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t/k-fault diagnosis algorithm of n-dimensional hypercube network based on the MM*model

Journal of Systems Engineering and Electronics 2018年第1期29卷 216-222页

作者： LIANG Jiarong ZHOU Ning YUN Long School of Computer and Electronics Information Guangxi UniversityNanning 530004China Guangxi Key Laboratory of Multimedia Communications and Network Technology Nanning 530004China

Compared with accurate diagnosis, the system’s selfdiagnosing capability can be greatly increased through the t/kdiagnosis strategy at most k vertexes to be mistakenly identified as faulty under the comparison model, where k is typically a small number. Based on the Preparata, Metze, and Chien(PMC)model, the n-dimensional hypercube network is proved to be t/kdiagnosable. In this paper, based on the Maeng and Malek(MM)*model, a novel t/k-fault diagnosis(1≤k≤4) algorithm of ndimensional hypercube, called t/k-MM*-DIAG, is proposed to isolate all faulty processors within the set of nodes, among which the number of fault-free nodes identified wrongly as faulty is at most k. The time complexity in our algorithm is only O(2~n n~2).

关键词： hypercube network t/k-diagnosis algorithm multiprocessor systems the Maeng and Malek(MM)* model Preparata Metze and Chien(PMC)

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Spatially Coupled Codes via Bidirectional Block Markov Superposition Transmission

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Tsinghua Science and technology 2024年第3期29卷 656-670页

作者： Gaoyan Li Shancheng Zhao Haiqiang Chen Jinming Wen College of Information Science and Technology Jinan UniversityGuangzhou 510632China Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi UniversityNanning 530004China

In this paper,we present a new class of spatially coupled codes obtained by using both non-recursive and recursive block-oriented *** resulting codes are termed as bidirectional block Markov superposition transmission(BiBMST)***,we perform an iterative decoding threshold analysis according to protograph-based extrinsic information transfer(PEXIT)charts for the BiBMST codes over the binary erasure channels(BECs).Secondly,we derive the generator and parity-check matrices of the BiBMST ***,extensive numerical results are presented to show the advantages of the proposed BiBMST ***,our numerical results show that,under the constraint of an equal decoding latency,the BiBMST codes perform better than the recursive BMST(rBMST)***,the simulation results show that,in finite-length regime,negligible performance gain is obtained by increasing the encoding *** solve this limitation by introducing partial superposition,and the resulting codes are termed as partially-connected BiBMST(PC-BiBMST)*** results have confirmed the advantages of the PC-BiBMST codes over the original BiBMST *** also present extensive simulation results to show the performance advantages of the PC-BiBMST codes over the spatially coupled low-density parity-check(SC-LDPC)codes,spatially coupled generalized LDPC(SC-GLDPC)codes,and the original BiBMST codes in the finite-length regime.

关键词： block Markov superposition transmission(BMST) protograph-based extrinsic information transfer(PEXIT) capacity-approaching codes spatial coupling

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Practical decoy-state quantum secure direct communication

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Science China(Physics,Mechanics & Astronomy) 2021年第12期64卷 68-75页

作者： Xin Liu Zijian Li Di Luo Chunfeng Huang Di Ma Minming Geng Junwei Wang Zhenrong Zhang Kejin Wei Guangxi Key Laboratory for Relativistic Astrophysics School of Physical Science and TechnologyGuangxi UniversityNanning 530004China Guangxi Key Laboratory of Multimedia Communications and Network Technology School of ComputerElectronicsand InformationGuangxi UniversityNanning 530004China CAS Quantum Network Co. LtdShanghai 201315China

Quantum secure direct communication(QSDC) has been demonstrated in both fiber-based and free-space channels using attenuated lasers. Decoy-state QSDC by exploiting four decoy states has been proposed to address the problem of photon-numbersplitting attacks caused by the use of attenuated lasers. In this study, we present an analysis of the practical aspects of decoy-state QSDC. First, we design a two-decoy-state protocol that only requires two decoy states, thereby significantly reducing experimental complexity. Second, we successfully perform full parameter optimization for a real-life QSDC system by introducing a genetic algorithm. Our simulation results show that the two-decoy-state protocol could be the best choice for developing a practical QSDC system. Furthermore, full optimization is crucial for a high-performance QSDC system. Our work serves as a major step toward the further development of practical decoy-state QSDC systems.

关键词： quantum secure direct communication decoy-state method full parameter optimization

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MIFNet: Learning Modality-Invariant Features for Generalizable Multimodal Image Matching

arXiv

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arXiv 2025年

作者： Liu, Yepeng Sun, Zhichao Yu, Baosheng Zhao, Yitian Du, Bo Xu, Yongchao Cheng, Jun National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan430070 China Lee Kong Chian School of Medicine Nanyang Technological University 308232 Singapore Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Zhejiang Ningbo315211 China 1 Fusionpolis Way #21-01 Connexis South Tower Singapore138632 Singapore

Many keypoint detection and description methods have been proposed for image matching or registration. While these methods demonstrate promising performance for single-modality image matching, they often struggle with multimodal data because the descriptors trained on single-modality data tend to lack robustness against the non-linear variations present in multimodal data. Extending such methods to multimodal image matching often requires well-aligned multimodal data to learn modality-invariant descriptors. However, acquiring such data is often costly and impractical in many real-world scenarios. To address this challenge, we propose a modality-invariant feature learning network (MIFNet) to compute modality-invariant features for keypoint descriptions in multimodal image matching using only single-modality training data. Specifically, we propose a novel latent feature aggregation module and a cumulative hybrid aggregation module to enhance the base keypoint descriptors trained on single-modality data by leveraging pretrained features from Stable Diffusion models. We validate our method with recent keypoint detection and description methods in three multimodal retinal image datasets (CF-FA, CF-OCT, EMA-OCTA) and two remote sensing datasets (Optical-SAR and Optical-NIR). Extensive experiments demonstrate that the proposed MIFNet is able to learn modality-invariant feature for multimodal image matching without accessing the targeted modality and has good zero-shot generalization ability. The source code will be made publicly available. Copyright © 2025, The Authors. All rights reserved.

关键词： Graph neural networks

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Smoothing Neural network for Non-Lipschitz Optimization with Linear Inequality Constraints

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Chinese Journal of Electronics 2021年第4期30卷 634-643页

作者： YU Xin WU Lingzhen XIE Mian WANG Yanlin XU Liuming LU Huixia XU Chenhua Department of Computer and Electronic Information Guangxi University The Guangxi Key Laboratory of Multimedia Communications and Network Technology School of Computer Science and Engineering Guilin University of Aerospace Technology Department of Electrical Engineering Guangxi University

This paper presents a smoothing neural network to solve a class of non-Lipschitz optimization problem with linear inequality constraints. The proposed neural network is modelled with a differential inclusion equation, which introduces the smoothing approximate techniques. Under certain conditions, we prove that the trajectory of neural network reaches the feasible region in finite time and stays there thereafter, and that any accumulation point of the solution is a stationary point of the original optimization problem. Furthermore, if all stationary points of the optimization problem are isolated,then the trajectory converges to a stationary point of the optimization problem. Two typical numerical examples are given to verify the effectiveness of the proposed neural network.

关键词： Non-Lipschitz optimization Neural networks Stationary points Convergence

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RFID network planning based on improved brain storm optimization algorithm

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The Journal of China Universities of Posts and Telecommunications 2022年第5期29卷 30-39页

作者： Lin Zihan Zheng Jiali Xie Xiaode Feng Minyu He Siyi School of Computer Electrics and InformationGuangxi UniversityNanning 530004China Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi UniversityNanning 530004China

In order to improve the service quality of radio frequency identification(RFID) systems, multiple objectives should be comprehensively considered. An improved brain storm optimization algorithm GABSO, which incorporated adaptive learning operator and golden sine operator into the original brain storm optimization(BSO) algorithm, was proposed to solve the problem of RFID network planning(RNP). GABSO algorithm introduces learning operator and golden sine operator to achieve a balance between exploration and development. Based on GABSO algorithm, an optimization model is established to optimize the position of the reader. The GABSO algorithm was tested on the RFID model and dataset, and was compared with other methods. The GABSO algorithm’s tag coverage was increased by 9.62% over the Cuckoo search(CS) algorithm, and 7.70% over BSO. The results show that the GABSO algorithm could be successfully applied to solve the problem of RNP.

关键词： radio frequency identification(RFID) RFID network planning(RNP) brain storm optimization golden sine operator

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Improved sparrow search algorithm for RFID network planning

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The Journal of China Universities of Posts and Telecommunications 2023年第1期30卷 93-102页

作者： Zhang Jiangbo Zheng Jiali Quan Yixuan Lin Zihan Xie Xiaode School of Computer Electrics and InformationGuangxi UniversityNanning 530004China Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi UniversityNanning 530004China

To solve the problem that the performance of the coverage,interference rate,load balance andweak power in the radio frequency identification(RFID)network *** paper proposes an elite opposition-based learning and Lévy flight sparrow search algorithm(SSA),which is named elite opposition-based learning and Levy flight SSA(ELSSA).First,the algorithm initializes the population by an elite opposed-based learning strategy to enhance the diversity of the ***,Lévy flight is introduced into the scrounger’s position update formula to solve the situation that the algorithm falls into the local optimal *** has a probability that the current position is changed by Lévy *** method can jump out of the local optimal *** the end,the proposed method is compared with particle swarm optimization(PSO)algorithm,grey wolf optimzer(GWO)algorithm and SSA in the multiple simulation *** simulated results showed that,under the same number of readers,the average fitness of the ELSSA is improved respectively by 3.36%,5.67%and 18.45%.By setting the different number of readers,ELSSA uses fewer readers than other *** conclusion shows that the proposed method can ensure a satisfying coverage by using fewer readers and achieving higher comprehensive performance.

关键词： radio frequency identification network planning sparrow search algorithm elite opposition-based learning Levy flight

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A DV-Hop positioning algorithm based on the glowworm swarm optimisation of mixed chaotic strategy

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International Journal of Security and networks 2019年第1期14卷 23-33页

作者： Song, Ling Zhao, Liqin Ye, Jin School of Computer and Electronic Information Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi University Nanning530004 China

DV-Hop, as a typical location algorithm without ranging, is widely used in node localisation of wireless sensor networks. However, in the third phase of DV-Hop, a least square method is used to solve the nonlinear equations. Using this method to locate the unknown nodes will produce large coordinates errors, poor stability of positioning accuracy, low location coverage and high energy consumption. An improved localisation algorithm based on hybrid chaotic strategy (MGDV-Hop) is proposed in this paper. Firstly, a glowworm swarm optimisation of hybrid chaotic strategy based on chaotic mutation and chaotic inertial weight updating (MC-GSO) is proposed. Then, MC-GSO is used to replace the least square method in estimating node coordinates. By establishing the error fitness function, the linear solution of coordinates is transformed into a two-dimensional combinatorial optimisation problem. Simulation results show that the average location error is reduced, while the location coverage is increased and the energy consumption is decreased. © 2019 Inderscience Enterprises Ltd.

关键词： Energy utilization

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Double-branch fusion network with a parallel attention selection mechanism for camouflaged object detection

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Science China(Information Sciences) 2023年第6期66卷 258-266页

作者： Junjiang XIANG Qing PAN Zhengrong ZHANG Songnian FU Yuwen QIN Advanced Institute of Photonics Technology School of Information EngineeringGuangdong University of Technology Guangdong Provincial Key Laboratory of Photonics Information Technology Guangdong University of Technology Guangxi Key Laboratory of Multimedia Communications and Network Technology School of Computer Electronics and InformationGuangxi University

To meet the challenge of camouflaged object detection （COD）,which has a high degree of intrinsic similarity between the object and background,this paper proposes a double-branch fusion network（DBFN）with a parallel attention selection mechanism （PASM）.In detail,a schismatic receptive field block（SRF）combined with an attention mechanism for low-level information is performed to learn texture features in one branch,and an integration of the SRF,a hybrid attention mechanism （HAM）,and a depth feature polymerization module （DFPM）is employed for high-level information to extract detection features in the other ***,both texture features and detection features are input into the PASM to acquire selective expression ***,the final result is obtained after further selective matrix optimization with atrous spatial pyramid pooling （ASPP）and a residual channel attention block （RCAB）being applied *** results on three public datasets verify that our method outperforms the state-of-the-art methods in terms of four evaluation metrics,i.e.,mean absolute error （MAE）,weighted F βmeasure （Fβω）,structural measure （Sα）,and E-measure （Eφ）

关键词： camouflaged object detection attention mechanism feature extraction feature aggregation texture information fuzzy boundary

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Hybrid gray wolf optimization-cuckoo search algorithm for RFID network planning

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The Journal of China Universities of Posts and Telecommunications 2021年第6期28卷 91-102页

作者： Quan Yixuan Zheng Jiali Xie Xiaode Lin Zihan Luo Wencong School of Computer Electrics and InformationGuangxi UniversityNanning 530004China Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi UniversityNanning 530004China

In recent years,with the rapid development of Internet of things(IoT)technology,radio frequency identification(RFID)technology as the core of IoT technology has been paid more and more attention,and RFID network planning(RNP)has become the primary *** with the traditional methods,meta-heuristic method is widely used in *** at the target requirements of RFID,such as fewer readers,covering more tags,reducing the interference between readers and saving costs,this paper proposes a hybrid gray wolf optimization-cuckoo search(GWO-CS)*** method uses the input representation based on random gray wolf search and evaluates the tag density and location to determine the combination performance of the reader's propagation *** with particle swarm optimization(PSO)algorithm,cuckoo search(CS)algorithm and gray wolf optimization(GWO)algorithm under the same experimental conditions,the coverage of GWO-CS is 9.306%higher than that of PSO algorithm,6.963%higher than that of CS algorithm,and 3.488%higher than that of GWO *** results show that the GWO-CS algorithm cannot only improve the global search range,but also improve the local search depth.

关键词： radio frequency identification(RFID) gray wolf optimization(GWO)algorithm cuckoo search(CS)algorithm dynamic adjustment of discovery probability directional mutation

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