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arXiv 2019年

作者： Li, Minghan Cao, Xiangyong Zhao, Qian Zhang, Lei Gao, Chenqiang Meng, Deyu School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Networks and Network Security Xi'an Jiaotong University Shaanxi China Department of Computing Hong Kong Polytechnic University Hung Hom Hong Kong Chongqing Key Laboratory of Signal and Information Processing Chongqing University of Posts and Telecommunications Chongqing400065 China

Video rain/snow removal from surveillance videos is an important task in the computer vision community since rain/snow existed in videos can severely degenerate the performance of many surveillance system. Various methods have been investigated extensively, but most only consider consistent rain/snow under stable background scenes. Rain/snow captured from practical surveillance camera, however, is always highly dynamic in time with the background scene transformed occasionally. To this issue, this paper proposes a novel rain/snow removal approach, which fully considers dynamic statistics of both rain/snow and background scenes taken from a video sequence. Specifically, the rain/snow is encoded as an online multi-scale convolutional sparse coding (OMS-CSC) model, which not only finely delivers the sparse scattering and multi-scale shapes of real rain/snow, but also well encodes their temporally dynamic configurations by real-time ameliorated parameters in the model. Furthermore, a transformation operator imposed on the background scenes is further embedded into the proposed model, which finely conveys the dynamic background transformations, such as rotations, scalings and distortions, inevitably existed in a real video sequence. The approach so constructed can naturally better adapt to the dynamic rain/snow as well as background changes, and also suitable to deal with the streaming video attributed its online learning mode. The proposed model is formulated in a concise maximum a posterior (MAP) framework and is readily solved by the ADMM algorithm. Compared with the state-of-the-art online and offline video rain/snow removal methods, the proposed method achieves better performance on synthetic and real videos datasets both visually and quantitatively. Specifically, our method can be implemented in relatively high efficiency, showing its potential to real-time video rain/snow removal. Copyright © 2019, The Authors. All rights reserved.

关键词： Convolution

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

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Journal of Physics: Conference Series 2020年第1期1651卷

作者： Chao Wang Liang Zhang Yi Cheng XueMei Li Xianliang Wu Key Lab of Ministry of Education of Intelligent Computing & Signal Processing Anhui University Hefei Anhui 230601 China AnHui Province Key Laboratory of simulation and design for Electronic information system Hefei Normal university Hefei Anhui 230601 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.

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A Novel Tri-band Frequency Selective Surface

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Journal of Physics: Conference Series 2020年第1期1651卷

作者： Shenbing Wu Mingsheng Chen Chao Wang Xianliang Wu Key Lab of Ministry of Education of Intelligent Computing & Signal Processing Anhui University Hefei Anhui 230601 China School of Electronic Engineering Huainan Normal University Huainan Anhui 232001 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 λ×0.126 λ. 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.

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Automatic Text Summarization Based on Transformer and Switchable Normalization

Automatic Text Summarization Based on Transformer and Switch...

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

作者： Tao Luo Kun Guo Hong Guo College of Mathematics and Computer Sciences Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Key Laboratory of Spatial Data Mining & Information Sharing Ministry of Education Fuzhou University Fuzhou China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

With the development of text summarization research, the methods based on RNN with the Encoder-Decoder model gradually become the mainstream. However, RNN tends to forget previous context information and leads to the lack of original information. That will reduce the accuracy of the generated summarizations. The Transformer model uses the self-attention mechanism to encode and decode historical information so that it can achieve better performance than RNN in learning context information. In this paper, a text summarization model based on transformer and switchable normalization is proposed. The accuracy of the model is improved by optimizing the normalization layer. Compared with other models, the new model has a great advantage in understanding words' semantics and associations. Experimental results in English Gigaword dataset show that the proposed model can achieve high ROUGE values and make the summarization more readable.

关键词： Decoding Switches Context modeling Vocabulary Task analysis Data mining Semantics

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Design of two-turns loop antenna with magnetic core for efficient wireless powering of medical telemetry

Design of two-turns loop antenna with magnetic core for effi...

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2017 International Applied Computational Electromagnetics Society Symposium in China, ACES-China 2017

作者： Song, Yong Song, Kaihong Wu, Xianliang Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China

ISBN: (纸本)9780996007856

We present an inductive powering system of a two-turns loop antenna which includes external and implanted antennas each with a cylinder magnetic core. It can wirelessly transfer power to loop antenna of a compact plane structure in cortex. Compared to single antenna without magnetic core, it can achieve highly-efficient (24.8%) wireless power transferring to implanted loop antenna and reduce the SAR to 83.2%. By improving the link power transfer efficiency and decreasing the SAR, we optimize the antenna's structural parameter. Then, we propose different liquid of concentrations to simulate human skin, which is used for power transmission experiment at 100MHz. © 2017 Applied Computational Electromagnetics Society.

关键词： Loop antennas

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Analysis of mutual inductance between non-coaxial rectangular coils with rectangular cross-section

Analysis of mutual inductance between non-coaxial rectangula...

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2017 International Applied Computational Electromagnetics Society Symposium in China, ACES-China 2017

作者： Pan, Wenlong Song, Kaihong Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China

ISBN: (纸本)9780996007856

Magnetic coupling resonance wireless power transmission is a new technology to realize the wireless transmission of energy by means of high frequency coupling resonance between the coils. The variation of the coupling coil size and the relative position will change the mutual inductance between the windings, which will finally affect the characteristics of the infinite power transmission system. Using rectangular coil with rectangular cross-section as the analysis object, to research the calculation method of mutual inductance between non-coaxial rectangular coils, by subdividing and summation method to deduce the theory formula of the mutual inductance, to support wireless power transmission which consists of rectangular coils. In all cases, the formula programming calculation, the FEM software simulation and the experimental measurement results are consistent, which proves the correctness of the calculation method. © 2017 Applied Computational Electromagnetics Society.

关键词： Computer software

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Recent Developments in Service Function Chaining (SFC) and Network Slicing in Backhaul and Metro Networks in Support of 5G

Recent Developments in Service Function Chaining (SFC) and N...

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International Conference on Transparent Optical Networks

作者： Adrian Farrel Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Electrical Engineering and Automation Anhui University Hefei China

In this invited paper we examine the role of Service Function Chaining (SFC) and Network Slicing in providing support for emerging 5G services as data is carried across backhaul and metro networks. The way in which backhaul and metro networks are managed and operated affects the ability of those networks to support a wide range of services that will be enabled by 5G connectivity. Those services will place exacting demands on the bandwidth, delay bounds, and jitter delivered to users, but may vary significantly over relatively short time periods. SFC and Network Slicing offer opportunities for software-driven coordination of network resources to marshal them so as to best deliver the network behaviour that will support the demands of the services. We present the latest proposals towards standardisation of SFC and Network Slicing in the Internet Engineering Task Force (IETF) and discuss how these concepts are applied in optical networks through the Metro-Haul project.

关键词： 5G mobile communication Network slicing Multiprotocol label switching Integrated optics Routing Optical fiber networks Network function virtualization

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Controlling The Uncertain Time-Delayed Lipschitz Nonlinear System: The Finite-time Non-fragile Design Approach

Controlling The Uncertain Time-Delayed Lipschitz Nonlinear S...

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第30届中国控制与决策会议

作者： Longfang Li Qilong Ai Shuping He School of Electrical Engineering and Automation Anhui University Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University

The finite-time non-fragile H controller design problem of a class of Lipschitz nonlinear systems with uncertainties and time-delays is studied. The objective is to design a suitable non-fragile controller which makes the Lipschitz nonlinear system be finite-time bounded and satisfies the given H performance index. By choosing a proper Lyapunov function and using linear matrix inequalities, a sufficient condition for the existence of finite-time non-fragile H controller is given and proved. Then, the designed method is described as an optimization one. Finally, the effectiveness and feasibility of the proposed design method is illustrated by the simulation result.

关键词： finite-time bounded Lipschitz nonlinear systems uncertain non-fragile H∞ controller

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AttentionDTA: prediction of drug–target binding affinity using attention model

AttentionDTA: prediction of drug–target binding affinity us...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Qichang Zhao Fen Xiao Mengyun Yang Yaohang Li Jianxin Wang Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education Xiangtan University Xiangtan Hunan China School of Computer Science and Engineering Central South University Changsha Hunan China Old Dominion University Norfolk Virginia USA

ISBN: (纸本)9781728118680

In bioinformatics, machine learning-based prediction of drug-target interaction (DTI) plays an important role in virtual screening of drug discovery. DTI prediction, which have been treated as a binary classification problem, depends on the concentration of two molecules, the interaction between two molecules, and other factors. The degree of affinity between a drug molecule (such as a drug compound) and a target molecule (such as a receptor or protein kinase) reflects how tightly the drug binds to a particular target and is quantified by the measurement which can reflect more detailed and specific information than binary relationship. In this study, we proposed an end-to-end model, named AttentionDTA, based on deep learning, which associates attention mechanism to predict the binding affinity of DTI. The novelty in this work is to use attentional mechanisms to consider which subsequences in a protein are more important for a drug and which subsequences in a drug are more important for a protein when predicting its affinity. So that the representational ability of the model is stronger. The model uses one-dimensional Convolution Neural Networks (1D-CNNs) to extract the abstract information of drug and protein, and makes the drug and protein representations mutually adapt through the attention mechanisms. We evaluate our model on two established drug-target affinity benchmark datasets, Davis and KIBA. The model outperforms DeepDTA, a state-of-the-art deep learning method for drug-target binding affinity prediction, with better Mean Squared Error (MSE), Concordance Index (CI), rm 2 , and Area Under Precision Recall Curve (AUPR). Our results show that the attention-based model can effectively extract effective representations by calculating the weight of the representation between the drug and the protein. Finally, we visualize the attention weight. It proves our model can obtain the information of binding sites.

关键词： bioinformatics biology computing convolutional neural nets drugs learning (artificial intelligence) mean square error methods molecular biophysics pattern classification proteins Mean square error Pharmaceutical Preparations biology computing molecular biology Pattern recognition Bioinformatics binding affinity Drug Compounding Protein affinity Diffusion Tensor Imaging Proteins

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

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Journal of Physics: Conference Series 2019年第1期1284卷

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 Hefei 230039 China

In this paper, we propose a new user grouping and power allocation scheme based on beamforming for downlink non-orthogonal multiple access systems. The 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 large. In addition, a power allocation scheme that can maximize the sum capacity while satisfying a certain fairness index is proposed. From 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.

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