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5th IEEE International Conference on Computational Electromagnetics, ICCEM 2019

作者： Wang, Yulong Zhao, Ran Huang, Zhixiang Wu, Xianliang Key Laboratory of Intelligent Computing Signal Processing Ministry of Education Anhui University Hefei China

ISBN: (纸本)9781538671115

In this paper, a discontinuous Galerkin time domain (DGTD) method is proposed to solve electromagnetic radiation and scattering problems. In this method, for the electromagnetic radiation problem, the dipole source excitation is introduced by the Total field / Scattering field (TF/SF) boundary condition. On the basis of TF/SF boundary condition, an equivalent principle (EP) based boundary condition is also introduced. For the electromagnetic scattering problem, the plane wave excitation is introduced by pure scattered field method. For all the cases, uniaxial perfectly matched layer (UPML) is used as the truncated boundary condition to enhance the reflection wave absorption. At the same time, the method uses nodal local basis function (for spatial discretization) and Verlet method (for time integration) to solve Maxwell equation. Numerical results show the effectiveness and accuracy of the algorithm. © 2019 IEEE.

关键词： Boundary conditions

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PRISTA-Net: Deep Iterative Shrinkage Thresholding Network for Coded Diffraction Patterns Phase Retrieval

arXiv

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arXiv 2023年

作者： Liu, Aoxu Fan, Xiaohong Yang, Yin Zhang, Jianping The School of Mathematics and Computational Science Xiangtan University Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan411105 China The School of Mathematics and Computational Science Xiangtan University the Key Laboratory of Intelligent Computing & Information Processing the Ministry of Education Xiangtan411105 China The School of Mathematics and Computational Science Xiangtan University the National Center for Applied Mathematics in Hunan Xiangtan411105 China

The problem of phase retrieval (PR) involves recovering an unknown image from limited amplitude measurement data and is a challenge nonlinear inverse problem in computational imaging and image processing. However, many of the PR methods are based on black-box network models that lack interpretability and plug-and-play (PnP) frameworks that are computationally complex and require careful parameter tuning. To address this, we have developed PRISTA-Net, a deep unfolding network (DUN) based on the first-order iterative shrinkage thresholding algorithm (ISTA). This network utilizes a learnable nonlinear transformation to address the proximal-point mapping sub-problem associated with the sparse priors, and an attention mechanism to focus on phase information containing image edges, textures, and structures. Additionally, the fast Fourier transform (FFT) is used to learn global features to enhance local information, and the designed logarithmic-based loss function leads to significant improvements when the noise level is low. All parameters in the proposed PRISTA-Net framework, including the nonlinear transformation, threshold parameters, and step size, are learned end-to-end instead of being manually set. This method combines the interpretability of traditional methods with the fast inference ability of deep learning and is able to handle noise at each iteration during the unfolding stage, thus improving recovery quality. Experiments on Coded Diffraction Patterns (CDPs) measurements demonstrate that our approach outperforms the existing state-of-the-art methods in terms of qualitative and quantitative evaluations. Our source codes are available at https://***/liuaxou/PRISTA-Net. Copyright © 2023, The Authors. All rights reserved.

关键词： Mapping

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A Low SAR, Small size, High gain, Circularly polarized Antenna for Wearable Application 2

A Low SAR, Small size, High gain, Circularly polarized Anten...

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2020 2nd International Conference on Artificial Intelligence Technologies and Application, ICAITA 2020

作者： Wang, Chao Zhang, Liang Cheng, Yi Li, XueMei Wu, Xianliang Key Lab of Ministry of Education of Intelligent Computing and Signal Processing Anhui University Hefei Anhui230601 China AnHui Province Key Laboratory of Simulation and Design for Electronic Information System Hefei Normal University Hefei Anhui230601 China

A planar, small size, high gain, low specific absorption rate (SAR) and circularly-polarized (CP) wearable antenna is proposed in this paper. The antenna is fabricated on a substrate with a dielectric constant of 3.5 with a thickness of 0.5 mm and sized at 42×42 mm only. Both the simulation results show that the overlap bandwidth of impedance and axial ratio of the proposed antenna is 13.7% (5.15-5.91 GHz). To isolate the antenna against the influence of the human body, a artificial magnetic conductor (AMC) plane is added on the reverse side of the radiator. The AMC structure effectively isolates the human body from the radiation of the antenna and reduces the specific absorption rate (SAR) of it by more than 96.1% at 5.5 GHz, which improves the antenna gain and the peak gain reaches 5.09 dBi at 5.5 GHz. The wearable performance of the antenna was analyzed, and it is observed that it can sustain good performance even under human body loading. Beside, the antenna has a small size, which makes it ideal for wearable applications. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Wearable antennas

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Component edge connectivity of hypercube-like networks

arXiv

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arXiv 2021年

作者： Liu, Dong Li, Pingshan Zhang, Bicheng Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education School of Mathematical and Computational Sicence Xiangtan University Hunan Xiangtan41105 China

As a generalization of the traditional connectivity, the g-component edge connectivity cλg(G) of a non-complete graph G is the minimum number of edges to be deleted from the graph G such that the resulting graph has at least g components. Hypercube-like networks (HL-networks for short) are obtained by manipulating some pairs of edges in hypercubes, which contain several famous interconnection networks such as twisted cubes, Möbius cubes, crossed cubes, locally twisted cubes. In this paper, we determine the (g + 1)-component edge connectivity of the n-dimensional HL-networks for g ≤ 2[n/2], n ≥ 8. Copyright © 2021, The Authors. All rights reserved.

关键词： Graph theory

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GK: A Graphormer Architecture Based Knowledge Graph Reasoning Model for Link Prediction

GK: A Graphormer Architecture Based Knowledge Graph Reasonin...

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IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Yueiia Wu Jian-tao Zhou Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing College of Computer Science Inner Mongolia University National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Hohhot China

Knowledge Graph has become a dominant research field in graph theory, but its incompleteness and sparsity hinder its application in various fields. Knowledge Graph Reasoning aims to alleviate these problems by deducing new knowledge or identifying false knowledge from existing knowledge. Recently, the Graph Convolution Network (GCN) based method is one of the most advanced methods to realize knowledge graph reasoning. However, it still suffers from some problems such as incomplete neighbor information aggregation and slow training speed. This paper proposes a knowledge graph reasoning model named GK for the link prediction task, which obtains better performance than existing GCN-based methods by introducing Graphormer into knowledge reasoning. The GK first proposes that nodes and their surroundings can be regarded as a hierarchical architecture that enables the model to capture more practical reasoning information to improve prediction accuracy. In addition, to accelerate the training speed of the model on the large-scale Knowledge Graph, we present a faster shortest path-finding method F-SPF in the edge coding process. Extensive experimental results show that the GK model can obtain the state-of-the-art prediction results of current GCN-based methods and can improve the training speed.

关键词： Training Knowledge engineering Knowledge graphs Predictive models Prediction algorithms Feature extraction Cognition

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A Hybrid Tourism Recommendation System Based on Multi-Objective Evolutionary Algorithm and Re-Ranking

SSRN

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SSRN 2022年

作者： Cao, Ruifen Li, Zijue Wei, Pijing Tian, Ye Zheng, Chunhou Information Materials and Intelligent Sensing Laboratory of Anhui Province School of Computer Science and Technology Anhui University Heifei230601 China Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Artificial Intelligence Anhui University Hefei230601 China Institutes of Physical Science and Information Technology Anhui University Hefei230601 China

The tourism recommendation system (TRS) can intelligently recommend the next attractions for users according to their historical visit records. To improve tourists’ travel experience, it is very important for TRS to select the attractions that users are interested in from a large number of attractions. Most tourism recommendation methods only consider accuracy as an evaluation index, which can no longer meet the needs of users who are increasingly seeking for diversified attractions. Actually, there is often a conflict between accuracy and diversity, and how to balance these two conflict objectives is a difficulty problem for recommendation system. In order to solve the problem, a hybrid recommendation model based on multi-objective optimization and re-ranking (MOEA/D-HRR) is proposed in this study. To overcome the limited predictive power of a single recommendation algorithm, MOEA/D-HRR mixes three different classical techniques by weighted summation of their recommended results, and then improve the diversity of the recommendation list by using the re-ranking algorithm based on Maximal Marginal Relevance (MMR). In addition, we construct a multi-objective optimization problem in which accuracy and diversity are formulated as two objectives, and use a multi-objective evolutionary algorithm (MOEA) to optimize the weights and parameter of the re-ranking algorithm. A lot of experiments on two public tourism datasets and a new tourism dataset were carried out. Experimental results showed that the model could provide users with diversified attractions while ensuring the accuracy of recommendation attraction. © 2022, The Authors. All rights reserved.

关键词： Multiobjective optimization

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RGB-T Saliency Detection via Robust Graph Learning and Collaborative Manifold Ranking 14th

RGB-T Saliency Detection via Robust Graph Learning and Colla...

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14th International Conference on Bio-inspired computing: Theories and Applications, BIC-TA 2019

作者： Sun, Dengdi Li, Sheng Ding, Zhuanlian Luo, Bin Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China School of Internet Anhui University Hefei230039 China

ISBN: (纸本)9789811534140

Visual saliency detection inspired by brain cognitive mechanisms is an important component of computer vision, aiming at automatically highlight salient visual objects from the image background. In complex real scenarios, integrating multiple different yet complementary feature representations has been proved to be an effective way for boosting saliency detection performance. In this paper, we propose a novel collaborative algorithm for saliency detection by adaptively incorporating information from grayscale and thermal images. Specifically, we first construct an affinity graph model via robust graph learning to characterize the feature similarities and location relationships between two different image patches under each modal. Based on these affinity graphs, a collaborative manifold ranking method cross-modality consistent constraints is designed to effectively and efficiently infer the salient score for each patch. Moreover, we introduce a weight for each modality to describe the reliability, and integrate them into the graph-based manifold ranking scheme to achieve an adaptive weighted fusion of different source data. For optimization, we propose some iterative algorithms to efficiently solve the models with several subproblems. Experiments on the grayscale-thermal datasets demonstrate the superior performance of the new method over the state-of-the-art approaches. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Graphic methods

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Dual-Graph Regularized Sparse Low-Rank Matrix Recovery for Tag Refinement 14th

Dual-Graph Regularized Sparse Low-Rank Matrix Recovery for T...

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14th International Conference on Bio-inspired computing: Theories and Applications, BIC-TA 2019

作者： Sun, Dengdi Bao, Yuanyuan Ge, Meiling Ding, Zhuanlian Luo, Bin Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China School of Internet Anhui University Hefei230039 China

ISBN: (纸本)9789811534140

In recent years, extremely large amounts of images with manual tags are easily available in many social websites such as Twitter, Flickr, and Instagram. However, these user-provided tags are often imprecise and incomplete, which inevitably limits the performances of image retrieval and other related applications. To this end, tag refitment technology aims at improving the quality of images tags automatically, and has been a fundamental challenge in Internet era. In this paper, we propose a novel dual-graph regularized sparse low-rank matrix recovery method, referred to as DGSLR briefly, to infer and improve the manual tags. Specifically, our DGSLR model first suppress the sparse noisy tags by norm, and decompose simultaneously the residual low-rank matrix to learning the low-dimensional vector representations of images and tags. Moreover, the visual similarities and the tag pairwise correlation are fully exploited to smooth the decomposition results by using the dual lapalcian graph regularization terms. For optimization, an improved alternative iteration strategy is designed to solve the resulting objective function. Extensive experiments on the tasks of tag refinement demonstrate the superior performance of the proposed algorithms over several representative methods, especially when the manual tag data is highly noisy and sparse. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Image enhancement

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A Novel Tri-band Frequency Selective Surface 2

A Novel Tri-band Frequency Selective Surface

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2020 2nd International Conference on Artificial Intelligence Technologies and Application, ICAITA 2020

作者： Wu, Shenbing Chen, Mingsheng Wang, Chao Wu, Xianliang Key Lab of Ministry of Education of Intelligent Computing and Signal Processing Anhui University Hefei Anhui230601 China School of Electronic Engineering Huainan Normal University Huainan Anhui232001 China

This paper presents a novel tri-band frequency miniaturized frequency selection surface (FSS). The proposed FSS consists of single-node folding and single-node folding bending. The single-layer FSS exhibits three stop bands centered at 2.45, 3.61 and 5.79 GHz, with bandwidths of 0.42, 0.48 and 0.70 GHz, respectively. The proposed FSS has better miniaturization characteristics, and its cell size is 0.126 ?×0.126 ?. The three stop band frequencies of the FSS can be independently controlled by simply changing the length of the corresponding branch. In addition, the proposed FSS is symmetrical about the center and exhibits good resonance stability in both TE and TM polarization. The prototype of the proposed FSS was fabricated and tested, compared with the simulation results, the FSS showed stable performance. This method provides ideas for designing multiband FSS in the future. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Frequency selective surfaces

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Multi-Object Localization and Irrelevant-Semantic Separation for Nuclei Segmentation in Histopathology Images

Multi-Object Localization and Irrelevant-Semantic Separation...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Ya Tang Xiongjun Ye Xuanya Li Zhineng Chen Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan China National Cancer Center Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China Baidu Inc. Beijing China School of Computer Science Fudan University Shanghai China

Automated segmentation of nuclei in histopathology images is critical for cancer diagnosis and prognosis. Due to the high variability of nuclei morphology, numerous nuclei overlapping, and the wide existence of nuclei clusters, this task still remains challenging. In this paper, we propose an effective nuclei segmentation method for histopathology images based on a novel neural network for multi-object localization and irrelevant-semantic separation (MI-Net), which includes a multi-object localization module (MOLM), a deep boundary awareness module (DBAM), and an irrelevant semantic separation module (ISSM). Specifically, the MOLM is used to calibrate features to capture more comprehensive information about the location of nuclei. It alleviates the problem of cell adhesion in histopathology images. The DBAM is intended to extract boundary information and solve the blurred boundary problem effectively. The ISSM is used to separate foreground features from background features and effectively address the problem of complex backgrounds in histopathology images. It also solves the low contrast problem between the target objects and the background. We evaluate MI-Net on the famous MoNuSeg dataset and compare it with ten state-of-the-art methods. MI-Net surpasses the best-performed method by margins from 1.12% to 2.29% over standard evaluation metrics, showing its effectiveness on nuclei segmentation.

关键词： Location awareness Image segmentation Histopathology Semantics Neural networks signal processing Task analysis

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