- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2019 IEEE International Conference on Manipulation, Manufacturing and Measurement on the Nanoscale (3M-NANO) - Zhenjiang, China (2019.8.4-2019.8.8)] 2019 IEEE International Conference on Manipulation, Manufacturing and Measurement on the Nanoscale (3M-NANO) - Modification of Wettability Property of NITI Alloy by Laser Texturing and Carbon Ion Implantation
摘要： Nonlocal self-similarity of images has attracted considerable interest in the field of image processing and has led to several state-of-the-art image denoising algorithms, such as block matching and 3-D, principal component analysis with local pixel grouping, patch-based locally optimal wiener, and spatially adaptive iterative singular-value thresholding. In this paper, we propose a computationally simple denoising algorithm using the nonlocal self-similarity and the low-rank approximation (LRA). The proposed method consists of three basic steps. First, our method classifies similar image patches by the block-matching technique to form the similar patch groups, which results in the similar patch groups to be low rank. Next, each group of similar patches is factorized by singular value decomposition (SVD) and estimated by taking only a few largest singular values and corresponding singular vectors. Finally, an initial denoised image is generated by aggregating all processed patches. For low-rank matrices, SVD can provide the optimal energy compaction in the least square sense. The proposed method exploits the optimal energy compaction property of SVD to lead an LRA of similar patch groups. Unlike other SVD-based methods, the LRA in SVD domain avoids learning the local basis for representing image patches, which usually is computationally expensive. The experimental results demonstrate that the proposed method can effectively reduce noise and be competitive with the current state-of-the-art denoising algorithms in terms of both quantitative metrics and subjective visual quality.
关键词： self-similarity,Back projection,patch grouping,image denoising,low-rank approximation (LRA),singular value decomposition (SVD)
更新于2025-09-23 15:21:01
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[IEEE 2019 International Applied Computational Electromagnetics Society Symposium - China (ACES) - Nanjing, China (2019.8.8-2019.8.11)] 2019 International Applied Computational Electromagnetics Society Symposium - China (ACES) - Double-Ridge Waveguide Mode Analysis based on FDTD-SVD Method
摘要： In this paper,hybrid finite-difference time-domain method(FDTD) and singular value decomposition(SVD),denoted by FDTD-SVD,is applied to analyze the double-ridge waveguide dominant mode. Double-ridge waveguide (WRD) without fillets and WRD with small fillets are analyzed.The simulation results show WRD without fillets can achieve a single-mode operation of 10TE from 35GHz to 112 GHz. For WRD with small fillets, its single-mode operation frequency band is 35-116GHz.These results are almost consistent with the detailed parametric study result(38-120GHz)based on Ansys HFSS, which proves that the FDTD-SVD method is simple and more effective. In addition, the transmission performance of WRD can be improved by adding fillets and enlarging the radius of fillets.
关键词： mode analysis,singular value decomposition(SVD),finite-difference time-domain method(FDTD),double-ridge waveguide(WRD)
更新于2025-09-23 15:21:01
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[IEEE 2017 IEEE 44th Photovoltaic Specialists Conference (PVSC) - Washington, DC (2017.6.25-2017.6.30)] 2017 IEEE 44th Photovoltaic Specialist Conference (PVSC) - Simulation of the performances of multijunction solar cells with improved voltage by transfer and scattering matrix methods
摘要： In this paper, we propose a virtual spatial modulation (VSM) scheme that performs index modulation on the virtual parallel channels resulting from the singular value decomposition of the multi-input-multi-output channels. The VSM scheme conveys information through both the indices of the virtual parallel channels and the M -ary modulated symbols. We derive a closed-form upper bound on the average bit error probability (ABEP), which considers the impact of imperfect channel estimation. Moreover, the asymptotic ABEP is also studied, which characterizes the error ?oor under imperfect channel estimation and the resulting diversity order as well as the coding gain under perfect channel estimation. Computer simulations verify the analysis and show that the VSM scheme can outperform the existing pre-coding aided spatial modulation schemes under the same spectral ef?ciency.
关键词： Singular value decomposition (SVD),average bit error probability,spatial modulation,pre-coding
更新于2025-09-23 15:19:57
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[IEEE 2020 IEEE/SICE International Symposium on System Integration (SII) - Honolulu, HI, USA (2020.1.12-2020.1.15)] 2020 IEEE/SICE International Symposium on System Integration (SII) - Defect detection on Polycrystalline solar cells using Electroluminescence and Fully Convolutional Neural Networks
摘要： In this paper, we propose a virtual spatial modulation (VSM) scheme that performs index modulation on the virtual parallel channels resulting from the singular value decomposition of the multi-input-multi-output channels. The VSM scheme conveys information through both the indices of the virtual parallel channels and the M -ary modulated symbols. We derive a closed-form upper bound on the average bit error probability (ABEP), which considers the impact of imperfect channel estimation. Moreover, the asymptotic ABEP is also studied, which characterizes the error ?oor under imperfect channel estimation and the resulting diversity order as well as the coding gain under perfect channel estimation. Computer simulations verify the analysis and show that the VSM scheme can outperform the existing pre-coding aided spatial modulation schemes under the same spectral ef?ciency.
关键词： Singular value decomposition (SVD),average bit error probability,spatial modulation,pre-coding
更新于2025-09-23 15:19:57
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[IEEE 2019 44th International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz) - Paris, France (2019.9.1-2019.9.6)] 2019 44th International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz) - The Upper Branch Broadening in Ultrastrongly Coupled THz Landau Polaritons
摘要： In this paper, we propose a virtual spatial modulation (VSM) scheme that performs index modulation on the virtual parallel channels resulting from the singular value decomposition of the multi-input-multi-output channels. The VSM scheme conveys information through both the indices of the virtual parallel channels and the M -ary modulated symbols. We derive a closed-form upper bound on the average bit error probability (ABEP), which considers the impact of imperfect channel estimation. Moreover, the asymptotic ABEP is also studied, which characterizes the error ?oor under imperfect channel estimation and the resulting diversity order as well as the coding gain under perfect channel estimation. Computer simulations verify the analysis and show that the VSM scheme can outperform the existing pre-coding aided spatial modulation schemes under the same spectral ef?ciency.
关键词： spatial modulation,average bit error probability,Singular value decomposition (SVD),pre-coding
更新于2025-09-19 17:13:59
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[IEEE 2019 PhotonIcs & Electromagnetics Research Symposium - Spring (PIERS-Spring) - Rome, Italy (2019.6.17-2019.6.20)] 2019 PhotonIcs & Electromagnetics Research Symposium - Spring (PIERS-Spring) - A Singular Value Decomposition Based Approach for Classifying Concealed Objects in Short Range Polarimetric Radar Imaging
摘要： In current research one of the main challenges in short range synthetic aperture radar (SAR) is electrically small structures and objects, which tend to unclear reinforced or through the wall objects, object orientation angle, and obscure contribution to extract the position of concealed multiple small objects. In this paper, ultra-wide-band (UWB) polarimetric radar was used to study reinforced objects and for estimation of object angle at short range. Electrically small 1D periodic mesh, 2D periodic meshes and di?erently oriented small objects or meshes could not be distinguished in conventional SAR images. A radar system with transmit and receive antennae mounted on a two dimensional scanning grid was used. The aim is non-destructive testing of built structures, in concrete slab manufacturing and for use in the renovation process. UWB short range radar data and images corresponding to di?erent polarization states were analysed by using singular value decomposition (SVD). To perform decomposition, the proposed approach applies SVD to image data matrices produced from the back projection algorithm (BPA) to classify the di?erent objects and identify the object angle. Then, sets of singular-components of di?erent polarization states are analysed to classify objects. Also, the BPA algorithm is performed to construct the object images from the polarimetric radar signals. The object re?ection varied with the polarimetric state of the UWB radar, which contributes to di?erent object signatures (i.e., object intensity) since the object signature depends on the orientation, the size, and the number of objects. Object orientation with respect to the radar system and object anisotropy could be determined from the ratio of the di?erent polarimetric singular-components. This proposed complex data analysis method demonstrates the usefulness of the SVD using BPA in extracting more information about and for classifying an object.
关键词： back projection algorithm (BPA),object classification,ultra-wide-band (UWB) polarimetric radar,Synthetic aperture radar (SAR),singular value decomposition (SVD)
更新于2025-09-19 17:13:59
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[IEEE 2019 Days on Diffraction (DD) - St. Petersburg, Russia (2019.6.3-2019.6.7)] 2019 Days on Diffraction (DD) - Novel types of mode dispersion of optical vortices in twisted optical fibers
摘要： Nonlocal self-similarity of images has attracted considerable interest in the field of image processing and has led to several state-of-the-art image denoising algorithms, such as block matching and 3-D, principal component analysis with local pixel grouping, patch-based locally optimal wiener, and spatially adaptive iterative singular-value thresholding. In this paper, we propose a computationally simple denoising algorithm using the nonlocal self-similarity and the low-rank approximation (LRA). The proposed method consists of three basic steps. First, our method classifies similar image patches by the block-matching technique to form the similar patch groups, which results in the similar patch groups to be low rank. Next, each group of similar patches is factorized by singular value decomposition (SVD) and estimated by taking only a few largest singular values and corresponding singular vectors. Finally, an initial denoised image is generated by aggregating all processed patches. For low-rank matrices, SVD can provide the optimal energy compaction in the least square sense. The proposed method exploits the optimal energy compaction property of SVD to lead an LRA of similar patch groups. Unlike other SVD-based methods, the LRA in SVD domain avoids learning the local basis for representing image patches, which usually is computationally expensive. The experimental results demonstrate that the proposed method can effectively reduce noise and be competitive with the current state-of-the-art denoising algorithms in terms of both quantitative metrics and subjective visual quality.
关键词： patch grouping,Back projection,low-rank approximation (LRA),singular value decomposition (SVD),image denoising,self-similarity
更新于2025-09-19 17:13:59