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Conference on Local Computer Networks (LCN)

作者： Junfeng Jin Baohua Zhao Hao Zhou School of Computer Science University of Science and Technology Hefei Anhui China Province Key Laboratory of Software in Computing and Communication Hefei Anhui China School of Computer Science University of Science and Technology of China Hefei Anhui

In this work, we study a link-weighted and distance-constrained channel assignment problem in multi-channel mesh networks with stationary router nodes, such as community wireless networks. A good channel assignment is given as the one which can minimize interference of each link from its neighboring links and subsequently improve the network throughput. In response to it, we introduce an interference metric, namely, min-max i-value of an edge (MMIE), to explicitly accounts for interference among links that are at distance one. In addition, we further show that the link-weighted and distance-constrained channel assignment problem with respect to the interference metric is NP-hard in computation. This guides us to propose a new heuristic channel assignment algorithm called LD-CA algorithm. We study the performance of our algorithm by implementing it in a wireless simulation environment consisting of 25 nodes, each equipped with one 802.11 wireless card. The ns-2 simulation results show that in a multi-channel environment, the LD-CA algorithm significantly outperforms previously proposed channel assignment schemes by minimizing MMIE of all links.

关键词： Wireless mesh networks Interference Mesh networks Throughput Spread spectrum communication Laboratories Computational modeling Switches Conferences Computer network management

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Multiscale Bilateral Attention Fusion Network for Pansharpening

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5828-5843页

作者： Guo, Zhongyuan Li, Jiawei Lei, Jia Liu, Jinyuan Zhou, Shihua Wang, Bin Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of the Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Dalian University of Technology School of Mechanical Engineering Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1061 New Zealand University of Ulster Intelligent Systems Research Center LondonderryBT48 7JL United Kingdom Bulgarian Academy of Sciences IICT Sofia1000 Bulgaria

High-resolution multispectral (HRMS) images combine spatial and spectral information originating from panchromatic (PAN) and reduced-resolution multispectral (LRMS) images. Pansharpening performs well and is widely used to obtain HRMS images. However, most pansharpening approaches determine the ratio of PAN and LRMS images through direct interpolation, which may introduce artifacts and distort the color of the fused results. To address this issue, an unsupervised progressive pansharpening framework, MSBANet, is proposed, which adopts a multistage fusion strategy. Each stage contains an attention interactive extraction module (AIEM) and a multiscale bilateral fusion module (MBFM). The AIEM extracts spatial and spectral features from input images and captures the correlations between features. The MBFM can efficiently integrate information from the AIEM and improve MSBANet context awareness. We design a hybrid loss function that enhances the ability of the fusion network to store spectral and texture details. In qualitative and quantitative experimental studies on four datasets, MSBANet outperformed state-of-the-art pansharpening techniques. The code will be released. © 2024 IEEE.

关键词： Data mining

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Blockchain-based Distributed Storage System for Task Offloading in Vehicular Edge computing

Blockchain-based Distributed Storage System for Task Offload...

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

作者： Yutian Zhang Bing Tang Qing Yang Xiaoyuan Zhang Jincheng Luo Hunan Key Laboratory for Service Computing and Novel Software Technology School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China School of Information and Communication Engineering Guangzhou Maritime University Guangzhou China

With the deepening requirements of development for the Internet of Vehicles technology, intelligent vehicles need to complete the complex task in a fast response time to meet the needs, which brings great challenges to reliable vehicle service provision. In order to solve this problem, we propose a task offloading scheme into the Internet of Vehicles, which can reduce the execution time of tasks. In this paper, we introduce a location-aware distributed storage system called LDSS to support task offloading. Data can be written, stored, computed and read out through different mobile edge servers. Firstly, the identity of the vehicle is stored on the blockchain, and then the vehicle needs to offload the data to the nearest mobile edge server. Then, the storage node and computing node are selected based on the time optimization strategy. Finally, by predicting the location of the vehicle, the mobile edge server nearest to the predicted vehicle location is selected as the access node for result downloading. After validating the identity of vehicle based on blockchain technology, the output result is returned to the vehicle. The experiments show that our proposed task offloading scheme based on blockchain can effectively achieve the effect of low latency and faster task response, as well as ensure data security.

关键词： Blockchains Servers Reliability Time factors Task analysis Low latency communication Optimization

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Immune genetic programming based on register-stack structure

Immune genetic programming based on register-stack structure

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Congress on Evolutionary Computation

作者： Zeming Zhang Wenjian Luo Xufa Wang Nature Inspired Computation and Applications Laboratory Department of Computer Science and Technology University of Science and Technology Hefei China Nature Inspired Computation and Applications Laboratory Department of Computer Science and Technology and Anhui Key Laboratory of Software in Computing and Communication University of Science and Technology Hefei China

Inspired by biological immune principles, a novel Immune Genetic Programming based on Register-Stack structure (rs-IGP) is proposed in this paper. In rs-IGP, an antigen represents a problem to be solved, and an antibody represents a candidate solution. A flexible and efficient antibody representation based on register-stack structure is designed for rs-IGP. Three populations are adopted in rs-IGP, i.e. the common population, the elitist population and the self set. The immune genetic operators are also developed, including clone operator, recombination operator, mutation operator, hypermutation operator, crossover operator and negative selection operator. The experimental results demonstrate that rs-IGP has better performance.

关键词： Genetic programming Evolutionary computation Genetic mutations Cloning Biological system modeling Problem-solving Genetic algorithms Gene expression

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Accepted Influence Maximization under Linear Threshold Model on Large-Scale Social Networks

Accepted Influence Maximization under Linear Threshold Model...

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IEEE International Conference on Trust, Security and Privacy in computing and communications (TrustCom)

作者： Xiaojuan Yang Jiaxing Shang Linjiang Zheng Dajiang Liu Shu Fu Baohua Qiang Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education College of Computer Science Chongqing University Chongqing China School of Microelectronics and Communication Engineering Chongqing University Chongqing China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9781665416597

The influence maximization (IM) problem, which aims to find $k$ most influential individuals from a social network to maximize the influence spread, has been extensively studied. Existing works all rely on the assumption that influenced individuals will definitely try to propagate the information to their neighbors through social trust. However, in real-world this assumption can be over-simplistic since trust-levels among different individuals usually exhibit high diversity. As a result, an influenced individual may choose not to further propagate the information to his neighbors. Motivated by the observation, in this paper we propose a new accepted influence maximization (AIM) problem where the influenced individuals are further divided into two subgroups, i.e., accepted and active, where only active individuals will continue to propagate the information. We prove this problem is NP-hard and the objective function is submodular, based on which a greedy algorithm is proposed with ( $1-1/e-\epsilon$ ) approximation guarantee. Considering the low computational efficiency of the greedy algorithm, we further propose a scalable path-based algorithm ALDAG. We conduct experiments on real datasets and the results demonstrate the effectiveness and efficiency of our method.

关键词： Greedy algorithms Data privacy Social networking (online) Computational modeling Heuristic algorithms Linear programming Approximation algorithms

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Matrix Classifier On Dynamic Functional Connectivity For Mci Identification

Matrix Classifier On Dynamic Functional Connectivity For Mci...

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IEEE International Conference on Image Processing

作者： Lei Zhou Liang Zhang Xiao Bai Jun Zhou School of Computer Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing State Key Laboratory of Software Development Environment Beihang University Beijing School of Information and Communication Technology Griffith University Nathan

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

One of the most popular method for Alzheimer's disease (AD) diagnosis is exploring the Brain functional connectivity (FC) from resting-state functional magnetic resonance imaging (RS-fMRI). To early prevent AD, it is crucial to distinguish AD and and its preclinical stage, mild cognitive impairment (MCI) and early MCI (eMCI). In many existing works, dynamic functional connectivity (dFC) which contains rich spatiotemporal information has been exploited for the MCI and eMCI identification. However, most of these dFC based methods only consider the correlation between discrete brain status while ignore the valuable spatiotemporal information contained in dFC. To overcome this limitation, we propose a matrix classifier based method on the dFC signal for MCI and eMCI identification. Specifically, we first represent the dFC correlations by matrix features which contain rich spatiotemporal information and then learn the support matrix machines (SMM) to classify AD and its preclinical stage. Experiments on 600 real people data provide by the Alzheimer's Disease Neuroimaging Initiative (ADNI) demonstrate that our proposed matrix classifier based method outperforms other FC and dFC based methods for both normal controls (NC)/MCI identification and NC/eMCI identification.

关键词： Feature extraction Correlation Dementia Microsoft Windows Spatiotemporal phenomena Training

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Scheduling Model for Cognitive Radio

Scheduling Model for Cognitive Radio

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3rd International Conference on Cognitive Radio Oriented Wireless Networks and communications (CrownCom 2008)

作者： Ping Zhu Jinglong Li Xufa Wang Department of Computer Science and Technology Anhui Province Key Laboratory of Software in Computing and Communication University of Science and Technology Hefei Anhui China Department of Computer Science and Technology University of Science and Technology of China Hefei Anhui China

ISBN: (纸本)9781424423019

The priority queue model divides CR (cognitive radio) system users into primary user and secondary user. The primary user has preemptive priority over secondary user, while the same priority level users are FIFO served. This paper models the CR system based on scheduling technology. The secondary users are further divided into different priority levels. A hybrid priority dynamic policy, which indicates primary user's preemptive priority and secondary user's nonpreemptive priority, is developed to reduce spectrum switch overhead during spectrum leasing process. Theoretical analyzing shows that hybrid priority reduces the spectrum switch overhead and the hybrid priority improvement is a non-negative mono-increasing function of priority level. Based on the proposed policy, CR scheduling model is built and CR scheduling rule is provided and proved. Experimental comparison between CR scheduling and priority queue shows that CR scheduling gets a smaller weighted staying time sum than priority queue and the improvement increases as system becomes more congested.

关键词： Cognitive radio Priority queue CR schedule

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A Parallel NURBS-based Isogeometric Analysis Framework on Multi-core CPUs

A Parallel NURBS-based Isogeometric Analysis Framework on Mu...

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2012年全国理论计算机科学学术年会(NCTCS2012)

作者： Licai Guo Zhangjin Huang School of Computer Science and Technology University of Science and Technology of China PR China USTC-SICT Joint Laboratory on Network and Key Laboratory of Software in Computing and Communication Anhui Province PR China USTC-SICT Joint Laboratory on Network and Communication PR China School of Computer Science and Technology University of Science and Technology of China PR China

parallel framework for NURBS-based isogeometric analysis on multi-core cpus is presented in this *** framework is composed of two parts:(1) construction of the stiffness matrix and right hand side vector; (2) a parall...

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Speaker recognition with mel scale-based wavelet packet decomposition and AR-volterra model

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ICIC Express Letters, Part B: Applications 2016年第10期7卷 2087-2094页

作者： Yang, Shuying Guo, Jijn Jiang, Jiaojiao School of Computer and Communication Engineering Tianjin University of Technology No. 391 Binshui Xidao Xiqing District Tianjin300384 China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology No. 391 Binshui Xidao Xiqing District Tianjin300384 China

In order to improve the rate of speaker recognition, this paper proposes a new scheme with Mel scale-based Wavelet Packet Decomposition and AR-Volterra model. First, the speech signal is decomposed by wavelet packet of Mel scale, and calculate energy spectrum of the sub-band signals. The sub-bands of low energy are discarded according to weighting the proportion ofenergy for all-bands. Second, chaos ofsub-band signals are determined by the largest Lyapunov exponent. Ifsub-band is chaotic, Volterra adaptive model will be used to extract features. Otherwise, the autoregressive model will be used to extract features. Third, linear and nonlinear characteristic parameters will be used to speaker recognition. Hidden Markov model is used to recognize speaker. The experimental results show the extracted features have been obviously improved. © 2016 ICIC International.

关键词： Hidden Markov models

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Evolutionary Design of Relatively Large Combinational Circuits with an Extended Stepwise Dimension Reduction

Evolutionary Design of Relatively Large Combinational Circui...

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IEEE International Symposium on Dependable, Autonomic and Secure computing (DASC)

作者： Zhifang Li Wenjian Luo Lihua Yue Xufa Wang School of Computer Science and Technology University of Science and Technology Hefei Anhui China Anhui Key Laboratory of Software in Computing and Communication University of Science and Technology Hefei Anhui China University of Science and Technology of China Hefei Anhui CN

In this paper, an eXtended Stepwise Dimension Reduction approach (XSDR) to evolutionary design of relatively large combinational logic circuits is proposed. In our previous work, a Stepwise Dimension Reduction approach (SDR) is introduced. The SDR divides a circuit into several layers. The layers are evolved one after another. However, some layers are difficult to be evolved. The XSDR improves the SDR by decomposing the original truth table of a layer to two truth tables. The new truth tables after decomposing are easy to be evolved. The proposed method has been tested with multipliers and the circuits taken from the Microelectronics Center of North Carolina (MCNC) benchmark library. The experimental results demonstrate that the XSDR extensively improves the performance of the SDR in terms of the number of fitness evaluations and the computational time.

关键词： Combinational circuits Hardware Circuit testing Evolutionary computation Electronic circuits Computer science Laboratories Assembly Microelectronics Benchmark testing

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