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Edge Detection of COVID-19 CT Image Based on GF_SSR, Improved Multiscale Morphology, and Adaptive Threshold 认领 引用 被引量：2
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作者 Shouming Hou Chaolan Jia +3 位作者 Kai Li Liya Fan Jincheng Guo Mackenzie Brown 《Computer Modeling in Engineering & Sciences》 SCIE EI 2022年第7期81-94,共14页
Edge detection is an effective method for image segmentation and feature extraction.Therefore,extracting weak edges with the inhomogeneous gray of Corona Virus Disease 2019(COVID-19)CT images is extremely important.Mu... Edge detection is an effective method for image segmentation and feature extraction.Therefore,extracting weak edges with the inhomogeneous gray of Corona Virus Disease 2019(COVID-19)CT images is extremely important.Multiscale morphology has been widely used in the edge detection of medical images due to its excellent boundary detection accuracy.In this paper,we propose a weak edge detection method based on Gaussian filtering and singlescale Retinex(GF_SSR),and improved multiscale morphology and adaptive threshold binarization(IMSM_ATB).As all the CT images have noise,we propose to remove image noise by Gaussian filtering.The edge of CT images is enhanced using the SSR algorithm.In addition,based on the extracted edge of CT images using improved Multiscale morphology,a particle swarm optimization(PSO)algorithm is introduced to binarize the image by automatically getting the optimal threshold.To evaluate our method,we use images from three datasets,namely COVID-19,Kaggle-COVID-19,and COVID-Chestxray,respectively.The average values of results are worthy of reference,with the Shannon information entropy of 1.8539,the Precision of 0.9992,the Recall of 0.8224,the F-Score of 1.9158,running time of 11.3000.Finally,three types of lesion images in the COVID-19 dataset are selected to evaluate the visual effects of the proposed algorithm.Compared with the other four algorithms,the proposed algorithm effectively detects the weak edge of the lesion and provides help for image segmentation and feature extraction. 展开更多
关键词 COVID-19 SSR multiscale morphology PSO adaptive threshold edge detection
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Self-FAGCFN:Graph-Convolution Fusion Network Based on Feature Fusion and Self-Supervised Feature Alignment for Pneumonia and Tuberculosis Diagnosis 认领 引用 被引量：1
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作者 Junding Sun Wenhao Tang +5 位作者 Lei Zhao Chaosheng Tang Xiaosheng Wu Zhaozhao Xu Bin Pu Yudong Zhang 《Journal of Bionic Engineering》 SCIE EI CSCD 2025年第4期2012-2029,共18页
Feature fusion is an important technique in medical image classification that can improve diagnostic accuracy by integrating complementary information from multiple sources.Recently,Deep Learning(DL)has been widely us... Feature fusion is an important technique in medical image classification that can improve diagnostic accuracy by integrating complementary information from multiple sources.Recently,Deep Learning(DL)has been widely used in pulmonary disease diagnosis,such as pneumonia and tuberculosis.However,traditional feature fusion methods often suffer from feature disparity,information loss,redundancy,and increased complexity,hindering the further extension of DL algorithms.To solve this problem,we propose a Graph-Convolution Fusion Network with Self-Supervised Feature Alignment(Self-FAGCFN)to address the limitations of traditional feature fusion methods in deep learning-based medical image classification for respiratory diseases such as pneumonia and tuberculosis.The network integrates Convolutional Neural Networks(CNNs)for robust feature extraction from two-dimensional grid structures and Graph Convolutional Networks(GCNs)within a Graph Neural Network branch to capture features based on graph structure,focusing on significant node representations.Additionally,an Attention-Embedding Ensemble Block is included to capture critical features from GCN outputs.To ensure effective feature alignment between pre-and post-fusion stages,we introduce a feature alignment loss that minimizes disparities.Moreover,to address the limitations of proposed methods,such as inappropriate centroid discrepancies during feature alignment and class imbalance in the dataset,we develop a Feature-Centroid Fusion(FCF)strategy and a Multi-Level Feature-Centroid Update(MLFCU)algorithm,respectively.Extensive experiments on public datasets LungVision and Chest-Xray demonstrate that the Self-FAGCFN model significantly outperforms existing methods in diagnosing pneumonia and tuberculosis,highlighting its potential for practical medical applications. 展开更多
关键词 Feature fusion Self-supervised feature alignment Convolutional neural networks Graph convolutional networks Class imbalance Feature-centroid fusion
A Review of Device-Free Indoor Positioning for Home-Based Care of the Aged:Techniques and Technologies 认领 引用 被引量：2
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作者 Geng Chen Lili Cheng +2 位作者 Rui Shao Qingbin Wang Shuihua Wang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第6期1901-1940,共40页
With the development of urbanization,the problem of neurological diseases brought about by population aging has gradually become a social problem of worldwide concern.Aging leads to gradual degeneration of the central... With the development of urbanization,the problem of neurological diseases brought about by population aging has gradually become a social problem of worldwide concern.Aging leads to gradual degeneration of the central nervous system,shrinkage of brain tissue,and decline in physical function in many elderlies,making them susceptible to neurological diseases such as Alzheimer’s disease(AD),stroke,Parkinson’s and major depressive disorder(MDD).Due to the influence of these neurological diseases,the elderly have troubles such as memory loss,inability to move,falling,and getting lost,which seriously affect their quality of life.Tracking and positioning of elderly with neurological diseases and keeping track of their location in real-time are necessary and crucial in order to detect and treat dangerous and unexpected situations in time.Considering that the elderly with neurological diseases forget to wear a positioning device or have mobility problems due to carrying a positioning device,device-free positioning as a passive positioning technology that detects device-free individuals is more suitable than traditional active positioning for the home-based care of the elderly with neurological diseases.This paper provides an extensive and in-depth survey of device-free indoor positioning technology for home-based care and an in-depth analysis of the main features of current positioning systems,as well as the techniques,technologies andmethods they employ,fromthe perspective of the needs of the elderly with neurological conditions.Moreover,evaluation criteria and possible solutions of positioning techniques for the home-based care of the elderly with neurological conditions are proposed.Finally,the opportunities and challenges for the development of indoor positioning technology in 6G mobile networks for home-based care of the elderly with neurological diseases are discussed.This review has provided comprehensive and effective tracking and positioning techniques,technologies and methods for the elderly,by which we can obtain the location information of the elderly in real-time and make home-based care more comfortable and safer for the elderly with neurological diseases. 展开更多
关键词 Home-based care device-free neurological diseases indoor positioning positioning techniques and technologies positioning accuracy
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Multimodal Classification of Alzheimer’s Disease Based on Kolmogorov-Arnold Graph Attention Network 认领 引用
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作者 Xiaosheng Wu Ruichao Tian +2 位作者 Zhaozhao Xu Shuihua Wang Yudong Zhang 《Journal of Bionic Engineering》 SCIE EI CSCD 2025年第5期2717-2730,共14页
Alzheimer’s Disease(AD),a prevalent neurodegenerative disorder characterized by memory loss and cognitive decline,poses significant challenges for individuals and society.Multimodal data fusion has emerged as a promi... Alzheimer’s Disease(AD),a prevalent neurodegenerative disorder characterized by memory loss and cognitive decline,poses significant challenges for individuals and society.Multimodal data fusion has emerged as a promising approach for AD diagnosis,with Graph Convolutional Networks(GCNs)effectively capturing irregular brain information.However,traditional GCN methods face limitations in representing and integrating multimodal data,often resulting in feature mismatch.In this study,we propose a novel Kolmogorov-Arnold Graph Attention Network(KAGAN)model to address this issue through semantic-level alignment.KAGAN incorporates a Multimodal Feature Construction method(MuStaF)to extract structural and functional features from T1-and T2-weighted images,and a Multimodal Graph Adjacency Matrix Construction method(MuGAC)to integrate clinical information,modeling intricate relationships across modalities.Experiments conducted on the ADNI dataset demonstrate the superiority of KAGAN in AD/CN/MCI classification,achieving an accuracy of 98.29±1.21%.This highlights KAGAN’s potential for early AD diagnosis by enabling interactive learning and fusion of multimodal features at the semantic level.The source code of our proposed model and the related datasets are available at http://gffzz188fe103f8f1460asc66o6quv0vvo66wx.ffgz.tsg.suse.edu.cn/sheeprra/KAGAN. 展开更多
关键词 Alzheimer’s disease diagnosis Multimodal data fusion Feature mismatch problem GAT KAN
Two kinds of tension in fiber Bragg gratings with cladding etched as the sinusoidal function 认领 引用 被引量：8
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作者 王国东 杨莹丽 《Optoelectronics Letters》 EI 2010年第1期48-50,共3页
A new grating structure with cladding etched as the sinusoidal function along the length direction is introduced. The cladding area of the grating varies as the sinusoidal function along fiber axes. The coupled mode t... A new grating structure with cladding etched as the sinusoidal function along the length direction is introduced. The cladding area of the grating varies as the sinusoidal function along fiber axes. The coupled mode theory is used to analyze its performance. Compared with the linearly chirped grating, the new structure exhibits an ideal box spectrum with steep edges when the tension is applied on the gratings in two different ways. For the type B grating, without tension after writing, it has a bigger bandwidth utilization (BWU) than that of the type A grating with tension after writing. Compared with the type A grating, the type B one is more adaptable in fiber-optic communications and sensor systems. 展开更多
关键词 线性啁啾光栅 正弦函数 蚀刻 包层 纤维 光纤通讯 耦合模理论 带宽利用率
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Least-Square Support Vector Machine and Wavelet Selection for Hearing Loss Identification 认领 引用 被引量：3
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作者 Chaosheng Tang Deepak Ranjan Nayak Shuihua Wang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2020年第10期299-313,共15页
Hearing loss(HL)is a kind of common illness,which can significantly reduce the quality of life.For example,HL often results in mishearing,misunderstanding,and communication problems.Therefore,it is necessary to provid... Hearing loss(HL)is a kind of common illness,which can significantly reduce the quality of life.For example,HL often results in mishearing,misunderstanding,and communication problems.Therefore,it is necessary to provide early diagnosis and timely treatment for HL.This study investigated the advantages and disadvantages of three classical machine learning methods:multilayer perceptron(MLP),support vector machine(SVM),and least-square support vector machine(LS-SVM)approach andmade a further optimization of the LS-SVM model via wavelet entropy.The investigation illustrated that themultilayer perceptron is a shallowneural network,while the least square support vector machine uses hinge loss function and least-square optimizationmethod.Besides,a wavelet selection method was proposed,and we found db4 can achieve the best results.The experiments showed that the LS-SVM method can identify the hearing loss disease with an overall accuracy of three classes as 84.89±1.77,which is superior to SVM andMLP.The results show that the least-square support vector machine is effective in hearing loss identification. 展开更多
关键词 Hearing loss wavelet entropy multilayer perceptron least square support vector machine
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DeepGCN based on variable multi‐graph and multimodal data for ASD diagnosis 认领 引用 被引量：1
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作者 Shuaiqi Liu Siqi Wang +3 位作者 Chaolei Sun Bing Li Shuihua Wang Fei Li 《CAAI Transactions on Intelligence Technology》 SCIE EI 2024年第4期879-893,共15页
Diagnosing individuals with autism spectrum disorder(ASD)accurately faces great chal-lenges in clinical practice,primarily due to the data's high heterogeneity and limited sample size.To tackle this issue,the auth... Diagnosing individuals with autism spectrum disorder(ASD)accurately faces great chal-lenges in clinical practice,primarily due to the data's high heterogeneity and limited sample size.To tackle this issue,the authors constructed a deep graph convolutional network(GCN)based on variable multi‐graph and multimodal data(VMM‐DGCN)for ASD diagnosis.Firstly,the functional connectivity matrix was constructed to extract primary features.Then,the authors constructed a variable multi‐graph construction strategy to capture the multi‐scale feature representations of each subject by utilising convolutional filters with varying kernel sizes.Furthermore,the authors brought the non‐imaging in-formation into the feature representation at each scale and constructed multiple population graphs based on multimodal data by fully considering the correlation between subjects.After extracting the deeper features of population graphs using the deep GCN(DeepGCN),the authors fused the node features of multiple subgraphs to perform node classification tasks for typical control and ASD patients.The proposed algorithm was evaluated on the Autism Brain Imaging Data Exchange I(ABIDE I)dataset,achieving an accuracy of 91.62%and an area under the curve value of 95.74%.These results demon-strated its outstanding performance compared to other ASD diagnostic algorithms. 展开更多
关键词 machine learning medical image processing medical signal processing
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Authenticated Digital Currency Redaction with Stronger Privacy and Usability 认领 引用
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作者 Tang Yongli Li Ying +2 位作者 Zhao Zongqu Li Yuanhong Guo Rui 《China Communications》 SCIE CSCD 2024年第6期219-236,共18页
With the promotion of digital currency,how to effectively solve the authenticity,privacy and usability of digital currency issuance has been a key problem.Redactable signature scheme(RSS)can provide the verification o... With the promotion of digital currency,how to effectively solve the authenticity,privacy and usability of digital currency issuance has been a key problem.Redactable signature scheme(RSS)can provide the verification of the integrity and source of the generated sub-documents and solve the privacy problem in digital currency by removing blocks from the signed documents.Unfortunately,it has not realized the consolidation of signed documents,which can not solve the problem of merging two digital currencies.Now,we introduce the concept of weight based on the threshold secret sharing scheme(TSSS)and present a redactable signature scheme with merge algorithm(RSS-MA)using the quasi-commutative accumulator.Our scheme can reduce the communication overhead by utilizing the merge algorithm when transmitting multiple digital currency signatures.Furthermore,this can effectively hide the scale of users’private monetary assets and the number of transactions between users.While meeting the three properties of digital currency issuance,in order to ensure the availability of digital currency after redacting,editors shall not remove the relevant identification information block form digital currency.Finally,our security proof and the analysis of efficiency show that RSS-MA greatly improves the communication and computation efficiency when transmitting multiple signatures. 展开更多
关键词 authenticity verification digital currency privacy protection RSS-MA TSSS usability
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Overbooking-Enabled Task Scheduling and Resource Allocation in Mobile Edge Computing Environments 认领 引用
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作者 Jixun Gao Bingyi Hu +3 位作者 Jialei Liu Huaichen Wang Quanzhen Huang Yuanyuan Zhao 《Intelligent Automation & Soft Computing》 SCIE 2023年第7期1-16,共16页
Mobile Edge Computing(MEC)is proposed to solve the needs of Inter-net of Things(IoT)users for high resource utilization,high reliability and low latency of service requests.However,the backup virtual machine is idle w... Mobile Edge Computing(MEC)is proposed to solve the needs of Inter-net of Things(IoT)users for high resource utilization,high reliability and low latency of service requests.However,the backup virtual machine is idle when its primary virtual machine is running normally,which will waste resources.Overbooking the backup virtual machine under the above circumstances can effectively improve resource utilization.First,these virtual machines are deployed into slots randomly,and then some tasks with cooperative relationship are off-loaded to virtual machines for processing.Different deployment locations have different resource utilization and average service response time.We want tofind a balanced solution that minimizes the average service response time of the IoT application while maximizing resource utilization.In this paper,we propose a task scheduler and exploit a Task Deployment Algorithm(TDA)to obtain an optimal virtual machine deployment scheme.Finally,the simulation results show that the TDA can significantly increase the resource utilization of the system,while redu-cing the average service response time of the application by comparing TDA with the other two classical methods.The experimental results confirm that the perfor-mance of TDA is better than that of other two methods. 展开更多
关键词 Mobile edge computing overbooking resource utilization service response time task deployment algorithm
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DGFE-Mamba:Mamba-Based 2D Image Segmentation Network 认领 引用 被引量：2
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作者 Junding Sun Kaixin Chen +4 位作者 Shuihua Wang Yudong Zhang Zhaozhao Xu Xiaosheng Wu Chaosheng Tang 《Journal of Bionic Engineering》 SCIE EI CSCD 2025年第4期2135-2150,共16页
In the field of medical image processing,combining global and local relationship modeling constitutes an effective strategy for precise segmentation.Prior research has established the validity of Convolutional Neural ... In the field of medical image processing,combining global and local relationship modeling constitutes an effective strategy for precise segmentation.Prior research has established the validity of Convolutional Neural Networks(CNN)in modeling local relationships.Conversely,Transformers have demonstrated their capability to effectively capture global contextual information.However,when utilized to address CNNs’limitations in modeling global relationships,Transformers are hindered by substantial computational complexity.To address this issue,we introduce Mamba,a State-Space Model(SSM)that exhibits exceptional proficiency in modeling long-range dependencies in sequential data.Given Mamba’s demonstrated potential in 2D medical image segmentation in previous studies,we have designed a Dual-encoder Global-local Feature Extraction Network based on Mamba,termed DGFE-Mamba,to accurately capture and fuse long-range dependencies and local dependencies within multi-scale features.Compared to Transformer-based methods,the DGFE-Mamba model excels in comprehensive feature modeling and demonstrates significantly improved segmentation accuracy.To validate the effectiveness and practicality of DGFE-Mamba,we conducted tests on the Automatic Cardiac Diagnosis Challenge(ACDC)dataset,the Synapse multi-organ CT abdominal segmentation dataset,and the Colorectal Cancer Clinic(CVC-ClinicDB)dataset.The results showed that DGFE-Mamba achieved Dice coefficients of 92.20,83.67,and 94.13,respectively.These findings comprehensively validate the effectiveness and practicality of the proposed DGFE-Mamba architecture. 展开更多
关键词 Medical image segmentation Mamba CNN Attention Mechanism
MSAMamba-UNet:A Lightweight Multi-Scale Adaptive Mamba Network for Skin Lesion Segmentation 认领 引用 被引量：1
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作者 Shouming Hou Jianchao Hou +2 位作者 Yuteng Pang Aoyu Xia Beibei Hou 《Journal of Bionic Engineering》 SCIE EI CSCD 2025年第6期3209-3225,共17页
Segmenting skin lesions is critical for early skin cancer detection.Existing CNN and Transformer-based methods face challenges such as high computational complexity and limited adaptability to variations in lesion siz... Segmenting skin lesions is critical for early skin cancer detection.Existing CNN and Transformer-based methods face challenges such as high computational complexity and limited adaptability to variations in lesion sizes.To overcome these limitations,we introduce MSAMamba-UNet,a lightweight model that integrates two novel architectures:Multi-Scale Mamba(MSMamba)and Adaptive Dynamic Gating Block(ADGB).MSMamba utilizes multi-scale decomposition and a parallel hierarchical structure to enhance the delineation of irregular lesion boundaries and sensitivity to small targets.ADGB dynamically selects convolutional kernels with varying receptive fields based on input features,improving the model’s capacity to accommodate diverse lesion textures and scales.Additionally,we introduce a Mix Attention Fusion Block(MAF)to enhance shallow feature representation by integrating parallel channel and pixel attention mechanisms.Extensive evaluation of MSAMamba-UNet on the ISIC 2016,ISIC 2017,and ISIC 2018 datasets demonstrates competitive segmentation accuracy with only 0.056 M parameters and 0.069 GFLOPs.Our experiments revealed that MSAMamba-UNet achieved IoU scores of 85.53%,85.47%,and 82.22%,as well as DSC scores of 92.20%,92.17%,and 90.24%,respectively.These results underscore the lightweight design and effectiveness of MSAMamba-UNet. 展开更多
关键词 Transformer Segmenting skin lesions Mamba Lightweight model Multi-scale
Realization of Rough Set Approximation Toplogical Operations Based on Formal Concept Analysis 认领 引用
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作者 Huilai Zhi 《International Journal of Intelligence Science》 2014年第3期65-69,共5页
There is an intimate correlation between rough set theory and formal concept analysis theory, so rough set approximations can be realized by means of formal concept analysis. For any given multiple valued information ... There is an intimate correlation between rough set theory and formal concept analysis theory, so rough set approximations can be realized by means of formal concept analysis. For any given multiple valued information system, the realization of rough set approximation operation has two major steps, firstly convert the information system from multiple valued one to single valued formal context, secondly realize rough set approximation operations aided by concept lattice, which is equivalent to a query operation under some necessary conditions. 展开更多
关键词 Rough Set Approximation Operation Formal Context Concept Lattice
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Weighted Attribute Based Conditional Proxy Re-Encryption in the Cloud 认领 引用
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作者 Xixi Yan Jing Zhang Pengyu Cheng 《Computers, Materials & Continua》 SCIE EI 2025年第4期1399-1414,共16页
Conditional proxy re-encryption(CPRE)is an effective cryptographic primitive language that enhances the access control mechanism and makes the delegation of decryption permissions more granular,but most of the attribu... Conditional proxy re-encryption(CPRE)is an effective cryptographic primitive language that enhances the access control mechanism and makes the delegation of decryption permissions more granular,but most of the attribute-based conditional proxy re-encryption(AB-CPRE)schemes proposed so far do not take into account the importance of user attributes.A weighted attribute-based conditional proxy re-encryption(WAB-CPRE)scheme is thus designed to provide more precise decryption rights delegation.By introducing the concept of weight attributes,the quantity of system attributes managed by the server is reduced greatly.At the same time,a weighted tree structure is constructed to simplify the expression of access structure effectively.With conditional proxy re-encryption,large amounts of data and complex computations are outsourced to cloud servers,so the data owner(DO)can revoke the user’s decryption rights directly with minimal costs.The scheme proposed achieves security against chosen plaintext attacks(CPA).Experimental simulation results demonstrated that the decryption time is within 6–9 ms,and it has a significant reduction in communication and computation cost on the user side with better functionality compared to other related schemes,which enables users to access cloud data on devices with limited resources. 展开更多
关键词 Cloud service conditional proxy re-encryption user revocation weighted attribute
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MSO-DETR: Metric space optimization for few-shot object detection 认领 引用 被引量：3
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作者 Haifeng Sima Manyang Wang +2 位作者 Lanlan Liu Yudong Zhang Junding Sun 《CAAI Transactions on Intelligence Technology》 SCIE EI 2024年第6期1515-1533,共19页
In the metric-based meta-learning detection model,the distribution of training samples in the metric space has great influence on the detection performance,and this influence is usually ignored by traditional meta-det... In the metric-based meta-learning detection model,the distribution of training samples in the metric space has great influence on the detection performance,and this influence is usually ignored by traditional meta-detectors.In addition,the design of metric space might be interfered with by the background noise of training samples.To tackle these issues,we propose a metric space optimisation method based on hyperbolic geometry attention and class-agnostic activation maps.First,the geometric properties of hyperbolic spaces to establish a structured metric space are used.A variety of feature samples of different classes are embedded into the hyperbolic space with extremely low distortion.This metric space is more suitable for representing tree-like structures between categories for image scene analysis.Meanwhile,a novel similarity measure function based on Poincarédistance is proposed to evaluate the distance of various types of objects in the feature space.In addition,the class-agnostic activation maps(CCAMs)are employed to re-calibrate the weight of foreground feature information and suppress background information.Finally,the decoder processes the high-level feature information as the decoding of the query object and detects objects by predicting their locations and corresponding task encodings.Experimental evaluation is conducted on Pascal VOC and MS COCO datasets.The experiment results show that the effectiveness of the authors’method surpasses the performance baseline of the excellent few-shot detection models. 展开更多
关键词 few-shot object detection hyperbolic space meta-learning metric space
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COVID-19 Detection via a 6-Layer Deep Convolutional Neural Network 认领 引用 被引量：5
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作者 Shouming Hou Ji Han 《Computer Modeling in Engineering & Sciences》 SCIE EI 2022年第2期855-869,共15页
Many people around the world have lost their lives due to COVID-19.The symptoms of most COVID-19 patients are fever,tiredness and dry cough,and the disease can easily spread to those around them.If the infected people... Many people around the world have lost their lives due to COVID-19.The symptoms of most COVID-19 patients are fever,tiredness and dry cough,and the disease can easily spread to those around them.If the infected people can be detected early,this will help local authorities control the speed of the virus,and the infected can also be treated in time.We proposed a six-layer convolutional neural network combined with max pooling,batch normalization and Adam algorithm to improve the detection effect of COVID-19 patients.In the 10-fold cross-validation methods,our method is superior to several state-of-the-art methods.In addition,we use Grad-CAM technology to realize heat map visualization to observe the process of model training and detection. 展开更多
关键词 COVID-19 deep learning convolutional neural network max pooling batch normalization Adam Grad-CAM
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Inertial projection algorithms for convex feasibility problem 认领 引用 被引量：2
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作者 Yazheng Dang Yan Gao Lihua Li 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2012年第5期734-740,共7页
The purpose of this paper is to apply inertial technique to string averaging projection method and block-iterative projection method in order to get two accelerated projection algorithms for solving convex feasibility... The purpose of this paper is to apply inertial technique to string averaging projection method and block-iterative projection method in order to get two accelerated projection algorithms for solving convex feasibility problem.Compared with the existing accelerated methods for solving the problem,the inertial technique employs a parameter sequence and two previous iterations to get the next iteration and hence improves the flexibility of the algorithm.Theoretical asymptotic convergence results are presented under some suitable conditions.Numerical simulations illustrate that the new methods have better convergence than the general projection methods.The presented algorithms are inspired by the inertial proximal point algorithm for finding zeros of a maximal monotone operator. 展开更多
关键词 convex feasibility problem inertial technique string averaging block iteration asymptotic convergence
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MAPFUNet:Multi-attention Perception-Fusion U-Netfor Liver Tumor Segmentation 认领 引用 被引量：1
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作者 Junding Sun Biao Wang +3 位作者 Xiaosheng Wu Chaosheng Tang Shuihua Wang Yudong Zhang 《Journal of Bionic Engineering》 SCIE EI CSCD 2024年第5期2515-2539,共25页
The second-leading cause of cancer-related deaths globally is liver cancer.The treatment of liver cancers depends heavily on the accurate segmentation of liver tumors from CT scans.The improved method based on U-Net h... The second-leading cause of cancer-related deaths globally is liver cancer.The treatment of liver cancers depends heavily on the accurate segmentation of liver tumors from CT scans.The improved method based on U-Net has achieved good perfor-mance for liver tumor segmentation,but these methods can still be improved.To deal with the problems of poor performance from the original U-Net framework in the segmentation of small-sized liver tumors and the position information of tumors that is seriously lost in the down-sampling process,we propose the Multi-attention Perception-fusion U-Net(MAPFU-Net).We propose the Position ResBlock(PResBlock)in the encoder stage to promote the feature extraction capability of MAPFUNet while retaining the position information regarding liver tumors.A Dual-branch Attention Module(DWAM)is proposed in the skip connections,which narrows the semantic gap between the encoder's and decoder's features and enables the network to utilize the encoder's multi-stage and multi-scale features.We propose the Channel-wise ASPP with Atten-tion(CAA)module at the bottleneck,which can be combined with multi-scale features and contributes to the recovery of micro-tumor feature information.Finally,we evaluated MAPFUNet on the LITS2017 dataset and the 3DIRCADB-01 dataset,with Dice values of 85.81 and 83.84%for liver tumor segmentation,which were 2.89 and 7.89%higher than the baseline model,respectively.The experiment results show that MAPFUNet is superior to other networks with better tumor feature representation and higher accuracy of liver tumor segmentation.We also extended MAPFUNet to brain tumor segmentation on the BraTS2019 dataset.The results indicate that MAPFUNet performs well on the brain tumor segmentation task,and its Dice values on the three tumor regions are 83.27%(WT),84.77%(TC),and 76.98%(ET),respectively. 展开更多
关键词 Liver tumor segmentation Small tumors Position information Attention Multi-scale features
Multi-graph Networks with Graph Pooling for COVID-19 Diagnosis 认领 引用 被引量：1
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作者 Chaosheng Tang Wenle Xu +3 位作者 Junding Sun Shuihua Wang Yudong Zhang Juan Manuel Górriz 《Journal of Bionic Engineering》 SCIE EI CSCD 2024年第6期3179-3200,共22页
Convolutional Neural Networks(CNNs)have shown remarkable capabilities in extracting local features from images,yet they often overlook the underlying relationships between pixels.To address this limitation,previous ap... Convolutional Neural Networks(CNNs)have shown remarkable capabilities in extracting local features from images,yet they often overlook the underlying relationships between pixels.To address this limitation,previous approaches have attempted to combine CNNs with Graph Convolutional Networks(GCNs)to capture global features.However,these approaches typically neglect the topological structure information of the graph during the global feature extraction stage.This paper proposes a novel end-to-end hybrid architecture called the Multi-Graph Pooling Network(MGPN),which is designed explicitly for chest X-ray image classification.Our approach sequentially combines CNNs and GCNs,enabling the learning of both local and global features from individual images.Recognizing that different nodes contribute differently to the final graph representation,we introduce an NI-GTP module to enhance the extraction of ultimate global features.Additionally,we introduce a G-LFF module to fuse the local and global features effectively. 展开更多
关键词 Convolutional neural networks Graph convolutional networks Graph pooling COVID-19
Policy-Based Group Signature Scheme from Lattice 认领 引用
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作者 Yongli Tang Yuanhong Li +2 位作者 Qing Ye Ying Li Xiaojun Wang 《Computers, Materials & Continua》 SCIE EI 2022年第8期4069-4085,共17页
Although the existing group signature schemes from lattice have been optimized for efficiency,the signing abilities of eachmember in the group are relatively single.It may not be suitable for complex applications.Insp... Although the existing group signature schemes from lattice have been optimized for efficiency,the signing abilities of eachmember in the group are relatively single.It may not be suitable for complex applications.Inspired by the pioneering work of Bellare and Fuchsbauer,we present a primitive called policy-based group signature.In policy-based group signatures,group members can on behalf of the group to sign documents that meet their own policies,and the generated signatures will not leak the identity and policies of the signer.Moreover,the group administrator is allowed to reveal the identity of signer when a controversy occurs.Through the analysis of application scenarios,we concluded that the policy-based group signature needs to meet two essential security properties:simulatability and traceability.And we construct a scheme of policy-based group signature from lattice through techniques such as commitment,zero-knowledge proof,rejection sampling.The security of our scheme is proved to be reduced to the module short integer solution(MSIS)and module learning with errors(MLWE)hard assumptions.Furthermore,we make a performance comparison between our scheme and three lattice-based group signature schemes.The result shows that our scheme has more advantages in storage overhead and the sizes of key and signature are decreased roughly by 83.13%,46.01%,respectively,compared with other schemes. 展开更多
关键词 Group signature policy-based signature lattice-based cryptography zero-knowledge proof
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Method for improving MapReduce performance by prefetching before scheduling 认领 引用
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作者 张霄宏 Feng Shengzhong +1 位作者 Fan Jianping Huang Zhexue 《High Technology Letters》 EI CAS 2012年第4期343-349,共7页
In this paper, a prefetching technique is proposed to solve the performance problem caused by remote data access delay. In the technique, the map tasks which will cause the delay are predicted first and then the input... In this paper, a prefetching technique is proposed to solve the performance problem caused by remote data access delay. In the technique, the map tasks which will cause the delay are predicted first and then the input data of these tasks will be preloaded before the tasks are scheduled. During the execution, the input data can be read from local nodes. Therefore, the delay can be hidden. The technique has been implemented in Hadoop-0. 20.1. The experiment results have shown that the technique reduces map tasks causing delay, and improves the performance of Hadoop MapRe- duce by 20%. 展开更多
关键词 cloud computing distributed computing prefetching MapReduce scheduling
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