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Maxwell-hydrodynamic model for simulating nonlinear terahertz generation from plasmonic metasurfaces

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arXiv 2017年

作者： Fang, Ming Huang, Zhixiang Sha, Wei E.I. Wu, Xianliang Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230039 China College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Department of Electrical and Electronic Engineering University of Hong Kong

The interaction between electromagnetic field and plasmonic nanostructures leads to both strong linear and nonlinear behaviors. In this paper, a time-domain hydrodynamic model for describing the motion of electrons in plasmonic nanostructures is presented, in which both surface and bulk nonlinearity are considered. A coupled Maxwell-hydrodynamic system capturing full-wave physics and free electron dynamics is numerically solved with the parallel finite-difference time-domain (FDTD) method. The validation of the proposed method is presented by simulating a plasmonic metasurface. The linear response is compared with the Drude dispersion model and the nonlinear terahertz emission from a difference-frequency generation process is theoretically analyzed. The work is fundamentally important to design nonlinear plasmonic nanodevices, especially for efficient and broadband THz emitters. Copyright © 2017, The Authors. All rights reserved.

关键词： Plasmonics

Spatio-Temporal Coherent Snapshot Compressed Imaging with Time-Varying Composite Phase Masks

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SSRN

SSRN 2023年

作者： Zhang, Ru Zhang, Cheng Zhang, Liru Chen, Mingsheng Cheng, Hong Shen, Chuan Zhang, Quanbin Wei, Sui Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Anhui Province Hefei230601 China Advanced Laser Technology Laboratory of Anhui Province National University of Defense Technology Anhui Province Hefei230037 China

Snapshot compressed imaging (SCI) can encode an image sequence into a single-frame measurement and has the advantages of low requirements for physical sensors, low bandwidth/ memory requirements, and a fast acquisition speed. However, existing snapshot compressed imaging mainly focuses on incoherent imaging, such as video/hyperspectral imaging, and research on coherent imaging is rare. In this paper, snapshot compressed sensing is introduced for coherent imaging conditions, and a spatio-temporal coherent snapshot compressed imaging method with time-varying composite phase masks is proposed. Numerical experiments demonstrate the effectiveness and superiority of our proposed spatio-temporal coherent snapshot compressed imaging with time-varying composite phase masks method (CSCI-CPM) in terms of the reconstruction quality and robustness for both self-sparse and complex cases. © 2023, The Authors. All rights reserved.

关键词： Numerical methods

Seismic Event Detection via Deep Multi-Task Learning

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Seismic Event Detection via Deep Multi-Task Learning

International Joint Conference on Neural Networks (IJCNN)

作者： Yang Yu Lei Zhang Jiakai Shen Qingcai Wang Guiquan Liu School of Computer Science and Technology University of Science and Technology of China Hefei China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei China Department of Computer Science and Technology Nanjing University Nanjing China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

It is fundamental to detect seismic events reliably and efficiently when processing continuous waveform data recorded by seismic stations. Recently, convolutional neural network (CNN) based detecting methods have been proposed for seismic events detection and obtained great success in this area, where the learning of seismic event detecting network of all seismic stations is considered as one learning task and numerous lab.led data need to be collected for training the detecting network. However, they tend to ignore the differences between seismic stations caused by geographic position. Moreover, due to a few seismic activities and high cost of manual data lab.ling, in some areas, the lab.led data for seismic event detecting tasks is insufficient. Under this condition, these methods always encounter over-fitting problem leading to bad detection performance. In this paper, we propose a multi-task based framework based on convolutional neural network for accurate seismic event detection under the condition of insufficient lab.led data. Specifically, we first cluster the seismic stations into several station clusters and treat the learning of seismic event detecting network of every station cluster as a learning task, and then we propose a deep multi-task network named detectMTIA among multiple tasks. Experimental results on a real-world seismic dataset with nine stations demonstrate the effectiveness of the proposed framework, especially when the lab.led data is insufficient.

关键词： Task analysis Clustering algorithms

Remote Sensing Image Segmentation Using Vision Mamba and Multi-Scale Multi-Frequency Feature Fusion

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arXiv 2024年

作者： Cao, Yice Liu, Chenchen Wu, Zhenhua Yao, Wenxin Xiong, Liu Chen, Jie Huang, Zhixiang The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Electronics and Information Engineering Anhui University Hefei230601 China The 38th Research Institute China Electronics Technology Group Corporation Hefei230088 China

As remote sensing imaging technology continues to advance and evolve, processing high-resolution and diversified satellite imagery to improve segmentation accuracy and enhance interpretation efficiency emerg as a pivotal area of investigation within the realm of remote sensing. Although segmentation algorithms based on CNNs and Transformers achieve significant progress in performance, balancing segmentation accuracy and computational complexity remains challenging, limiting their wide application in practical tasks. To address this, this paper introduces state space model (SSM) and proposes a novel hybrid semantic segmentation network based on vision Mamba (CVMH-UNet). This method designs a cross-scanning visual state space block (CVSSBlock) that uses cross 2D scanning (CS2D) to fully capture global information from multiple directions, while by incorporating convolutional neural network branches to overcome the constraints of Vision Mamba (VMamba) in acquiring local information, this approach facilitates a comprehensive analysis of both global and local features. Furthermore, to address the issue of limited discriminative power and the difficulty in achieving detailed fusion with direct skip connections, a multi-frequency multi-scale feature fusion block (MFMSBlock) is designed. This module introduces multi-frequency information through 2D discrete cosine transform (2D DCT) to enhance information utilization and provides additional scale local detail information through point-wise convolution branches. Finally, it aggregates multi-scale information along the channel dimension, achieving refined feature fusion. Findings from experiments conducted on renowned datasets of remote sensing imagery demonstrate that proposed CVMH-UNet achieves superior segmentation performance while maintaining low computational complexity, outperforming surpassing current leading-edge segmentation algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

A Novel Solution of Underdetermined Equations for DGTD based on Prior knowledge

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A Novel Solution of Underdetermined Equations for DGTD based...

International Conference on Microwave and Millimeter Wave Technology Proceedings

作者： Qi Qi Xinyuan Cao Mingsheng Chen Zhixiang Huang Yi Liu Xianliang Wu Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei Anhui Province Key Laboratory of Simulation and Design for Electronic Information System Hefei Normal University Hefei China School of Electronic Engineering Huainan Normal University Huainan China

ISBN: (数字)9781728157337

ISBN: (纸本)9781728157344

To accelerate solving transient electromagnetic problems by discontinuous Galerkin time domain (DGTD) method, a solution of underdetermined equations is established based on prior knowledge. In the proposed method, nodal basis function and a leapfrog scheme are used to Maxwell curl equations for spatial and time discretization. Electromagnetic fields in each element of computation domain are updated individually by applying conventional DGTD in initial time. When the electromagnetic wave covers the domain, the fields of all elements are updated as a whole. The underdetermined equations are established by randomly extracting rows from global mass matrix. Taking the results of a few previous time steps as the prior knowledge, a sparse transform is constructed. Then the underdetermined system of equations can be solved by recovery algorithms. Meanwhile,in order to maintain the advantage of low computational complexity, a restart mechanism is presented. The validity of the proposed method is verified by numerical experiments.

关键词： Microwave technology Millimeter wave technology Transforms Sparse matrices Electromagnetic fields Transient analysis Time-domain analysis

An enumeration of 1-perfect ternary codes

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arXiv 2021年

作者： Shi, Minjia Krotov, Denis S. Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Mathematical Sciences Anhui University Anhui Hefei China State Key Laboratory of Integrated Service Networks Xidian University Xi’an710071 China Sobolev Institute of Mathematics Novosibirsk630090 Russia

MSC Codes 94B25We study codes with parameters of the ternary Hamming (n=(3m−1)/2, 3n−m, 3) code, i.e., ternary 1-perfect codes. The rank of the code is defined to be the dimension of its affine span. We characterize ternary 1-perfect codes of rank n − m + 1, count their number, and prove that all such codes can be obtained from each other by a sequence of two-coordinate switchings. We enumerate ternary 1-perfect codes of length 13 obtained by concatenation from codes of lengths 9 and 4;we find that there are 93241327 equivalence classes of such codes. Copyright © 2021, The Authors. All rights reserved.

关键词： Equivalence classes

Shot Transition Detection Based on Locally Linear Embedding

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Shot Transition Detection Based on Locally Linear Embedding

2010 International Conference on Measuring Technology and Mechatronics Automation(ICMTMA 2010)(2010年检测技术与机电自动化国际会议)

作者： MaoSheng Fu DaMing Zhang Min Kong Bin Luo Key Lab of Intelligent Computing & Signal Processing of Ministry of Education Anhui University Hefei230039. P. R. China Department of Computer Science and Technology in West Anhui University Anhui Lu'an 237012. P. R. China

ISBN: (纸本)9781424450015

Detection of shot transitions servers as the preliminary step to video indexing and retrieval. Locally linear embedding (LLE) algorithm fails when it is applied to video with multi-shot. In this paper, we present a novel framework of shot transitions detection. The method involves two processes: First we extract the manifold feature of shot transition using LLE through addition of virtual frames on an enriched set, and then they are classified by *** show that the recognition rate of shot transition is reached over 90％.

关键词： shot transition detection locally linear embedding feature extraction

Simulating Maxwell-Schrödinger Equations by High-Order Symplectic FDTD Algorithm

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arXiv 2019年

作者： Xie, Guoda Huang, Zhixiang Fang, Ming Sha, Wei E.I. Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230601 China Key Laboratory of Micro-nano Electronic Devices and Smart Systems of Zhejiang Province College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China

—A novel symplectic algorithm is proposed to solve the Maxwell-Schrödinger (M-S) system for investigating light-matter interaction. Using the fourth-order symplectic integration and fourth-order collocated differences, Maxwell-Schrödinger equations are discretized in temporal and spatial domains, respectively. The symplectic finite-difference time-domain (SFDTD) algorithm is developed for accurate and efficient study of coherent interaction between electromagnetic fields and artificial atoms. Particularly, the Dirichlet boundary condition is adopted for modeling the Rabi oscillation problems under the semi-classical framework. To implement the Dirichlet boundary condition, image theory is introduced, tailored to the high-order collocated differences. For validating the proposed SFDTD algorithm, three-dimensional numerical studies of the population inversion in the Rabi oscillation are presented. Numerical results show that the proposed high-order SFDTD(4,4) algorithm exhibits better numerical performance than the conventional FDTD(2,2) approach at the aspects of accuracy and efficiency for the long-term simulation. The proposed algorithm opens up a promising way towards a high-accurate energy-conservation modeling and simulation of complex dynamics in nanoscale light-matter interaction. Copyright © 2019, The Authors. All rights reserved.

关键词： Finite difference time domain method

Alignment-Free RGB-T Salient Object Detection: A Large-scale Dataset and Progressive Correlation Network

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arXiv 2024年

作者： Wang, Kunpeng Chen, Keke Li, Chenglong Tu, Zhengzheng Luo, Bin Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University China Anhui Provincial Key Laboratory of Security Artificial Intelligence School of Artificial Intelligence Anhui University China

Alignment-free RGB-Thermal (RGB-T) salient object detection (SOD) aims to achieve robust performance in complex scenes by directly leveraging the complementary information from unaligned visible-thermal image pairs, without requiring manual alignment. However, the lab.r-intensive process of collecting and annotating image pairs limits the scale of existing benchmarks, hindering the advancement of alignment-free RGB-T SOD. In this paper, we construct a large-scale and high-diversity unaligned RGB-T SOD dataset named UVT20K, comprising 20,000 image pairs, 407 scenes, and 1256 object categories. All samples are collected from real-world scenarios with various challenges, such as low illumination, image clutter, complex salient objects, and so on. To support the exploration for further research, each sample in UVT20K is annotated with a comprehensive set of ground truths, including saliency masks, scribbles, boundaries, and challenge attributes. In addition, we propose a Progressive Correlation Network (PCNet), which models inter- and intra-modal correlations on the basis of explicit alignment to achieve accurate predictions in unaligned image pairs. Extensive experiments conducted on unaligned and aligned datasets demonstrate the effectiveness of our method. Copyright © 2024, The Authors. All rights reserved.

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