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Analog spatiotemporal feature extraction for cognitive radio-frequency sensing with integrated photonics

Light(Science & Applications) 2024年第3期13卷 478-487页

作者： Shaofu Xu Binshuo Liu Sicheng Yi Jing Wang Weiwen Zou State Key Laboratory of advanced Optical communication Systems and Networks Intelligent Microwave Lightwave Integration Innovation Center(imLic)Department of Electronic EngineeringShanghai Jiao Tong UniversityShanghaiChina

Analog feature extraction(AFE)is an appealing strategy for low-latency and efficient cognitive sensing systems since key features are much sparser than the Nyquist-sampled ***,applying AFE to broadband radio-frequency(RF)scenarios is challenging due to the bandwidth and programmability bottlenecks of analog electronic ***,we introduce a photonics-based scheme that extracts spatiotemporal features from broadband RF signals in the analog *** feature extractor structure inspired by convolutional neural networks is implemented on integrated photonic circuits to process RF signals from multiple antennas,extracting valid features from both temporal and spatial *** of the tunability of the photonic devices,the photonic spatiotemporal feature extractor is trainable,which enhances the validity of the extracted ***,a digital-analog-hybrid transfer learning method is proposed for the effective and low-cost training of the photonic feature *** validate our scheme,we demonstrate a radar target recognition task with a 4-GHz instantaneous *** results indicate that the photonic analog feature extractor tackles broadband RF signals and reduces the sampling rate of analog-to-digital converters to 1/4 of the Nyquist sampling while maintaining a high target recognition accuracy of 97.5%.Our scheme offers a promising path for exploiting the AFE strategy in the realm of cognitive RF sensing,with the potential to contribute to the efficient signal processing involved in applications such as autonomous driving,robotics,and smart factories.

关键词： scheme driving smart

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Combo Packet:An Encryption Traffic Classification Method Based on Contextual Information

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Computers, Materials & Continua 2024年第4期79卷 1223-1243页

作者： Yuancong Chai Yuefei Zhu Wei Lin Ding Li State Key Laboratory of Mathematical Engineering and advanced computing Information Engineering UniversityZhengzhou450001China

With the increasing proportion of encrypted traffic in cyberspace, the classification of encrypted traffic has becomea core key technology in network supervision. In recent years, many different solutions have emerged in this *** methods identify and classify traffic by extracting spatiotemporal characteristics of data flows or byte-levelfeatures of packets. However, due to changes in data transmission mediums, such as fiber optics and satellites,temporal features can exhibit significant variations due to changes in communication links and transmissionquality. Additionally, partial spatial features can change due to reasons like data reordering and *** with these challenges, identifying encrypted traffic solely based on packet byte-level features is significantlydifficult. To address this, we propose a universal packet-level encrypted traffic identification method, ComboPacket. This method utilizes convolutional neural networks to extract deep features of the current packet andits contextual information and employs spatial and channel attention mechanisms to select and locate effectivefeatures. Experimental data shows that Combo Packet can effectively distinguish between encrypted traffic servicecategories (e.g., File Transfer Protocol, FTP, and Peer-to-Peer, P2P) and encrypted traffic application categories (e.g.,BitTorrent and Skype). Validated on the ISCX VPN-non VPN dataset, it achieves classification accuracies of 97.0%and 97.1% for service and application categories, respectively. It also provides shorter training times and higherrecognition speeds. The performance and recognition capabilities of Combo Packet are significantly superior tothe existing classification methods mentioned.

关键词： Encrypted traffic classification packet-level convolutional neural network attention mechanisms

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Physical Layer Encryption of OFDM-PON Based on Quantum Noise Stream Cipher with Polar Code

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China communications 2024年第3期21卷 174-188页

作者： Xu Yinbo Gao Mingyi Zhu Huaqing Chen Bowen Xiang Lian Shen Gangxiang Jiangsu Engineering Research center of Novel Optical Fiber Technology and communication Network Suzhou Key Laboratory of Advanced Optical Communication Network Technologythe School of Electronic and Information EngineeringSoochow UniversityNo.1 Shizi StreetSuzhou 215006China

Orthogonal frequency division multiplexing passive optical network(OFDM-PON) has superior anti-dispersion property to operate in the C-band of fiber for increased optical power budget. However,the downlink broadcast exposes the physical layer vulnerable to the threat of illegal eavesdropping. Quantum noise stream cipher(QNSC) is a classic physical layer encryption method and well compatible with the OFDM-PON. Meanwhile, it is indispensable to exploit forward error correction(FEC) to control errors in data transmission. However, when QNSC and FEC are jointly coded, the redundant information becomes heavier and thus the code rate of the transmitted signal will be largely reduced. In this work, we propose a physical layer encryption scheme based on polar-code-assisted QNSC. In order to improve the code rate and security of the transmitted signal, we exploit chaotic sequences to yield the redundant bits and utilize the redundant information of the polar code to generate the higher-order encrypted signal in the QNSC scheme with the operation of the *** experimentally demonstrate the encrypted 16/64-QAM, 16/256-QAM, 16/1024-QAM, 16/4096-QAM QNSC signals transmitted over 30-km standard single mode fiber. For the transmitted 16/4096-QAM QNSC signal, compared with the conventional QNSC method, the proposed method increases the code rate from 0.1 to 0.32 with enhanced security.

关键词： physical layer encryption polar code quantum noise stream cipher

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Experimental demonstration of a quantum downstream access network in continuous variable quantum key distribution with a local local oscillator

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Photonics Research 2024年第6期12卷 1262-1273页

作者： DENGKE QI XIANGYU WANG ZHENGHUA LI JIAYU MA ZIYANG CHEN YUEMING LU SONG YU State Key Laboratory of Information Photonics and Optical communications Beijing University of Posts and TelecommunicationsBeijing 100876China Key Laboratory of Trustworthy Distributed computing and Service(MoE) Beijing University of Posts and TelecommunicationsBeijing 100876China State Key Laboratory of advanced Optical communication Systems and Networks School of Electronicsand Center for Quantum Information TechnologyPeking UniversityBeijing 100871China

Quantum networks provide opportunities and challenges across a range of intellectual and technical frontiers,including quantum computation,communication,and *** traditional communication networks,quantum networks utilize quantum bits rather than classical bits to store and transmit *** key distribution(QKD)relying on the principles of quantum mechanics is a key component in quantum networks and enables two parties to produce a shared random secret key,thereby ensuring the security of data *** this work,we propose a cost-effective quantum downstream access network structure in which each user can get their corresponding key information through terminal *** on this structure,we demonstrate the first four-end-users quantum downstream access network in continuous variable QKD with a local local *** contrast to point-to-point continuous variable QKD,the network architecture reevaluates the security of each user and accounts for it accordingly,and each user has a lower tolerance for excess noise as the overall network expands with more ***,the feasibility of the experiment is based on the analysis of the theoretical model,noise analysis,and multiple techniques such as the particle filter and adaptive equalization algorithm used to suppress excess *** results show that each user can get a low level of excess noise and can achieve secret key rates of 546 kbps,535 kbps,522.5 kbps,and 512.5 kbps under a transmission distance of 10 km,respectively,with the finite-size block of 1×10~*** not only verifies the good performance but also provides the foundation for the future multi-user quantum downstream access networks.

关键词： quantum oscillator local

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A Review of Intelligent Configuration and Its Security for Complex Networks

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Chinese Journal of Electronics 2024年第4期33卷 920-947页

作者： Yue ZHAO Bin YANG Fei TENG Xianhua NIU Ning HU Bo TIAN Science and Technology on communication Security Laboratory School of Computer and Information Engineering Chuzhou University School of computing and Artificial Intelligence Southwest Jiaotong University School of Computer and Software Engineering Xihua University Cyberspace Institute of advanced Technology Guangzhou University

Complex networks are becoming more complex because of the use of many components with diverse technologies. In fact, manual configuration that makes each component interoperable has breed latent danger to system security. There is still no comprehensive review of these studies and prospects for further research. According to the complexity of component configuration and difficulty of security assurance in typical complex networks, this paper systematically reviews the abstract models and formal analysis methods required for intelligent configuration of complex networks, specifically analyzes, and compares the current key technologies such as configuration semantic awareness, automatic generation of security configuration, dynamic deployment, and verification evaluation. These technologies can effectively improve the security of complex networks intelligent configuration and reduce the complexity of operation and maintenance. This paper also summarizes the mainstream construction methods of complex networks configuration and its security test environment and detection index system, which lays a theoretical foundation for the formation of the comprehensive effectiveness verification capability of configuration security. The whole lifecycle management system of configuration security process proposed in this paper provides an important technical reference for reducing the complexity of network operation and maintenance and improving network security.

关键词： Complex networks Intelligent configuration Configuration security Software defined network Lifecycle management SOFTWARE ALGORITHM AGENT

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Advancing brain-inspired computing with hybrid neural networks

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National Science Review 2024年第5期11卷 56-71页

作者： Faqiang Liu Hao Zheng Songchen Ma Weihao Zhang Xue Liu Yansong Chua Luping Shi Rong Zhao center for Brain-Inspired computing Research Optical Memory National Engineering Research CenterTsinghua University-China Electronics Technology HIK Group Co.Joint Research Center for Brain-inspired ComputingIDGMc Govern Institute for Brain Research Department of Precision Instrument Tsinghua University Neuromorphic computing Laboratory China Nanhu Academy of Electronics and Information Technology

Brain-inspired computing, drawing inspiration from the fundamental structure and information-processing mechanisms of the human brain, has gained significant momentum in recent years. It has emerged as a research paradigm centered on brain–computer dual-driven and multi-network integration. One noteworthy instance of this paradigm is the hybrid neural network(HNN), which integrates computer-science-oriented artificial neural networks(ANNs) with neuroscience-oriented spiking neural networks(SNNs). HNNs exhibit distinct advantages in various intelligent tasks, including perception, cognition and learning. This paper presents a comprehensive review of HNNs with an emphasis on their origin, concepts, biological perspective, construction framework and supporting systems. Furthermore, insights and suggestions for potential research directions are provided aiming to propel the advancement of the HNN paradigm.

关键词： brain-inspired computing hybrid neural network dual-brain driven multi-network integration neuromorphic system

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Symmetrization of quasi-regular patterns with periodic tilting of regular polygons

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Computational Visual Media 2024年第3期10卷 559-576页

作者： Zhengzheng Yin Yao Jin Zhijian Fang Yun Zhang Huaxiong Zhang Jiu Zhou Lili He School of Computer Science and Technology Zhejiang Sci-Tech UniversityHangzhou 310018China Zhejiang Provincial Innovation center of advanced Textile Technology Shaoxing 312000China School of Media Engineering Communication University of ZhejiangHangzhou 310018China

Computer-generated aesthetic patterns arewidely used as design materials in various fields. Themost common methods use fractals or dynamicalsystems as basic tools to create various patterns. Toenhance aesthetics and controllability, some researchershave introduced symmetric layouts along with thesetools. One popular strategy employs dynamical systemscompatible with symmetries that construct functionswith the desired symmetries. However, these aretypically confined to simple planar symmetries. Theother generates symmetrical patterns under theconstraints of tilings. Although it is slightly moreﬂexible, it is restricted to small ranges of tilingsand lacks textural variations. Thus, we proposed anew approach for generating aesthetic patterns bysymmetrizing quasi-regular patterns using general kuniformtilings. We adopted a unified strategy toconstruct invariant mappings for k-uniform tilings thatcan eliminate texture seams across the tiling ***, we constructed three types of symmetriesassociated with the patterns: dihedral, rotational, andreﬂection symmetries. The proposed method can beeasily implemented using GPU shaders and is highlyefficient and suitable for complicated tiling with regularpolygons. Experiments demonstrated the advantages of our method over state-of-the-art methods in terms ofﬂexibility in controlling the generation of patterns withvarious parameters as well as the diversity of texturesand styles.

关键词： quasi-regular patterns(QRP) k-uniform tilings invariant mappings symmetry aesthetic patterns

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Strengthening-softening transition and maximum strength in Schwarz nanocrystals

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Nano Materials Science 2024年第3期6卷 320-328页

作者： Hanzheng Xing Jiaxi Jiang Yujia Wang Yongpan Zeng Xiaoyan Li center for advanced Mechanics and Materials Applied Mechanics LaboratoryDepartment of Engineering MechanicsTsinghua UniversityBeijing100084China Institute of High Performance computing(IHPC) Agency for ScienceTechnology and Research(A*STAR)Singapore138632Singapore

Recently,a Schwarz crystal structure with curved grain boundaries(GBs)constrained by twin-boundary(TB)networks was discovered in nanocrystalline Cu through experiments and atomistic *** Cu with nanosized Schwarz crystals exhibited high strength and excellent thermal ***,the grainsize effect and associated deformation mechanisms of Schwarz nanocrystals remain ***,we performed large-scale atomistic simulations to investigate the deformation behaviors and grain-size effect of nanocrystalline Cu with Schwarz *** simulations showed that similar to regular nanocrystals,Schwarz nanocrystals exhibit a strengthening-softening transition with decreasing grain *** critical grain size in Schwarz nanocrystals is smaller than that in regular nanocrystals,leading to a maximum strength higher than that of regular *** simulations revealed that the softening in Schwarz nanocrystals mainly originates from TB migration(or detwinning)and annihilation of GBs,rather than GB-mediated processes(including GB migration,sliding and diffusion)dominating the softening in regular *** analyses of simulation data further showed that compared with those in regular nanocrystals,the GB-mediated processes in Schwarz nanocrystals are suppressed,which is related to the low volume fraction of amorphous-like GBs and constraints of TB *** smaller critical grain size arises from the suppression of GB-mediated processes.

关键词： Schwarz nanocrystal Curved grain boundary Atomistic simulation Grain size effect Maximum strength

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A survey on federated learning:a perspective from multi-party computation

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Frontiers of Computer Science 2024年第1期18卷 93-103页

作者： Fengxia LIU Zhiming ZHENG Yexuan SHI Yongxin TONG Yi ZHANG Institute of Artificial Intelligence and Key Laboratory of Mathematics Informatics Behavioral Semantics Beihang UniversityBeijing 100191China State Key Laboratory of Software Development Environment and advanced Innovation center for Future Blockchain and Privacy computing Beihang UniversityBeijing 100191China Pengcheng Laboratory Shenzhen 518055China Zhongguancun Laboratory Beijing 100190China Institute for Mathematical Sciences and Engineering Research center of Financial computing and Digital Engineering Renmin University of ChinaBeijing 100872China

Federated learning is a promising learning paradigm that allows collaborative training of models across multiple data owners without sharing their raw *** enhance privacy in federated learning,multi-party computation can be leveraged for secure communication and computation during model *** survey provides a comprehensive review on how to integrate mainstream multi-party computation techniques into diverse federated learning setups for guaranteed privacy,as well as the corresponding optimization techniques to improve model accuracy and training *** also pinpoint future directions to deploy federated learning to a wider range of applications.

关键词： sfederated learning multi-party ycomputation privacy-preserving data mining distributed learning

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Dual Frequency Transformer for Efficient SDR-to-HDR Translation

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Machine Intelligence Research 2024年第3期21卷 538-548页

作者： Gang Xu Qibin Hou Ming-Ming Cheng Tianjin Media computing center College of Computer ScienceNankai UniversityTianjin300000China

The SDR-to-HDR translation technique can convert the abundant standard-dynamic-range (SDR) media resources to high-dynamic-range (HDR) ones, which can represent high-contrast scenes, providing more realistic visual experiences. While recent vision Transformers have achieved promising performance in many low-level vision tasks, there are few works attempting to leverage Transformers for SDR-to-HDR translation. In this paper, we are among the first to investigate the performance of Transformers for SDR-to-HDR translation. We find that directly using the self-attention mechanism may involve artifacts in the results due to the inappropriate way to model long-range dependencies between the low-frequency and high-frequency components. Taking this into account, we advance the self-attention mechanism and present a dual frequency attention (DFA), which leverages the self-attention mechanism to separately encode the low-frequency structural information and high-frequency detail information. Based on the proposed DFA, we further design a multi-scale feature fusion network, named dual frequency Transformer (DFT), for efficient SDR-to-HDR translation. Extensive experiments on the HDRTV1K dataset demonstrate that our DFT can achieve better quantitative and qualitative performance than the recent state-of-the-art methods. The code of our DFT is made publicly available at https://***/CS-GangXu/DFT.

关键词： Standard-dynamic-range to high-dynamic-range(SDR-to-HDR)translation Transformer dual frequency attention(DFA) frequency-aware feature decomposition efficient model

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