Fast parallel beam propagation method based on multi-core and many-core architectures

Shaaban, Adel; Sayed, Manal M.; Hameed, Mohamed Farhat O.; Saleh, Hassan; Gomaa, L.R.; Du, Yi-Chun; Obayya, S. S. A.

doi:10.1016/j.ijleo.2018.11.111

Cited by 13 publications

(8 citation statements)

References 24 publications

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“…The Beam Propagation Method (BPM) is a popular method to simulate propagation and scattering of waves in a non-homogeneous media 1 and has been frequently used to simulate and design optoelelctronic devices. [2][3][4][5][6][7] The BPM methods can be roughly divided in two groups. The first group, called FD BPM, employs the finite difference method.…”

Section: Introductionmentioning

confidence: 99%

Time-dependent dynamical Bragg diffraction in crystals by beam propagation method (BPM)

Krzywiński¹,

Halavanau²

2022

Preprint

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Section: Introductionmentioning

confidence: 99%

Time-dependent dynamical Bragg diffraction in crystals by beam propagation method (BPM)

Krzywiński¹,

Halavanau²

2022

Preprint

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“…The beam propagation method (BPM) is a popular method to simulate the propagation and scattering of waves in nonhomogeneous media (Ersoy, 2007) and has been frequently used to design and simulate opto-electronic devices (Sziklas & Siegman, 1975;Feit & Fleck, 1978;Shaaban et al, 2019;Hadley, 1992;Yamauchi et al, 1995;Scalora & Crenshaw, 1994). The BPMs can be roughly divided into two groups.…”

Section: Introductionmentioning

confidence: 99%

Time-dependent dynamical Bragg diffraction in deformed crystals by the beam propagation method

Krzywiński

Halavanau

2022

Acta Cryst Sect A

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This paper describes how to efficiently solve time-dependent X-ray dynamic diffraction problems in distorted crystals with a fast Fourier transform based beam propagation method. Examples are given of using the technique to simulate the propagation of X-ray beams in deformed crystals in space and time domains relevant to the cavity-based X-ray free-electron lasers and X-ray free-electron laser self-seeding systems.

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“…Shi et al [19] proposed a method based on multi-feature learning, combining the linear and nonlinear features of hyperspectral remote sensing images to explore the linear and nonlinear boundaries between different features of remote sensing images. Shaaban et al [20] proposed a multi-core learning method based on Bayesian theory, which can efficiently fuse hundreds or thousands of nuclear features. However, ignoring the spectral and spatial characteristics of the pixels at the recognition boundary also limits the further improvement of target recognition accuracy.…”

Section: Introductionmentioning

confidence: 99%

High-Resolution Remote Sensing Image Information Extraction and Target Recognition Based on Multiple Information Fusion

Liu

Chang

2020

IEEE Access

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The current research on multiple information fusion of remote sensing images is mainly aimed at remote sensing images of specific satellite sensors, and cannot be extended to other types of data source images. For high-resolution remote sensing images, when its surface coverage changes significantly, most of the mainstream algorithms are difficult to restore satisfactorily. The algorithm proposed in this paper combines the sparse representation and the spectral, spatial, and temporal features of remote sensing images for the first time to solve the above problems. The algorithm proposed in this paper first simulates the human visual mechanism, and obtains the spatial, spectral, and temporal features of the remote sensing image through the spatial spectral dictionary learning and the time-varying weight learning model. Secondly, local constraints are added to the extraction of temporal features to obtain temporal and geographical change information of heterogeneous remote sensing images. Then, a sparse representation model combining space-spectrum-time features is proposed to extract features of high-resolution remote sensing images. Finally, based on the VGG-16 network, this paper proposes a target recognition network with deep fully convolutional network, and uses the extracted feature map as the input of the target recognition network to realize the target recognition of the remote sensing image. Experimental results show that the method proposed in this paper can improve the accuracy of target recognition and improve the accuracy of recognition.

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Fast parallel beam propagation method based on multi-core and many-core architectures

Cited by 13 publications

References 24 publications

Time-dependent dynamical Bragg diffraction in crystals by beam propagation method (BPM)

Time-dependent dynamical Bragg diffraction in crystals by beam propagation method (BPM)

Time-dependent dynamical Bragg diffraction in deformed crystals by the beam propagation method

High-Resolution Remote Sensing Image Information Extraction and Target Recognition Based on Multiple Information Fusion

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