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Pricing Policy for a Dynamic Spectrum Allocation Scheme with Batch Requests and Impatient Packets in Cognitive Radio Networks

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Journal of systems Science and systems Engineering 2022年第2期31卷 133-149页

作者： Haixing Wu Shunfu Jin Wuyi Yue School of Information Science and Engineering Yanshan UniversityQinhuangdao 066004China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan UniversityQinhuangdao 066004China Department of Intelligence and Informatics Konan UniversityKobe 658-8501Japan

In cognitive radio networks(CRNs),multiple secondary users may send out requests simultaneously and one secondary user may send out multiple requests at one time,i.e.,request arrivals usually show an aggregate ***,a secondary user packet waiting in the buffer may leave the system due to impatience before it is transmitted,and this impatient behavior inevitably has an impact on the system *** to investigate the influence of the aggregate behavior of requests and the likelihood of impatience on a dynamic spectrum allocation scheme in CRNs,in this paper a batch arrival queueing model with possible reneging and potential transmission interruption is *** constructing a Markov chain and presenting a transition rate matrix,the steady-state distribution of the queueing model along with a dynamic spectrum allocation scheme is derived to analyze the stochastic behavior of the ***,some important performance measures such as the loss rate,the balk rate and the average delay of secondary user packets are ***,system experiments are carried out to show the change trends of the performance measures with respect to batch arrival rates of secondary user packets for different impatience parameters,different batch sizes of secondary user packets,and different arrival rates of primary user ***,a pricing policy for secondary users is presented and the dynamic spectrum allocation scheme is socially optimized.

关键词： Cognitive radio networks dynamic spectrum scheme batch arrival impatient packets Markov chain social optimization

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Multiscale Convolutional Transformer with Diverse-aware Feature Learning for Motor Imagery EEG Decoding

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IEEE Transactions on Cognitive and Developmental systems 2025年

作者： Hang, Wenlong Wang, Junliang Liang, Shuang Lei, Baiying Wang, Qiong Li, Guanglin Chen, Badong Qin, Jing Nanjing Tech University College of Computer Nanjing211816 China Nanjing Tech University Information Engineering Nanjing211816 China Nanjing University of Posts and Telecommunications School of Internet of Things Nanjing210023 China Shenzhen University School of Biomedical Engineering Shenzhen518060 China Guangdong Provincial Key Laboratory of Computer Vision Shenzhen518055 China Virtual Reality Technology Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China Shenzhen Institute of Advanced Technology Shenzhen518055 China Chinese Academy of Sciences Shenzhen518055 China Xi'an Jiaotong University Institute of Artificial Intelligence and Robotics Xi'an710049 China Hong Kong Polytechnic University School of Nursing Hung Hom Hong Kong

Electroencephalogram (EEG)-based motor imagery (MI) brain-computer interfaces (BCIs) have significant potential in improving motor function for neurorehabilitation. Despite recent advancements, learning diversified EEG features across different frequency ranges remains a significant challenge, as the homogenization of feature representations often limits the generalization capabilities of EEG decoding models for BCIs. In this paper, we propose a novel multiscale convolutional transformer framework for EEG decoding that integrates multiscale convolution, transformer, and diverse-aware feature learning scheme (MCTD) to tackle the above challenge. Specifically, we first capture multiple frequency features using dynamic one-dimensional temporal convolution with different kernel lengths. Subsequently, we incorporate convolutional layers and transformers with a contrastive learning scheme to extract discriminative local and global EEG features within a single frequency range. To mitigate the homogenization of features extracted from different frequency ranges, we propose a novel decorrelation regularization. It enables multiscale convolutional transformers produce less correlated features with each other, thereby enhancing the overall expressiveness of EEG decoding model. The performance of MCTD is evaluated on four public MI-based EEG datasets, including the BCI competition III 3a and IV 2a, the BNCI 2015- 001, and the OpenBMI. For the average Kappa/Accuracy scores, MCTD obtains improvements of 3.58%/2.68%, 3.09%/2.20%, 2.33%/1.54%, and 4.44%/2.22%, over the state-of-the-art method on four EEG datasets, respectively. Experimental results demonstrate that our method exhibits superior performance. © 2016 IEEE.

关键词： Brain computer interface

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Design for Testability Features of Godson-3 Multicore Microprocessor

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Journal of computer Science & technology 2011年第2期26卷 302-313页

作者：齐子初刘慧李向库胡伟武 Key Laboratory of Computer System and Architecture Chinese Academy of Sciences Institute of Computing Technology Chinese Academy of Sciences Loongson Technologies Corporation Limited

This paper describes the design for testability （DFT） challenges and techniques of Godson-3 microprocessor, which is a scalable multicore processor based on the scalable mesh of crossbar （SMOC） on-chip network and targets high-end applications. Advanced techniques are adopted to make the DFT design scalable and achieve low-power and low-cost test with limited IO resources. To achieve a scalable and flexible test access, a highly elaborate test access mechanism （TAM） is implemented to support multiple test instructions and test modes. Taking advantage of multiple identical cores embedding in the processor, scan partition and on-chip comparisons are employed to reduce test power and test time. Test compression technique is also utilized to decrease test time. To further reduce test power, clock controlling logics are designed with ability to turn off clocks of non-testing partitions. In addition, scan collars of CACHEs are designed to perform functional test with low-speed ATE for speed-binning purposes, which poses low complexity and has good correlation results.

关键词： DFT （design for testability） TAM （test access mechanism） multicore processor low power test

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QoS Requirement Generation and Algorithm Selection for Composite Service Based on Reference Vector

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Journal of computer Science & technology 2009年第2期24卷 357-372页

作者：吴邦欲支志雄徐世杰顾明孙家广 School of Software Tsinghua University Key Laboratory for Information System Security Ministry of Education of China Department of Computer Science and Technology Tsinghua University

Under SOA （Service-Oriented Architecture）, composite service is formed by aggregating multiple component services together in a given workflow. One key criterion of this research topic is QoS composition. Most work on service composition mainly focuses on the algorithms about how to compose services according to assumed QoS, without considering where the required QoS comes from and the selection of user preferred composition algorithm among those with different computational cost and different selection results. In this paper, we propose to strengthen current service composition mechanism by generation of QoS requirement and its algorithm selection based on the QoS reference vectors which are calculated optimally from the existing individual services＇ QoS by registry to represent QoS overview about the best QoS, the worst （or most economical） QoS, or the average QoS of all composite services. To implement QoS requirement, which is determined according to QoS overview, this paper introduces two selection algorithms as two kinds of experiment examples, one aiming at the most accurate service selection and the other chasing for trade-off between selection cost and result. Experimental results show our mechanism can help the requester achieve his expected composite service with appropriate QoS requirement and customized selection algorithm.

关键词： service-oriented architecture service composition quality of services service selection

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Unsupervised image-to-image translation by semantics consistency and self-attention

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Optoelectronics Letters 2022年第3期18卷 175-180页

作者： ZHANG Zhibin XUE Wanli FU Guokai The Key Laboratory of Computer Vision and System of Ministry of Education Tianjin Key Laboratory of Intelligence Computing and Novel Software TechnologyTianjin University of TechnologyTianjin 300384China

Unsupervised image-to-image translation is a challenging task for computer vision. The goal of image translation is to learn a mapping between two domains, without corresponding image pairs. Many previous works only focused on image-level translation but ignored image features processing, which led to a certain semantics loss, such as the changes of the background of the generated image, partial transformation, and so on. In this work, we propose a method of image-to-image translation based on generative adversarial nets(GANs). We use autoencoder structure to extract image features in the generator and add semantic consistency loss on extracted features to maintain the semantic consistency of the generated image. Self-attention mechanism at the end of generator is used to obtain long-distance dependency in image. At the same time, as expanding the convolution receptive field, the quality of the generated image is enhanced. Quantitative experiment shows that our method significantly outperforms previous works. Especially on images with obvious foreground, our model shows an impressive improvement.

关键词： translation features semantic

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Global Spatial-Temporal Information-based Residual ConvLSTM for Video Space-Time Super-Resolution

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IEEE Transactions on Multimedia 2025年

作者： Fu, Congrui Yuan, Hui Jiang, Shiqi Zhang, Guanghui Shen, Liquan Hamzaoui, Raouf Center of Experimental Teaching China Shandong University School of Control Science and Engineering China Ministry of Education Key Laboratory of Machine Intelligence and System Control China Shandong University School of software Shandong Jinan China Shandong University School of Computer Science and Technology China Shanghai University School of Communication and Information Engineering Shanghai China De Montfort University School of Engineering and Sustainable Development LeicesterLE1 9BH United Kingdom

By converting low-frame-rate, low-resolution videos into high-frame-rate, high-resolution ones, space-time video super-resolution techniques can enhance visual experiences and facilitate more efficient information dissemination. We propose a convolutional neural network (CNN) for space-time video super-resolution, namely GIRNet. Our method combines long-term global information and short-term local information from the video to better extract complete and accurate spatial-temporal information. To generate highly accurate features and thus improve performance, the proposed network integrates a feature-level temporal interpolation module with deformable convolutions and a global spatial-temporal information-based residual convolutional long short-term memory (convLSTM) module. In the feature-level temporal interpolation module, we leverage deformable convolution, which adapts to deformations and scale variations of objects across different scene locations. This provides a more efficient solution than conventional convolution for extracting features from moving objects. Our network effectively uses forward and backward feature information to determine inter-frame offsets, leading to the direct generation of interpolated frame features. In the global spatial-temporal information-based residual convLSTM module, the first convLSTM is used to derive global spatial-temporal information from the input features, and the second convLSTM uses the previously computed global spatial-temporal information feature as its initial cell state. This second convLSTM adopts residual connections to preserve spatial information, thereby enhancing the output features. Experiments on the Vimeo90K dataset show that the proposed method outperforms open source state-of-the-art techniques in peak signal-to-noise-ratio (by 1.45 dB, 1.14 dB, and 0.2 dB over STARnet, TMNet, and 3DAttGAN, respectively), structural similarity index(by 0.027, 0.023, and 0.006 over STARnet, TMNet, and 3DAttGAN, respectiv

关键词： Long short-term memory

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Seamless virtual machine live migration on network security enhanced hypervisor

Seamless virtual machine live migration on network security ...

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2009 2nd IEEE International Conference on Broadband Network and Multimedia technology, IEEE IC-BNMT2009

作者： Xianqin, Chen Han, Wan Sumei, Wang Xiang, Long State Key Laboratory of Virtual Reality Technology System School of Computer Science and Technology Beijing University of Aeronautics and Astronautics Beijing 100191 China

ISBN: (纸本)9781424450053

Since the virtual network traffic is invisible outside the hypervisor, it is impossible for traditional network-base security devices to harness the attacks happened in virtual computing environment. Industry and academies adopt the network security enabled hypervisor (NSE-H) to protect virtual machines (VM) residing in the virtual network. In this paper, we identified the insufficiency of the existing live migration implementation, which prevents itself from providing transparent VM relocation between NSE-Hs. This occurs because the contemporary migration implementation only takes VM encapsulated states into account, but ignores VM related security context(SC) needed by NSE-H embedded security engines (SE). We presented a comprehensive live migration framework for the NSE-H, considering both the execution context encapsulated in VM instance and the VM related security context within the SEs. We built a prototype system of the framework based on stateful firewall enabled Xen hypervisor. Our experiment was performed with realistic applications and the results demonstrate that the solution complements the insufficiency without introducing significant performance downgrade. Even in the worst case, the downtime that occurs during migration increases no more than 15%, comparing to existing implementation. ©2009 IEEE.

关键词： Network security

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Real-time approximate soft shadow rendering with bidirectional penumbra map

Real-time approximate soft shadow rendering with bidirection...

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International Conference on Educational and Information technology

作者： Wang, Lili Zhen, Jingchao Shen, Zhe State Key Laboratory of Virtual Reality Technology and System Beihang University Beijing China

ISBN: (纸本)9781424480340

The present traditional penumbra map method can only render the outer part of the penumbra, which breaks down when the umbra region significantly decreases because of the large light sources and the large distance between the occluder and the shadowed objects. This happens for very large area light source or as the occluder moves away from the objects it shadows. This paper proposes a real-time soft shadow rendering method based on bidirectional penumbra map generated in image space. It first extracts the edge of the shadow in the shadow map without rendering the whole scenes again. Then it extends the edges both inside and outside to obtain the inner and outer penumbra region, and calculate the change of lighting intensity along the two directions to generate the bidirectional penumbra map. Lastly, the final image is rendered with this penumbra map and the shadow map. This method solves the distortion problem of the traditional penumbra map method when the umbra region significantly decreases. It has only two rendering passes and approximates the soft shadow in image space, thus can be design to run entirely on GPU. The result of our experiments shows the efficiency of our approach. © 2010 IEEE.

关键词： Maps

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Concurrent Competitive Wave Approach toHyper-Distributed Hyper-Parallel AIProcessing

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Journal of computer Science & technology 1997年第6期12卷 543-554页

作者：帅典勋 Department of Computer Science East China University of Science and Technology Shanghai State Key Laboratory of Intelligence Technology and System Tsinghua University Beijing

This paper preseflts a new approach of the synchronous homogeneous concurrent propagation of competitive waves for the purpose of hyper-distributed hyper-parallel heuristic problem-solving. The concurrent algorithm, mechanism and their properties are given. In comparison with the traditional AI algorithms, the approach is featured by the knowledge-based problem-solving in the distributed parallel environment, the feasibility for hardware implementation and the various applications.

关键词： Hyper-distributed hyper-parallel processing synchronous homogeneous competitive wave heuristic search implicit AND/OR graph concurrent algorithm

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Behavior Consistency Computation for Workflow Nets With Unknown Correspondence

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IEEE/CAA Journal of Automatica Sinica 2018年第1期5卷 281-291页

作者： Mimi Wang Guanjun Liu Peihai Zhao Chungang Yan Changjun Jiang Department of Computer Science and Technology the Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China IEEE

Consistency degree calculation is established on the basis of known correspondence, but in real life, the correspondence is generally unknown, so how to calculate consistency of two models under unknown correspondence has become a problem. For this condition, we should analyze unknown correspondence due to the influence of different *** this paper we obtain the relations of transitions based on event relations using branching processes, and build a behavioral matrix of relations. Based on the permutation of behavioral matrix, we express different correspondences, and define a new formula to compute the maximal consistency degree of two workflow nets. Additionally, this paper utilizes an example to show these definitions, computation as well as the advantages.

关键词： Behavioral matrix branching process consistency degree unknown correspondence workflow net(WF-nets)

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