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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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Color analysis of iron surface structure fabricated by circularly polarized femtosecond laser

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Guangxue Jingmi Gongcheng/Optics and Precision Engineering 2015年 23卷 52-56页

作者： Su, Ya-Hui Han, Xiao-Hu Fan, Zhen-Zhu Zhang, Hua-Yong Zhong, Hong-Mei School of Electrical Engineering and Automation Anhui University Hefei230601 China School of Electrical and Information Engineering Anhui University Hefei230601 China Key Laboratory of Intelligent Computing and Signal Processing of the Ministry of Education Anhui University Hefei230039 China

A method to induce multi-colors on an iron surface by using Circularly Polarized Femtosecond Laser (CPFL) processing was proposed. The subwavelength ripples were fabricated by CPFL scanning over the iron surface where covered by randomly distributed nanoparticles with diameters from 50 nm to 190 nm. A while light was used to irradiate the treated iron surface, and the relation between surface colors and incident angles was explored. By changing the incident angles of a white light from 0° to 80°, diverse colors were displayed on the laser-treated iron surface. In order to understand the underlying mechanism of the experimental results, a model of spherical nanoparticles spread randomly on ripples of the iron surface was built by Finite-difference Time Domain (FDTD). The experiments show that the surface reflection spectrum is varied from blue to red by increasing the incident angle of white light, which is consistent with the experimental results. Furthermore, it is found that the reflection spectrum shifts towards blue and the peak increases by decreasing the sizes of nanoparticles. This work may find potential applications in the fields of information storage, identifying codes and anti-counterfeiting patterns. © 2015, Chinese Academy of Sciences. All right reserved.

关键词： Femtosecond lasers

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A Reconstruction Method for Cross-Cut Shredded Documents Based on the Extreme Learning Machine Algorithm

Research Square

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Research Square 2022年

作者： Zhang, Zhenghui Zou, Juan Zheng, Jinhua Yang, Shengxiang Gong, Dunwei Pei, Tingrui Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan University Hunan Xiangtan411105 China Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Information Engineering College of Xiangtan University Hunan Xiangtan411105 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom School of Information and Electronic Engineering China University of Mining and Technology Xuzhou221116 China

Reconstruction of cross-cut shredded text documents (RCCSTD) has important applications for information security and judicial evidence collection. The traditional method of manual construction is a very time-consuming task, so the use of computer-assisted efficient reconstruction is a crucial research topic. Fragment consensus information extraction and fragment pair compatibility measurement are two fundamental processes in RCCSTD. Due to the limitations of the existing classical methods of these two steps, only documents with specific structures or characteristics can be spliced, and pairing error is larger when the cutting is more fine-grained. In order to reconstruct the fragments more effectively, this paper improves the extraction method for consensus information and constructs a new global pairwise compatibility measurement model based on the extreme learning machine algorithm. The purpose of the algorithm’s design is to exploit all available information and computationally suggest matches to increase the algorithm’s ability to discriminate between data in various complex situations, then find the best neighbor of each fragment for splicing according to pairwise compatibility. The overall performance of our approach in several practical experiments is illustrated. The results indicate that the matching accuracy of the proposed algorithm is better than that of the previously published classical algorithms and still ensures a higher matching accuracy in the noisy datasets, which can provide a feasible method for RCCSTD intelligent systems in real scenarios. © 2022, CC BY.

关键词： Knowledge acquisition

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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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An Improved Adaptive Exponential Smoothing Model for Short-term Travel Time Forecasting of Urban Arterial Street

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Acta Automatica Sinica 2008年第11期34卷 1404-1409页

作者： Zhi-Peng LI Hong YU Yun-Cai LIU Fu-Qiang LIU Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 P. R. China Intelligent Transportation System Department Shanghai Electrical Apparatus Research Institute Shanghai 200063 P. R. China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200240 P. R. China

Short-term forecasting of travel time is essential for the success of intelligent transportation system. In this paper, we review the state-of-art of short-term traffic forecasting models and outline their basic ideas, related works, advantages and disadvantages of each model. An improved adaptive exponential smoothing (IAES) model is also proposed to overcome the drawbacks of the previous adaptive exponential smoothing model. Then, comparing experiments are carried out under normal traffic condition and abnormal traffic condition to evaluate the performance of four main branches of forecasting models on direct travel time data obtained by license plate matching (LPM). The results of experiments show each model seems to have its own strength and weakness. The forecasting performance of IASE is superior to other models in shorter forecasting horizon (one and two step forecasting) and the IASE is capable of dealing with all kind of traffic conditions.

关键词： Travel time short-term forecasting license plate matching (LPM) exponential smoothing

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Automatic Segmentation of Nasopharyngeal Carcinoma in CT Images Using Dual Attention and Edge Detection

Automatic Segmentation of Nasopharyngeal Carcinoma in CT Ima...

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

作者： Qizhi Wang Wei Huang Yuan Zhang Xuanya Li Xiongjun Ye Kai Hu Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan China Department of Radiology the First Hospital of Changsha Changsha China Baidu Inc. Beijing China National Cancer Center Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China

Nasopharyngeal carcinoma (NPC) is a malignant tumor with a high incidence. Accurate segmentation of the tumor region in Computed Tomography (CT) images of NPC is the key to treatment. However, the features of uneven grayscale values and hazy boundaries of NPC regions make accurate NPC segmentation particularly challenging. To address these problems, we propose an accurate and effective NPC segmentation method using Dual Attention and Edge Detection Convolutional Neural Network (DAED-Net). Firstly, we combine a 2.5D convolutional neural network with UNet++ and propose a new backbone called Dual-dimension Dense UNet (DD-UNet), which can extract more beneficial features from 3D images. Secondly, a Dual Attention Module (DAM) is proposed to help the model better segment the target region of NPC by efficiently collecting spatial and channel attention information from feature maps. Moreover, an Edge Detection Module (EDM) is introduced in the network to enhance the segmentation of the target contours. Finally, we evaluate the proposed DAED-Net on the public MICCAI 2019 StructSeg NPC dataset from different perspectives. Numerical and visual results show that the proposed method outperforms nine state-of-the-art segmentation methods and yields more accurate NPC segmentation results.

关键词： Image segmentation Visualization Three-dimensional displays Dams Computed tomography Image edge detection Feature extraction

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A Weighted Flat Lattice Transformer-based Knowledge Extraction Architecture for Chinese Named Entity Recognition

A Weighted Flat Lattice Transformer-based Knowledge Extracti...

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International Conference on Computer Supported Cooperative Work in Design

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

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Named Entity Recognition (NER) is one of the contents of Knowledge Extraction (KE) that transforms data into knowledge representation. However, Chinese NER faces the problem of lacking clear word boundaries that limit the effectiveness of the KE. Although the flat lattice Transformer (FLAT) framework, which converts lattice structure into a flat structure including a set of spans, can effectively improve this problem and obtain advanced results, there still exist the problems of insensitivity to entity importance weights and insufficient feature learning. This paper proposes a weighted flat lattice Transformer architecture for Chinese NER, namely WFLAT. The WFLAT first adds a weight matrix into self-attention calculation, which can obtain finer-grained partitioning of entities to improve experimental performance, and then adopts a multi-layer Transformer encoder with each layer using a multi-head self-attention mechanism. Extensive experimental results on benchmarks demonstrate that our proposed KE model can obtain state-of-the-art performance for the Chinese NER task.

关键词： Representation learning Computational modeling Memory management Lattices Named entity recognition Transforms Knowledge representation

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Risk analysis of system security based on evidence theory

Risk analysis of system security based on evidence theory

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International Conference on Computer Design and Applications (ICCDA)

作者： Peida Xu Yong Deng Jianling Xu Xiaoyan Su Key Laboratory of Digital Agricultural Early-warning Technology Ministry of Agriculture of China Beijing China Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education Xiangtan University China School of Electronics & Information Technology Shanghai Jiaotong University China School of Electronics & Information Technology Shanghai Jiaotong University Shanghai China Hangzhou Key Laboratory of E-Business and Information Security Hangzhou Normal University Shanghai Zhejiang China Grain Economics Institute Nanjing University of Finance and Economics Nanjing China

ISBN: (纸本)9781424471645;9781424471638

Risk evaluation is very important to the design and improvement of physical protection systems. In this paper, an evaluation method of multi-source information fusion is proposed based on the D-S evidence theory. In the proposed method, each individual component of the protection system in the simulated plane is modeled. Then, the threat report of each component according to the specific tactics is determined based on its real environment. Finally, the comprehensive threat distribution is obtained based on through the D-S evidence theory to combine multi-sources information. The proposed method can easily applied to the evaluation of the effectiveness of the protection system. We make the total threat of the protection system lowest through changes of the protection resources allocation. A numerical example is used to illustrate the efficiency of the proposed method.

关键词： Risk analysis Protection Information security Information technology Computer security Laboratories Environmental economics educational technology Agriculture Finance

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On self-dual and LCD double circulant and double negacirculant codes over Fq+ uFq

arXiv

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arXiv 2018年

作者： Shi, Minjia Zhu, Hongwei Qian, Liqin Sok, Lin Sol, Patrick Key Laboratory of Intelligent Computing Signal Processing Ministry of Education Anhui University No.3 Feixi Road Hefei Anhui230039 China School of Mathematical Sciences Anhui University Hefei Anhui230601 China National Mobile Communications Research Laboratory Southeast University China Department of Mathematics Royal University of Phnom Penh Phnom Penh12156 Cambodia CNRS LAGA University of Paris 8 Saint-Denis93526 France

Double circulant codes of length 2n over the semilocal ring R = Fq + uFq, u2 = u, are studied when q is an odd prime power, and-1 is a square in Fq. Double negacirculant codes of length 2n are studied over R when n is even and q is an odd prime power. Exact enumeration of self-dual and LCD such codes for given length 2n is given. Employing a duality-preserving Gray map, self-dual and LCD codes of length 4n over Fq are constructed. Using random coding and the Artin conjecture, the relative distance of these codes is bounded below. The parameters of examples of the modest length are computed. Several such codes are optimal. Copyright © 2018, The Authors. All rights reserved.

关键词：

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SpreadFGL: Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation

arXiv

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arXiv 2024年

作者： Zhong, Luying Pi, Yueyang Chen, Zheyi Yu, Zhengxin Miao, Wang Chen, Xing Min, Geyong College of Computer and Data Science Fuzhou University China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University China Engineering Research Center of Big Data Intelligence Ministry of Education China School of Computing and Communications University of Lancaster United Kingdom School of Engineering Computing and Mathematics University of Plymouth United Kingdom Department of Computer Science University of Exeter United Kingdom

Federated Graph Learning (FGL) has garnered widespread attention by enabling collaborative training on multiple clients for semi-supervised classification tasks. However, most existing FGL studies do not well consider the missing inter-client topology information in real-world scenarios, causing insufficient feature aggregation of multi-hop neighbor clients during model training. Moreover, the classic FGL commonly adopts the FedAvg but neglects the high training costs when the number of clients expands, resulting in the overload of a single edge server. To address these important challenges, we propose a novel FGL framework, named SpreadFGL, to promote the information flow in edge-client collaboration and extract more generalized potential relationships between clients. In SpreadFGL, an adaptive graph imputation generator incorporated with a versatile assessor is first designed to exploit the potential links between subgraphs, without sharing raw data. Next, a new negative sampling mechanism is developed to make SpreadFGL concentrate on more refined information in downstream tasks. To facilitate load balancing at the edge layer, SpreadFGL follows a distributed training manner that enables fast model convergence. Using real-world testbed and benchmark graph datasets, extensive experiments demonstrate the effectiveness of the proposed SpreadFGL. The results show that SpreadFGL achieves higher accuracy and faster convergence against state-of-the-art algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph algorithms

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