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Proceedings of the 2019 2nd International Conference on Signal Processing and Machine Learning

作者： Zhenxing Luo Lulu Zhao Wanyong Tian Dan Yang Yiyuan Chen Jiabin Yu Jianjun Li Science and Technology on Communication Information Security Control Laboratory Jiaxing China CETC Key Laboratory of Data Link Technology Xi'An China Hangzhou Dianzi University Hangzhou China

ISBN: (纸本)9781450372213

The Discrete Event System (DEVS)[1] Specification provides a reference standard for the model design and simulation development of complex discrete event state system. It designs a formal mechanism to describe discrete event state, which is composed of a set of strictly abstract mathematical symbols, and provides a strict description mechanism and execution logic for the modeling and Simulation of discrete event state system, it ensures the normalization, reusability and simulation operation ability of the model. As a special link system, data link[2] is different from general communication system. It is mainly used between different military platforms to ensure information sharing. By linking sensors, command and control system and weapon platform according to the specified message format and communication protocol, the information system that can automatically transmit the formatted data of battlefield situation, command and guidance, tactical coordination, weapon control, etc., in real time can form a close and efficient tactical link relationship between different combat platforms. In this paper, DEVS is used to simulate the Reporting Responsibility for Air, Surface, and Land tracks in MIL-STD-6016B.

关键词： DEVS data link ADEVS Modeling and Simulation

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Edge-cloud cooperation driven surface roughness classification method for selective laser melting

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Advanced Engineering Informatics 2025年 66卷

作者： Ma, Shuaiyin Huang, Yuming Liu, Yang Yan, Zhiqiang Lv, Jingxiang Cai, Wei School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an710121 China Shaanxi Union Research Centre of University and Enterprise for 5G+ Industrial Internet Communication Terminal Technology Xi'an University of Posts and Telecommunications Xi'an710121 China Department of Management and Engineering Linköping University LinköpingSE-581 83 Sweden Industrial Engineering and Management University of Oulu Oulu90570 Finland Key Laboratory of Road Construction Technology and Equipment of MOE Chang'an University Xi'an710064 China College of Engineering and Technology Southwest University Chongqing400715 China

Additive manufacturing (AM) technology is extensively utilized in aerospace and industrial manufacturing. However, parts built using AM are susceptible to spheroidization, porosity, cracks, and poor surface quality, making it difficult to establish an actionable product quality degree. Hence, developing a reasonable method to equate product quality with a new degree and further analyzing the product quality based on these standards has proven effective for enhancing part quality in AM. To achieve this goal, this paper proposes a surface roughness classification method that utilizes surface roughness analysis and sample enhancement. This method leverages edge cloud cooperation to efficiently analyze and integrate data from different sensors, enabling real-time monitoring and adjustment of the manufacturing process. Subsequently, the quality degree analysis system was developed utilizing matter-element extension cloud model. Furthermore, a bidirectional-gated recurrent unit (Bi-GRU) based model for quality classification and recognition has been established, with Wasserstein generative adversarial network (WGAN) employed for sample enhancement to address the issue of imbalanced column classification and to enhance the accuracy of both classification and recognition. Finally, the results obtained from this case study demonstrate through comparative experiments that the proposed method for classifying surface roughness can accurately identify 98% of prepared samples. © 2025 The Authors

关键词： Inspection

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data link modeling and simulation based on DEVS 2

Data link modeling and simulation based on DEVS

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2nd International Conference on Signal Processing and Machine Learning, SPML 2019

作者： Luo, Zhenxing Zhao, Lulu Tian, Wanyong Yang, Dan Chen, Yiyuan Yu, Jiabin Li, Jianjun Science and Technology on Communication Information Security Control Laboratory Jiaxing314033 China CETC Key Laboratory of Data Link Technology Xi'An710071 China Hangzhou Dianzi University Hangzhou310018 China

ISBN: (纸本)9781450372213

关键词： Discrete event simulation

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Local Partial Zero-Forcing Combining for Cell-Free Massive MIMO Systems

arXiv

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arXiv 2022年

作者： Zhang, Jiayi Zhang, Jing Björnson, Emil Ai, Bo The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-Speed Railway System Beijing Jiaotong University Beijing100044 China The Department of Electrical Engineering Linköping University Linköping Sweden The Department of Computer Science KTH Royal Institute of Technology Kista Sweden The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-Speed Railway System Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China

Cell-free massive multiple-input multiple-output (MIMO) provides more uniform spectral efficiency (SE) for users (UEs) than cellular technology. The main challenge to achieve the benefits of cell-free massive MIMO is to realize signal processing in a scalable way. In this paper, we consider scalable full-pilot zero-forcing (FZF), partial FZF (PFZF), protective weak PFZF (PWPFZF), and local regularized ZF (LRZF) combining by exploiting channel statistics. We derive closed-form expressions of the uplink SE for FZF, PFZF, and PWPFZF combining with large-scale fading decoding over independent Rayleigh fading channels, taking channel estimation errors and pilot contamination into account. Moreover, we investigate the impact of the number of pilot sequences, antennas per AP, and APs on the performance. Numerical results show that LRZF provides the highest SE. However, PWPFZF is preferable when the number of pilot sequences is large and the number of antennas per AP is small. The reason is that PWPFZF has lower computational complexity and the SE expression can be computed in closed-form. Furthermore, we investigate the performance of PWPFZF combining with fractional power control and the numerical results show that it improves the performance of weak UEs and realizes uniformly good service for all UEs in a scalable fashion. Copyright © 2022, The Authors. All rights reserved.

关键词： Power control

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Ship Segmentation on High-Resolution Sar Image by a 3D Dilated Multiscale U-Net

Ship Segmentation on High-Resolution Sar Image by a 3D Dilat...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Jichao Li Chubing Guo Shuiping Gou Yuanbo Chen Miao Wang Jia-Wei Chen Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xidian University Xi'an China CETC Key laboratory of Data Link Technology Beijing China Beijing Huahang Radio Measurement and Research Institute Beijing China

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

Targets detection and segmentation in a synthetic aperture radar (SAR) image is a vital step for its interpretation. It is quite challenging for most conventional methods due to complex background and the speckle. Furthermore, the sizes of targets in a scene are variable. Inspired by the success of neural networks in computer vision, In this paper, we propose a 3D dilated multi-scale U-shape convolutional neural network (3DDM-UNet). In the proposed method, we first build a 3D image block via a multiscale stationary wavelet transform to exploit the structural information of targets with various sizes. Then, the built 3D image block is fed into a 3D dilated multiscale U-Net. To train the proposed network, we build a dataset from a scene of SAR image with various sizes and shapes of ship targets. Finally, the trained network is employed to the testing set to obtain the segmentation results. Experimental results on test images show that the proposed method achieved better performance than conventional methods.

关键词： Radar polarimetry Three-dimensional displays Image segmentation Kernel Synthetic aperture radar Marine vehicles Object detection

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Towards 6G wireless communication networks:vision, enabling technologies, and new paradigm shifts

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Science China(Information Sciences) 2021年第1期64卷 5-78页

作者： Xiaohu YOU Cheng-Xiang WANG Jie HUANG Xiqi GAO Zaichen ZHANG Mao WANG Yongming HUANG Chuan ZHANG Yanxiang JIANG Jiaheng WANG Min ZHU Bin SHENG Dongming WANG Zhiwen PAN Pengcheng ZHU Yang YANG Zening LIU Ping ZHANG Xiaofeng TAO Shaoqian LI Zhi CHEN Xinying MA Chih-Lin I Shuangfeng HAN Ke LI Chengkang PAN Zhimin ZHENG Lajos HANZO Xuemin (Sherman) SHEN Yingjie Jay GUO Zhiguo DING Harald HAAS Wen TONG Peiying ZHU Ganghua YANG Jun WANG Erik G.LARSSON Hien Quoc NGO Wei HONG Haiming WANG Debin HOU Jixin CHEN Zhe CHEN Zhangcheng HAO Geoffrey Ye LI Rahim TAFAZOLLI Yue GAO H.Vincent POOR Gerhard P.FETTWEIS Ying-Chang LIANG National Mobile Communications Research Laboratory School of Information Science and EngineeringSoutheast University Purple Mountain Laboratories Shanghai Institute of Fog Computing Technology (SHIFT) ShanghaiTech University Research Center for Network Communication Peng Cheng Laboratory State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications National Engineering Laboratory for Mobile Network Technologies Beijing University of Posts and Telecommunications National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China (UESTC) China Mobile Research Institute School of Electronics and Computer Science University of Southampton Department of Electrical and Computer Engineering University of Waterloo Global Big Data Technologies Centre (GBDTC) University of Technology Sydney School of Electrical and Electronic Engineering The University of Manchester LiFi Research and Development Centre Institute for Digital CommunicationsSchool of EngineeringThe University of Edinburgh Huawei Technologies Canada Co. Ltd. Huawei Technologies Department of Electrical Engineering (ISY) Link?ping University Institute of Electronics Communications & Information Technology (ECIT)Queen's University Belfast State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast University School of Electrical and Computer Engineering Georgia Institute of Technology 5G Innovation Centre University of Surrey Princeton University Vodafone Chair Mobile Communications Systems Technische Universit?t Dresden Center for Intelligent Networking and Communications (CINC) University of Electronic Science and Technology of China (UESTC)

The fifth generation(5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability,and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation(6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle(UAV) communication networks, thus achieving a space-airground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6GHz, millimeter wave(mmWave), terahertz(THz),and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence(AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable f

关键词： 6G vision network architecture air interface and transmission technologies space-air-ground-sea integrated network all spectra artificial intelligence network security

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Hyperspectral Target Detection via Multiple Instance LSTM Target Localization Network

Hyperspectral Target Detection via Multiple Instance LSTM Ta...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Xiaoying Chen Xiuxiu Wang Chubing Guo Chao Chen Shuiping Gou Tao Yu Changzhe Jiao Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Xidian University Xi'an P. R. China CETC Key laboratory of Data Link Technology Xi'an China IBM Research San Jose CA USA Laboratory of Spectral Imaging Technique Xi'an Institute of Optics and Precision Mechanics Chinese Academy Sciences Xi'an China CAS Key Laboratory of Spectral Imaging Technology Xi'an China

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

Modeling target detection problem given inaccurate annotations as a multiple instance learning (MIL) problem is an effective way for addressing the ground truth uncertainties of remotely sensed hyperspectral imagery. In this paper, we propose a hyperspectral target detection method based on 1D convolution neural network (1DCNN) feature extraction and long short term memory network (LSTM) under the MIL framework, where the LSTM features for each hyperspectral pixel is further refined by a scoring network as to discriminate the real target instance from the inaccurately labeled hyperspectral regions. The proposed method has achieved superior results on both simulated data and real hyperspectral data over the state-of-the-art methods, showing the prospects for further investigation.

关键词： Hyperspectral imaging Feature extraction Object detection Training Task analysis Long short term memory Location awareness

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Joint Channel Assignment and Power Allocation for NOMA-based D2D Communications with Imperfect CSI

Joint Channel Assignment and Power Allocation for NOMA-based...

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International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Tianqi Xing Nan Ma Ping Zhang State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China CETC Key Laboratory of Data Link Technology Xi'an China

In this paper, we propose a channel assignment and power allocation sheme for non-orthogonal multiple access (NOMA) device-to-device (D2D) communications. Our optimization goal is to maximize the sum rate of the D2D system, and considering the fairness factor simultaneously. Importantly, we innovatively consider the impact of imperfect channel state information (CSI) in our system, which makes our proposed algorithm more realistic and effective. We split the problem into two sub-problems: 1) one-to-one matching for the cellular user and D2D transmitter and finding the appropriate receivers for the D2D transmitter to form the D2D group; 2) power allocation for each D2D group. For the channel assignment, we derive a closed form expression of the scheduled data rate of D2D link with considering imperfect CSI by using mathematical approximation methods. And we transform the power allocation subproblem from a non-convex problem to convex one and use the Lagrangian multiplier method to get the optimal power allocation coefficients, considering the sensitivity of the successive interference cancellation (SIC) receiver. Moreover, the theoretical analysis and simulation results are presented to evaluate our proposed scheme for the NOMA-based D2D communications.

关键词： Device-to-device communication Receivers Resource management NOMA Transmitters Interference Channel allocation

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The 2D Materials Roadmap

arXiv

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arXiv 2025年

作者： Ren, Wencai Bøggild, Peter Redwing, Joan Marie Novoselov, Kostya Sun, Luzhao Qi, Yue Jia, Kaicheng Liu, Zhongfan Burton, Oliver Alexander-Webber, Jack Hofmann, Stephan Cao, Yang Long, Yu Yang, Quan-Hong Li, Dan Choi, Soo Ho Kim, Ki Kang Lee, Young Hee Li, Mian Huang, Qing Gogotsi, Yury Clark, Nicholas Carl, Amy Gorbachev, Roman Olsen, Thomas Rosen, Johanna Thygesen, Kristian Sommer Efetov, Dmitri K. Jessen, Bjarke S. Yankowitz, Matthew Barrier, Julien Kumar, Roshan Krishna Koppens, Frank H.L. Deng, Hui Li, Xiaoqin Dai, Siyuan Basov, D.N. Wang, Xinran Das, Saptarshi Duan, Xiangfeng Yu, Zhihao Borsch, Markus Ferrari, Andrea C. Huber, Rupert Kira, Mackillo Xia, Fengnian Wang, Xiao Wu, Zhong-Shuai Feng, Xinliang Simon, Patrice Cheng, Hui-Ming Liu, Bilu Xie, Yi Jin, Wanqin Nair, Rahul Raveendran Xu, Yan Zhang, Qing Katiyar, Ajit K. Ahn, Jong-Hyun Aharonovich, Igor Hersam, Mark C. Roche, Stephan Hua, Qilin Shen, Guozhen Ren, Tianling Zhang, Hao-Bin Koo, Chong Min Koratkar, Nikhil Pellegrini, Vittorio Young, Robert J. Qu, Bill Lemme, Max Pollard, Andrew J. Shenyang National Laboratory for Materials Science Institute of Metal Research Chinese Academy of Sciences 72 Wenhua Road Shenyang110016 China Technical University of Denmark Denmark The Pennsylvania State University United States University of Manchester United Kingdom Institute for Functional Intelligent Materials National University of Singapore Singapore Beijing Graphene Institute China University of Cambridge United Kingdom Department of Chemical Engineering The University of Melbourne Victoria Australia Nanoyang Group Tianjin Key Laboratory of Advanced Carbon and Electrochemical Energy Storage School of Chemical Engineering and Technology Tianjin University Tianjin300072 China The Hong Kong University of Science and Technology Hong Kong Center for Integrated Nanostructure Physics Institute for Basic Science Suwon16419 Korea Republic of Sungkyunkwan University Suwon16419 Korea Republic of Zhejiang Key Laboratory of Data-Driven High-Safety Energy Materials and Applications Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences China Drexel University United States Linköping University Sweden Ludwig-Maximilians-Universität München Germany University of Washington United States ICFO The Institute of Photonic Sciences Castelldefels Barcelona08860 Spain University of Michigan United States University of Texas Austin United States Auburn University United States Columbia University United States State Key Laboratory of Catalysis Dalian Institute of Chemical Physics Chinese Academy of Sciences Dalian China University of California Los Angeles United States Suzhou Laboratory Suzhou China School of Integrated Circuit Science and Engineering Nanjing University of Posts and Telecommunications Nanjing210023 China Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States University of Regensburg Germany Department of Electrical and Computer Engineering Yale University New Have

Over the past two decades, 2D materials have rapidly evolved into a diverse and expanding family of material platforms. Many members of this materials class have demonstrated their potential to deliver transformative impact on fundamental research and technological applications across different fields. In this roadmap, we provide an overview of the key aspects of 2D material research and development, spanning synthesis, properties and commercial applications. We specifically present roadmaps for high impact 2D materials, including graphene and its derivatives, transition metal dichalcogenides, MXenes as well as their heterostructures and moiré systems. The discussions are organized into thematic sections covering emerging research areas (e.g., twisted electronics, moiré nano-optoelectronics, polaritronics, quantum photonics, and neuromorphic computing), breakthrough applications in key technologies (e.g., 2D transistors, energy storage, electrocatalysis, filtration and separation, thermal management, flexible electronics, sensing, electromagnetic interference shielding, and composites) and other important topics (computational discovery of novel materials, commercialization and standardization). This roadmap focuses on the current research landscape, future challenges and scientific and technological advances required to address, with the intent to provide useful references for promoting the development of 2D materials. © 2025, CC BY.

关键词： Integrated optoelectronics

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SLAM Based on Double Layer Cubature Kalman Filter

SLAM Based on Double Layer Cubature Kalman Filter

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International Conference on Information Fusion

作者： Feng Yang Bo Jin Mengting Yan Yujuan Luo CETC Key Laboratory of Data Link Technology P. R. China Key Laboratory of Information Fusion Technology Ministry of Education P. R. China

ISBN: (数字)9780996452786

ISBN: (纸本)9781728118406

In the simultaneous localization and mapping (SLAM), the challenge is the large computational cost, low accuracy and instability. In SLAM system, cubature Kalman filter (CKF) has shown good performance. However, in terms of algorithm accuracy and stability, double layer cubature Kalman filter (DLCKF) is better than CKF. It calculates the predicted state at next moment through the inner layer CKF, which is more accurate than the predicted value obtained directly through motion model. The outer layer CKF then updates the predicted state with the measurement to obtain a more accurate estimate. Combined with the advantages of DLCKF, an innovative filter-based SLAM algorithm based on double layer cubature Kalman filter was established in this paper to solve the above problems. Simulation results are presented that the positioning error of the proposed algorithm is significantly reduced and the accuracy of mapping is greatly improved compared with traditional EKF-SLAM, UKF-SLAM and CKF-SLAM.

关键词： Simultaneous localization and mapping Kalman filters Robot kinematics Covariance matrices Prediction algorithms Mathematical model

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