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Against Mobile Collusive Eavesdroppers: Cooperative Secure Transmission and Computation in UAV-Assisted MEC Networks

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IEEE Transactions on Mobile Computing 2025年第6期24卷 5280-5297页

作者： Zhao, Mingxiong Wang, Zirui Guo, Kun Zhang, Rongqian Quek, Tony Q. S. Ministry of Education Engineering Research Center of Integration and Application of Digital Learning Technology Beijing100039 China Ministry of Education Engineering Research Center of Cyberspace Kunming650504 China Yunnan University of Finance and Economics Yunnan Key Laboratory of Service Computing Kunming650221 China Yunnan University Yunnan Key Laboratory of Software Engineering Kunming650091 China East China Normal University Shanghai Key Laboratory of Multidimensional Information Processing School of Communications and Electronics Engineering Shanghai200241 China Yunnan University School of Information Science and Engineering Kunming650500 China Singapore University of Technology and Design Information Systems Technology and Design 487372 Singapore National Pilot School of Software Yunnan University Kunming China

In Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) networks, the security of transmission faces significant challenges due to the vulnerabilities of line-of-sight links and potential eavesdropping on two-hop links. This paper addresses these challenges with an innovative Cooperative Secure Transmission and Computation strategy (CSTC), specifically engineered for time-slotted UAV-assisted MEC networks plagued by mobile collusive eavesdroppers. These eavesdroppers significantly bolster their interception capabilities through coordinated and optimized movements, escalating the security threats. To neutralize these risks, the proposed CSTC employs the UAV and remote devices as helper nodes to emit jamming signals, thereby thwarting eavesdropping activities, while simultaneously facilitating the efficient relay of users' tasks to the base station for advanced processing. The CSTC aims to maximize the sum Secrecy Transmission Rate (STR) satisfying task latency constraints. It involves a joint optimization of UAV trajectory, jamming beamformers, transmit power, and data offloading strategy to expedite task transmission. Additionally, a real-time computation scheduling approach is developed based on a newly defined metric, the Urgency Degree of Users (UDoU), to enhance task processing efficiency. Our extensive simulations validate that the CSTC not only elevates the sum STR but also consistently meets latency constraints, demonstrating its robustness against advanced mobile eavesdropping techniques. © 2002-2012 IEEE.

关键词： Jamming

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Gifts from Gallery: Advancing Image Retrieval via Unsupervised Asymmetric Feature Fusion

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IEEE Transactions on Multimedia 2025年

作者： Zhou, Wengang Wu, Hui Wang, Min Li, Houqiang University of Science and Technology of China CAS Key Laboratory of Technology in Geospatial Information Processing and Application System Department of Electronic Engineering and Information Science Hefei230027 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230030 China

Asymmetric retrieval systems, characterized by the deployment of models with varying capacities on platforms with differing computational and storage resources, pose a challenge in balancing retrieval efficiency and accuracy. The recent introduction of the Asymmetric Feature Fusion (AFF) paradigm has shown promise by enhancing existing asymmetric retrieval systems through feature fusion on the gallery side. However, its reliance on extensive human-annotated data hinders practical applicability. To this end, we introduce an innovative unsupervised training method tailored for AFF. Leveraging multiple gallery models as feature extractors, our approach exploits similarities among images encoded by these models as pseudolabels. For each gallery model, we calculate its adaptive weight through an in-depth exploration of contextual relationships among ranking list images in its embedding spaces. Subsequently, these weights are utilized to effectively fuse the image similarities encoded by different gallery models into a more robust one. The fused image similarities provide powerful pseudo-supervision for training AFF. Our unsupervised training approach enhances the generality and utility of AFF in real-world scenarios, particularly when labeled data is limited or expensive to obtain. Exhaustive experiments on various landmark retrieval datasets demonstrate the superiority of our method. © 1999-2012 IEEE.

关键词： Multimedia systems

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A SAR imaging method based on generalized minimax-concave penalty

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Science China(information Sciences) 2019年第2期62卷 233-239页

作者： Zhonghao WEI Bingchen ZHANG Yirong WU University of Chinese Academy of Sciences Key Laboratory of Technology in Geospatial Information Processing and Application Systems Institute of Electronics Chinese Academy of Sciences Institute of Electronics Chinese Academy of Sciences

Dear editor,Sparse signal processing offers a framework for synthetic aperture radar （SAR） imaging [1, 2]. As an efficient tool in sparse signal processing, L1minimization is often used in the reconstruction of SAR images. When implemented in SAR imaging [3–5], L1minimization offers significant improvement in the properties by suppressing the sidelobes and clutter. However, L1minimization is known to be a biased estimator. The L1minimization based algorithms such as the iterative

关键词： SAR A SAR imaging method based on generalized minimax-concave penalty

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Sampled-data Observer Design for a Class of Stochastic Nonlinear systems Based on the Approximate Discrete-time Models

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IEEE/CAA Journal of Automatica Sinica 2017年第3期4卷 507-511页

作者： Xinxin Fu Yu Kang Pengfei Li Department of Automation University of Science and Technology of China State Key Laboratory of Fire Science Department of AutomationInstitute of Advanced TechnologyUniversity of Science and Technology of China Key Laboratory of Technology in GeoSpatial Information Processing and Application System Chinese Academy of Sciences IEEE

In this paper,we studied the approximate sampleddata observer design for a class of stochastic nonlinear ***-Maruyama approximation was investigated in this paper because it is the basis of other higher precision numerical methods,and it preserves important structures of the nonlinear ***,the form of Euler-Maruyama model is simple and easy to be *** results provide a reference for sampled-data observer design method for such stochastic nonlinear systems,and may be useful to many practical control applications,such as tracking control in mechanical *** the effectiveness of the approach is demonstrated by a simulation example.

关键词： Approximation model exponentially bounded sampled-data observer stochastic nonlinear

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A New Approach to Airborne High Resolution SAR Motion Compensation for Large Trajectory Deviations

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电子学报(英文版) 2012年第4期21卷 764-769页

作者： MENG Dadi HU Donghui DING Chibiao Key Laboratory of Technology in Geospatial Information Processing and Application System Chinese Academy of Sciences Beijing 100190 China Institute of Electronics Chinese Academy of Sciences Beijing 100190 China

The Two-step algorithm (TSA) is widely used for trajectory deviation compensation of airborne Synthetic aperture radar (SAR), by which most of the mo- tion errors are compensated before Range curve migration compensation (RCMC) and the residual after the RCMC. We found that the RCMC in the presence of the residual motion errors results in additional range shift errors hard to be compensated for. Based on theoretical investiga- tions, this shortage of TSA is reported and a new compen- sation scheme which greatly alleviates the RCMC induced errors is proposed. Besides, range resampling considering the residual motion errors is very convinent, which, how- ever, is boresome in TSA. The new method is effective to compensate the high resolution SAR systems for large tra- jectory deviations, which is hard to be achieved by TSA because of the uncompensated errors. Results on the sim- ulated data are provided to demonstrate the effectiveness of the new method.

关键词：机载合成孔径雷达高分辨率SAR 运动补偿弹道偏差运动误差 TSA 偏差补偿误差范围 Synthetic aperture radar (SAR), Motion compensation, Space variant.

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Rapid face image retrieval with pose-based local features and multi-reference re-ranking

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Journal of Computational information systems 2013年第1期9卷 223-230页

作者： Zhen, Haiyang Zeng, Lingzi Zhang, Rong Yin, Dong Department of Electronic Eng. and Information Science University of Science and Technology of China Hefei 230027 China Key Laboratory of Geospatial Information Processing and Application System Technology Chinese Academy of Sciences Beijing 100190 China

The performance of state-of-art image retrieval systems using Bag-of-Words representation and textual retrieval methods degrades quickly when applied to face images because their local features can not suffer variations of illumination, pose and expression of human face. Moreover, most discriminative facial features are typically high-dimensional and global, and not suitable for rapid retrieval. Motivated by the observation that local features and global features are complementary, we propose a rapid face image retrieval approach using both local and global features in this paper. To ensure the high speed of retrieval, an inverted index is built by quantizing pose-based local features specially designed for face images on component level. Candidate images with low precision and high recall can be obtained using inverted index of visual words in the retrieval stage. Global facial features and multi-reference method are used to obtain a final retrieval list with high precision. Experiments on a 160k face dataset downloaded from the internet show our approach can achieve a high precision and a high speed. © 2013 Binary information Press.

关键词： Indexing (of information)

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Star extraction by star sensors for daytime images affected by atmospheric turbulence

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Chinese Journal of Aeronautics 2025年

作者： Wanxiang GOU Yinhu ZHAN Chonghui LI Shuai TONG Yong ZHENG Yuan YANG Hanxu LI College of Geospatial Information Information Engineering University Zhengzhou 450000 China Key Laboratory of Smart Earth Beijing 100029 China Key Laboratory of Spatiotemporal Perception and Intelligent Processing Ministry of Natural Resources Zhengzhou 450000 China Collaborative Innovation Center of BDS Navigation Application Technology Henan Province Zhengzhou 450000 China

Daytime star images captured by dedicated near-space star sensors are characterized by short exposures, high noise, and low Signal-to-Noise Ratios (SNRs). Such imaging is also affected by atmospheric turbulence, causing optical phenomena, such as scintillation, distortion, and jitter. This causes difficulty in recording high-precision star images during the daytime. This study proposes an adaptive star point extraction method based on dynamically predicting stars’ positions. First, it predicts the approximate position of stars based on the star catalog, sensor attitude, observation time, and other information, improving the extraction accuracy. Second, it employs a regional SNR sorting method that adaptively selects star images with higher SNRs, suppressing the scintillation effect and enhancing the SNR of star images. Third, depending on the star’s motion trajectory characteristics on the image plane, it utilizes the centroid smoothing method for extraction, thus overcoming the impact of star drift. Field experiments demonstrate that the proposed method can effectively overcome star scintillation, drift, and irregular imaging caused by atmospheric turbulence, achieving a 100% success rate. Moreover, the extraction accuracy improves by more than 80% compared to traditional adaptive methods, attaining a value of 0.05 pixels (0.5’’), thereby meeting the requirements of daytime astronomical attitude determination and positioning.

关键词： Astronomical navigation Star sensor Daytime star image Atmospheric turbulence Star extraction Centroid accuracy

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Self-Tuning Asynchronous Filter for Linear Gaussian System and applications

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IEEE/CAA Journal of Automatica Sinica 2018年第6期5卷 1054-1061页

作者： Wenjun Lv Yu Kang Yunbo Zhao Department of Automation University of Science and Technology of ChinaHefei 230027China State Key Laboratory of Fire Science Department of Automation and Institute of Advanced TechnologyUniversity of Science and Technology of ChinaHefei 230027China Key Laboratory of Technology in GeoSpatial Information Processing and Application System Chinese Academy of SciencesBeijing 100190China College of Information Engineering Zhejiang University of TechnologyHangzhou 310023China

In this paper, optimal filtering problem for a class of linear Gaussian systems is studied. The system states are updated at a fast uniform sampling rate and the measurements are sampled at a slow uniform sampling rate. The updating rate of system states is several times the sampling rate of measurements and the multiple is constant. To solve the problem,we will propose a self-tuning asynchronous filter whose contributions are twofold. First, the optimal filter at the sampling times when the measurements are available is derived in the linear minimum variance sense. Furthermore, considering the variation of noise statistics, a regulator is introduced to adjust the filtering coefficients adaptively. The case studies of wheeled robot navigation system and air quality evaluation system will show the effectiveness and practicability in engineering.

关键词： Air quality evaluation system linear Gaussian system wheeled robot navigation system

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Passive Super-Low Frequency electromagnetic prospecting technique

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Frontiers of Earth Science 2017年第2期11卷 248-267页

作者： Nan WANG Shanshan ZHAO Jian HUI Qiming QIN Institute of Remote Sensing and GIS School of Earth and Space Sciences Peking University Beijing 100871 China Key Laboratory of Technology in Geospatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing 100190 China

The Super-Low Frequency （SLF） electromag- netic prospecting technique, adopted as a non-imaging remote sensing tool for depth sounding, is systematically proposed for subsurface geological survey. In this paper, we propose and theoretically illustrate natural source magnetic amplitudes as SLF responses for the first step. In order to directly calculate multi-dimensional theoretical SLF responses, modeling algorithms were developed and evaluated using the finite difference method. The theore- tical results of three-dimensional （3-D） models show that the average normalized SLF magnetic amplitude responses were numerically stable and appropriate for practical interpretation. To explore the depth resolution, three-layer models were configured. The modeling results prove that the SLF technique is more sensitive to conductive objective layers than high resistive ones, with the SLF responses of conductive objective layers obviously show- ing uprising amplitudes in the low frequency range. Afterwards, we proposed an improved Frequency-Depth transformation based on Bostick inversion to realize the depth sounding by empirically adjusting two parameters. The SLF technique has already been successfully applied in geothermal exploration and coalbed methane （CBM） reservoir interpretation, which demonstrates that the proposed methodology is effective in revealing low resistive distributions. Furthermore, it siginificantly contributes to reservoir identification with electromagnetic radiation anomaly extraction. Meanwhile, the SLF inter- pretation results are in accordance with dynamic production status of CBM reservoirs, which means it could provide an economical, convenient and promising method for exploring and monitoring subsurface geo-objects.

关键词： Super-Low Frequency (SLF) three-dimen-sional modeling frequency-depth transformation geother-mal exploration coalbed methane (CBM) electromagneticradiation (EMR)

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A Differentiable Framework for Image-Based SAR Target Reconstruction with 3D Gaussian Splatting 2

A Differentiable Framework for Image-Based SAR Target Recons...

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2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Liu, Zinan Niu, Shengren Qiu, Xiaolan Peng, Lingxiao Zhong, Lihua Ding, Chibiao Chinese Academy of Sciences The National Key Laboratory of Microwave Imaging Technology Aerospace Information Research Institute Beijing China Chinese Academy of Sciences Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Aerospace Information Research Institute Suzhou China Chinese Academy of Sciences Key Laboratory of Technology in Geospatial Information Processing and Application Systems Aerospace Information Research Institute Beijing China

ISBN: (纸本)9798331515669

Image-based synthetic aperture radar (SAR) target three-dimensional (3D) reconstruction is an important application for extracting target information from high-resolution two-dimensional (2D) SAR images. However, due to the complex mechanism of SAR imaging, developing a data-driven, fully automated 3D reconstruction framework with explicit physical constraints is very difficult. Thus, this paper proposes a differentiable framework for image-based SAR target reconstruction with 3D Gaussian Splatting, which can automatically realize the 3D target reconstruction from multi-view SAR images. Specifically, the proposed differentiable framework accumulates and generates simulated SAR images according to the SAR imaging geometry and projection during the forward simulation stage. By calculating the loss between the simulated and ground truth SAR images and backpropagating, the spatial positions of the 3D Gaussians are continuously adjusted until convergence, generating the 3D point cloud model of the target. The experiment is conducted on the constructed T72 SAR image dataset and the reconstruction results demonstrate the effectiveness of the proposed framework. © 2024 IEEE.

关键词： 3D reconstruction

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