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A two-branch multiscale spectral-spatial feature extraction network for hyperspectral image classification

Journal of information and Intelligence 2024年第3期2卷 224-235页

作者： Aamir Ali Caihong Mu Zeyu Zhang Jian Zhu Yi Liu key laboratory of Intelligent Perception and Image Understanding of ministry of education Collaborative Innovation Center of Quantum Information of Shaanxi ProvinceSchool of Artificial IntelligenceXidian UniversityXi'an 710071China school of Electronic Engineering Xidian UniversityXi'an 710071China

In the field of hyperspectral image(HSI)classification in remote sensing,the combination of spectral and spatial features has gained considerable *** addition,the multiscale feature extraction approach is very effective at improving the classification accuracy for HSIs,capable of capturing a large amount of intrinsic ***,some existing methods for extracting spectral and spatial features can only generate low-level features and consider limited scales,leading to low classification results,and dense-connection based methods enhance the feature propagation at the cost of high model *** paper presents a two-branch multiscale spectral-spatial feature extraction network(TBMSSN)for HSI *** design the mul-tiscale spectral feature extraction(MSEFE)and multiscale spatial feature extraction(MSAFE)modules to improve the feature representation,and a spatial attention mechanism is applied in the MSAFE module to reduce redundant information and enhance the representation of spatial fea-tures at *** we densely connect series of MSEFE or MSAFE modules respectively in a two-branch framework to balance efficiency and effectiveness,alleviate the vanishing-gradient problem and strengthen the feature *** evaluate the effectiveness of the proposed method,the experimental results were carried out on bench mark HsI datasets,demonstrating that TBMSSN obtained higher classification accuracy compared with several state-of-the-art methods.

关键词： Hyperspectral image classification Multiscale spectral-spatial information Two-branch architecture

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Automatic recognition of depression based on audio and video:A review

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World Journal of Psychiatry 2024年第2期14卷 225-233页

作者： Meng-Meng Han Xing-Yun Li Xin-Yu Yi Yun-Shao Zheng Wei-Li Xia Ya-Fei Liu Qing-Xiang Wang Shandong Mental Health Center Shandong UniversityJinan 250014Shandong ProvinceChina key laboratory of Computing Power network and information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353Shandong ProvinceChina Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and TechnologyQilu University of Technology(Shandong Academy of Sciences)Jinan 250353Shandong ProvinceChina Shandong Provincial key laboratory of computer networks Shandong Fundamental Research Center for Computer ScienceJinan 250353Shandong ProvinceChina Department of Ward Two Shandong Mental Health CenterShandong UniversityJinan 250014Shandong ProvinceChina

Depression is a common mental health *** current depression detection methods,specialized physicians often engage in conversations and physiological examinations based on standardized scales as auxiliary measures for depression ***-biological markers-typically classified as verbal or non-verbal and deemed crucial evaluation criteria for depression-have not been effectively *** physicians usually require extensive training and experience to capture changes in these *** in deep learning technology have provided technical support for capturing non-biological *** researchers have proposed automatic depression estimation(ADE)systems based on sounds and videos to assist physicians in capturing these features and conducting depression *** article summarizes commonly used public datasets and recent research on audio-and video-based ADE based on three perspectives:Datasets,deficiencies in existing research,and future development directions.

关键词： Depression recognition Deep learning Automatic depression estimation System Audio processing Image processing Feature fusion Future development

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Convolution-Transformer for Image Feature Extraction

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computer Modeling in Engineering & Sciences 2024年第10期141卷 87-106页

作者： Lirong Yin Lei Wang Siyu Lu Ruiyang Wang Youshuai Yang Bo Yang Shan Liu Ahmed AlSanad Salman A.AlQahtani Zhengtong Yin Xiaolu Li Xiaobing Chen Wenfeng Zheng Department of Geography and Anthropology Louisiana State UniversityBaton RougeLA70803USA School of Automation University of Electronic Science and Technology of ChinaChengdu610054China College of computer and information Sciences King Saud UniversityRiyadh11574Saudi Arabia College of Resources and Environmental Engineering Key Laboratory of Karst Georesources and Environment(Guizhou University)Ministry of EducationGuiyang550025China School of Geographical Sciences Southwest UniversityChongqing400715China School of Electrical and computer Engineering Louisiana State UniversityBaton RougeLA70803USA

This study addresses the limitations of Transformer models in image feature extraction,particularly their lack of inductive bias for visual *** to Convolutional Neural networks(CNNs),the Transformers are more sensitive to different hyperparameters of optimizers,which leads to a lack of stability and slow *** tackle these challenges,we propose the Convolution-based Efficient Transformer Image Feature Extraction network(CEFormer)as an enhancement of the Transformer *** model incorporates E-Attention,depthwise separable convolution,and dilated convolution to introduce crucial inductive biases,such as translation invariance,locality,and scale invariance,into the Transformer ***,we implement a lightweight convolution module to process the input images,resulting in faster convergence and improved *** results in an efficient convolution combined Transformer image feature extraction *** results on the ImageNet1k Top-1 dataset demonstrate that the proposed network achieves better accuracy while maintaining high computational *** achieves up to 85.0%accuracy across various model sizes on image classification,outperforming various baseline *** integrated into the Mask Region-ConvolutionalNeuralnetwork(R-CNN)framework as a backbone network,CEFormer outperforms other models and achieves the highest mean Average Precision(mAP)*** research presents a significant advancement in Transformer-based image feature extraction,balancing performance and computational efficiency.

关键词： Transformer E-Attention depth convolution dilated convolution CEFormer

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An Efficient and Fast Area Optimization Approach for Mixed Polarity Reed-Muller Logic Circuits

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Chinese Journal of Electronics 2024年第5期33卷 1165-1180页

作者： Yuhao ZHOU Zhenxue HE Jianhui JIANG Xiaojun ZHAO Fan ZHANG Limin XIAO Xiang WANG School of Software Engineering Tongji University key laboratory of Agricultural Big Data of Hebei Province Hebei Agricultural University School of computer Science and Engineering Beihang University School of Electronic and information Engineering Beihang University

Area has become one of the main bottlenecks restricting the development of integrated circuits. The area optimization approaches of existing XNOR/OR-based mixed polarity Reed-Muller(MPRM) circuits have poor optimization effect and efficiency. Given that the area optimization of MPRM logic circuits is a combinatorial optimization problem, we propose a whole annealing adaptive bacterial foraging algorithm(WAA-BFA), which includes individual evolution based on Markov chain and Metropolis acceptance criteria, and individual mutation based on adaptive probability. To address the issue of low conversion efficiency in existing polarity conversion approaches, we introduce a fast polarity conversion algorithm(FPCA). Moreover, we present an MPRM circuits area optimization approach that uses the FPCA and WAA-BFA to search for the best polarity corresponding to the minimum circuits area. Experimental results demonstrate that the proposed MPRM circuits area optimization approach is effective and can be used as a promising EDA tool.

关键词： Area optimization Combinatorial optimization problem Fast polarity conversion algorithm Fast polarity conversion algorithm Mixed polarity Reed-Muller Mixed polarity Reed-Muller Bacterial foraging algorithm Bacterial foraging algorithm Mixed polarity Reed-Muller Bacterial foraging algorithm BACTERIAL FORAGING ALGORITHM MINIMIZATION EXTRACTION network

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An Improved Iterated Greedy Algorithm for Solving Rescue Robot Path Planning Problem with Limited Survival Time

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computers, Materials & Continua 2024年第7期80卷 931-947页

作者： Xiaoqing Wang Peng Duan Leilei Meng Kaidong Yang School of computer Science Liaocheng UniversityLiaocheng252000China Shandong key laboratory of Optical Communication Science and Technology School of Physics Science and Information TechnologyLiaocheng UniversityLiaocheng252059China

Effective path planning is crucial for mobile robots to quickly reach rescue destination and complete rescue tasks in a post-disaster *** this study,we investigated the post-disaster rescue path planning problem and modeled this problem as a variant of the travel salesman problem(TSP)with life-strength *** address this problem,we proposed an improved iterated greedy(IIG)***,a push-forward insertion heuristic(PFIH)strategy was employed to generate a high-quality initial ***,a greedy-based insertion strategy was designed and used in the destruction-construction stage to increase the algorithm’s exploration ***,three problem-specific swap operators were developed to improve the algorithm’s exploitation ***,an improved simulated annealing(SA)strategy was used as an acceptance criterion to effectively prevent the algorithm from falling into local *** verify the effectiveness of the proposed algorithm,the Solomon dataset was extended to generate 27 instances for ***,the proposed IIG was compared with five state-of-the-art *** parameter analysiswas conducted using the design of experiments(DOE)Taguchi method,and the effectiveness analysis of each component has been verified one by *** results indicate that IIGoutperforms the compared algorithms in terms of the number of rescue survivors and convergence speed,proving the effectiveness of the proposed algorithm.

关键词： Rescue robot path planning life strength improved iterative greedy algorithm problem-specific swap operators

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Distributed generalized Nash equilibrium seeking:event-triggered coding-decoding-based secure communication

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Science China(information Sciences) 2024年第7期67卷 271-284页

作者： Shaofu YANG Wenying XU Wangli HE Jinde CAO School of computer Science and Engineering Southeast University School of Mathematics Southeast University key laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology

In this paper, we consider the distributed generalized Nash equilibrium(GNE) seeking problem in strongly monotone games. The transmission among players is implemented through a digital communication network with limited bandwidth. For improving communication efficiency or/and security, an event-triggered coding-decoding-based communication is first proposed, where the data(decision variable) are first mapped to a series of finite-level codewords and, only when an event condition is satisfied, then sent to the neighboring agents. Moreover, a distributed communication-efficient GNE seeking algorithm is constructed accordingly,and the overrelaxation scheme is further taken into consideration. Through primal-dual analysis, the proposed algorithm is proven to converge to a variational GNE with fixed step-sizes by recasting it as an inexact forward-backward iteration. Finally, numerical simulations illustrate the benefit of the proposed algorithms in terms of saving communication resources.

关键词： noncooperative games event-triggered communication coding-decoding quantization

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GAN-DIRNet:A Novel Deformable Image Registration Approach for Multimodal Histological Images

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computers, Materials & Continua 2024年第7期80卷 487-506页

作者： Haiyue Li Jing Xie Jing Ke Ye Yuan Xiaoyong Pan Hongyi Xin Hongbin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of ChinaShanghai200240China Department of Pathology Ruijin HospitalShanghai Jiao Tong UniversitySchool of MedicineShanghai200025China Department of computer Science and Engineering Shanghai Jiao Tong UniversityShanghai200240China Global Institute of Future Technology Shanghai Jiao Tong UniversityShanghai200240China

Multi-modal histological image registration tasks pose significant challenges due to tissue staining operations causing partial loss and folding of *** neural network(CNN)and generative adversarial network(GAN)are pivotal inmedical image ***,existing methods often struggle with severe interference and deformation,as seen in histological images of conditions like Cushing’s *** argue that the failure of current approaches lies in underutilizing the feature extraction capability of the discriminator *** this study,we propose a novel multi-modal registration approach GAN-DIRNet based on GAN for deformable histological image *** begin with,the discriminators of two GANs are embedded as a new dual parallel feature extraction module into the unsupervised registration networks,characterized by implicitly extracting feature descriptors of specific ***,modal feature description layers and registration layers collaborate in unsupervised optimization,facilitating faster convergence and more precise ***,experiments and evaluations were conducted on the registration of the Mixed National Institute of Standards and Technology database(MNIST),eight publicly available datasets of histological sections and the Clustering-Registration-Classification-Segmentation(CRCS)dataset on the Cushing’s *** results demonstrate that our proposed GAN-DIRNet method surpasses existing approaches like DIRNet in terms of both registration accuracy and time efficiency,while also exhibiting robustness across different image types.

关键词： Histological images registration deformable registration generative adversarial network cushing’s disease machine learning computer vision

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Experimental full-duplex amplify-and-forward relay scheme for OFDM with power gain control

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Journal of information and Intelligence 2024年第5期2卷 375-387页

作者： Pu Yang Xiang-Gen Xia Qingyue Qu Yi Liu The State key laboratory of Integrated Service network Xidian UniversityXian 710071China The Department of Electrical and computer Engineering University of DelawareNewark DE 19716USA

The fundamental challenges for full-duplex(FD)relay networks are the self-interference cancellation(SIC)and the cooperative transmission design at the *** paper presents a practical amplify-and-forward(AF)FD one-way relay scheme for orthogonal frequency division multiplexing(OFDM)transmission with multi-domain *** is found that the residual self-interference(SI)signals at the relay can be regarded as an equivalent multipath *** effects of the residual sI at the relay are incorporated into the equivalent end-to-end channel model,and the inter-block interference can be removed at the destination by using cyclic prefix(CP)*** on the equivalent multipath model,we present a solution for optimizing the amplification factor on the performance of signal-to-interference-plus-noise ratio(SINR)when the equivalent multipath length is longer than the ***,a practical one way FD relay network with 3 nodes is built and *** simulation and measured results verify the superior performance of the proposed scheme.

关键词： Full-duplex Amplify-and-forward Gain control Self-interference cancellation

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High-Level Security Dimension Evaluation of Blockchain BaaS System and key Technology

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China Communications 2024年第6期21卷 176-191页

作者： Chen Wanfa Zheng Qing’an Chen Shuzhen Fu Hongyi Chen Liang Fujian Police College Fuzhou 350007China computer Science and information Security Management Department Fujian Police CollegeFuzhou 350007China Fujian Province key laboratory for Digital network Crime Supervision and Prevention Fuzhou 350007China Beijing university of Posts and Telecommunications Beijing 100876China Fujian Provincial Prison Administration Fuzhou 350002China

In recent years,blockchain technology integration and application has gradually become an important driving force for new technological innovation and industrial *** blockchain technology and applications are developing rapidly,the emerging security risks and obstacles have gradually become *** can still find security issues in blockchain systems and conduct attacks,causing increasing losses from network attacks every *** response to the current demand for blockchain application security detection and assessment in all industries,and the insufficient coverage of existing detection technologies such as smart contract detectiontechnology,this paper proposes a blockchain core technology security assessment system model,and studies the relevant detection and assessment key technologies and systems.A security assessment scheme based on a smart contract and consensus mechanism detection scheme is *** the underlying blockchain architecture supports the traceability of detection results using super ***,the functionality and performance of the system were tested,and the test results show that the model and solutions proposed in this paper have good feasibility.

关键词： blockchain security consensus mechanis smart contract

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U-Net Based Dual-Pooling Segmentation of Bone Metastases in Thoracic SPECT Bone Scintigrams

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Journal of computer and Communications 2024年第4期12卷 60-71页

作者： Yang He Qiang Lin Yongchun Cao Zhengxing Man key laboratory of China’ s Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University Lanzhou China School of Mathematics and computer Science Northwest Minzu University Lanzhou China

In order to enhance the performance of the CNN-based segmentation models for bone metastases, this study proposes a segmentation method that integrates dual-pooling, DAC, and RMP modules. The network consists of distinct feature encoding and decoding stages, with dual-pooling modules employed in encoding stages to maintain the background information needed for bone scintigrams diagnosis. Both the DAC and RMP modules are utilized in the bottleneck layer to address the multi-scale problem of metastatic lesions. Experimental evaluations on 306 clinical SPECT data have demonstrated that the proposed method showcases a substantial improvement in both DSC and Recall scores by 3.28% and 6.55% compared the baseline. Exhaustive case studies illustrate the superiority of the methodology.

关键词： Tumor Bone Metastasis Bone Scintigram Lesion Segmentation CNN Dual Pooling

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