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TechRxiv 2023年

作者： Li, Yingsong Wang, Yifan Li, Yibing Sun, Qian The College of Information and Communication Engineering Harbin Engineering University Harbin150001 China The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230601 China

To address the significant performance degradation of conventional underdetermined blind source separation algorithms for frequency-hopping (FH) signals under time-frequency (TF) overlapping conditions, this paper presents a novel three-stage scheme based on the TF distribution of FH signals. In the first stage, key parameters of the FH signal are estimated using a TF binary graph. In the second step, the initial mixing matrix is estimated for non-overlapping and overlapping carrier frequencies employing density peaks clustering and tensor decomposition methods, respectively. In the third step, the final mixing matrix, directions of arrival (DOA), and source signals are estimated using the expectation-maximization algorithm within the nonnegative matrix factorization model. Finally, different segments of the FH signals are spliced together based on the DOAs of different source signals. Comprehensive experimental results demonstrate the superior performance of the proposed algorithm compared to state-of-the-art algorithms. Even at a signal-to-noise ratio of 5 dB, the correlation coefficient of the estimated source signals can still reach 0.91. © 2023, CC BY.

关键词： Blind source separation

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A Wideband Circularly Polarized Folded Transmitarray Antenna With Linearly Polarized Feed 7

A Wideband Circularly Polarized Folded Transmitarray Antenna...

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7th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2024

作者： Sun, Xiangdong Guo, Xulong Li, Yin Zhang, Yongliang Li, Yingsong Inner Mongolia University School of Electronic Information Engineering Hohhot010021 China Inner Mongolia University Inner Mongolia Branch of National Computer Network Emergency Response Technical Team Coordination Center of China Hohhot010020 China Peng Cheng Laboratory Shenzhen518000 China Inner Mongolia University School of Transportation Institute Hohhot010021 China Ministry of Education Anhui University Key Laboratory of Intelligent Computing and Signal Processing China

ISBN: (纸本)9798350384437

A low-profile, high-gain, wideband circularly polarized folded transmitarray antenna (CPFTA) is proposed, and a polarization-selective linear-to-circular polarization (LP-CP) conversion element is introduced. The CPFTA consists of a linearly polarized (LP) magnetoelectric dipole antenna and a circularly polarized (CP) helical patch antenna. The CPFTA demonstrates excellent performance with a 3dB gain bandwidth of 29.5% and a 3dB axial ratio (AR) bandwidth of 40.9%. © 2024 IEEE.

关键词： Circular polarization

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Deep generative network for image inpainting with gradient semantics and spatial-smooth attention

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Journal of Visual Communication and Image Representation 2024年 98卷

作者： Sheng, Ziqi Xu, Wenbo Lin, Cong Lu, Wei Ye, Long School of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security Technology Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University Guangzhou510006 China Applied Laboratory of Dig Data and Education Statistics School of Statistics and Mathematics Guangdong University of Finance and Economics Guangzhou510320 China State Key Laboratory of Media Convergence and Communication Communication University of China Beijing100024 China

As a powerful forgery operation in the image content security area, image inpainting based on deep generative networks can yield visually appealing outputs but often produces ambiguous artifacts, especially in boundary and high semantic areas. To address this issue, a novel end-to-end network with gradient semantics and spatial-smooth attention (GS-SSA) is proposed, which combines a gradient learning network and an image inpainting network. The gradient learning network is meant to properly anticipate the gradient semantics in the hole region and get a complete gradient semantic map. The image inpainting network utilizes the complete gradient semantic map to better repair the missing pixels and obtain the final inpainting results. Moreover, spatial-smooth attention is introduced into the image inpainting network, considering both the spatial structural relations and the surrounding information in the hole region. Experimental results on public datasets show the superiority of the proposed method, especially in large-hole region tasks. © 2023 Elsevier Inc.

关键词： Semantics

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Rethinking Few-shot Class-incremental Learning: Learning from Yourself

arXiv

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arXiv 2024年

作者： Tang, Yu-Ming Peng, Yi-Xing Meng, Jingke Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University China Peng Cheng Laboratory Shenzhen China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University China

Few-shot class-incremental learning (FSCIL) aims to learn sequential classes with limited samples in a few-shot fashion. Inherited from the classical class-incremental learning setting, the popular benchmark of FSCIL uses averaged accuracy (aAcc) and last-task averaged accuracy (lAcc) as the evaluation metrics. However, we reveal that such evaluation metrics may not provide adequate emphasis on the novel class performance, and the continual learning ability of FSCIL methods could be ignored under this benchmark. In this work, as a complement to existing metrics, we offer a new metric called generalized average accuracy (gAcc) which is designed to provide an extra equitable evaluation by incorporating different perspectives of the performance under the guidance of a parameter α. We also present an overall metric in the form of the area under the curve (AUC) along the α. Under the guidance of gAcc, we release the potential of intermediate features of the vision transformers to boost the novel-class performance. Taking information from intermediate layers which are less class-specific and more generalizable, we manage to rectify the final features, leading to a more generalizable transformer-based FSCIL framework. Without complex network designs or cumbersome training procedures, our method outperforms existing FSCIL methods at aAcc and gAcc on three datasets. See codes at 1 Copyright © 2024, The Authors. All rights reserved.

关键词： Complex networks

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Non-Markovian effect enhanced quantum noises in a coherent Ising machine

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Optics Express 2025年第11期33卷 22208-22221页

作者： Fan, Chen-Rui Lu, Bo Yao, Yi-Xuan Ai, Qing Wang, Chuan School of Artificial Intelligence Beijing Normal University Beijing100875 China Laboratory for Advanced Computing and Intelligence Engineering Zhengzhou450001 China School of Physics and Astronomy Applied Optics Beijing Area Major Laboratory Beijing100875 China Key Laboratory of Multiscale Spin Physics Ministry of Education Beijing Normal University Beijing100875 China

Combinatorial optimization problems (COPs) constitute a fundamental class of computational challenges with extensive applications across scientific and industrial domains. The emergence of the coherent Ising machine (CIM) as a computational paradigm has demonstrated exceptional capabilities in rapidly generating arbitrary spin configurations and effectively solving large-scale COPs. Despite its promising potential, the CIM framework encounters an inherent limitation related to amplitude heterogeneity. Current approaches to this limitation primarily rely on Gaussian approximations to model quantum noise in the system’s amplitude dynamics within the original CIM framework. However, these methods generally fail to account for the temporal and periodic non-Markovian characteristics of pulses in practical environments. In this study, we introduce temporally correlated non-Markovian noise into the dynamics of both conventional and spiking neural network-based CIM systems, investigating their performance through comprehensive hyperparameter analysis. Our method incorporates a detailed examination of hyperparameter selection and its influence on algorithmic efficiency. Through extensive numerical simulations, we demonstrate that the integration of non-Markovian noise significantly accelerates variance evolution in both canonical coordinates and amplitude dynamics over time. The proposed approach not only exhibits enhanced computational performance but also shows superior scalability for large-scale problem instances. These findings suggest that our approach offers distinct advantages in addressing complex COPs, potentially opening new avenues for efficient optimization in various applications. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Combinatorial optimization

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Learnable Prompt as Pseudo-Imputation: Rethinking the Necessity of Traditional EHR Data Imputation in Downstream Clinical Prediction 25

Learnable Prompt as Pseudo-Imputation: Rethinking the Necess...

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Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1

作者： Weibin Liao Yinghao Zhu Zhongji Zhang Yuhang Wang Zixiang Wang Xu Chu Yasha Wang Liantao Ma School of Computer Science Peking University Beijing China & Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China School of Software and Microelectronics Peking University Beijing China & Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China School of Computer Science Peking University Beijing China Center on Frontiers of Computing Studies Peking University Beijing China Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China & Peking University Information Technology Institute (Tianjin Binhai) Peking University Tianjin China National Engineering Research Center for Software Engineering Peking University Beijing China & Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China

ISBN: (纸本)9798400712456

Analyzing the health status of patients based on Electronic Health Records (EHR) is a fundamental research problem in medical informatics. The presence of extensive missing values in EHR makes it challenging for deep neural networks (DNNs) to directly model the patient's health status. Existing DNNs training protocols, including Impute-then-Regress Procedure and Jointly Optimizing of Impute-n-Regress Procedure, require the additional imputation models to reconstruction missing values. However, Impute-then-Regress Procedure introduces the risk of injecting imputed, non-real data into downstream clinical prediction tasks, resulting in power loss, biased estimation, and poorly performing models, while Jointly Optimizing of Impute-n-Regress Procedure is also difficult to generalize due to the complex optimization space and demanding data requirements. Inspired by the recent advanced literature of learnable prompt in the fields of NLP and CV, in this work, we rethought the necessity of the imputation model in downstream clinical tasks, and proposed Learnable Prompt as Pseudo-Imputation (PAI) as a new training protocol to assist EHR analysis. PAI no longer introduces any imputed data but constructs a learnable prompt to model the implicit preferences of the downstream model for missing values, resulting in a significant performance improvement for all state-of-the-arts EHR analysis models on four real-world datasets across two clinical prediction tasks. Further experimental analysis indicates that PAI exhibits higher robustness in situations of data insufficiency and high missing rates. More importantly, as a plug-and-play protocol, PAI can be easily integrated into any existing or even imperceptible future EHR analysis models. The code of this work is deployed publicly available at https://***/MrBlankness/PAI to help the research community reproduce the results and assist the EHR analysis tasks.

关键词： electronic health records

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Upper Bounds on Lifetime of Three Dimensional Extended UWB Sensor Networks*

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Chinese Journal of Electronics 2023年第2期19卷 307-312页

作者： Juan Xu Changjun Jiang Aihuang Guo Yongfa Hong Shu Li Cheng Wang School of Electronics and Information Engineering Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing of the Ministry of Education Tongji University Shanghai China College of Information Science and Engineering Shandong University of Science and Technology Qingdao China

The upper bounds on lifetime of three dimensional extended Time hopping impulse radio Ultrawide band (TH-IR UWB) sensor networks are derived using percolation theory arguments. The TH-IR UWB sensor network consists of $n$ sensor nodes distributed in a cube of edge length $n^{1/3}$ according to a Poisson point process of unit intensity. It is shown that for such a static three dimensional extended TH-IR UWB sensor network, the upper bound on the lifetime is of order $O(n^{-1})$ , while in the ideal case, the upper bound on the lifetime is longer than that of a static network by a factor of $n^{2/3}$ . Therefore sensor nodes moving randomly in the deployment area can improve the upper bound on network lifetime. The results also reveal that the upper bounds on network lifetime decrease with the number of nodes $n$ , thus extended TH-IR UWB sensor networks aren't prone to be employed in large-scale network.

关键词： Wireless communication Wireless sensor networks Upper bound Protocols

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A Lumped Elements Integrated Miniaturization Super-Wideband Antenna

A Lumped Elements Integrated Miniaturization Super-Wideband ...

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International Workshop on Antenna Technology: Small Antennas and Novel Metamaterials, iWAT

作者： Yinfeng Xia Yingsong Li Wei Xue Luyu Zhao Eugene Sinkevich Vladimir Mordachev College of Information and Communication Engineering Harbin Engineering University Harbin China The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China EMC R&D Laboratory Belarusian State University of Informatics and Radioelectronics Minsk Belarus

ISBN: (数字)9798350314755

ISBN: (纸本)9798350314762

A lumped elements integrated miniaturization super-wideband antenna is proposed, which is fed by coplanar waveguide (CPW) that consists of feeding line, extended ground plane, three S-shaped-slot rectangle radiation patches and several integrated lumped elements. The three S-shaped-slot rectangle radiation patches are electromagnetically coupled by the balanced CPW feeding line. To enhance the bandwidth of the designed antenna, several lumped elements are integrated into the antenna. The designed antenna is modeled in CST. By co-simulation and optimizing using CST and ADS. A -10 dB fractional bandwidth of 169.5% can be achieved which ranges from 0.8 GHz-9.7 GHz. Moreover, the proposed antenna has good omnidirectional radiation patterns in the operating band.

关键词： Conferences Rectangular waveguides Bandwidth Antenna feeds Distance measurement Coplanar waveguides Planar waveguides

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PPVF: An Efficient Privacy-Preserving Online Video Fetching Framework with Correlated Differential Privacy

arXiv

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arXiv 2024年

作者： Zhang, Xianzhi Zhou, Yipeng Wu, Di Sheng, Quan Z. Hu, Miao Xiao, Linchang The School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China The Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-Sen University Ministry of Education China The School of Computing Macquarie University NSW2109 Australia

Online video streaming has evolved into an integral component of the contemporary Internet landscape. Yet, the disclosure of user requests presents formidable privacy challenges. As users stream their preferred online videos, their requests are automatically seized by video content providers, potentially leaking users’ privacy. Unfortunately, current protection methods are not well-suited to preserving user request privacy from content providers while maintaining high-quality online video services. To tackle this challenge, we introduce a novel Privacy-Preserving Video Fetching (PPVF) framework, which utilizes trusted edge devices to pre-fetch and cache videos, ensuring the privacy of users’ requests while optimizing the efficiency of edge caching. More specifically, we design PPVF with three core components: (1) Online privacy budget scheduler, which employs a theoretically guaranteed online algorithm to select non-requested videos as candidates with assigned privacy budgets. Alternative videos are chosen by an online algorithm that is theoretically guaranteed to consider both video utilities and available privacy budgets. (2) Noisy video request generator, which generates redundant video requests (in addition to original ones) utilizing correlated differential privacy to obfuscate request privacy. (3) Online video utility predictor, which leverages federated learning to collaboratively evaluate video utility in an online fashion, aiding in video selection in (1) and noise generation in (2). Finally, we conduct extensive experiments using real-world video request traces from Tencent Video. The results demonstrate that PPVF effectively safeguards user request privacy while upholding high video caching performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Video streaming

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Risk-Aware SDN Defense Framework Against Anti-Honeypot Attacks Using Safe Reinforcement Learning

Risk-Aware SDN Defense Framework Against Anti-Honeypot Attac...

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作者： Gao, Dongying Guo, Caiwei Zhang, Yi Ji, Wen Lv, Zhilei Li, Zheng Zhang, Kunsan Lin, Ruibin State Grid Fujian Information & Telecommunication Company Fuzhou China Department of Information and Communication Engineering & Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University Xiamen China State Grid Zhangzhou Power Supply Company Zhangzhou China State Grid Info-Telecom Great Power Science and Technology Co. Ltd. Fuzhou China

The development of multiple attack methods by external attackers in recent years poses a huge challenge to the security and efficient operation of software-defined networks (SDN), which are the core of operational controllers and data storage. Therefore, it is critical to ensure that the normal process of network interaction between SDN servers and users is protected from external attacks. In this paper, we propose a risk-aware SDN defense framework based on safe reinforcement learning (SRL) to counter multiple attack actions. Specifically, the defender uses SRL to maximize the utility by choosing to provide a honeypot service or pseudo-honeypot service within predefined security constraints, while the external attacker maximizes the utility by choosing an anti-honeypot attack or masquerade attack. To describe the system risk in detail, we introduce the risk level function to model the simultaneous dynamic attack and defense processes. Simulation results demonstrate that our proposed risk-aware scheme improves the defense utility by 17.5% and 142.4% and reduces the system risk by 42.7% and 59.6% compared to the QLearning scheme and the Random scheme, respectively. © 2024 John Wiley & Sons Ltd.

关键词： Adversarial machine learning

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