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AMTrans:Auto-Correlation Multi-Head Attention Transformer for Infrared Spectral Deconvolution

清华大学学报自然科学版（英文版） 2025年第3期30卷 1329-1341页

作者： Lei Gao Liyuan Cui Shuwen Chen Lizhen Deng Xiaokang Wang Xiaohong Yan Hu Zhu College of Electronic and Optical Engineering & College of Flexible Electronics(Future Technology) Nanjing University of Posts and TelecommunicationsNanjing 210003China Jiangsu Province Key Lab on Image Processing and Image Communication Nanjing University of Posts and TelecommunicationsNanjing 210003China National Engineering Research Center of Communication and Network Technology Nanjing University of Posts and TelecommunicationsNanjing 210003China School of Computer Science and Technology Hainan UniversityHaikou 570228China

Infrared spectroscopy analysis has found widespread applications in various fields due to advancements in technology and industry *** improve the quality and reliability of infrared spectroscopy signals,deconvolution is a crucial preprocessing *** by the transformer model,we propose an Auto-correlation Multi-head attention Transformer(AMTrans)for infrared spectrum sequence *** auto-correlation attention model improves the scaled dot-product attention in the *** utilizes attention mechanism for feature extraction and implements attention computation using the auto-correlation *** auto-correlation attention model is used to exploit the inherent sequence nature of spectral data and to effectively recovery spectra by capturing auto-correlation patterns in the *** proposed model is trained using supervised learning and demonstrates promising results in infrared spectroscopic *** comparing the experiments with other deconvolution techniques,the experimental results show that the method has excellent deconvolution performance and can effectively recover the texture details of the infrared spectrum.

关键词： spectroscopy spectral deconvolution transformer auto-correlation mechanism

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3D Physical Layer Secure Transmission for UAV-Assisted Mobile Communications Without Locations of Eavesdroppers 18th

3D Physical Layer Secure Transmission for UAV-Assisted Mobi...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Yu, Wenlu Li, Jianqiang Fan, Xin Wang, Guopeng Li, Guangkai Luo, Chuanwen Hong, Yi Chen, Ting School of Information Science and Technology Beijing Forestry University Beijing100083 China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing100029 China Zhejiang Ocean University Zhoushan316022 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9783031714665

The flexibility and cost-effectiveness of unmanned aerial vehicles (UAVs) in a wide range of scenarios have made them indispensable tools for mobile communications. However, UAV communications are also facing increasing security threats, especially in open spaces where they are vulnerable to potential eavesdroppers. In this paper, we employ an extra UAV as a cooperative jammer to enhance UAV-to-ground communication security by sending artificial noise to effectively resist passive eavesdroppers. Unlike previous work, we focus on the dynamic scenario where the source UAV and the ground user move simultaneously. In this scenario, we propose a joint optimization algorithm of trajectory and power for the jamming UAV to obtain the optimal jamming strategy. Due to the absence of eavesdroppers’ locations, the goal of the optimization is to maximize a secure region. Numerical results demonstrate that our optimization algorithm can significantly enlarge the secure region compared to randomly selecting a position of the jamming UAV. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Jamming

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基于Seed-PCG法的列车-轨道-地基土三维随机振动GPU并行计算方法

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Journal of Central South University 2024年第1期31卷 302-316页

作者：朱志辉冯杨杨啸李昊邹有 National Engineering Research Center of High-speed Railway Construction Technology Changsha 410075China School of Civil Engineering Central South UniversityChangsha 410075China School of Computer Science and Engineering Center South UniversityChangsha 410083China Information and Network Center Central South UniversityChangsha 410083China

为了解决列车-轨道-地基土三维有限元模型随机多样本计算效率低的问题,本文提出了一种基于Seed-PCG法的高效并行计算方法。基于有限元法和虚拟激励法建立轨道不平顺激励下的三维列车-轨道-地基土耦合随机振动分析模型;针对车致地基土随... 详细信息

为了解决列车-轨道-地基土三维有限元模型随机多样本计算效率低的问题,本文提出了一种基于Seed-PCG法的高效并行计算方法。基于有限元法和虚拟激励法建立轨道不平顺激励下的三维列车-轨道-地基土耦合随机振动分析模型;针对车致地基土随机振动分析产生的多右端项线性方程组求解问题,采用Seed-PCG方法进行求解。通过PCG方法求解种子系统得到的Krylov子空间进行投影,以改进其余线性方程组的初始解和对应的初始残量,有效提高了PCG法的收敛速度,最后,在MATLABCUDA混合平台上开发了并行计算程序。数值算例表明:相同计算平台下的该方法相比多点同步算法获得了104.2倍的加速;相比PCG法逐个求解方案减少了18%的迭代次数,获得了1.21倍的加速。

关键词： Seed-PCG法多右端项线性方程组随机振动 GPU并行计算列车-轨道-地基土耦合模型

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Coverage-Guaranteed Prediction Sets for Out-of-Distribution Data

arXiv

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arXiv 2024年

作者： Zou, Xin Liu, Weiwei School of Computer Science Institute of Artificial Intelligence National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China

Out-of-distribution (OOD) generalization has attracted increasing research attention in recent years, due to its promising experimental results in real-world applications. In this paper, we study the confidence set prediction problem in the OOD generalization setting. Split conformal prediction (SCP) is an efficient framework for handling the confidence set prediction problem. However, the validity of SCP requires the examples to be exchangeable, which is violated in the OOD setting. Empirically, we show that trivially applying SCP results in a failure to maintain the marginal coverage when the unseen target domain is different from the source domain. To address this issue, we develop a method for forming confident prediction sets in the OOD setting and theoretically prove the validity of our method. Finally, we conduct experiments on simulated data to empirically verify the correctness of our theory and the validity of our proposed method. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

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A Self-Supervised Monocular Depth Estimation Framework Based on Detail Recovery and Feature Fusion

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IEEE Access 2025年 13卷 105126-105140页

作者： Shun Li Chongzheng Huang Xiangzhe Li Zhengyou Liang School of Computer Electronics and Information Guangxi University Nanning China School of Information Engineering Guangxi Polytechnic of Construction Nanning China Guangxi Key Laboratory of Multimedia Communications and Network Technology Nanning China

With the rapid development of autonomous driving and robotics, the demand for high-precision depth estimation is growing steadily. The emergence of self-supervised learning methods has reduced the reliance on expensive and hard-to-obtain ground truth data. However, existing models still face challenges in design, such as insufficient detail recovery, inadequate feature fusion, and limited global contextual understanding, which lead to deficiencies in the accuracy and precision of the generated depth maps. To address these challenges, this paper proposes a novel self-supervised monocular depth estimation network, Enhanced Depth Fusion Net (EDFNet). Based on the encoder-decoder architecture, EDFNet introduces three key modules: the Adaptive Selective Attention Module (ASAM), the Multi-Dimensional Attention Fusion Module (MDAFM), and the Hybrid Parallel Convolution Block (HPCB). Specifically, ASAM selectively emphasizes critical features and spatial locations in images to enhance the model’s ability to capture details. MDAFM captures feature information from different perspectives and levels to address the problem of critical information loss, while HPCB effectively captures and fuses local and global information through parallel processing. The synergy of these modules enables EDFNet to generate high-quality depth maps in various complex scenarios. Experimental results on the KITTI dataset demonstrate that the proposed method significantly outperforms state-of-the-art approaches across multiple evaluation metrics, including Abs Rel, Sq Rel, RMSE, $\delta \lt 1.25$ , effectively mitigating the issues of detail loss and edge blurring in depth maps. Additionally, EDFNet demonstrates excellent generalization performance on the Make3D, NYU Depth V2, and Cityscapes datasets.

关键词： Depth measurement Accuracy Feature extraction Adaptation models Convolutional neural networks Attention mechanisms Transformers Self-supervised learning network architecture Decoding

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Learning Dual-Domain Calibration and Distance-Driven Correlation Filter:A Probabilistic Perspective for UAV Tracking

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computers, Materials & Continua 2023年第12期77卷 3741-3764页

作者： Taiyu Yan Yuxin Cao Guoxia Xu Xiaoran Zhao Hu Zhu Lizhen Deng Jiangsu Province Key Lab on Image Processing and Image Communication Nanjing University of Posts and TelecommunicationsNanjingChina School of Computer Science Qufu Normal UniversityQufuChina National Engineering Research Center of Communication and Network Technology Nanjing University of Posts and TelecommunicationsNanjingChina

Unmanned Aerial Vehicle(UAV)tracking has been possible because of the growth of intelligent information technology in smart cities,making it simple to gather data at any time by dynamically monitoring events,people,the environment,and other aspects in the *** traditional filter creates a model to address the boundary effect and time filter degradation issues in UAV tracking *** these methods ignore the loss of data integrity terms since they are overly dependent on numerous explicit previous regularization *** light of the aforementioned issues,this work suggests a dual-domain Jensen-Shannon divergence correlation filter(DJSCF)model address the probability-based distance measuring issue in the event of filter *** two-domain weighting matrix and JS divergence constraint are combined to lessen the impact of sample imbalance and *** new tracking models that are based on the perspectives of the actual probability filter distribution and observation probability filter distribution are proposed to translate the statistical distance in the online tracking model into response *** model is roughly transformed into a linear equality constraint issue in the iterative solution,which is then solved by the alternate direction multiplier method(ADMM).The usefulness and superiority of the suggested strategy have been shown by a vast number of experimental findings.

关键词： Dual-domain weighting distance measure correlation filter ADMM

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Self-Learning Based Dependable Offloading Optimization in Semi-Trusted Vehicular Edge Computing and networks

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IEEE Transactions on Vehicular technology 2025年第5期74卷 8141-8157页

作者： Li, Xuehan Jing, Tao Li, Ruinian Wang, Xiaoxuan Yan, Yu Fan, Xin Huo, Yan Yu, Fei Richard Beijing Jiaotong University School of Electronic and Information Engineering National Engineering Research Center of Advanced Network Technologies Beijing100044 China Bowling Green State University Department of CS Bowling GreenOH43403 United States Beijing Forestry University School of Information Science and Technology Beijing100083 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

As vehicular edge computing and networks (VECONs) emerge, Internet of vehicles (IoVs) is gaining significant advantages. To provide users with dependable services, VECONs need to offer efficient and secure task offloading services. Unfortunately, little effort has been put into addressing security issues such as the risk of privacy exposure when offloading data. Modeling and optimizing offloading dependability are affected by these issues. To address these concerns, a dependable three-layer offloading framework involving blockchain technology is presented in this paper, with fully homomorphic encryption (FHE) algorithm incorporated in order to mitigate privacy exposure risks. The framework divides tasks reasonably and introduces a novel measure of offloading dependability called cost from energy consumption, delay, and privacy exposure risk (cEDP) for constructing dependable offloading optimization. Additionally, a self-learning based dependable privacy preservation offloading (SDPPO) algorithm is presented that is capable of solving the optimal offloading problem in a decentralized manner. Simulation results show that the SDPPO algorithm outperforms its counterparts in terms of faster convergence speed, better cEDP convergence results, and improved scalability and tunability characteristics. © 1967-2012 IEEE.

关键词： Differential privacy

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LGDB-Net: Dual-Branch Path for Building Extraction from Remote Sensing Image 30

LGDB-Net: Dual-Branch Path for Building Extraction from Remo...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Zhang, Ronghuan Zhao, Jing Li, Ming Zou, Qingzhi Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Shandong University of Traditional Chinese Medicine School of Intelligence and Information Engineering Jinan China

ISBN: (纸本)9798331515966

Extracting buildings from remote sensing images using deep learning techniques is a widely applied and crucial task. Convolutional Neural networks (CNNs) adopt hierarchical feature representation, showcasing powerful capabilities in extracting local information but facing challenges in capturing global features. Transformers can address this limitation, but they perform poorly in extracting local features and significantly increase memory requirements and computational complexity. To overcome these challenges, we propose a method for building extraction from remote sensing images called LGDB-Net (Local-Global Dual-Branch network), employing a dual-branch approach. Firstly, inspired by Swin Transformer, we designed GB-Former(Global Branch-Former) as the backbone network to model global information. We use a linear multi-head self-attention mechanism to reduce time and memory complexity while maintaining a large global receptive field. Additionally, we replace the traditional multi-layer perceptron with a convolution-enhanced multi-layer perceptron to improve channel feature representation, reduce model parameters, and enhance segmentation performance. Secondly, we use multiple Depth-wise Conv3×3 + LN (Layer Normalization) + GeLU (Gaussian Error Linear Unit) modules as the auxiliary branch for local detailed feature extraction. Finally, we adopt a multi-scale feature fusion strategy to integrate feature information from both branches. We conduct a series of experiments on three datasets: WHU, Massachusetts, and Inria. The experimental results demonstrate that the proposed method not only effectively improves segmentation accuracy with lower building omission and commission rates but also significantly reduces model parameters and computational complexity. © 2024 IEEE.

关键词： Convolutional neural networks

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Cooperative Caching Strategy Based on Two-Layer Caching Model for Remote Sensing Satellite networks

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computers, Materials & Continua 2023年第5期75卷 3903-3922页

作者： Rui Xu Xiaoqiang Di Hao Luo Hui Qi Xiongwen He Wenping Lei School of Computer Science and Technology Changchun University of Science and TechnologyChangchun130022China School of Information Engineering Henan Institute of Science and TechnologyXinxiang453003China Jilin Province Key Laboratory of Network and Information Security Changchun University of Science and TechnologyChangchun130022China Information Center Changchun University of Science and TechnologyChangchun130022China Beijing Institute of Spacecraft System Engineering Beijing100094China Beijing Institute of Space Mechanic and Electricity Beijing100094China

In Information Centric networking(ICN)where content is the object of exchange,in-network caching is a unique functional feature with the ability to handle data storage and distribution in remote sensing satellite *** up cache space at any node enables users to access data nearby,thus relieving the processing pressure on the ***,the existing caching strategies still suffer from the lack of global planning of cache contents and low utilization of cache resources due to the lack of fine-grained division of cache *** address the issues mentioned,a cooperative caching strategy(CSTL)for remote sensing satellite networks based on a two-layer caching model is *** two-layer caching model is constructed by setting up separate cache spaces in the satellite network and the ground *** caching of popular contents in the region at the ground station to reduce the access delay of users.A content classification method based on hierarchical division is proposed in the satellite network,and differential probabilistic caching is employed for different levels of *** cached content is also dynamically adjusted by analyzing the subsequent changes in the popularity of the cached *** the two-layer caching model,ground stations and satellite networks collaboratively cache to achieve global planning of cache contents,rationalize the utilization of cache resources,and reduce the propagation delay of remote sensing *** results show that the CSTL strategy not only has a high cache hit ratio compared with other caching strategies but also effectively reduces user request delay and server load,which satisfies the timeliness requirement of remote sensing data transmission.

关键词： Information centric networking caching strategy two-layer caching model hierarchical division

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A Derivative Matrix-Based Covert Communication Method in Blockchain

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computer Systems Science & engineering 2023年第7期46卷 225-239页

作者： Xiang Zhang Xiaona Zhang Xiaorui Zhang Wei Sun Ruohan Meng Xingming Sun School of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China School of Hydrology and Water Resources Nanjing University of Information Science&TechnologyNanjing210044China Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Key Laboratory of Hydrometeorological Disaster Mechanism andWarning ofMinistry ofWater Resources Nanjing210044China Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) NanjingUniversity of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China School of Computer Science Engineering Nanyang Technological UniversitySingapore

The data in the blockchain cannot be tampered with and the users are anonymous,which enables the blockchain to be a natural carrier for covert ***,the existing methods of covert communication in blockchain suffer from the predefined channel structure,the capacity of a single transaction is not high,and the fixed transaction behaviors will lower the concealment of the communication ***,this paper proposes a derivation matrix-based covert communication method in *** uses dual-key to derive two types of blockchain addresses and then constructs an address matrix by dividing addresses into multiple layers to make full use of the redundancy of ***,to solve the problem of the lack of concealment caused by the fixed transaction behaviors,divide the rectangular matrix into square blocks with overlapping regions and then encrypt different blocks sequentially to make the transaction behaviors of the channel addresses match better with those of the real ***,the linear congruence algorithm is used to generate random sequence,which provides a random order for blocks encryption,and thus enhances the security of the encryption *** results show that this method can effectively reduce the abnormal transaction behaviors of addresses while ensuring the channel transmission efficiency.

关键词： Covert communication blockchain concealment security capacity derivation

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