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2024 Photonics and Electromagnetics Research Symposium, PIERS 2024

作者： Song, Haoming Zhang, Yifan Zhang, Biao Lv, Yanfang Zhang, Lanjie Ministry of Information Industry Beijing Information Science and Technology University Key Laboratory of Information and Communication Systems China Ministry of Education Beijing Information Science and Technology University Key Laboratory of Modern Measurement and Control Technology China

ISBN: (纸本)9798350375909

Sea surface salinity (SSS) as an important indicator of ocean water circulation, affects global ecology and climate change. However, the accuracy of L-band satellite sea surface salinity retrieval products has been severely affected in coastal regions due to factors such as Radio Frequency Interference (RFI) and Land Sea Continuation (LSC). In this study, coastal regions observational data from the Soil Motion Active Passive (SMAP) satellite, and established the LGBM model for SSS retrieval. The results indicated that LGBM performed excellently in the retrieval of SSS in coastal regions. In comparison to in-situ SSS, the LGBM SSS demonstrated a mean bias (MB) of 0.11psu, a root mean squares error (RMSE) of 0.68psu. This paper also added the bias between LGBM SSS and in situ SSS for secondary correction of LGBM. This correction reduced the MB to 0.03psu, the RMSE to 0.54psu. This study affirms the feasibility of using LGBM for SSS retrieval in coastal regions. Future efforts will focus on integrating more auxiliary data that may influence SSS in coastal regions, aiming to further enhance the precision of the coastal regions' SSS retrieval product. © 2024 IEEE.

关键词： Radio interference

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Multi-scale network for single image deblurring based on ensemble learning module

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Multimedia Tools and Applications 2025年第11期84卷 9045-9064页

作者： Wu, WenBo Pan, Yun Su, Na Wang, JingTao Wu, ShaoChuan Xu, ZeKun Yu, YouJian Liu, YaPeng State Key Laboratory of Media Convergence and Communication Communication University of China Beijing100024 China School of Computer and Cyberspace Security Communication University of China Beijing100024 China School of Electronics and Information Engineering Harbin Institute of Technology Harbin150080 China College of Computer and Control Engineering Northeast Forestry University Harbin150040 China School of Computer and Information Engineering Tianjin Chengjian University Tianjin300384 China School of Computer Science and Technology Anhui University Hefei230601 China

In this paper, we have identified two primary issues with current multi-scale image deblurring methods. On the one hand, the blurring scale is ignored. On the other hand, the context information of images is not fully utilized, and the spatial detail information of images is lost. To better solve the above problems, on top of that, this paper proposes a multi-scale network for single image deblurring. The designed network contains two parts: 1) Ensemble Learning Model: The Weak Learning Model in this module connects multiple weak learners in series to increase network depth. Moreover, through multi-scale feature ensemble module, the output features and intermediate features of different weak learner are integrated, and finally strong learner is used to enhance the features with semantic information at different scales;2) Spatial Feature Enhancement Module: This module can preserve spatial detail information of images and compensate for the disadvantages of losing image spatial details due to continuous downsampling in the Ensemble Learning Model structure. Our proposed deblurring model has been extensively evaluated on several benchmark datasets, and the model achieves superior performance compared to the most advanced deblurring methods. In particular, the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) values are 33.57 dB and 0.965 on the GoPro dataset, respectively, with PSNR and SSIM improving by 0.49 dB and 0.002 over NAFNet, and 0.65 dB and 0.004 over Restormer. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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A Dynamic and Secure Join Query Protocol for Multi-User Environment in Cloud Computing

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IEEE Transactions on Cloud Computing 2025年第2期13卷 512-525页

作者： Li, Hongjun He, Debiao Feng, Qi Yang, Xiaolin Luo, Qingcai Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China Wuhan University School of Cyber Science and Engineering Wuhan430072 China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network & Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China National Key Laboratory of Security Communication Chengdu610041 China Inspur Cloud Information Technology Company Ltd. Shandong Jinan250000 China Inspur Academy of Science and Technology Shandong Jinan250101 China

The development of cloud computing needs to continuously improve and perfect the privacy-preserving techniques for the user’s confidential data. Multi-user join query, as an important method of data sharing, allows multiple legitimate data users to perform join query over the data owner’s encrypted database. However, some existing join query protocols may face some challenges in the practical application, such as practicality, security, and efficiency. In this article, we put forward a dynamic and secure join query protocol in the multi-user environment. Compared with some existing protocols, the proposed protocol has the following advantages. On the one hand, we utilize the dynamic oblivious cross tags structure to realize an efficient join query with forward and backward security. On the other hand, we combine the randomizable distributed key-homomorphic pseudo-random functions with join query to support multiple data users, which can provide resilience against the single user’s key leakage and resist collusion attacks between the cloud server and a subset of data users. We formally define and prove the security of proposed protocol. In addition, we give a detailed analysis of computation and communication overheads to demonstrate the efficiency of proposed protocol. Finally, we carry out some experimental evaluations to further demonstrate the superiority of functionality and efficiency. © 2013 IEEE.

关键词： Structured Query Language

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A deep learning model for classifying the thoughts of multiple individuals based on visual event 4

A deep learning model for classifying the thoughts of multip...

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4th IEEE International Conference on Electrical, Computer, and Energy Technologies, ICECET 2024

作者： Luvsansambuu, Uurtsaikh Tserendondog, Tengis Bat-Erdene, Munkbayar Amar, Batmunkh School of Information and Communication Technology Mongolian University of Science and Technology Dept. of Electronics Ulaanbaatar14191 Mongolia SICT Mongolian University of Science and Technology Dept. of Information Network and Security Ulaanbaatar14919 Mongolia

ISBN: (纸本)9798350395914

In numerous research studies, it is revealed that the accuracy of EEG classification depends on how well subjects can accurately reproduce thoughts. The main question was: Who can use it? Is it restricted to individuals previously trained to reproduce thoughts, or is it accessible to everyone? Therefore, the goal of this study was to develop a thought classification model for controlling the illumination of light that can be used by everyone. In this research, we collected EEG data from 122 volunteers unfamiliar with the basic test environment. The aim was to create a deep learning model for classifying the thoughts of these individuals. Each volunteer was assigned the task of mentally simulating the process of illuminating and dimming the light, using their visual imagination as part of the Vision event. This paper illustrates the implementation of our deep learning model with Brain-Computer Interface for light control. The result of testing of the model to classify three distinct thoughts, achieved the accuracy of 82.66% ± 2%. © 2024 IEEE.

关键词： Contrastive Learning

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Target-Before-Detect Algorithm for Passive MIMO Radar Based on Improved Particle Filter

Target-Before-Detect Algorithm for Passive MIMO Radar Based ...

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2022 Global Conference on Robotics, Artificial Intelligence and information technology, GCRAIT 2022

作者： Zhou, Yawen Zhu, Jiawei Song, Tingsong Yao, Siyi Lai, Rui Guo, Yuning School of Electronic Science and Technology University Chengdu China Information Security Control Laboratory Science and Technology on Communication Jiaxing China

ISBN: (数字)9781665481922

ISBN: (纸本)9781665481922

In order to improve the target detection performance of passive multiple-input-multiple-output (PMR) radar system, a Target-Before-Detect (TBD) algorithm based on improved particle filter is proposed in this paper. As a classical TBD algorithm, particle filter algorithm can realize the gain accumulation of target signal in time domain and effectively improve the performance of target detection. Although this algorithm has performed well in passive single-input-single-output (SISO) radar (PSR) systems, it cannot be directly applied to PMR systems. Therefore, an improved particle filtering algorithm based on passive MIMO radar is proposed in this paper. This method takes advantage of the PMR spatial accumulation and proposes a novel particle weight calculation method, which uses the false alarm probability distribution of detector to calculate the weight of the particle, so as to obtain the gain accumulation of the target in time and space. The simulation results show that the detection probability of the proposed algorithm can reach 90% even when the target-path signal-To-noise ratio (SNR) is low. Meanwhile, the good detection performance of the proposed algorithm is verified by comparing with the single frame detection results. © 2022 IEEE.

关键词： Probability distributions

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Minimum dispersion beamforming using modified conjugate gradient method

Minimum dispersion beamforming using modified conjugate grad...

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2021 International Conference on Electronic information Engineering and Computer communication, EIECC 2021

作者： Song, Jinyang Wang, Wei Chen, Yan Science and Technology on Communication Information Security Control Laboratory China Cetc 36 Zhejiang Jiaxing China

ISBN: (数字)9781510653207

ISBN: (纸本)9781510653191

In order to solve linear constraint minimize dispersion problem in beamforming with non-Gaussian signals or noise, this paper proposed a real time adaptive beamforming technique based on modified conjugate gradient (MCG) method. Compared to conventional adaptive method, MCG have faster convergence rate than stochastic gradient (SG) method, and comparable convergence rate with recursive least square (RLS) method with lower complexity. And it simplifies the procedure of conjugate gradient method with one iteration per snapshots by using inexact line search scheme. Simulation has demonstrated the superior performance in output SINR and its convergence rate. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Beamforming

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Employing a CNN Detector to Identify AI-Generated Images and Against Attacks on AI Systems 1

Employing a CNN Detector to Identify AI-Generated Images and...

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1st International Conference on Cryptography and information security, VCRIS 2024

作者： Truong, Phi-Ho Nguyen, Tien-Dung Truong, Xuan-Hung Nguyen, Nhat-Hai Pham, Duy-Trung Vietnam Academy of Cryptography Techniques Department of Information Security Viet Nam School of Information and Communication Technology Hanoi University of Science and Technology Viet Nam Binh Thuan Provincial Police Department of Cyber Security Viet Nam

ISBN: (纸本)9798331529949

The advancement of artificial intelligence (AI) technology has made Generative AI a significant concern for many. This technology generates fake images through highly complex algorithms. It involves a detailed analysis of the original image, extracting features like color, context, and feature, which are then used to build a neural network. This network is combined with computer graphics to produce an image that resembles the original. Attackers often use these fake images to attack AI systems, making the detection and prevention of such images a pressing issue. In this study, we propose a method to detect fake images using a detector built on CNN models. Our experimental results demonstrate that the proposed detectors achieve over 95% average accuracy on the test datasets, indicating their potential applicability to real-world problems. © 2024 IEEE.

关键词： Generative adversarial networks

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Research and Implementation of Credit Investigation Sharing Platform Based on Double Blockchain

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Computers, Materials & Continua 2023年第6期75卷 5193-5211页

作者： Han Yanyan Wei Wanqi Dou Kaili Li Peng College of Information Engineering Xidian UniversityXi’an710071China Department of Electronic and Communication Engineering Beijing Electronic Science and Technology InstituteBeijing100070China Department of Cyberspace Security Beijing Electronic Science and Technology InstituteBeijing100070China

As the development of the modern economy is increasingly insep-arable from credit support,the traditional credit investigation mode has yet to meet this *** of the difficulties in conventional credit data sharing among credit investigation agencies,poor data portability,and centralized supervision,this paper proposes a data-sharing scheme for credit investigation agencies based on a double *** the problems such as difficult data sharing,difficult recovery of damaged data,and accessible data leakage between institutions and users with non-traditional credit inves-tigation data other than credit,this paper proposes a data-sharing scheme for credit investigation subjects based on the digital *** on the above two solutions,this paper designs a double blockchain credit data-sharing plat-form based on the“public chain+alliance chain”from credit investigation agencies’and visiting subjects’*** sharing platform uses the alliance chain as the management chain to solve the problem of complex data sharing between credit bureaus and centralized supervision,uses the public chain as the use chain to solve the problem of complex data sharing between the access subject and the credit bureaus,uses the interplanetary file system and digital envelope and other technologies to solve the problem of difficult recovery of damaged data,data leakage,and other *** the upload test,the average upload speed reaches 80.6 M/*** average download speed of the system is 88.7 M/s after the download *** multi-thread stress test tests the linkage port on the system package,and the average response time for the hypertext transfer protocol(HTTP)is 0.6 *** system performance and security analysis show that the sharing platform can provide safe and reliable credit-sharing services for organizations and users and high working efficiency.

关键词： Dual blockchain credit data sharing truffle framework digital envelope ipfs

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PMB filter based distributed tracking of multiple extended targets under different resolutions 100

PMB filter based distributed tracking of multiple extended t...

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100th IEEE Vehicular technology Conference, VTC 2024-Fall

作者： Li, Yue Gao, Lin Wei, Ping Li, Wanchun Zhang, Huaguo Mu, Hao University of Electronic Science and Technology of China School of Information and Communication Engineering China National Key Laboratory of Electromagnetic Space Security China

ISBN: (纸本)9798331517786

A key feature of extended target (ET) is that it can produce multiple measurements, thus providing detailed information such as size, orientation. However, due to the measurement number decrease, the ET tends to be a point target (PT) when it becomes far from the sensor, leading to disability of estimating the extensions. In this paper we consider the problem of distributed ETs tracking based on multiple sensors. In such a case the measurements decrease of an ET can be compensated by other sensors, so that the tracking performance can be maintained. In the proposed algorithm the targets are modeled by Poisson multi-Bernoulli (PMB) random finite set (RFS) and the state of each target consists of two parts representing the possibility of being ET and PT, respectively. In the local filtering state, interaction between ET and PT states is considered in the prediction step for target identity change between ET and PT, based on which the local PMB filter is achieved for seamlessly tracking ETs and PTs. In the fusion part a generalized covariance intersection (GCI) based criterion is proposed to fuse the posteriors of each sensor. The performance of proposed algorithm is verified via simulations. © 2024 IEEE.

关键词： Target tracking

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Container-Based IoT Architectures: Use Case for Visual Person Counting 5

Container-Based IoT Architectures: Use Case for Visual Perso...

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5th Symposium of the Norwegian AI Society, NAIS 2023

作者： Veiga, Tiago Asad, Hafiz Areeb Kraemer, Frank Alexander Bach, Kerstin Department of Computer Science Norwegian University of Science and Technology Trondheim Norway Department of Information Security and Communication Technology Norwegian University of Science and Technology Trondheim Norway

The design and deployment of combined Internet-of-Things (IoT) and Artificial Intelligence (AI) solutions present several challenges, some of which are handled by modular logical models for the flow of data and containerized architectures that provide virtualized environments to encapsulate different functionalities in a network. The combinations of containers result in reusable and general solutions, even leading to visual composition solutions with recent deployment platforms. General requirements for these solutions are the capability of handling data acquisition, maintenance, and model training from zero information. This paper studies the deployment process for a use case of visual person counting from cameras located in outdoor areas. We show how a containerized solution fulfills the particular requirements for the use case, illustrating how the design of the modular architecture, data pipelines, and exposed services contribute to enhancing adaptive behavior through learning based on the context of the environment. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Containers

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