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General Assembly and Scientific Symposium, URSI

作者： Fei Ma Yuexian Wang Ling Wang Yanyun Gong School of Electronics and Information Northwestern Polytechnical University Xi'an Shaanxi China The Science and Technology on Communication Information Security Control Laboratory Jiaxing Zhejiang China

In this paper, a new approach without iteration is proposed for direct position determination (DPD) with Doppler shifts in the presence of unknown direction-dependent mutual coupling (DDMC). The special banded symmetric Toeplitz structure of mutual coupling matrix and Doppler shift containing location information can be exploited for constructing a space-time transformation matrix, and the spectral function which is utilized to obtain the locations of sources is established via the orthogonality between space-time transformation matrix and noise subspace. Simulation results show that our solution can accurately determine the emitter position in a single-step without a priori information of the DDMC.

关键词： Doppler shift Location awareness Mutual coupling Symmetric matrices Simulation Spatial resolution Computational complexity

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Hydamc: A Hybrid Detection Approach for Misuse of Cryptographic Algorithms in Closed-Source Software

Hydamc: A Hybrid Detection Approach for Misuse of Cryptograp...

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IEEE International Conference on Trust, security and Privacy in Computing and communications (TrustCom)

作者： Haoling Fan Fangyu Zheng Jingqiang Lin Lingjia Meng Mingyu Wang Qiang Wang Shijie Jia Yuan Ma State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China Data Assurance and Communication Security Research Center Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China School of Cryptography University of Chinese Academy of Sciences Beijing China School of Cyber Security University of Science and Technology of China Hefei China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Cryptographic algorithms are fundamental to secure software development, but security vulnerabilities can arise during implementation, usage, and when calling third-party libraries. As security standards continue to evolve, software updates have become an inevitable trend, and detecting cryptographic algorithm misuse is crucial to ensure compliance with these standards during the update process. However, closed-source software presents challenges in detecting cryptographic algorithm misuse. To enhance the security ecosystem of software, we designed a hybrid detection approach for detecting misuses in closed-source software related to weak cryptographic algorithms, short keys, insecure working modes, and insecure padding modes. Our hybrid detection tool uses both static and dynamic detection methods to collect log information through a logging mechanism in binary executable files. The collected data is cleaned using a data cleaning strategy and analyzed to extract key features, generating test reports to help developers and experts identify cryptographic algorithm security issues. We tested 24 software applications from app stores and found that 62.5% had weak algorithm implementations or usage, 83.3% supported short keys, and 50% supported insecure padding modes. Finally, we provided actionable recommendations to mitigate identified issues.

关键词： Privacy Heuristic algorithms Software algorithms Ecosystems Feature extraction Market research Software

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Relay Satellite Assisted LEO Constellation NOMA communication System

arXiv

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arXiv 2024年

作者： Zhang, Xuyang Yue, Xinwei Han, Zhihao Li, Tian Shen, Xia Wang, Yafei Liu, Rongke Key Laboratory of Information and Communication Systems Ministry of Information Industry China Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science and Technology University Beijing100101 China 54th Research Institute of China Electronics Technology Group Corporation Shijiazhuang Hebei050081 China Beijing100191 China School of Electronic and Information Engineering Beihang University Beijing100191 China Shenzhen Beihang Emerging Industry Technology Research Institute Shenzhen518063 China

This paper proposes a relay satellite assisted low earth orbit (LEO) constellation non-orthogonal multiple access combined beamforming (R-NOMA-BF) communication system, where multiple antenna LEO satellites deliver information to ground non-orthogonal users. To measure the service quality, we formulate a resource allocation problem to minimize the second-order difference between the achievable capacity and user request traffic. Based on the above problem, joint optimization for LEO satellite-cell assignment factor, NOMA power and BF vector is taken into account. The optimization variables are analyzed with respect to feasibility and non-convexity. Additionally, we provide a pair of effective algorithms, i.e., doppler shift LEO satellite-cell assisted monotonic programming of NOMA with BF vector (D-mNOMA-BF) and ant colony pathfinding based NOMA exponential cone programming with BF vector (A-eNOMA-BF). Two compromise algorithms regarding the above are also presented. Numerical results show that: 1) D-mNOMA-BF and A-eNOMA-BF algorithms are superior to that of orthogonal multiple access based BF (OMA-BF) and polarization multiplexing schemes;2) With the increasing number of antennas and single satellite power, R-NOMA-BF system is able to expand users satisfaction;and 3) By comparing various imperfect successive interference cancellation, the performance of A-mNOMA-BF algorithm exceeds D-mNOMA-BF. Copyright © 2024, The Authors. All rights reserved.

关键词： Satellite communication systems

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A Cross-Task Transfer SAR Target Recognition Method Based on AMFFNet and IACGAN

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Wan, Xunyang Du, Xiaolin Cui, Guolong Chen, Xiaolong Zheng, Jibin Yantai University School of Computer and Control Engineering Yantai264005 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Naval Aviation University Yantai264001 China Xidian University National Key Laboratory of Radar Signal Processing Xi'an710071 China

Convolutional neural networks (CNN) have shown excellent capability in synthetic aperture radar (SAR) target recognition. However, obtaining sufficient training samples for improving network recognition is difficult due to the deficiency of labeled SAR image data. To tackle the problem, a cross-task transfer method for SAR target recognition is proposed in this paper, which is based on a designed attention multi-scale feature fusion network (AMFFNet) and an improved auxiliary classifier generative adversarial network (IACGAN). In this method, firstly, a data augmentation method based on you only cut twice (YOCT) is devised for expanding the training samples. Secondly, the partial weight parameters of AMFFNet are initialized by the pre-trained IACGAN discriminator. Finally, AMFFNet is fine-tuned using the training samples. Additionally, on the network structure of AMFFNet, multi-scale features are extracted by adopting a multi-scale depth separable convolution module, which allows the model to acquire more informative features. Attention mechanisms are employed in this network to enhance its ability to capture important features while reducing the effects of noise. A multi-level feature fusion structure is used to further raise the representational capability of the model. Experimental results on multiple open-source real SAR datasets show that the proposed method can achieve better recognition performance under different percentages of training data, and the effectiveness and superiority of the method are confirmed by its robustness to noise. © 1965-2011 IEEE.

关键词： Convolutional neural networks

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Uncertainty-Guided Diffusion Model for Camouflaged Object Detection

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IEEE Transactions on Multimedia 2025年

作者： Yang, Jinsheng Zhong, Bineng Liang, Qihua Mo, Zhiyi Zhang, Shengping Song, Shuxiang Guangxi Normal University Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guilin541004 China Guangxi Normal University Guangxi Key Lab of Multi-Source Information Mining & Security China Wuzhou University Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou543002 China Harbin Institute of Technology School of Computer Science and Technology Weihai264209 China

Recently, diffusion models have significantly improved the performance of Camouflaged Object Detection (COD) by adding noise to a mask and iteratively denoising it to match the target distributions. Due to the direct extraction of features from noisy masks and the lack of conditional constraints on a prediction area, the diffusion model may deviate from a correct prediction range and produces mispredictions in regions with high uncertainty. To address this issue, we propose an uncertainty-guided diffusion model (UGDNet) for COD, which explicitly quantifies uncertainty and integrates it as an anchor condition into the diffusion models to provide an initialization of the diffusion regions. The core idea is first to utilize a probability representation and transformer to explicitly model uncertainty, aiming to identify areas where a model may generate overconfident mispredictions. Then, we use the uncertainty as an anchor condition to provide a reference prediction range for the diffusion model, guiding each step of the diffusion process. Furthermore, we use uncertainty to guide feature aggregation, prompting the model to pay extra attention to the semantic features of regions with high uncertainty to refine the segmentation results further. The experimental results indicate that our proposed UGDNet achieves higher accuracy than existing state-of-the-art models on five COD benchmarks, including COD10K, NC4K, CAMO, CHAMELEON, and CDS2K. © 1999-2012 IEEE.

关键词： Semantics

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Forgery Detection Scheme of Deep Video Frame-rate Up-conversion Based on Dual-stream Multi-scale Spatial-temporal Representation

Forgery Detection Scheme of Deep Video Frame-rate Up-convers...

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IEEE International Conference on Trust, security and Privacy in Computing and communications (TrustCom)

作者： Qing Gu Xiangling Ding Dengyong Zhang Ce Yang The School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan P. R. China Hunan University of Science and Technology The State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Zhengzhou Xinda Institute of Advanced Technology Guangdong Key Laboratory of Information Security Technology The School of Computer and Communication Engineering Changsha University of Science and Technology Changsha P. R. China

Video Frame-Rate Up-Conversion (FRUC) is originally designed to produce the high frame-rate video by periodically inserting new frames between two adjacent frames. However, it can also be utilized to synthesize the faked high frame-rate videos or spliced videos for malicious intents. The existing FRUC detection methods can efficiently identify its occurrence by exploring the blurring effects or deformed structures left over from the traditional FRUC methods. But the video FRUC has been substantially improved in the past years, especially in this deep learning era. Deep learning-based video FRUC (Deep FRUC) weakens the visual traces of the traditional ones such that it is challenging for the current FRUC detectors. In this paper, we propose a forensics algorithm based on dual-stream multi-scale spatial-temporal representation for Deep FRUC. Specifically, we develop a multi-scale receptive field strategy, and attention scheme to learn hidden tampered trace and spatial-temporal representation, respectively. Besides, frame residual and noise residual attention streams are complementary learning from the spatial-temporal dimension, in which the former can capture contrast differences and tampered abnormal boundaries, while the latter can explode noise inconsistencies between original and tampered frames. Experimental results show that the proposed algorithm can effectively achieve the best detection accuracy compared with the existing FRUC forensics works under the Deep FRUC video datasets.

关键词： Location awareness Deep learning Visualization Privacy Forensics Computational modeling Detectors

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Conditional Adversarial Domain Adaption based on Self-attention

Journal of Network Intelligence

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Journal of Network Intelligence 2022年第1期7卷 175-188页

作者： Zhao, Li-Quan Zhang, Yu-Peng Teng, Zi-Ming Jiang, Zi-Cong Cui, Ying Kan, Zhong-Feng Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China College of information and communication Jilin University Changchun130012 China Graduate school of Information Science and Electrical Engineering Kyushu University Fukuoka819-0395 Japan Zhuhai Power Supply Bureau Guangdong Electric Power Corporation Zhuhai519000 China Jilin Power Supply Bureau Jilin Electric Power Corporation Jilin132012 China

Transfer learning is a deep learning method. It transforms knowledge in the source domain and feeds it to the target domain to improve the performance of the target domain model. The conditional adversarial domain adaption method is one of a number of transfer learning methods. It uses Resnet as the feature extraction network;however, Resnet can only determine the relationship between local features, and so its feature extraction accuracy is limited. To overcome this problem, a self-attention layer has been designed in order to determine the relationship among all features. This layer is added between the fourth layer and fifth layer of Resnet. Besides, a cross-entropy function is introduced into loss function, and a sigmoid layer is designed and used as the out layer in conditional adversarial domain adaption. These produce gradient disappearance in the deep network, and influence the discriminator training effect. To solve that problem, a least squares function is used as to design the loss function. The new type of loss function does not require the sigmoid function. Using theoretical analysis, we explain how this new loss function can be used to solve the gradient disappearance problem, thus rendering the network structure more stable. We compare our proposed method with other datasets, including Joint Adaptation Networks, adversarial domain adaptation, conditional adversarial domain adaption, Resnet-50, domain-adversarial neural network on imageCLEF-DA, Office-Home, and Office-31. Our simulation results show that the proposed conditional adversarial domain adaption based on self-attention method has the fastest convergence speed and highest accuracy the among different datasets. © 2022, Taiwan Ubiquitous information CO LTD. All rights reserved.

关键词： Computer vision

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Application of Integrated Sensing and communication in Structural Health Monitoring

Application of Integrated Sensing and Communication in Struc...

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International Conference on Wireless and Optical communications ( WOCC)

作者： Jie Yang Chao-Kai Wen Shi Jin Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China Institute of Communications Engineering National Sun Yat-sen University Kaohsiung Taiwan National Mobile Communications Research Laboratory Southeast University Nanjing China

ISBN: (数字)9798331539658

ISBN: (纸本)9798331539665

Integrated sensing and communication (ISAC) is a primary goal in future cellular systems. Numerous researchers have explored how cellular systems can incorporate sensing capabilities in various use cases, such as intruder detection, monitoring, tracking, and collision avoidance. However, the potential of ISAC in structural health monitoring (SHM), which involves detecting slow and subtle changes in structures, remains unexplored. This study introduces a pioneering problem model and a solution framework for SHM using ISAC technology, employing reconfigurable intelligent surfaces (RIS) as reference points for monitoring building structures. Our method is inspired by the dynamic phase configuration of RIS to generate varying radio signals, which differ from background interference and thus facilitate the acquisition of precise radio measurements from the reference points. We conduct a theoretical analysis of these radio measurements through the lens of Fisher information. Our findings confirm that increasing observation time, adding more receivers, and optimizing RIS phases significantly enhance the position error bound (PEB) and orientation error bound (OEB) of the reference points, meeting the stringent accuracy requirements of SHM. Furthermore, we develop a Bayesian inference model to recognize structural states based on the calculated PEB and OEB, enabling the validation of detection probabilities of cellular systems in identifying damaged structural conditions. Our comprehensive theoretical and numerical analysis reveals the capability of ISAC to effectively detect millimeter-level deformations.

关键词： Wireless communication Wireless sensor networks Deformation Numerical analysis Buildings Receivers Reconfigurable intelligent surfaces Integrated sensing and communication Numerical models Monitoring

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Deep Learning-Based LOS and NLOS Parameters Estimation, Tracking, and Identification

Deep Learning-Based LOS and NLOS Parameters Estimation, Trac...

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IEEE International Conference on communications in China Workshops (ICCC)

作者： Kai Chen Jun Wang Jie Yang Le Liang Chao-Kai Wen Shi Jin National Mobile Communications Research Laboratory Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing China Institute of Communications Engineering National Sun Yat-sen University Kaohsiung Taiwan

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

Millimeter-wave (mmWave) communication systems have large bandwidths and, combined with large-scale antenna arrays and beamforming technology, facilitate higher resolution of angle and delay measurements. This, in turn, provides opportunities for integrated sensing and communication (ISAC). Accurate and reliable estimation of mmWave channel state information is crucial for ISAC. In this paper, we propose a deep learning-based method for estimating channel multipath parameters, tracking, and identifying line-of-sight (LOS) and non-line-of-sight (NLOS) paths. Specifically, we leverage a network named “you only look once (YOLO)”, a highly efficient object detection network in computer vision, to detect the angles of arrival (AOAs) and angles of departure (AODs) of multipaths from received radio frequency signals. Moreover, we use a tracking-by-detection algorithm, ByteTrack, to track the variation of each path's AOA and AOD under a mobile scenario. Thanks to these vision-based channel estimation approaches, we reveal that LOS and NLOS paths exhibit different trajectory properties, allowing us to precisely identify LOS and NLOS paths, a challenge originally in the context of ISAC. We validate the proposed estimation, tracking, and identification methods on real measurement data and demonstrate that the proposed estimation method shows better performance compared to existing methods.

关键词： Antenna measurements Computer vision Simultaneous localization and mapping Millimeter wave measurements Estimation Channel estimation Transforms Integrated sensing and communication Millimeter wave communication Antenna arrays

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Fabrication of lithium niobate metasurfaces via a combination of FIB and ICP-RIE

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Chinese Optics Letters 2022年第11期20卷 95-99页

作者： Chunyan Jin Wei Wu Lei Cao Bofeng Gao Jiaxin Chen Wei Cai Mengxin Ren Jingjun Xu Key Laboratory of Weak-Light Nonlinear Photonics Ministry of EducationSchool of Physics and TEDA Applied Physics InstituteNankai UniversityTianjin 300071China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjin 300308China Collaborative Innovation Center of Extreme Optics Shanxi UniversityTaiyuan 030006China

Lithium niobate(LN)metasurfaces have emerged as a new platform for manipulating electromagnetic ***,we report a fabrication technique for LN nano-grating metasurfaces by combining focused ion beam(FIB)milling with inductively coupled plasma reactive ion etching(ICP-RIE).Steep sidewalls with angles larger than 80°are *** quasi-bound states in the continuum are observed from our *** measured transmission spectra show good agreement with the numerical simulations,confirming the high quality of the fabricated *** technique can be applied to fabricate the LN metasurfaces with sharp resonances for various applications in optical communications,on-chip photonics,laser physics,sensing,and so on.

关键词： lithium niobate metasurfaces fabrication FIB ICP-RIE

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