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A Text Image Watermarking Algorithm Based on Image Enhancement

computers, Materials & Continua 2023年第10期77卷 1183-1207页

作者： Baowei Wang Luyao Shen Junhao Zhang Zenghui Xu Neng Wang School of Computer Nanjing University of Information Science and TechnologyCollaborative Innovation Center of Jiangsu Atmospheric Environment and Equipment TechnologyDigital Forensics Engineering Research Center of Digital Forensics Ministry of EducationNanjing210044China School of Software Nanjing University of Information Science and TechnologyNanjing210044China

Digital watermarking technology is adequate for copyright protection and content *** needs to be more research on the watermarking algorithm after printing and *** at the problem that existing anti-print scanning text image watermarking algorithms cannot take into account the invisibility and robustness of the watermark,an anti-print scanning watermarking algorithm suitable for text images is *** algorithm first performs a series of image enhancement preprocessing operations on the printed scanned image to eliminate the interference of incorrect bit information on watermark embedding and then uses a combination of Discrete Wavelet Transform(DWT)-Singular Value Decomposition(SVD)to embed the *** show that the average Normalized Correlation(NC)of the watermark extracted by this algorithm against attacks such as Joint Photographic Experts Group(JPEG)compression,JPEG2000 compression,and print scanning is above ***,the average NC of the watermark extracted after print scanning attacks is greater than 0.964,and the average Bit Error Ratio(BER)is 5.15%.This indicates that this algorithm has strong resistance to various attacks and print scanning attacks and can better take into account the invisibility of the watermark.

关键词： Print-resistant scanning image enhancement DWT SVD embedding intensity

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CA-DBMNet: a channel attention based dual branch multi-scale network for depth map super-resolution

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Multimedia Tools and Applications 2025年第4期84卷 1577-1596页

作者： Miao, Yongwei Zhang, Xinjie Sun, Yuliang Wang, Jinrong School of Information Science and Technology Hangzhou Normal University Hangzhou311121 China School of Computer Science and Technology Zhejiang Sci-Tech University Hangzhou310018 China School of Information Science and Technology Zhejiang Shuren University Hangzhou310015 China

Scene depth information plays a fundamental role and can be beneficial to various computer vision or visual robotics applications. The scene color image acquired by consumer depth sensors usually has a high resolution, whilst its depth map counterpart often performs low resolution or man-made artifacts. Due to its strong similarity in terms of scene structures between RGB-D pairs, taking the color image as prior information, this paper proposes a Dual Branch Multi-scale Network (CA-DBMNet) based on the channel attention mechanism which can effectively guide the task of depth map super-resolution (SR). The network consists of two branches–color image feature extraction branch and depth map super-resolution branch. The first branch adopts the feature pyramid structure to extract the color image features, capturing image features and structures at different scales. The second branch is composed of three modules: 1) A dense residual feature fusion (DRFF) module to integrate the extracted features from two branches with dense connection and residual learning;2) A channel multi-scale (CMS) module to exploit multi-scale features from depth feature maps;3) A channel attention (CA) module to effectively enhance the channel proportion of high-frequency components in the depth feature maps. Extensive experiments demonstrate that CA-DBMNet can effectively reconstruct the high-resolution depth map with complete scene structures and sharp edges. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Color

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Precise control of page cache for containers

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Frontiers of computer science 2024年第2期18卷 17-30页

作者： Kun WANG Song WU Shengbang LI Zhuo HUANG Hao FAN Chen YU Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Container-based virtualization isbecoming increasingly popular in cloud computing due to its efficiency and *** isolation is a fundamental property of *** works have indicated weak resource isolation could cause significant performance degradation for containerized applications and enhanced resource ***,current studies have almost not discussed the isolation problems of page cache which is a key resource for *** leverage memory cgroup to control page cache ***,existing policy introduces two major problems in a container-based ***,containers can utilize more memory than limited by their cgroup,effectively breaking memory ***,the Os kernel has to evict page cache to make space for newly-arrived memory requests,slowing down containerized *** paper performs an empirical study of these problems and demonstrates the performance impacts on containerized *** we propose pCache(precise control of page cache)to address the problems by dividing page cache into private and shared and controlling both kinds of page cache separately and *** do so,pCache leverages two new technologies:fair account(f-account)and evict on demand(EoD).F-account splits the shared page cache charging based on per-container share to prevent containers from using memory for free,enhancing memory *** EoD reduces unnecessary page cache evictions to avoid the performance *** evaluation results demonstrate that our system can effectively enhance memory isolation for containers and achieve substantial performance improvement over the original page cache management policy.

关键词： page cache memory cgroup container isolation cloud computing

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Traffexplainer: A Framework Toward GNN-Based Interpretable Traffic Prediction

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 559-573页

作者： Kong, Lingbai Yang, Hanchen Li, Wengen Zhang, Yichao Guan, Jihong Zhou, Shuigeng Tongji University Department of Computer Science and Technology Shanghai201804 China Fudan University School of Computer Science Shanghai200433 China

With the increasing traffic congestion problems in metropolises, traffic prediction plays an essential role in intelligent traffic systems. Notably, various deep learning models, especially graph neural networks (GNNs), achieve state-of-the-art performance in traffic prediction tasks but still lack interpretability. To interpret the critical information abstracted by traffic prediction models, we proposed a flexible framework termed Traffexplainer toward GNN-based interpretable traffic prediction. Traffexplainer is applicable to a wide range of GNNs without making any modifications to the original model structure. The framework consists of the GNN-based traffic prediction model and the perturbation-based hierarchical interpretation generator. Specifically, the hierarchical spatial mask and temporal mask are introduced to perturb the prediction model by modulating the values of input data. Then the prediction losses are backward propagated to the masks, which can identify the most critical features for traffic prediction, and further improve the prediction performance. We deploy the framework with five representative GNN-based traffic prediction models and analyze their prediction and interpretation performance on three real-world traffic flow datasets. The experiment results demonstrate that our framework can generate effective and faithful interpretations for GNN-based traffic prediction models, and also improve the prediction performance. The code will be publicly available at https://***/lingbai-kong/Traffexplainer. © 2024 The Authors.

关键词： Prediction models

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Time-Efficient Video Anomaly Detection With Parallel Computing and Twice-Reconstruction

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IEEE Sensors Journal 2025年第6期25卷 9887-9901页

作者： Qi, Xiaosha Chao, Xin Ji, Genlin Li, Le Changzhou Institute of Technology School of Computer Science and Information Engineering Jiangsu Changzhou213000 China Nanjing Normal University School of Geography Jiangsu Nanjing210023 China Nanjing Normal University School of Computer and Electronic Information Jiangsu Nanjing210023 China

To tackle two prevalent challenges in video anomaly detection, misclassification of abnormal frames as normal and the prolonged runtime of existing methods, this article proposes a time-efficient anomaly detection method utilizing twice-reconstruction and parallel computing. The proposed method integrates three key innovations: a twice-reconstruction model to amplify the reconstruction error for abnormal frames, a memory module to better capture and utilize normal distribution patterns, and a parallel computing strategy to significantly accelerate anomaly detection. By reconstructing frames twice, our method enhances the model's anomaly detection capabilities. Meanwhile, a memory module is incorporated to retain normal distribution patterns more effectively, reducing the likelihood of misclassifying abnormal frames. Furthermore, parallel computing is employed to minimize runtime and boost detection efficiency. Unlike existing methods, TPR-VAD achieves both high detection accuracy and time efficiency, making it well-suited for real-world applications such as intelligent surveillance systems. Experimental results on the UCSD Ped2, CUHK Avenue, and ShanghaiTech datasets show that our method achieves superior detection accuracy, reaching 99.01%, 91.21%, and 82.77%, respectively, while significantly reducing runtime. © 2001-2012 IEEE.

关键词： Normal distribution

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Enhanced corrosion resistance and electrical conductivity of stainless steel bipolar plates by molybdenum ion implantation

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Journal of Iron and Steel Research International 2025年第4期32卷 1073-1084页

作者： Li Ding Hu-bin Zhang Rui-juan Wang Yong Pan Xin Zhang Meng Yang School of Automotive&Rail Transit Nanjing Institute of TechnologyNanjing211167JiangsuChina College of Safety Science and Engineering Nanjing Tech UniversityNanjing211816JiangsuChina College of Materials Science and Engineering Nanjing Tech UniversityNanjing211816JiangsuChina

To enhance the corrosion resistance and electrical conductivity, the surface of 316L stainless steel was modified by the ion implantation of Mo. By investigating various accelerating voltages and implantation doses, it was found that the corrosion resistance of stainless steel was enhanced by 50%-80% and the surface conductivity by 15%-28% at most. The minimum stabilized current density is 0.72 μA/cm^(2). This is due to the formation of a Cr and Mo riched modified layer on the surface of the stainless steel. Mo oxides synergize with Cr oxides in the form of a solid solution to enhance the corrosion resistance of passivation films on the stainless steel surface. The optimum parameters were Cr in the proportion of 6%-8% and Mo in the proportion of 4%-5%.

关键词： Bipolar plate Hydrogen fuel cell Ion implantation 316L stainless steel Corrosion resistance Interfacial contact resistance

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Dual uncertainty-guided multi-model pseudo-label learning for semi-supervised medical image segmentation

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Mathematical Biosciences and Engineering 2024年第2期21卷 2212-2232页

作者： Qiu, Zhanhong Gan, Weiyan Yang, Zhi Zhou, Ran Gan, Haitao School of Computer Science Hubei University of Technology Wuhan430068 China

Semi-supervised medical image segmentation is currently a highly researched area. Pseudo-label learning is a traditional semi-supervised learning method aimed at acquiring additional knowledge by generating pseudo-labels for unlabeled data. However, this method relies on the quality of pseudo-labels and can lead to an unstable training process due to differences between samples. Additionally, directly generating pseudo-labels from the model itself accelerates noise accumulation, resulting in low-confidence pseudo-labels. To address these issues, we proposed a dual uncertainty-guided multi-model pseudo-label learning framework (DUMM) for semi-supervised medical image segmentation. The framework consisted of two main parts: The first part is a sample selection module based on sample-level uncertainty (SUS), intended to achieve a more stable and smooth training process. The second part is a multi-model pseudo-label generation module based on pixel-level uncertainty (PUM), intended to obtain high-quality pseudo-labels. We conducted a series of experiments on two public medical datasets, ACDC2017 and ISIC2018. Compared to the baseline, we improved the Dice scores by 6.5% and 4.0% over the two datasets, respectively. Furthermore, our results showed a clear advantage over the comparative methods. This validates the feasibility and applicability of our approach. © 2024 American Institute of Mathematical sciences. All rights reserved.

关键词： Image segmentation

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Automated object recognition in high-resolution optical remote sensing imagery

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National science Review 2023年第6期10卷 38-41页

作者： Yazhou Yao Tao Chen Hanbo Bi Xinhao Cai Gensheng Pei Guoye Yang Zhiyuan Yan Xian Sun Xing Xu Hai Zhang School of Computer Science and Engineering Nanjing University of Science and Technology Aerospace Information Research Institute Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Key Laboratory of Network Information System Technology (NIST) Aerospace Information Research Institute Chinese Academy of Sciences Department of Computer Science and Technology Tsinghua University School of Computer Science and Engineering University of Electronic Science and Technology of China Pazhou Laboratory (Huangpu) School of Mathematics Northwest University

INTRODUCTION With the rapid development of remote sensing technology,high-quality remote sensing images have become widely *** automated object detection and recognition of these images,which aims to automatically locate objects of interest in remote sensing images and distinguish their specific categories,is an important fundamental task in the *** provides an effective means for geospatial object monitoring in many social applications,such as intelligent transportation,urban planning,environmental monitoring and homeland security.

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Performance Analysis of a Novel Relay Selection Scheme for Wireless-Powered Cluster-Based Multi-Hop Cognitive Relay Networks

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IEEE Transactions on Cognitive Communications and Networking 2024年第3期11卷 1551-1562页

作者： Sun, Hui Naraghi-Pour, Mort Qian, Yuwen Sheng, Weixing Han, Yubing Nanjing University of Science and Technology School of Electronic and Optical Engineering Nanjing210094 China Louisiana State University Division of Electrical and Computer Engineering School of Electrical Engineering and Computer Science Baton RougeLA70803 United States

In this paper, we study the performance of wireless-powered cluster-based multi-hop cognitive relay networks (MCRNs), where secondary nodes harvest energy from multiple dedicated power beacons (PBs) and share the spectrum with multiple primary receivers (PRs) in the underlay paradigm. For this system, we propose a hop-by-hop relay selection scheme called the largest decoding set (LDS). In each stage, relay selection is based on the harvested energy from PBs and the channel state information (CSI) of both the interference links to PRs and the relaying links in the subsequent hop. Considering both harvested energy and maximum interference constraints, we derive the exact end-to-end outage probability and show that the results closely match those obtained from simulations. Moreover, the asymptotic end-to-end outage probabilities in two different scenarios are derived to provide more valuable insights. Numerical results show that the outage probability of the proposed LDS scheme is very close to that of the best-path scheduling (BPS) scheme, which provides a lower bound on outage probability but requires global CSI before the secondary source transmits. We also compare the LDS scheme with other relay selection schemes that have appeared recently in the literature and show that LDS has the best outage performance. © 2024 IEEE.

关键词： Cognitive radio

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PCB Surface Defect Detection based on YOLOv8n

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第12期51卷 2017-2025页

作者： You, Rui Wang, Zhifeng School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

In the electronic manufacturing industry, accurate detection of PCB defects is crucial as it directly impacts product quality and reliability. The primary challenges in PCB defect detection include missed detections and false alarms, particularly concerning micro-defects. This study proposes an enhanced PCB defect detection algorithm based on YOLOv8, which incorporates the Global Attention Module (GAM), Partial Convolution Layer (PConv3), and a multi-head detection strategy. The GAM improves the model’s sensitivity to micro-defects by capturing and weighting global context information through spatial and channel attention mechanisms applied to the input feature map. The PConv3 optimizes feature extraction, minimizing false alarms due to information loss. The multi-head detection strategy identifies defects at varying scales, preserving detailed information and enhancing the detection of small-sized defects. Experimental results demonstrate that the improved algorithm achieves a 3.6% increase in average precision while meeting real-time detection requirements. © (2024), (International Association of Engineers). All rights reserved.

关键词： Deep Learning Global Attention Module Multi-head Detection PCB Defect Detection PConv3 YOLOv8

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