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arXiv 2024年

作者： Zhang, Cui Zhang, Wenjun Wu, Qiong Fan, Pingyi Cheng, Nan Chen, Wen Letaief, Khaled B. The School of Internet of Things Engineering Wuxi Institute of Technology Wuxi214122 China The School of Internet of Things Engineering Jiangnan University Wuxi214122 China The Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China The State Key Lab. of ISN School of Telecommunications Engineering Xidian University Xi’an710071 China The Department of Electronic Engineering Shanghai JiaoTong University Shanghai200240 China Hong Kong

The Internet of Vehicles (IoV) network can address the issue of limited computing resources and data processing capabilities of individual vehicles, but it also brings the risk of privacy leakage to vehicle users. Applying blockchain technology can establish secure data links within the IoV, solving the problems of insufficient computing resources for each vehicle and the security of data transmission over the network. However, with the development of the IoV, the amount of data interaction between multiple vehicles and between vehicles and base stations, roadside units, etc., is continuously increasing. There is a need to further reduce the interaction volume, and intelligent data compression is key to solving this problem. The VIB technique facilitates the training of encoding and decoding models, substantially diminishing the volume of data that needs to be transmitted. This paper introduces an innovative approach that integrates blockchain with VIB, referred to as BVIB, designed to lighten computational workloads and reinforce the security of the network. We first construct a new network framework by separating the encoding and decoding networks to address the computational burden issue, and then propose a new algorithm to enhance the security of IoV networks. We also discuss the impact of the data extraction rate on system latency to determine the most suitable data extraction rate. An experimental framework combining Python and C++ has been established to substantiate the efficacy of our BVIB approach. Comprehensive simulation studies indicate that the BVIB consistently excels in comparison to alternative foundational methodologies. Copyright © 2024, The Authors. All rights reserved.

关键词： information leakage

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2-D GCANet Applied to Denoise 1-D EEG Signals in Online Remote Teaching Scenarios

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第4期8卷 1289-1302页

作者： Huang, Ting-Qing Wang, Chuan-Sheng Chen, Zhao-Qi Zhang, Fu-Quan Meng, Xiang-Long Grau, Antoni Chen, Yang Huang, Jing-Wei College of Computer and Control Engineering Minjiang University Fuzhou University Town No. 200 Xiyuangong Road Fuzhou China Department of Automatic Control Technical Polytechnic University of Catalonia Autonomous Region of Catalonia Barcelona Spain College of Computer and Big Data Fuzhou University Fuzhou University Town No. 2 Wulong Jiangbei Avenue Fuzhou China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music No. 2 Wannianchang Street Chenghua District Sichuan Province Chengdu City China Fuzhou Technology Innovation Center of intelligent Manufacturing information System Minjiang University Fuzhou University Town NO. 200 Xiyuangong Road Fuzhou China Fujian Province University No. 599 Quanxiu Road Juyuanzhou Ecological Civilization District Fujian Province Fuzhou City China College of Electronic Engineering Shandong University of Science and Technology No. 579 Qianwangang Road Huangdao District Qingdao China School of Mechanical and Automotive Engineering Fujian University of Technology No. 33 Xuefu South Road University New District Fujian Province Fuzhou City China

When EEG signals are used to assess the level of student engagement in online teaching tasks, they are often interfered by noise. It is a challenge to effectively remove these noises. Currently, deep learning methods have been applied to the field of EEG denoising. However, existing methods still have some problems. The denoised signals still have obvious noise residue, or the original EEG signals are damaged, and the model fitting speed is too slow. Image dehazing, as a typical denoising task in the field of image enhancement, has achieved great success in recent years. Therefore, inspired by advanced models in this field, we introduce CNN into EEG denoising tasks. In this paper, we take GCANet, an excellent image enhancement model, as an example. The dilated convolutions and gate fusion subnetworks included in GCANet enable more efficient EEG signal denoising. The results demonstrate that the proposed model effectively reduces noise while preserving essential features. Furthermore, in comparison to other state-of-the-art models, our proposed model exhibits enhanced robustness and faster convergence, as evidenced by achieving lower loss values after five epochs. Its good performance provides a new development idea for the field of EEG denoising. © 2023, Taiwan Ubiquitous information CO LTD. All rights reserved.

关键词： Image enhancement

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The Phenology and Water Level Time-Series Mangrove Index for Improved Mangrove Monitoring

SSRN

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SSRN 2024年

作者： Sun, Weiwei Huang, Ke Yang, Gang Fu, Bolin Chen, Chao Meng, Xiangchao Feng, Tian Ge, Yong Department of Geography and Spatial Information Techniques Ningbo University Ningbo315211 China Zhejiang Collaborative Innovation Center Ningbo Universities Collaborative Innovation Center for Land and Marine Spatial Utilization and Governance Research Ningbo315211 China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences & Natural Resources Research Chinese Academy of Sciences Beijing100101 China College of Geomatics and Geoinformation Guilin University of Technology Guilin541006 China School of Geography Science and Geomatics Engineering Suzhou University of Science and Technology Suzhou215009 China

Mangroves are declining and degrading under the influence of human activities and natural factors, making their accurate mapping and dynamic monitoring essential. However, most of the existing mangrove indices based on the spectral characteristics of multispectral images have some limitations in recognition accuracy and universality. Therefore, this study aimed to develop a robust and efficient Phenology and Water level Time-series Mangrove Index (PWTMI) for mangrove monitoring. PWTMI is constructed by combining spectral and temporal characteristics from dense time-series multispectral data, wherein phenology and water level time-series characteristics are extracted from NDVI and MNDWI time series. The results show that PWTMI outperforms existing multispectral-based mangrove indices and has an accuracy similar to a hyperspectral-based mangrove index, with overall accuracy ranging from 91.49% to 98.83% and F1 score ranging from 0.91 to 0.98 in four typical areas in China, indicating great potential for long time-series and large-scale mangrove monitoring. © 2024, The Authors. All rights reserved.

关键词： Water levels

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Corrigendum to “Improved Algorithm for Permutation Testing” [Theoretical Computer science 986 (2024) 114316]

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Theoretical Computer science 2024年 999卷

作者： Xiaojin Zhang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

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Repairing Reed-Solomon Codes with Less Bandwidth

Repairing Reed-Solomon Codes with Less Bandwidth

引用

IEEE International Symposium on information Theory

作者： Shu Liu Yunqi Wan Chaoping Xing National Key Laboratory on Wireless Communications University of Electronic Science and Technology of China Chengdu China Theory Lab Central Research Institute 2012 Labs Huawei Technology Co. Ltd. Shenzhen China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

Guruswami and Wootters first provided a decoding framework for repairing Reed-Solomon codes. There is a series of work after Guruswami-Wootters' repairing scheme. In particular, based on this framework a repairing scheme achieving the cut-set bound was presented by Tamo, Ye and Barg. Guruswami-Wootters' repairing scheme can be modified so that we require downloading less data, i.e., less communication bandwidth. We illustrate our improvement by two examples given in the pioneer paper by Guruswami and Wootters. These examples show that our repairing scheme can save bandwidth $(1-R)^{2}n$ and $(1-2R)n$ over the base field, respectively, where $R$ is the code rate and $n$ is the code length.

关键词： Reed-Solomon codes Codes Bandwidth Decoding

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A Scanner Matching Method based on Interior-point BFGS Algorithm 5

A Scanner Matching Method based on Interior-point BFGS Algor...

引用

5th International Workshop on Advanced Patterning Solutions, IWAPS 2021

作者： Chen, Yuguang Li, Sikun He, Jianfang Zhang, Libin Shi, Weijie Tang, Ming Wei, Yayi Wang, Xiangzhao Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Science Laboratory of Information Optics and Opto-Electronic Technology Shanghai China School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing China Integrated Circuit Advanced Process RD Center Institute of Microelectronics Chinese Academy of Sciences Beijing China Dongfang Jingyuan Electron Limited Beijing China National Engineering Laboratory for Next Generation Internet Access System School of Optical and Electronic Information Wuhan China

ISBN: (纸本)9781665420785

A scanner matching method based on interior-point BFGS algorithm is proposed. This method minimizes the critical dimension (CD) differences by optimizing parameters of illumination sources, including inner partial coherence factor, outer partial coherence factor, numerical aperture and opening angle of illumination poles for dipole and quadrupole sources. Simulations of scanner matching are performed. Scanners with annular, dipole and quadrupole sources are matched in the simulations. Through-pitch Line/space patterns are used as the matching targets. The results of these simulations show that, the proposed method can reduce root-mean-square of CD differences after matching. In the majority of the simulation cases, the proposed method is faster than methods based on the Levenberg-Marquardt algorithm, and could even achieve better result. Analyses on the proposed method are given. © 2021 IEEE.

关键词： Photolithography

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Learning superpoint graph cut for 3D instance segmentation 22

Learning superpoint graph cut for 3D instance segmentation

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Proceedings of the 36th International Conference on Neural information Processing systems

作者： Le Hui Linghua Tang Yaqi Shen Jin Xie Jian Yang PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China

ISBN: (纸本)9781713871088

3D instance segmentation is a challenging task due to the complex local geometric structures of objects in point clouds. In this paper, we propose a learning-based superpoint graph cut method that explicitly learns the local geometric structures of the point cloud for 3D instance segmentation. Specifically, we first oversegment the raw point clouds into superpoints and construct the superpoint graph. Then, we propose an edge score prediction network to predict the edge scores of the superpoint graph, where the similarity vectors of two adjacent nodes learned through cross-graph attention in the coordinate and feature spaces are used for regressing edge scores. By forcing two adjacent nodes of the same instance to be close to the instance center in the coordinate and feature spaces, we formulate a geometry-aware edge loss to train the edge score prediction network. Finally, we develop a superpoint graph cut network that employs the learned edge scores and the predicted semantic classes of nodes to generate instances, where bilateral graph attention is proposed to extract discriminative features on both the coordinate and feature spaces for predicting semantic labels and scores of instances. Extensive experiments on two challenging datasets, ScanNet v2 and S3DIS, show that our method achieves new state-of-the-art performance on 3D instance segmentation.

关键词：

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Fluid Antenna Multiple Access Assisted Integrated Data and Energy Transfer: Outage and Multiplexing Gain Analysis

arXiv

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arXiv 2024年

作者： Lin, Xiao Zhao, Yizhe Yang, Halvin Hu, Jie Wong, Kai-Kit School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China Department of Electronic and Electrical Engineering University College London Torrington Place WC1E 7JE United Kingdom Yonsei Frontier Lab Yonsei University Seoul Korea Republic of

Fluid antenna multiple access (FAMA) exploits spatial opportunities in wireless channels through port switching to overcome multiuser interference, achieving better performance than traditional fixed MIMO systems. Integrated Data and Energy Transfer (IDET) is capable of providing both wireless data transfer (WDT) and wireless energy transfer (WET) services for low-power devices. This paper investigates an FAMA-assisted IDET system, where a base station (BS) equipped with N fixed antennas provides dedicated IDET services to N user equipments (UEs). Each UE is equipped with a single fluid antenna, while the power splitting (PS) approach is conceived for coordinating WDT and WET. Under the Rayleigh channel model, we derive both exact expressions and approximate closed forms for the outage probabilities of WDT and WET, where the fluid antenna (FA) at each UE selects the optimal port to maximize either the signal-to-interference-plus-noise ratio (SINR) or the energy harvesting power (EHP). The IDET outage probabilities are defined and subsequently derived and approximated into closed-forms. Further, multiplexing gains of the proposed system are defined and analyzed to evaluate the performance. Further, we analyze the IDET outage probabilities and multiplexing gains of the proposed system. Additionally, to provide a more general analysis, we extend our analytical framework to the Rician channel model. Numerical results validate the theoretical analysis while also illustrating that the trade-off is achieved between WDT and WET performance by exploiting different port selection strategies and numbers of UEs. Copyright © 2024, The Authors. All rights reserved.

关键词： Energy transfer

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Mask-guided Salient Feature Mining for Cloth-Changing Person Re-identification

Mask-guided Salient Feature Mining for Cloth-Changing Person...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Liman Jiang Canlong Zhang Lei Wu Zhixin Li Zhiwen Wang Chunrong Wei Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Guangxi Key Lab of Multi-Source Information Mining & Security Guangxi Normal University Guilin China School of Electronic Engineering Guangxi University of Science and Technology Liuzhou China Teachers College for Vocational and Technical Education Guangxi Normal University Guilin China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Cloth-changing person re-identification (CC-ReID) aims at retrieving pedestrians with changing clothes across multiple cameras. Most methods often focus on exploiting discriminative biometric features for resisting the clothing changes. Nevertheless, simply concatenating various features not only increases the computations, but also introduces redundant information. In this paper, we propose a Mask-guided Salient Feature Mining (MSFM) to learn cloth-irrelevant features. Specifically, we introduce human parsing results in the data pre-processing stage, and exploit region-specific cues by performing pixel-level mask on the parsing results. Besides, a Multi-local Attention (MLA) is proposed, where the model can focus on local cues in horizontal direction and obtain robust identity-related representations. Meanwhile, we introduce a part loss supervised by selective masking regions for capturing fine-grained features and constraining clothing features. Extensive experiments on two public cloth-changing datasets demonstrate our proposed MSFM can achieve superior performance over existing state-of-the-art methods.

关键词： Biometrics Pedestrians Computational modeling Clothing Cameras Data mining

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Explainable deep stacking ensemble model for accurate and transparent brain tumor diagnosis

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Computers in Biology and Medicine 2025年 191卷 110166-110166页

作者： Haque, Rezaul Khan, Mahbub Alam Rahman, Hamdadur Khan, Shakil Siddiqui, Md Ismail Hossain Limon, Zishad Hossain Swapno, S M Masfequier Rahman Appaji, Abhishek Department of Computer Science and Engineering East West University A 2 Jahurul Islam Ave Dhaka1212 Bangladesh Department of Management Information System Pacific State University 3424 Wilshire Blvd. 12th Floor Los Angeles CA90010 United States Department of Management of Information System International American University 3440 Wilshire Blvd STE 1000 Los AngelesCA90010 United States Department of Business Analytics International American University 3440 Wilshire Blvd STE 1000 Los AngelesCA90010 United States Department of Engineering/Industrial Management Westcliff University IrvineCA92614 United States Department of Computer Science Westcliff University IrvineCA92614 United States Department Of Computer Science and Engineering Bangladesh University of Business and Technology Mirpur-2 Dhaka1216 Bangladesh Department of Medical Electronics Engineering B.M.S. College of Engineering Bangalore560019 India Maastricht University University Eye Clinic Maastricht Maastricht Netherlands

Early detection of brain tumors in MRI images is vital for improving treatment results. However, deep learning models face challenges like limited dataset diversity, class imbalance, and insufficient interpretability. Most studies rely on small, single-source datasets and do not combine different feature extraction techniques for better classification. To address these challenges, we propose a robust and explainable stacking ensemble model for multiclass brain tumor classification. To address these challenges, we propose a stacking ensemble model that combines EfficientNetB0, MobileNetV2, GoogleNet, and Multi-level CapsuleNet, using CatBoost as the meta-learner for improved feature aggregation and classification accuracy. This ensemble approach captures complex tumor characteristics while enhancing robustness and interpretability. The proposed model integrates EfficientNetB0, MobileNetV2, GoogleNet, and a Multi-level CapsuleNet within a stacking framework, utilizing CatBoost as the meta-learner to improve feature aggregation and classification accuracy. We created two large MRI datasets by merging data from four sources: BraTS, Msoud, Br35H, and SARTAJ. To tackle class imbalance, we applied Borderline-SMOTE and data augmentation. We also utilized feature extraction methods, along with PCA and Gray Wolf Optimization (GWO). Our model was validated through confidence interval analysis and statistical tests, demonstrating superior performance. Error analysis revealed misclassification trends, and we assessed computational efficiency regarding inference speed and resource usage. The proposed ensemble achieved 97.81% F1 score and 98.75% PR AUC on M1, and 98.32% F1 score with 99.34% PR AUC on M2. Moreover, the model consistently surpassed state-of-the-art CNNs, Vision Transformers, and other ensemble methods in classifying brain tumors across individual four datasets. Finally, we developed a web-based diagnostic tool that enables clinicians to interact with the proposed mo

关键词： Magnetic resonance imaging

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