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Braille Character Segmentation Algorithm Based on Gaussian Diffusion

computers, Materials & Continua 2024年第4期79卷 1481-1496页

作者： Zezheng Meng Zefeng Cai Jie Feng Hanjie Ma Haixiang Zhang Shaohua Li School of Computer Science and Technology Zhejiang Sci-Tech UniversityHangzhou310018China

Optical braille recognition methods typically employ existing target detection models or segmentation modelsfor the direct detection and recognition of braille characters in original braille images. However, these methodsneed improvement in accuracy and generalizability, especially in densely dotted braille image environments. Thispaper presents a two-stage braille recognition framework. The first stage is a braille dot detection algorithmbased on Gaussian diffusion, targeting Gaussian heatmaps generated by the convex dots in braille images. Thisis applied to the detection of convex dots in double-sided braille, achieving high accuracy in determining thecentral coordinates of the braille convex dots. The second stage involves constructing a braille grid using traditionalpost-processing algorithms to recognize braille character information. Experimental results demonstrate that thisframework exhibits strong robustness and effectiveness in detecting braille dots and recognizing braille charactersin complex double-sided braille image datasets. The framework achieved an F1 score of 99.89% for Braille dotdetection and 99.78% for Braille character recognition. Compared to the highest accuracy in existing methods,these represent improvements of 0.08% and 0.02%, respectively.

关键词： computer vision image recognition optical braille recognition

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YOLO-VSI: An Improved YOLOv8 Model for Detecting Railway Turnouts Defects in Complex Environments

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computers, Materials & Continua 2024年第11期81卷 3261-3280页

作者： Chenghai Yu Zhilong Lu School of Computer Science and Technology Zhejiang Sci-Tech UniversityHangzhou310018China

Railway turnouts often develop defects such as chipping,cracks,and wear during *** not detected and addressed promptly,these defects can pose significant risks to train operation safety and passenger *** advances in defect detection technologies,research specifically targeting railway turnout defects remains *** address this gap,we collected images from railway inspectors and constructed a dataset of railway turnout defects in complex *** enhance detection accuracy,we propose an improved YOLOv8 model named YOLO-VSS-SOUP-Inner-CIoU(YOLO-VSI).The model employs a state-space model(SSM)to enhance the C2f module in the YOLOv8 backbone,proposed the C2f-VSS module to better capture long-range dependencies and contextual features,thus improving feature extraction in complex *** the network’s neck layer,we integrate SPDConv and Omni-Kernel Network(OKM)modules to improve the original PAFPN(Path Aggregation Feature Pyramid Network)structure,and proposed the Small Object Upgrade Pyramid(SOUP)structure to enhance small object detection ***,the Inner-CIoU loss function with a scale factor is applied to further enhance the model’s detection *** to the baseline model,YOLO-VSI demonstrates a 3.5%improvement in average precision on our railway turnout dataset,showcasing increased accuracy and *** on the public NEU-DET dataset reveal a 2.3%increase in average precision over the baseline,indicating that YOLO-VSI has good generalization capabilities.

关键词： YOLO railway turnouts defect detection mamba FPN(Feature Pyramid Network)

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ATG-Net: Improved Feature Pyramid Network for Aerial Object Detection

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International Journal of Advanced computer science and Applications 2024年第11期15卷 1185-1192页

作者： Zheng, Junbao Yang, ChangHui Gui, Jiangsheng School of Computer Science and Technology Zhejiang Sci-Tech University Zhejiang Hangzhou China

Object detection in aerial images is gradually gaining wide attention and application. However, given the prevalence of numerous small objects in the Unmanned Aerial Vehicle (UAV) aerial images, the extraction of superior fusion features is critical for the detection of small objects. However, feature fusion in many detectors does not fully consider the specific characteristics of the detection task. To obtain suitable features for the detection task, the paper proposes an improved Feature Pyramid Network (FPN) named ATG-Net, which aims to improve the feature fusion capability. Firstly, we propose an Adaptive Tri-Layer Weighting (ATW) module that adaptively assigns weights to each layer of the feature map according to its size and content complexity. Secondly, a Triple Feature Encoding (TFE) module is implemented, which can fuse feature maps from three different scales. Finally, the paper incorporates the Global Attention Mechanism (GAM) into the network, which includes improved channel attention mechanisms and spatial attention mechanisms. The experiments are conducted on the VisDrone2020 dataset, and the result shows that the network significantly outperforms the baseline detector and a variety of popular object detectors, which significantly improves the feature fusion capability of the network and the detection accuracy of small objects. © (2024), (science and Information Organization). All rights reserved.

关键词： Encoding (symbols)

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Sub-pixel multi-scale fusion network for medical image segmentation

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Multimedia Tools and Applications 2024年第41期83卷 89355-89373页

作者： Li, Jing Chen, Qiaohong Fang, Xian School of Computer Science and Technology Zhejiang Sci-Tech University Hangzhou310018 China

CNNs and Transformers have significantly advanced the domain of medical image segmentation. The integration of their strengths facilitates rich feature extraction but also introduces the challenge of mixed multi-scale feature fusion. To overcome this issue, we propose an innovative deep medical image segmentation framework termed Sub-pixel Multi-scale Fusion Network (SMFNet), which effectively incorporates the sub-pixel multi-scale feature fusion results of CNN and Transformer into the architecture. In particular, our design consists of three effective and practical modules. Primarily, we utilize the Sub-pixel Convolutional Module to synchronize the extracted features at multiple scales to a consistent resolution. In the next place, we develop the Three-level Enhancement Module to learn features from adjacent layers and perform information exchange. Lastly, we leverage the Hierarchical Adaptive Gate to fuse information from other contextual levels through the Sub-pixel Convolutional Module. Extensive experiments on the Synapse, ACDC, and ISIC 2018 datasets demonstrate the effectiveness of the proposed SMFNet, and our method is superior to other competitive CNN-based or Transformer-based segmentation methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Medical imaging

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A significant wave height prediction method with ocean characteristics fusion and spatiotemporal dynamic graph modeling

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Acta Oceanologica Sinica 2024年第12期43卷 13-33页

作者： Xiao Yin Taoxing Wu Jie Yu Xiaoyu He Lingyu Xu Department of Computer Engineering and Science Shanghai UniversityShanghai 200444China School of Computer Science and Technology Zhejiang Sci-Tech UniversityHangzhou 310018China

Accurate significant wave height(SWH)prediction is essential for the development and utilization of wave *** learning methods such as recurrent and convolutional neural networks have achieved good results in SWH ***,these methods do not adapt well to dynamic seasonal variations in wave *** this study,we propose a novel method—the spatiotemporal dynamic graph(STDG)neural *** method predicts the SWH of multiple nodes based on dynamic graph modeling and multi-characteristic ***,considering the dynamic seasonal variations in the wave direction over time,the network models wave dynamic spatial dependencies from long-and short-term pattern ***,to correlate multiple characteristics with SWH,the network introduces a cross-characteristic transformer to effectively fuse multiple ***,we conducted experiments on two datasets from the South China Sea and East China Sea to validate the proposed method and compared it with five prediction methods in the three *** experimental results show that the proposed method achieves the best performance at all predictive scales and has greater advantages for extreme value ***,an analysis of the dynamic graph shows that the proposed method captures the seasonal variation mechanism of the waves.

关键词： significant wave height forecasting dynamic seasonal variation dynamic graph neural networks

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A hyperledger fabric-based EMR sharing mechanisms with proxy re-encryption and IPFS

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Cluster Computing 2024年第5期27卷 6953-6983页

作者： Huang, Der-Chen Liu, Ling-Chun Deng, Yong-Yuan Chen, Chin-Ling Zeng, Kuang-Wei Department of Computer Science and Engineering National Chung-Hsing University Taichung402 Taiwan School of Information Engineering Changchun Sci-Tech University Jilin Changchun130600 China Department of Computer Science and Information Engineering Chaoyang University of Technology Taichung41349 Taiwan

With the advent of the information age, data storage has not only developed from paper information systems to electronic information system storage, but has also extended to cloud database storage methods. To date, we can see the application of big data and cloud in various fields and levels, making induction, arrangement, transmission, and exchange of data easier and faster. The electronic and systematisation of patient medical records in the medical environment is an example. The use of electronic medical records can help improve the quality of medical care, reduce duplication of treatment, and reduce the waste of examination resources. It has many advantages, but also many risks. Medical institutions may abuse electronic medical data without the consent of patients, leading to the leakage of patient privacy data and causing serious social impact. Therefore, this study proposes a medical record exchange and sharing mechanism based on the Hyperledger blockchain architecture combined with proxy reencryption. When pharmacists need to obtain medical records for related research, patients can decide whether to share medical records, and hospitals can also revoke pharmacist access rights at any time to achieve more private, secure, and convenient sharing of medical records. The proposed scheme meets various blockchain security requirements and the BAN (Burrows–Abadi–Needham) logic proof model (Burrows–Abadi–Needham) is applied to assess the correctness of the proposed scheme. The proposed scheme performs well in terms of computational and communication costs. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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AI-Enhanced Secure Data Aggregation for Smart Grids with Privacy Preservation

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computers, Materials & Continua 2025年第1期82卷 799-816页

作者： Congcong Wang Chen Wang Wenying Zheng Wei Gu School of Software Nanjing University of Information Science and TechnologyNanjing210044China School of Information Science and Engineering Zhejiang Sci-Tech UniversityHangzhou310018China State Key Laboratory of Public Big Data Guizhou UniversityGuiyang550025China School of Computer Science and Technology(School of Artificial Intelligence) Zhejiang Sci-Tech UniversityHangzhou310018China School of Computer Science(School of Cyber Science and Engineering) Nanjing University of Information Science and TechnologyNanjing210044China

As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy *** research emphasizes data security and user privacy concerns within smart ***,existing methods struggle with efficiency and security when processing large-scale *** efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent *** paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data *** approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user *** also explores the application of Boneh Lynn Shacham(BLS)signatures for user *** proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.

关键词： Smart grid data security privacy protection artificial intelligence data aggregation

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Full Scale-Aware Balanced High-Resolution Network for Multi-Person Pose Estimation

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computers, Materials & Continua 2023年第9期76卷 3379-3392页

作者： Shaohua Li Haixiang Zhang HanjieMa Jie Feng Mingfeng Jiang School of Computer Science and Technology Zhejiang Sci-Tech UniversityHangzhou310018China

Scale variation is amajor challenge inmulti-person pose *** scenes where persons are present at various distances,models tend to perform better on larger-scale persons,while the performance for smaller-scale persons often falls short of ***,effectively balancing the persons of different scales poses a significant *** this paper proposes a newmulti-person pose estimation model called FSANet to improve themodel’s performance in complex *** model utilizes High-Resolution Network(HRNet)as the backbone and feeds the outputs of the last stage’s four branches into the DCB *** dilated convolution-based(DCB)module employs a parallel structure that incorporates dilated convolutions with different rates to expand the receptive field of each ***,the attention operation-based(AOB)module performs attention operations at both branch and channel levels to enhance high-frequency features and reduce the influence of ***,predictions are made using the heatmap *** model can recognize images with diverse scales and more complex semantic *** results demonstrate that FSA Net achieves competitive results on the MSCOCO and MPII datasets,validating the effectiveness of our proposed approach.

关键词： computer vision high-resolution network human pose estimation

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Automated GUI widgets classification

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Frontiers of computer science 2023年第1期17卷 233-235页

作者： Kabir Sulaiman SAID Liming NIE Yuanchang LIN Yaowen ZHENG Zuohua DING School of Computer Science and Technology Zhejiang Sci-Tech UniversityHangzhou 310018China School of Computer Science and Engineering Nanyang Technological UniversitySingap

1 Introduction Graphical User Interface(GUI)widgets classification entails classifying widgets into their appropriate domain-specific types(e.g.,CheckBox and EditText)[1,2].The widgets classification is essential as it supports several software engineering tasks,such as GUI design and testing[1,3].The ability to obtain better widget classification performance has become one of the keys to the success of these *** in recent years have proposed many techniques for improving widget classification performance[1,2,4].For example,Moran et al.[1]proposed a deep learning technique to classify GUI widgets into their domain-specific *** authors used the deep learning algorithm,a Convolutional Neural Network(CNN)architecture,to classify the GUI *** et al.[2]proposed combining text-based and non-text-based models to improve the overall performance of GUI widget detection while classifying the widgets with the ResNet50 model.

关键词： GUI User classify

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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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