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An ultra-wideband pattern reconflgurable antenna based on graphene coating

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Chinese Physics B 2016年第11期25卷 625-631页

作者：姜彦南袁锐高喜王娇李思敏林诒玉 Guangxi key laboratory of Wireless Wideband Communication and Signal processing Guilin 541004 China key laboratory of cognitive radio and information processing (Ministry of Education) Guilin University of Electronic Technology Guilin 541004 China Guangxi Experiment Center of information Science Guilin University of Electronic Technology Guilin 541004 China

An ultra-wideband pattern reconfigurable antenna is *** antenna is a dielectric coaxial hollow monopole with a cylindrical graphene-based impedance surface *** consists of a graphene sheet coated onto the inner surface of a cylindrical substrate and a set of independent polysilicon DC gating pads mounted on the outside of the cylindrical *** changing the DC bias voltages to the different gating pads,the surface impedance of the graphene coating can be freely *** to the tunability of graphene＇s surface impedance,the radiation pattern of the proposed antenna can be reconfigured.A transmission line method is used to illustrate the physical mechanism of the proposed *** results show that the proposed antenna can reconfigure its radiation pattern in the omnidirectional mode with the relative bandwidth of 58.5% and the directional mode over the entire azimuth plane with the relative bandwidth of 67%.

关键词： wideband antenna impedance bandwidth directional illustrate hollow dielectric coated outside

Deep learning-based software engineering: progress,challenges, and opportunities

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Science China(information Sciences) 2025年第1期68卷 57-144页

作者： Xiangping CHEN Xing HU Yuan HUANG He JIANG Weixing JI Yanjie JIANG Yanyan JIANG Bo LIU Hui LIU Xiaochen LI Xiaoli LIAN Guozhu MENG Xin PENG Hailong SUN Lin SHI Bo WANG Chong WANG Jiayi WANG Tiantian WANG Jifeng XUAN Xin XIA Yibiao YANG Yixin YANG Li ZHANG Yuming ZHOU Lu ZHANG School of Journalism and Communication Sun Yat-sen University School of Software Technology Zhejiang University School of Software Engineering Sun Yat-sen University School of Software Dalian University of Technology School of Computer Science and Technology Beijing Institute of Technology key laboratory of High Confidence Software Technologies (Peking University) Ministry of EducationSchool of Computer Science Peking University State key laboratory for Novel Software Technology Nanjing University School of Computer Science and Engineering Beihang University Institute of information Engineering Chinese Academy of Sciences School of Computer Science Fudan University State key laboratory of Complex & Critical Software Environment (CCSE) School of Software Beihang University School of Computer and information Technology Beijing Jiaotong University School of Computer Science and Technology Harbin Institute of Technology School of Computer Science Wuhan University Huawei Technologies

Researchers have recently achieved significant advances in deep learning techniques, which in turn has substantially advanced other research disciplines, such as natural language processing, image processing, speech recognition, and software engineering. Various deep learning techniques have been successfully employed to facilitate software engineering tasks, including code generation, software refactoring, and fault localization. Many studies have also been presented in top conferences and journals, demonstrating the applications of deep learning techniques in resolving various software engineering tasks. However,although several surveys have provided overall pictures of the application of deep learning techniques in software engineering,they focus more on learning techniques, that is, what kind of deep learning techniques are employed and how deep models are trained or fine-tuned for software engineering tasks. We still lack surveys explaining the advances of subareas in software engineering driven by deep learning techniques, as well as challenges and opportunities in each subarea. To this end, in this study, we present the first task-oriented survey on deep learning-based software engineering. It covers twelve major software engineering subareas significantly impacted by deep learning techniques. Such subareas spread out through the whole lifecycle of software development and maintenance, including requirements engineering, software development, testing, maintenance, and developer collaboration. As we believe that deep learning may provide an opportunity to revolutionize the whole discipline of software engineering, providing one survey covering as many subareas as possible in software engineering can help future research push forward the frontier of deep learning-based software engineering more systematically. For each of the selected subareas,we highlight the major advances achieved by applying deep learning techniques with pointers to the available datasets i

关键词： deep learning software engineering software benchmark software artifact representation survey

Local-contrastive-learning machine with both generalization and adversarial robustness: A statistical physics analysis

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Science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 109-123页

作者： Mingshan Xie Yuchen Wang Haiping Huang PMI Lab School of Physics Sun Yat-sen University Guangdong Provincial key laboratory of Magnetoelectric Physics and Devices Sun Yat-sen University

Distinct from human cognitive processing, deep neural networks trained by backpropagation can be easily fooled by adversarial examples. To design a semantically meaningful representation learning, we discard backpropagation, and instead, propose a local contrastive learning, where the representations for the inputs bearing the same label shrink(akin to boson) in hidden layers,while those of different labels repel(akin to fermion). This layer-wise learning is local in nature, being biologically plausible.A statistical mechanics analysis shows that the target fermion-pair-distance is a key parameter. Moreover, the application of this local contrastive learning to MNIST benchmark dataset demonstrates that the adversarial vulnerability of standard perceptron can be greatly mitigated by tuning the target distance, i.e., controlling the geometric separation of prototype manifolds.

关键词： machine learning statistical mechanics adversarial robustness neural networks

NOMA-Based Collaborative Beam Hopping Frequency Allocation Mechanism for Future LEO Satellite Systems

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China Communications 2023年第6期20卷 321-338页

作者： Fei Zheng Zhao Pi Zou Zhou Miao Ye Hongbing Qiu Ministry of Education key laboratory of cognitive radio and information processing Guilin University of Electronic TechnologyGuilin 541004China State key laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China

Low Earth orbit(LEO) satellite systems provide terrestrial users with services that are not limited by geographical location. However, the conflict between existing allocation schemes and the business variability between beams is becoming increasingly prominent. Beam hopping technology allows for a more flexible and versatile approach to satellite resource allocation. This paper proposes a beam hopping pattern optimization scheme that jointly considers the interference threshold distance and beam service priority, reducing the inter-beam co-channel interference(CCI). In the cluster area, a non-orthogonal multiple access(NOMA)-based collaborative beam hopping(NCBH) scheme is proposed to minimize the cell-edge user(CEU) interference. Since there is a difference in channel gain between the CEU and cellcenter user(CCU), this scheme forms a NOMA cluster to perform power domain multiplexing and formulates a NOMA cluster pairing strategy according to the user location to reduce the CCI of the CEU. After NOMA cluster pairing, the optimal carrier frequency of the NOMA cluster is selected by a reinforcement learning algorithm. The simulation results verify the excellent performance of the proposed NCBH scheme regarding the user’s received power, transmission rate, and outage probability.

关键词： LEO satellite beam hopping NOMA resource allocation

Interpolation Fitting Algorithm in Time-Space Domain of Differential Barometric Altimetry

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Journal of Beijing Institute of Technology 2019年第1期28卷 119-128页

作者： Zhengqun Hu Lirong Zhang Guanyi Ma Yuanfa Ji National Astronomical Observatories Chinese Academy of Sciences key laboratory of cognitive radio and information processing Ministry of EducationGuilin University of Electronic Technology Guangxi key laboratory of Precision Navigation Technology and Application

In order to overcome the limitations of a unitary reference station in mobile communication positioning network differential barometric altimetry(DBA)and broaden the action scope of the reference station and improve positioning accuracy of elevation,an integrated interpolation algorithm model based on generalized extended approximation(GEA)algorithm and Kriging interpolation in time-space domain of reference station is *** the time domain,barometric measured data is considered the maximum value estimated by bilateral extension to avoid wrong direction of estimation,which is approaching true *** the spatial domain,barometric relevance among multiple reference stations is utilized,the weighted coefficients of multiple reference stations is calculated by the integrated algorithm model based on the GEA algorithm and Kriging *** impact of each reference station to the measured station is quantified,so that a virtual reference station is constructed,which can overcome the limitation of barometric correction by a unitary reference *** addition,the measurement error due to irregular change in atmospheric pressure will be eliminated.

关键词： differential barometric altimetry(DBA) multi reference station generalized extended approximation(GEA) Kriging algorithm

HSNR: A Network Representation Learning Algorithm Using Hierarchical Structure Embedding

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Chinese Journal of Electronics 2020年第6期29卷 1141-1152页

作者： YE Zhonglin ZHAO Haixing ZHU Yu XIAO Yuzhi College of Computer Qinghai Normal University Tibetan information processing and Machine Translation key laboratory of Qinghai Province key laboratory of Tibetan information processing Ministry of Education

Vertices in the same group tend to connect densely,and usually share common *** of different sizes reflect the relations of vertices in different ranges,and also reflect the features of different orders of the *** this work,we propose a novel network representation learning algorithm by introducing group features of vertices of different orders to learn more discriminative network representations,named as network representation learning algorithm using Hierarchical structure embedding(HSNR).HSNR algorithm firstly constructs hierarchical relations of network structures of different orders based on greedy algorithm and *** order to introduce hierarchical features into the network representation learning model,HSNR algorithm then introduces the idea of multi-relational modeling from knowledge representation,and converts the hierarchical relations into the triplet form between ***,HSNR proposes a joint learning model embedding vertex triplets into the network *** experimental results show that the HSRN algorithm presented has an excellent performance in network vertex classification task on three real-world datasets.

关键词： group features network vertex classification task network structures Combinatorial mathematics HSNR algorithm hierarchical features novel network representation discriminative network representations joint learning model Hierarchical structure embedding network representation learning model Data handling techniques vertices network representation learning algorithm HSRN algorithm graph theory Computer vision and image processing techniques hierarchical relations knowledge representation greedy algorithm learning (artificial intelligence) greedy algorithms Knowledge engineering techniques

New construction of highly nonlinear resilient S-boxes via linear codes

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Frontiers of Computer Science 2022年第3期16卷 175-181页

作者： Haixia ZHAO Yongzhuang WEI Ministry of Education key laboratory of cognitive radio and information processing Guilin University of Electronic TechnologyGuilin 541004China School of Mathematics and Computing Science Guilin University of Electronic TechnologyGuilin 541004China Guangxi key laboratory of Cryptography and information Security Guilin University of Electronic TechnologyGuilin 541004China

Highly nonlinear resilient functions play a crucial role in nonlinear combiners which are usual hardware oriented stream *** the past three decades,the main idea of construction of highly nonlinear resilient functions are benefited from concatenating a large number of affine ***,these resilient functions as core component of ciphers usually suffered from the guess and determine attack or algebraic attack since the n-variable nonlinear Boolean functions can be easily given rise to partial linear relations by fixing at most nil variables of *** to design highly nonlinear resilient functions(S-boxes)without concatenating a large number of nil variables affine subfunctions appears to be an important *** this article,a new construction of highly nonlinear resilient functions is *** functions consist of two classes *** specially,the first class(nonlinear part)contains both the bent functions with 2k variables and some affine subfUnctions with n/2-k variables which are attained by using[n/2-k,m,d]disjoint linear *** second class(linear part)includes some linear subfunctions with nil variables which are attained by using[n/2,m,d]disjoint linear *** is illustrated that these resilient functions have high nonlinearity and high algebraic *** particular,It is different from previous well-known resilient S-boxes,these new S-boxes cannot be directly decomposed into some affine subftinctions with nil variables by fixing at most nil *** means that the S-boxes(vectorial Boolean functions)which use these resilient functions as component functions have more favourable cryptography properties against the guess and determine attack or algebraic attacks.

关键词： stream cipher S-box disjoint linear codes resiliency nonlinearity

Hop distance fairness for wireless mesh network based on queue management

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Journal of Central South University 2012年第10期19卷 2832-2838页

作者：叶进王建新黄家玮李陶深 School of information Science and Engineering Central South University key laboratory of cognitive radio and information processing of Ministry of Education(Gulin University of Electronic Technology) School of Computer Electronics and InformationGuangxi University

Wireless mesh network （WMN） is a new multi-hop network for broadband accessing to intemet. However, there exists a server unfairness problem based on different hop distances in WMN. To solve this problem, the unfairness issue was analyzed in test-bed experiment and NS2 simulation. A dynamic queue management scheme E-QMMN was proposed, which allocates the queue buffer according to the hop distance of every flow. The experimental results show that the proposed scheme can not only increase the hop distance fairness of the legacy scheme at most 50%, but also reduce the average round trip time at least 29% in congested WMN environments.

关键词： wireless mesh network (WMN) hop distance queue management unfairness problem

Analysis of an improved acquisition method for high-dynamic BOC signal

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Journal of Systems Engineering and Electronics 2016年第6期27卷 1158-1167页

作者： Yi Pan Tianqi Zhang Gang Zhang Zhongtao Luo Chongqing key laboratory of Signal and information processing Chongqing University of Posts and Telecommunications

To reach a compromise between short acquisition time and excellent detection probability for the high-dynamic binary offset carrier (BOC) signal, an overall algorithm based on discrete polynomial-phase transform (DPT) and partial matching filter (PMF)-fast Fourier transform (FFT) algorithm is given. The high order items of received signals are removed by the method of DPT, and the PMF-FFT algorithm is redesigned for the BOC signal. The simulation experiments and theoretical analyses verify that the improved algorithm has attained improvement in acquisition performance. © 2016 Beijing Institute of Aerospace information.

关键词： Bins Mergers and acquisitions Titration