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Traffic Engineering in Dynamic Hybrid Segment Routing Networks

Computers, Materials & Continua 2021年第7期68卷 655-670页

作者： Yingya Guo Kai Huang Cheng Hu Jiangyuan Yao Siyu Zhou College of Mathematics and Computer Science Fuzhou UniversityFuzhou350000China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhou350000China Key Laboratory of Spatial Data Mining&Information Sharing Ministry of EducationFuzhou350003China School of Information Science and Technology Guangdong University of Foreign StudiesGuangzhou510006China School of Computer Science and Cyberspace Security Hainan UniversityHaiKou570228China Tandon School of Engineering New York UniversityNew York10012USA Department of Computing Hong Kong Polytechnic UniversityHong Hom999077Hong Kong

The emergence of Segment Routing(SR)provides a novel routing paradigm that uses a routing technique called source packet *** SR architecture,the paths that the packets choose to route on are indicated at the ingress *** with shortest-path-based routing in traditional distributed routing protocols,SR can realize a flexible routing by implementing an arbitrary flow splitting at the ingress *** the advantages of SR,it may be difficult to update the existing IP network to a full SR deployed network,for economical and technical *** partial of the traditional IP network to the SR network,thus forming a hybrid SR network,is a preferable *** the traffic is dynamically changing in a daily time,in this paper,we propose a Weight Adjustment algorithm WASAR to optimize routing in a dynamic hybrid SR *** algorithm can be divided into three steps:firstly,representative Traffic Matrices(TMs)and the expected TM are obtained from the historical TMs through ultrascalable spectral clustering ***,given the network topology,the initial network weight setting and the expected TM,we can realize the link weight optimization and SR node deployment optimization through a Deep Reinforcement Learning(DRL)***,we optimize the flow splitting ratios of SR nodes in a centralized online manner under dynamic traffic demands,in order to improve the network *** the evaluation,we exploit historical TMs to test the performance of the obtained routing configuration in *** extensive experimental results validate that our proposed WASAR algorithm has superior performance in reducing Maximum Link Utilization(MLU)under the dynamic traffic.

关键词： Traffic engineering routing optimization segment routing deep reinforcement learning ultra-scalable spectral clustering

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Bi-Contrastive Alignment for Incomplete Multi-view Clustering

Bi-Contrastive Alignment for Incomplete Multi-view Clusterin...

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Chinese Control and Decision Conference, CCDC

作者： Shengxia Gao Yaoying Wang Yuqing Shi Kaiwu Zhang Shiqiang Du Chinese National Information Technology Research Institute Northwest Minzu University Lanzhou China Key Laboratory of Minzu Languages and Cultures Intelligent Information Processing Gansu Province Northwest Minzu University Lanzhou China College of Electrical Engineering Northwest Minzu University Lanzhou China Key Laboratory of Linguistic and Cultural Computing of Ministry of Education Northwest Minzu University Lanzhou China College of Mathematics and Computer Science Northwest Minzu University Lanzhou China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Multi-view clustering (MVC) represents a significant challenge in the realms of machine learning and data mining. Recently, the advent of contrastive learning (CL) has catalyzed the development of numerous innovative methods within this field. The efficacy of these methods typically hinges on the availability of consistent and comprehensive data. Nevertheless, real-world scenarios of data collection and transmission frequently encounter issues like partial unaligned-views (PU), which result in incomplete datasets and pose formidable challenges for effective MVC. To address this prevalent issue, our paper introduces an innovative framework, termed bi-contrastive alignment for incomplete multi-view clustering (BCA). BCA specifically targets the challenge of incomplete data stemming from PU. It encompasses a unique module, semantic guided bi-contrastive alignment (SGBA), designed to counteract the PU by harnessing semantic similarities. Moreover, the framework augments data alignment by integrating a contrastive learning-based corrective component into the loss function. The comparative experiments demonstrate that the ARI metric of our method surpasses the second-best contrasting method on all four datasets.

关键词： Measurement Semantics Contrastive learning Machine learning Fasteners Data collection Benchmark testing

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A Millimeter-Wave Wideband Integrated Log-Periodic Antenna Array

A Millimeter-Wave Wideband Integrated Log-Periodic Antenna A...

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Cross Strait Quad-Regional Radio Science and Wireless Technology Conference (CSQRWC)

作者： Daosheng Huang Guanghui Xu Jie Wu Hui Xu Li-Xia Yang Zhi-Xiang Huang Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China

ISBN: (数字)9781728181813

ISBN: (纸本)9781728181820

The millimeter-Wave (mm-Wave) wideband integrated log-periodic antenna array based on multilayer PCB is proposed in this paper. The antenna array is simulated and designed to operate at the mm-Wave band of 32-47 GHz with the impedance bandwidth of 37.5%. Differential feeding is adopted to reduce the cross polarization, and EBG structure is used to improve the back lobe. The simulated maximum gain of the array is 11 dBi and the cross-polarization ratio is less than -40 dB. Due to its wide bandwidth, high gain and low cross polarization, the proposed array will be a suitable scheme for many mm-Wave applications.

关键词： Millimeter wave technology Nonhomogeneous media Broadband antennas Gain Wideband Antenna arrays Periodic structures

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Analysis of Asymptotically Tight Approximation ser for Cooperative NOMA Systems 19

Analysis of Asymptotically Tight Approximation ser for Coope...

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19th IEEE International Conference on Communication Technology, ICCT 2019

作者： Zhu, DIngcheng Yao, Yijing Xu, Lei Zhang, Hongwei Fang, Hongyu Li, Xiaohui Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Hefei China

ISBN: (纸本)9781728105352

The closed-form formula derivation of the power domain cooperative Non-Orthogonal Multiple Access (NOMA) system is of great significance for further improving the performance of the system. However, the system performance formulas of the channel capacity and the paired bit error rate Pairwise Error Probability (PEP) are too complicated, which have increased the difficulty in system performance optimization. Therefore, based on the Amplify Forward (AF) relay cooperative NOMA model, the signal Interference Noise Ratio (SINR) formulas of the two weak-user nodes are constructed. Through the assumption of that, the Symbol Error Rate (SER) of each user is fair, the simplification condition of Moment Generating Function (MGF) with the harmonic mean form is satisfied. Combined with the SER calculation formula of MGF, the system SER asymptotically tight approximation formula with simple structure is derived at high signal-to-noise ratio (SNR). The Monte Carlo simulation results show that, the formula can accurately describe the SER performance of the power domain cooperative NOMA system with the non-ideal Successive Interference Cancellation (SIC) system when SNR is high. Under the condition of certain total power, the optimal power distribution factor is solved in order to minimize the total system SER. © 2019 IEEE.

关键词： Bit error rate

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Dynamic Multi-user Detection Scheme Based on CVA-SSAOMP Algorithm in Uplink Grant-Free NOMA 19

Dynamic Multi-user Detection Scheme Based on CVA-SSAOMP Algo...

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19th IEEE International Conference on Communication Technology, ICCT 2019

作者： Zhang, Jian Bai, Shichao Xu, Lei Zhang, Hongwei Fang, Hongyu Li, Xiaohui Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Hefei China

ISBN: (纸本)9781728105352

In the uplink grant-free non-orthogonal multiple access (NOMA) scenario, since the active user at the sender has a structured sparsity transmission characteristic, the compressive sensing recovery algorithm is initially applied to the joint detection of the active user and the transmitted data. However, the existing compressive sensing recovery algorithms with unknown sparsity often require noise power or signal-to-noise ratio (SNR) as the priori conditions, which greatly reduces the algorithm adaptability in multi-user detection. Therefore, an algorithm based on cross validation aided structured sparsity adaptive orthogonal matching pursuit (CVA-SSAOMP) is proposed to realize multi-user detection in dynamic change communication scenario of channel state information (CSI). The proposed algorithm transforms the structured sparsity model into a block sparse model, and without the priori conditions above, the cross validation method in the field of statistics and machine learning is used to adaptively estimate the sparsity of active user through the residual update of cross validation. The simulation results show that, compared with the traditional orthogonal matching pursuit (OMP) algorithm, subspace pursuit (SP) algorithm and cross validation aided block sparsity adaptive subspace pursuit (CVA-BSASP) algorithm, the proposed algorithm can effectively improve the accurate estimation of the sparsity of active user and the performance of system bit error ratio (BER), and has the advantage of low-complexity. © 2019 IEEE.

关键词： Multiuser detection

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Disentangled Generation Network for Enlarged License Plate Recognition and A Unified Dataset

arXiv

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arXiv 2022年

作者： Li, Chenglong Yang, Xiaobin Wang, Guohao Zheng, Aihua Tan, Chang Jia, Ruoran Tang, Jin Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Artificial Intelligence Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Lab of Intelligent Computing and Signal Processing Ministry of Education Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China iFLYTEK Co. Ltd. Hefei230088 China

License plate recognition plays a critical role in many practical applications, but license plates of large vehicles are difficult to be recognized due to the factors of low resolution, contamination, low illumination, and occlusion, to name a few. To overcome the above factors, the transportation management department generally introduces the enlarged license plate behind the rear of a vehicle. However, enlarged license plates have high diversity as they are non-standard in position, size, and style. Furthermore, the background regions contain a variety of noisy information which greatly disturbs the recognition of license plate characters. Existing works have not studied this challenging problem. In this work, we first address the enlarged license plate recognition problem and contribute a dataset containing 9342 images, which cover most of the challenges of real scenes. However, the created data are still insufficient to train deep methods of enlarged license plate recognition, and building large-scale training data is very time-consuming and high labor cost. To handle this problem, we propose a novel task-level disentanglement generation framework based on the Disentangled Generation Network (DGNet), which disentangles the generation into the text generation and background generation in an end-to-end manner to effectively ensure diversity and integrity, for robust enlarged license plate recognition. Extensive experiments on the created dataset are conducted, and we demonstrate the effectiveness of the proposed approach in three representative text recognition frameworks. © 2022, CC BY.

关键词： Optical character recognition

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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 Verlet Time-stepping Nodal DGTD Method for Electromagnetic Scattering and Radiation 5

A Verlet Time-stepping Nodal DGTD Method for Electromagnetic...

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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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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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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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