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International Conference on intelligent Control and Information processing (ICICIP)

作者： Yanke Lu Fang Li Xiaonan Luo Guangxi Key Laboratory of Image and and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Center for Applied Mathematics of Guangxi (GUET) Guilin China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798331516147

ISBN: (纸本)9798331516154

Image restoration is a classic foundational visual task, aimed at recovering damaged images, such as those affected by compression, blurring, or noise, to high-definition clarity. Although current image enhancement techniques largely rely on convolutional neural networks, the efficient capabilities of Transformers demonstrated in complex visual tasks have not yet been extensively applied in the field of image restoration. This paper presents an optimized image restoration strategy based on the SwinlR model, which is applied to the task of underwater image restoration. In the shallow feature extraction phase, deep separable convolution technology is employed; while in the deep feature extraction phase, a Spatial Gated Feedforward Network (SGFN) is utilized. The experimental data suggests that the novel approach surpasses conventional techniques in effectiveness and has effectively minimized both the model's parameter quantity and computational demands.

关键词： Visualization Convolution Computational modeling Noise Information processing Logic gates Feature extraction Transformers Image restoration intelligent control

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GLSNet: A Global Guided Local Feature Stepwise Aggregation Network for polyp segmentation

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Biomedical signal processing and Control 2024年 87卷

作者： Pan, Xiaoying Ma, Chenyang Mu, Yaya Bi, Manrong Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi‘an710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Xi‘an710121 China School of Computer Science & Technology Xi'an University of Post & Telecommunications Xi‘an710121 China

Accurate polyp segmentation is of great significance for the prevention and diagnosis of early colon cancer. Transformer-based image segmentation models have been proposed for polyp segmentation with good results, however, these methods do not sufficiently consider the negative impact of background noise in different levels of features, resulting in the loss of local details. To alleviate this problem, we propose a GLSNet (A Global Guided Local Feature Stepwise Aggregation Network for polyp segmentation) network, including a spatial feature enhancement (SFE) module, a globally guided local feature enhancement (GLFE) module, and a feature stepwise aggregation (FSA) module. SFE can enhance the spatial feature representation of polyps to better capture the polyp information in the features. GLFE uses high-level features to filter noise in low-level features to capture polyp information hidden in shallow features;FSA fuses the semantic and positional information of polyps across scales to obtain the final segmentation results. Qualitative and quantitative experiments were conducted on 7 benchmark datasets, and the experimental results demonstrated that GLSNet outperforms other existing methods and has stronger generalization performance. In particular, we achieved a mean Dice of 92.9% on the large-scale Kvasir dataset, and mean Dice of 81.3% and 81.6% on CVC-ColonDB and ETIS, respectively, which are significantly higher than those of the competing methods. © 2023

关键词： Benchmarking

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A Smart Cuckoo Filter with Disjoint-set to Detect the Endless Loop

A Smart Cuckoo Filter with Disjoint-set to Detect the Endles...

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2022 Global Conference on Robotics, Artificial Intelligence and Information Technology, GCRAIT 2022

作者： Hua, Wendi Xie, Ping School of Computer Qinghai Normal University Key Laboratory of Internet of Things of Qinghai Province Qinghai China State Key Laboratory of Tibetan Intelligent Information Processing and Application Institute of Plateau Science and Sustainability Xining China

ISBN: (数字)9781665481922

ISBN: (纸本)9781665481922

Cuckoo filter is a new type of approximate membership query data structure that stores the fingerprints of set elements. Because of its higher performance than bloom filter, cuckoo filter has been developed in recent years and gradually replaced the bloom filter in many fields. However, the endless loop problem in cuckoo hashing makes element insertion inefficient and unstable, which hinders cuckoo filter development. To solve this problem, we propose a new variant of cuckoo filter called smart cuckoo filter. Smart cuckoo filter uses a variable bucket structure to save the element insertion information at each position. It keeps buckets aligned to memory, thereby reducing the space waste and inefficiency of data access. Smart cuckoo filter uses the disjoint-set to determine whether the element will fall into the endless loop before the element is inserted. Also, the insertion is friendly to the concurrency of the entire filter. The experiments demonstrate that the element insertion speed of smart cuckoo filter in the endless loops is better than that of the traditional cuckoo filter, which can reach 2.2x in some cases. © 2022 IEEE.

关键词： Data structures

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A Method to Verify Internal Parameters of Camera Calibration in World Coordinate System 6

A Method to Verify Internal Parameters of Camera Calibration...

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6th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2023

作者： Xing, Zheng Wang, Qingchun Li, Yingsong Qi, Junwei College of Information and Communication Engineering Harbin Engineering University Harbin150001 China Shanghai Yanding Information Technology Co. Ltd. Shanghai201210 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China

ISBN: (纸本)9798350399059

Aiming at the problem that reprojection error (RE) cannot reflect the camera calibration accuracy intuitively, this paper proposes a method to verify the camera calibration results in world coordinate system. Firstly, the checkerboard calibration target is photographed with the camera under test, and the internal parameters and distortion parameters of the camera are calculated by using the pinhole model and the Kannala-Brandt (KB) fisheye model, respectively. Then, the same camera is used to shoot two targets in the field of view (FOV), and the pixel coordinates of the two target points are detected from the image. Thirdly, the inverse projection model is used to calculate the distance between the two objects and the angle between the camera and the two objects, and the real distance and angle are compared. The experimental results show that the average distance errors of the pinhole model and the KB fisheye model are 0.34% and 0.35%, respectively, and the angle errors are 0.40% and 0.12%, respectively, which proves that the verification method can effectively characterize the calibration accuracy of the camera. © 2023 IEEE.

关键词： Calibration

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Identifying Outliers in Astronomical Images with Unsupervised Machine Learning

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Research in Astronomy and Astrophysics 2022年第8期22卷 74-84页

作者： Yang Han Zhiqiang Zou Nan Li Yanli Chen School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing 210023China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing 210023China Key Laboratory of Optical Astronomy National Astronomical ObservatoriesChinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China

Astronomical outliers,such as unusual,rare or unknown types of astronomical objects or phenomena,constantly lead to the discovery of genuinely unforeseen knowledge in *** unpredictable outliers will be uncovered in principle with the increment of the coverage and quality of upcoming survey ***,it is a severe challenge to mine rare and unexpected targets from enormous data with human inspection due to a significant *** learning is also unsuitable for this purpose because designing proper training sets for unanticipated signals is *** by these challenges,we adopt unsupervised machine learning approaches to identify outliers in the data of galaxy images to explore the paths for detecting astronomical *** comparison,we construct three methods,which are built upon the k-nearest neighbors(KNN),Convolutional Auto-Encoder(CAE)+KNN,and CAE+KNN+Attention Mechanism(att CAE_KNN)*** sets are created based on the Galaxy Zoo image data published online to evaluate the performance of the above *** show that att CAE_KNN achieves the best recall(78%),which is 53%higher than the classical KNN method and 22%higher than CAE+*** efficiency of att CAE_KNN(10 minutes)is also superior to KNN(4 h)and equal to CAE+KNN(10 minutes)for accomplishing the same ***,we believe that it is feasible to detect astronomical outliers in the data of galaxy images in an unsupervised ***,we will apply att CAE_KNN to available survey data sets to assess its applicability and reliability.

关键词： Galaxy Physical Data and Processes Galaxy:fundamental parameters

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Self-Dual Hadamard Bent Sequences

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Journal of Systems Science & Complexity 2023年第2期36卷 894-908页

作者： SHI Minjia LI Yaya CHENG Wei CRNKOVIC Dean KROTOV Denis SOLéPatrick Key Laboratory of Intelligent Computing Signal Processing Ministry of EducationSchool of Mathematical SciencesAnhui UniversityHefei 230601China State Key Laboratory of Information Security(Institute of Information Engineering) Chinese Academy of SciencesBeijing 100093China. LTCI Télécom ParisParis91120 PalaiseauFrance Secure-IC S.A.S. 104 Bd du Montparnasse75014 Paris Faculty of Mathematics University of RijekaNovosibirsk 630090Russia Sobolev Institute of Mathematics Novosibirsk 630090Russia Aix Marseille Univ CNRSCentrale MarseilleI2MMarseilleFrance

A new notion of bent sequence related to Hadamard matrices was introduced recently,motivated by a security application(Solé,et al.,2021).The authors study the self-dual class in length at most *** authors use three competing methods of generation:Exhaustion,Linear Algebra and Gr?bner *** Hadamard matrices and Bush-type Hadamard matrices provide many *** authors conjecture that if v is an even perfect square,a self-dual bent sequence of length v always *** authors introduce the strong automorphism group of Hadamard matrices,which acts on their associated self-dual bent *** authors give an efficient algorithm to compute that group.

关键词： Bent sequences bush-type Hadamard matrices Hadanard matrices PUF functions regular Hadamard matrices

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A L2 Band LHCP Antenna with Combination of Unequal Slots and Arc-Shaped Protruding Patches 5

A L2 Band LHCP Antenna with Combination of Unequal Slots and...

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5th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2022

作者： Meng, Lulu Qian, Sixian Li, Yingsong Huang, Zhixiang Yang, Lixia School of Electronic and Informationn Engineering Anhui University Hefei230601 China Anhui Province Key Laboratory of Target Recognition and Feature Extraction LuAn230088 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China

ISBN: (数字)9781665472111

ISBN: (纸本)9781665472111

A microstrip circular polarization (CP) patch antenna with a combination of unequal slots and arc-shaped protruding patches is proposed and designed, which can work for L2 band of GPS. The antenna is composed of ground plane, dielectric and radiation patch, where two unequal slots and arc-shaped protruding patches are loaded on the patch and placed along the diagonal. By adjusting the radius of arc-shape slots and protruding patches, Left-handed circular polarization (LHCP) is realized. Simulation and optimization results show that the CP antenna achieves axial ratio bandwidth of 10MHz and impedance bandwidth of 40MHz, providing a potential application for GPS. © 2022 IEEE.

关键词： Circular polarization

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Blockchain-Assisted Revocable Cross-Domain Authentication for Vehicular Ad-Hoc Networks

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Li, Ru Cui, Jie Zhang, Jing Wei, Lu Zhong, Hong He, Debiao Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Hefei230039 China Anhui University Anhui Engineering Laboratory of IoT Security Technologies Hefei230039 China Wuhan University School of Cyber Science and Engineering 430072 China

With the rapid development of vehicular ad-hoc networks (VANETs) and the increasing diversification of user demands, interactions between different management domains have become more frequent. Identity authentication is an effective way to establish cross-domain trust and secure communication. However, the existing cross-domain authentication schemes of VANETs are limited to the same management or authentication technology for each domain and rely on centralized cross-domain identity management. Even distributed management solutions encounter latency sensitivity, security and privacy challenges. To address these challenges, we propose a blockchain-assisted revocable cross-domain authentication scheme for VANETs. The proposed scheme can establish trust between domain entities by deploying different authentication methods and using distributed management to avoid single-point failures. In addition, the scheme can revoke the identity of malicious vehicles by updating the group public key, thereby ensuring the security and privacy of cross-domain Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication. This design avoids the additional impacts of blockchain technology constraints on the high mobility and real-time requirements of VANETs. Security analysis and performance evaluation show that our scheme can resist more attacks and has better security than other related schemes while also achieving a better balance between communication and computational cost. © 2025 IEEE.

关键词： Vehicular ad hoc networks

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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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Consistency-aware unsupervised label learning for cross-domain person re-identification

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Multimedia Tools and Applications 2024年 1-20页

作者： Geng, Yanbing Lian, Yongjian Cui, Fangshu Zhang, Xiaowei Zhou, Mingliang Zhang, Geao School of Data Science and Technology North University of China Shanxi Taiyuan030051 China Shandong Key Laboratory of Intelligent Information Processing School of Computer Science and Technology Qingdao University Qingdao266071 China School of Computer Science Chongqing University 174 Shazheng Street Shapingba District Chongqing400044 China College of Information Science and Engineering Northeastern University 11 Lane 3 Wenhua Road Heping District Liaoning Shenyang110819 China

Un-refined pseudo labels always disturb the cross-domain Re-ID performance in unsupervised clustering methods. In this paper, we propose a consistency-aware unsupervised label learning network to refine noisy labels for promising domain adaptation. Specifically, we use a hetero-generated label consistency constraint to mine possible label information, keep a reference consistency of the target pairs on the relative comparative characteristics. We then use a cross-granularity attention consistency constraint to refine the pseudo labels meanwhile learning multi-granularity discriminative representation, keeping a self-similarity consistency of the corresponding parts on the discriminative regions. Our model is fine-tuned by the refined pseudo-labels to reduce the domain gap while coping with the intra-domain variation. Experimental results demonstrate that the proposed method can achieve superior performance on several benchmark datasets. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Benchmarking

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