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Correction to: Enhancing cyber defense strategies with discrete multi-dimensional Z-numbers: a multi-attribute decision-making approach

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Complex & Intelligent Systems 2025年第7期11卷 1-1页

作者： Yao, Aiting Huang, Chen Zhang, Weiqi Dong, Chengzu Lu, Meiqu Mao, Junjun Liu, Xiao Li, Xuejun Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging School of Computer Science and Technology Anhui University Hefei China AI Techplus Pty Ltd Organization Melbourne Australia School of Information Technology Deakin University Geelong Australia School of Data Science Lingnan University Hongkong China School of Artificial Intelligence Guangxi Minzu University Nanning China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei China

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A New User Grouping and Power Allocation Scheme for Downlink Non-orthogonal Multiple Access Systems

A New User Grouping and Power Allocation Scheme for Downlink...

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2019 3rd International Conference on Data Mining, Communications and Information Technology (DMCIT 2019)

作者： Yin Wang Xiaohui Li Wenwu Wang Gongquan Zhang Hongwei Zhang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University

In this paper,we propose a new user grouping and power allocation scheme based on beamforming for downlink non-orthogonal multiple access *** proposed user grouping scheme can effectively reduce the interference from the other user and other beams as well,and can effectively improve the weak user rate especially when the SNR is not *** addition,a power allocation scheme that can maximize the sum capacity while satisfying a certain fairness index is *** the simulation results,the user grouping and power allocation method proposed in this paper can not only improve the overall system throughput performance,but also improve the fairness of weak users.

关键词： SNR A New User Grouping and Power Allocation Scheme for Downlink Non-orthogonal Multiple Access Systems

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Multi-channel Compressed Sensing Optimization Based on Singular Value Decomposition

Multi-channel Compressed Sensing Optimization Based on Singu...

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2019第十一届数字图像处理国际会议

作者： Cheng Zhang Yuanyuan Zhu Jun Tang Qianwen Chen Meiqin Wang Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University

Distributed compressed sensing theory is applied to many practical problems,ECG signal,color imaging,*** order to improve the reconstruction accuracy of multi-dimensional signals,this paper applies singular value decomposition to the multi-measure vector problem in DCS,then distributed compressed sensing reconstruction method based on singular value decomposition is *** method can achieve row orthogonality of the measurement matrix and does not affect the design of the reconstruction *** experiments verify the effectiveness of the proposed method,which can significantly improve the reconstruction quality of the signal and the robustness to noise.

关键词： Distributed compressed sensing (DCS) Multiple measurement vector problem(MMV) singular value decomposition(SVD) Separable Design

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A Second-order C-bandpass Frequency Selective Surface

A Second-order C-bandpass Frequency Selective Surface

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International Conference on Microwave and Millimeter Wave Technology Proceedings

作者： Shenbing Wu Mingsheng Chen Meng Kong Liang Zhang Key Lab of Intelligent Computing & Signal Processing Ministry of Education Anhui University School of Electronic Engineering Huainan Normal University AnHui Province Key Laboratory of Simulation and Design for Electronic Information System Hefei Normal University

ISBN: (数字)9781728157337

ISBN: (纸本)9781728157344

A novel C-bandpass frequency selective surface(FSS) exhibiting miniaturized periodic parts is developed in this study. Such FSS acts as a bandpass filter at 4.03--7.87GHz. The proposed FSS exhibits miniaturization characteristics with the unit-cell dimension 0.12λ*0.12λ and the total thickness less than λ/28. Meantime, the simulation results reveal that the second-order FSS exhibits better stability under a variety of incident directions and polarization modes.

关键词： Frequency selective surfaces Microwave technology Surface waves Simulation Millimeter wave technology Stability analysis Periodic structures

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The 1st Challenge on Remote Physiological signal Sensing (RePSS)

The 1st Challenge on Remote Physiological Signal Sensing (Re...

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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Xiaobai Li Hu Han Hao Lu Xuesong Niu Zitong Yu Antitza Dantcheva Guoying Zhao Shiguang Shan Center for Machine Vision and Signal Analysis University of Oulu Finland Key Laboratory of Intelligient Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology China STARS team INRIA France

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Remote measurement of physiological signals from videos is an emerging topic. The topic draws great interests, but the lack of publicly available benchmark databases and a fair validation platform are hindering its further development. For this concern, we organize the first challenge on Remote Physiological signal Sensing (RePSS), in which two databases of VIPL and OBF are provided as the benchmark for kin researchers to evaluate their approaches. The 1st challenge of RePSS focuses on measuring the average heart rate from facial videos, which is the basic problem of remote physiological measurement. This paper presents an overview of the challenge, including data, protocol, analysis of results and discussion. The top ranked solutions are highlighted to provide insights for researchers, and future directions are outlined for this topic and this challenge.

关键词： Videos Physiology Heart rate Testing Databases Cameras Training

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Multilevel Characteristic Basis Function Method with ACA for Accelerated Solution of Electrically Large Scattering Problems

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Transactions of Nanjing University of Aeronautics and Astronautics 2018年第3期35卷 449-454页

作者： Li Chenlu Sun Yufa Wang Zhonggen Wang Guohua Key Laboratory of Intelligent Computing & Signal Processing Ministry of EducationAnhui University College of Electrical and Information Engineering Anhui University of Science and Technology

The multilevel characteristic basis function method(MLCBFM)with the adaptive cross approximation(ACA)algorithm for accelerated solution of electrically large scattering problems is studied in this *** the conventional MLCBFM based on Foldy-Lax multiple scattering equations,the improvement is only made in the generation of characteristic basis functions(CBFs).However,it does not provide a change in impedance matrix filling and reducing matrix calculation procedure,which is *** reality,all the impedance and reduced matrix of each level of the MLCBFM have low-rank property and can be calculated ***,ACA is used for the efficient generation of two-level CBFs and the fast calculation of reduced matrix in this *** results are given to demonstrate the accuracy and efficiency of the method.

关键词： multilevel characteristic basis function method(MLCBFM) adaptive cross approximation(ACA) characteristic basis functions(CBFs) electromagnetic scattering

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Structure-aware random fourier kernel for graphs 21

Structure-aware random fourier kernel for graphs

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Proceedings of the 35th International Conference on Neural Information processing Systems

作者： Jinyuan Fang Qiang Zhang Zaiqiao Meng Shangsong Liang School of Computer Science and Engineering Sun Yat-sen University China and Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Hangzhou Innovation Center Zhejiang University China and College of Computer Science and Technology Zhejiang University China and AZFT Knowledge Engine Lab China School of Computing Science University of Glasgow United Kingdom and Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates School of Computer Science and Engineering Sun Yat-sen University China and Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China and Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates

ISBN: (纸本)9781713845393

Gaussian Processes (GPs) define distributions over functions and their generalization capabilities depend heavily on the choice of kernels. In this paper, we propose a novel structure-aware random Fourier (SRF) kernel for GPs that brings several benefits when modeling graph-structured data. First, SRF kernel is defined with a spectral distribution based on the Fourier duality given by the Bochner's theorem, transforming the kernel learning problem to a distribution inference problem. Second, SRF kernel admits a random Fourier feature formulation that makes the kernel scalable for optimization. Third, SRF kernel enables to leverage geometric structures by taking subgraphs as inputs. To effectively optimize GPs with SRF kernel, we develop a variational EM algorithm, which alternates between an inference procedure (E-step) and a learning procedure (M-step). Experimental results on five real-world datasets show that our model can achieve state-of-the-art performance in two typical graph learning tasks, i.e., object classification and link prediction.

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

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

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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

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Embedding dynamic attributed networks by modeling the evolution processes

arXiv

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arXiv 2020年

作者： Xu, Zenan Ou, Zijing Su, Qinliang Yu, Jianxing Quan, Xiaojun Lin, Zhenkun School of Data and Computer Science Sun Yat-sen University China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Key Lab. of Machine Intelligence and Advanced Computing Ministry of Education China

Network embedding has recently emerged as a promising technique to embed nodes of a network into low-dimensional vectors. While fairly successful, most existing works focus on the embedding techniques for static networks. But in practice, there are many networks that are evolving over time and hence are dynamic, e.g., the social networks. To address this issue, a high-order spatio-temporal embedding model is developed to track the evolutions of dynamic networks. Specifically, an activeness-aware neighborhood embedding method is first proposed to extract the high-order neighborhood information at each given timestamp. Then, an embedding prediction framework is further developed to capture the temporal correlations, in which the attention mechanism is employed instead of recurrent neural networks (RNNs) for its efficiency in computing and flexibility in modeling. Extensive experiments are conducted on four real-world datasets from three different areas. It is shown that the proposed method outperforms all the baselines by a substantial margin for the tasks of dynamic link prediction and node classification, which demonstrates the effectiveness of the proposed methods on tracking the evolutions of dynamic networks. Copyright © 2020, The Authors. All rights reserved.

关键词： Recurrent neural networks

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An End to End Method of Whisper Enhancement

An End to End Method of Whisper Enhancement

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IEEE International Conference on Information Communication and signal processing (ICICSP)

作者： Yan Huang HaiLun Lian Jian Zhou HuaBin Wang Liang Tao MOE Key Laboratory of Intelligent Computing and Signal Processing Anhui University Hefei China

In this paper, we propose a method of enhancing whisper, using whisper without any pretreatment combined with Wavenet. Our method is end-to-end, that is, inputing noised whisper to get clean whisper. The input to our method is the original whisper without any processing, reducing the loss of features caused by other operations. We use speech denoising Wavenet to enhance whisper. Wavenet can not only enhance whisper well, but also tackle the issue of intelligibility. Specifically, use symmetric dilated convolution to obtain noisy speech context, help the model to enhance the speech for better denoising effect. Experimental results show that the enchanced whisper gains better performance both in the aspect of speech quality and intelligibility.

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