the deep convolution neural network needs to carry out multilevel convolution and pooling operations to the entire image in order to extract a deep semantic features of the image. The detection models can get better results for big object. However, those models fail to detect small objects that have low resolution and are greatly influenced by noise because the features after repeated convolution operations of existing models do not fully represent the essential characteristics of the small objects. In this paper, we can achieve good detection a

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33

Igumnov, Leonid A., Ivan Markov, Aleksandr Lyubimov, and Valery Novikov. "Dynamic Response of Three-Dimensional Multi-Domain Piezoelectric Structures via BEM." Key Engineering Materials 769 (April 2018): 317–22. http://dx.doi.org/10.4028/www.scientific.net/kem.769.317.

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In this paper, a Laplace domain boundary element method is applied for transient dynamic analysis of three-dimensional multi-domain linear piezoelectric structures. Piezoelectric materials of homogeneous sub-domains may have arbitrary degree of anisotropy. The boundary element formulation is based on a weakly singular representation of the piezoelectric boundary integral equations in the Laplace domain. To compute the time-domain solutions a convolution quadrature formula is applied for the numerical inversion of Laplace transform. Presented multi-domain boundary element method is tested on a

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34

Li, Ruixue, Bo Yin, Yanping Cong, and Zehua Du. "Simultaneous Prediction of Soil Properties Using Multi_CNN Model." Sensors 20, no. 21 (2020): 6271. http://dx.doi.org/10.3390/s20216271.

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Soil nutrient prediction based on near-infrared spectroscopy has become the main research direction for rapid acquisition of soil information. The development of deep learning has greatly improved the prediction accuracy of traditional modeling methods. In view of the low efficiency and low accuracy of current soil prediction models, this paper proposes a soil multi-attribute intelligent prediction method based on convolutional neural networks, by constructing a dual-stream convolutional neural network model Multi_CNN that combines one-dimensional convolution and two-dimensional convolution, t

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35

Li, Wenmei, Huaihuai Chen, Qing Liu, Haiyan Liu, Yu Wang, and Guan Gui. "Attention Mechanism and Depthwise Separable Convolution Aided 3DCNN for Hyperspectral Remote Sensing Image Classification." Remote Sensing 14, no. 9 (2022): 2215. http://dx.doi.org/10.3390/rs14092215.

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Hyperspectral Remote Rensing Image (HRSI) classification based on Convolution Neural Network (CNN) has become one of the hot topics in the field of remote sensing. However, the high dimensional information and limited training samples are prone to the Hughes phenomenon for hyperspectral remote sensing images. Meanwhile, high-dimensional information processing also consumes significant time and computing power, or the extracted features may not be representative, resulting in unsatisfactory classification efficiency and accuracy. To solve these problems, an attention mechanism and depthwise sep

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Yang Jun, 杨军, 王顺 Wang Shun, and 周鹏 Zhou Peng. "Recognition and Classification for Three-Dimensional Model Based on Deep Voxel Convolution Neural Network." Acta Optica Sinica 39, no. 4 (2019): 0415007. http://dx.doi.org/10.3788/aos201939.0415007.

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Marston, Philip L. "Spatial surface convolution approximation of three‐dimensional leaky wave contributions to high‐frequency scattering." Journal of the Acoustical Society of America 100, no. 4 (1996): 2820. http://dx.doi.org/10.1121/1.416619.

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38

McGary, J. E., and A. L. Boyer. "An interactive, parallel, three-dimensional fast Fourier transform convolution dose calculation using a supercomputer." Medical Physics 24, no. 4 (1997): 519–22. http://dx.doi.org/10.1118/1.597934.

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Chehrazi, F., B. Yi, M. Sarfaraz, S. Naqvi, and C. Yu. "Three Dimensional Dose Reconstruction IMRT Verification using an EPID and the Convolution Superposition Algorithm." International Journal of Radiation Oncology*Biology*Physics 63 (October 2005): S514. http://dx.doi.org/10.1016/j.ijrobp.2005.07.870.

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TIMOSHIN, S. N., and F. T. SMITH. "Vortex/inflectional-wave interactions with weakly three-dimensional input." Journal of Fluid Mechanics 348 (October 10, 1997): 247–94. http://dx.doi.org/10.1017/s0022112097006447.

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The subtle impact of the spanwise scaling in nonlinear interactions between oblique instability waves and the induced longitudinal vortex field is considered theoretically for the case of a Rayleigh-unstable boundary-layer flow, at large Reynolds numbers. A classification is given of various flow regimes on the basis of Reynolds-stress mechanisms of mean vorticity generation, and a connection between low-amplitude non-parallel vortex/wave interactions and less-low-amplitude non-equilibrium critical-layer flows is discussed in more detail than in previous studies. Two new regimes of vortex/wave

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Chamgoulov, Ravil, Pierre Lane, and Calum MacAulay. "Optical Computed-Tomographic Microscope for Three-Dimensional Quantitative Histology." Analytical Cellular Pathology 26, no. 5-6 (2004): 319–27. http://dx.doi.org/10.1155/2004/209579.

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A novel optical computed‐tomographic microscope has been developed allowing quantitative three‐dimensional (3D) imaging and analysis of fixed pathological material. Rather than a conventional two‐dimensional (2D) image, the instrument produces a 3D representation of fixed absorption‐stained material, from which quantitative histopathological features can be measured more accurately. The accurate quantification of these features is critically important in disease diagnosis and the clinical classification of cancer. The system consists of two high NA objective lenses, a light source, a digital s

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Lutter, Liisa, Christopher J. Serpell, Mick F. Tuite, Louise C. Serpell, and Wei-Feng Xue. "Three-dimensional reconstruction of individual helical nano-filament structures from atomic force microscopy topographs." Biomolecular Concepts 11, no. 1 (2020): 102–15. http://dx.doi.org/10.1515/bmc-2020-0009.

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AbstractAtomic force microscopy, AFM, is a powerful tool that can produce detailed topographical images of individual nano-structures with a high signal-to-noise ratio without the need for ensemble averaging. However, the application of AFM in structural biology has been hampered by the tip-sample convolution effect, which distorts images of nano-structures, particularly those that are of similar dimensions to the cantilever probe tips used in AFM. Here we show that the tip-sample convolution results in a feature-dependent and non-uniform distribution of image resolution on AFM topographs. We

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Bourgeois, Aline, Philippe Joseph, and Jean Claude Lecomte. "Three-dimensional full wave seismic modelling versus one-dimensional convolution: the seismic appearance of the Grès d’Annot turbidite system." Geological Society, London, Special Publications 221, no. 1 (2004): 401–17. http://dx.doi.org/10.1144/gsl.sp.2004.221.01.22.

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Wu, Peida, Ziguan Cui, Zongliang Gan, and Feng Liu. "Three-Dimensional ResNeXt Network Using Feature Fusion and Label Smoothing for Hyperspectral Image Classification." Sensors 20, no. 6 (2020): 1652. http://dx.doi.org/10.3390/s20061652.

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In recent years, deep learning methods have been widely used in the hyperspectral image (HSI) classification tasks. Among them, spectral-spatial combined methods based on the three-dimensional (3-D) convolution have shown good performance. However, because of the three-dimensional convolution, increasing network depth will result in a dramatic rise in the number of parameters. In addition, the previous methods do not make full use of spectral information. They mostly use the data after dimensionality reduction directly as the input of networks, which result in poor classification ability in so

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Jiang, Xinrui, Ye Zhang, Qi Yang, Bin Deng, and Hongqiang Wang. "Millimeter-Wave Array Radar-Based Human Gait Recognition Using Multi-Channel Three-Dimensional Convolutional Neural Network." Sensors 20, no. 19 (2020): 5466. http://dx.doi.org/10.3390/s20195466.

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At present, there are two obvious problems in radar-based gait recognition. First, the traditional radar frequency band is difficult to meet the requirements of fine identification with due to its low carrier frequency and limited micro-Doppler resolution. Another significant problem is that radar signal processing is relatively complex, and the existing signal processing algorithms are poor in real-time usability, robustness and universality. This paper focuses on the two basic problems of human gait detection with radar and proposes a human gait classification and recognition method based on

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Lozano Torres, Jose Agustin, and Björn Malte Schäfer. "Three-dimensional weak gravitational lensing of the 21-cm radiation background." Monthly Notices of the Royal Astronomical Society 512, no. 4 (2022): 5135–52. http://dx.doi.org/10.1093/mnras/stac796.

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ABSTRACT We study weak gravitational lensing by the cosmic large-scale structure of the 21-cm radiation background in the 3D weak-lensing formalism. The interplay between source distance measured at finite resolution, visibility, and lensing terms is analysed in detail and the resulting total covariance Cℓ(k, k′) is derived. The effect of lensing correlates different multipoles through convolution, breaking the statistical homogeneity of the 21-cm radiation background. This homogeneity breaking can be exploited to reconstruct the lensing field $\hat{\phi }_{\rm \ell m}(\kappa)$ and noise-lensi

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Zhao, Huapeng, and Zhongxiang Shen. "Efficient Modeling of Three-Dimensional Reverberation Chambers Using Hybrid Discrete Singular Convolution-Method of Moments." IEEE Transactions on Antennas and Propagation 59, no. 8 (2011): 2943–53. http://dx.doi.org/10.1109/tap.2011.2158966.

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Yang Li-Xia, Ge De-Biao, and Wei Bing. "Three-dimensional finite-difference time-domain implementation for anisotropic dispersive medium using recursive convolution method." Acta Physica Sinica 56, no. 8 (2007): 4509. http://dx.doi.org/10.7498/aps.56.4509.

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Yoo, Hoon, та Jae-Young Jang. "Computational three-dimensional reconstruction in diffraction grating imaging by convolution with periodic δ-function array". Optik 249 (січень 2022): 168211. http://dx.doi.org/10.1016/j.ijleo.2021.168211.

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Zhang, Qi, Jianlong Chang, Gaofeng Meng, Shiming Xiang, and Chunhong Pan. "Spatio-Temporal Graph Structure Learning for Traffic Forecasting." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (2020): 1177–85. http://dx.doi.org/10.1609/aaai.v34i01.5470.

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Abstract:

As an indispensable part in Intelligent Traffic System (ITS), the task of traffic forecasting inherently subjects to the following three challenging aspects. First, traffic data are physically associated with road networks, and thus should be formatted as traffic graphs rather than regular grid-like tensors. Second, traffic data render strong spatial dependence, which implies that the nodes in the traffic graphs usually have complex and dynamic relationships between each other. Third, traffic data demonstrate strong temporal dependence, which is crucial for traffic time series modeling. To add

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