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A Fine-Grained RecognitionModel based on Discriminative Region Localization and Efficient Second-Order Feature Encoding 认领 引用
1
作者 Xiaorui Zhang Yingying Wang +3 位作者 Wei Sun Shiyu Zhou Haoming Zhang Pengpai Wang 《Computers, Materials & Continua》 SCIE EI 2026年第4期946-965,共20页
Discriminative region localization and efficient feature encoding are crucial for fine-grained object recognition.However,existing data augmentation methods struggle to accurately locate discriminative regions in comp... Discriminative region localization and efficient feature encoding are crucial for fine-grained object recognition.However,existing data augmentation methods struggle to accurately locate discriminative regions in complex backgrounds,small target objects,and limited training data,leading to poor recognition.Fine-grained images exhibit“small inter-class differences,”and while second-order feature encoding enhances discrimination,it often requires dual Convolutional Neural Networks(CNN),increasing training time and complexity.This study proposes a model integrating discriminative region localization and efficient second-order feature encoding.By ranking feature map channels via a fully connected layer,it selects high-importance channels to generate an enhanced map,accurately locating discriminative regions.Cropping and erasing augmentations further refine recognition.To improve efficiency,a novel second-order feature encoding module generates an attention map from the fourth convolutional group of Residual Network 50 layers(ResNet-50)and multiplies it with features from the fifth group,producing second-order features while reducing dimensionality and training time.Experiments on Caltech-University of California,San Diego Birds-200-2011(CUB-200-2011),Stanford Car,and Fine-Grained Visual Classification of Aircraft(FGVC Aircraft)datasets show state-of-the-art accuracy of 88.9%,94.7%,and 93.3%,respectively. 展开更多
关键词 Fine-grained recognition feature encoding data augmentation second-order feature discriminative regions
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DGRDet: Dynamic Gaussian Receptive Field Encoding-Based Spiking Neural Networks for Remote Sensing Object Detection 认领 引用
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作者 Li Chen Fan Zhang +3 位作者 Guangwei Xie Yanzhao Gao Xiaofeng Qi Mingqian Sun 《Computers, Materials & Continua》 SCIE EI 2026年第8期1247-1271,共25页
Remote sensing object detection aims to identify and localize specific targets in satellite or aerial imagery.Spiking Neural Networks(SNNs),benefiting from their implicit feedback-based and event-driven brain-inspired... Remote sensing object detection aims to identify and localize specific targets in satellite or aerial imagery.Spiking Neural Networks(SNNs),benefiting from their implicit feedback-based and event-driven brain-inspired dynamics,offer a promising solution to alleviate the high energy consumption of conventional ANN-based detection models.However,existing SNN-based approaches for remote sensing object detection—particularly for small,arbitrarily rotated objects—are still in their infancy and suffer from a substantial performance gap compared with ANN counterparts.In this work,we draw inspiration from the hierarchical sparse perception mechanisms of biological vision and integrate dynamic receptive field modulation into the encoding stage,proposing a high-precision spiking object detection framework tailored for remote sensing image.Specifically,we design a Hierarchical Feedback-based Gaussian Encoding(HFG)scheme,in which the parameters of Gaussian kernels are dynamically adjusted through spike-triggered top-down feedback connections.This mechanism enables the encoding process to adaptively respond to complex geometric variations of remote sensing objects,including rotation and scale changes.Based on the proposed encoding strategy,we develop DGRDet(Dynamic Gaussian Receptive Field Encoding-based Spiking Neural Networks for Remote Sensing Object Detection),a directly trained deep SNN detector for remote sensing image.Extensive evaluations on the large-scale public DOTA dataset demonstrate that DGRDet achieves competitive detection accuracy,outperforming existing SNN-based object detection methods.Moreover,compared with ANN models of comparable detection performance,DGRDet reduces spike activity by 81.31%and requires only 0.12%of the inference energy consumption,achieving a favorable balance between detection accuracy,efficiency,and energy efficiency. 展开更多
关键词 Remote sensing image object detection spiking neural networks(SNNs) hierarchical sparse dynamic gaussian encoding
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Beyond Classical Positional Encodings:A Learnable QFT-Inspired Framework for Transformer Language Models 认领 引用
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作者 Sara Tehsin Tallha Akram +2 位作者 Syed Rameez Naqvi Meshal Alharbi Abdulrahman Alabduljabbar 《Computers, Materials & Continua》 SCIE EI 2026年第9期159-182,共24页
Transformers have become the dominant architecture for sequence modeling in natural language processing;however,their effectiveness critically depends on how positional information is encoded.Conventional positional e... Transformers have become the dominant architecture for sequence modeling in natural language processing;however,their effectiveness critically depends on how positional information is encoded.Conventional positional encodings,while effective,may have limited structural flexibility for capturing complex global sequence relationships.Recent quantum-inspired approaches have sought to address this limitation,yetmany either oversimplify quantum principles or introduce substantial computational or hardware overhead.We introduce a novel Quantum Fourier Transform(QFT)-inspired positional encoding scheme for transformers,motivated by the structured frequency representation of the QFT.Unlike prior approaches that either emulate quantum operations superficially or require complex circuit constructions,the proposed method provides a learnable hybrid encoding that preserves quantuminspired structure while remaining aligned with hardware-efficient circuit primitives and structurally compatible with future near-term quantum implementations.Experiments on WikiText-103 indicate that the proposed encoding achieves competitive perplexity,improved robustness to input scrambling,and stable training behavior relative to alternative quantum-inspired baselines under the evaluated settings.Preliminary circuit-level simulations further suggest favorable noise resilience of the associated encoding primitives.These findings support the potential utility of incorporating quantum-inspired design principles into deep learning architectures and provide a foundation for future exploration at the interface of quantum computing and transformer-based natural language processing(NLP). 展开更多
关键词 LLMs positional encoding Quantum Fourier Transform positional embeddings hybrid quantumclassical models near-term quantum devices quantum transformer
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Multidimensional visual feature encoding and functional organization in the pigeon entopallium 认领 引用
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作者 Jun-Cai Zhu Min-Jie Zhu +4 位作者 Qing-Zhi He Peng Wu Xiao-Ke Niu Jiang-Tao Wang Zhi-Zhong Wang 《Zoological Research》 SCIE CSCD 2026年第2期487-502,共16页
Understanding how birds perceive and recognize visual objects remains a fundamental question in neuroscience.The entopallium,a key node in the avian tectofugal pathway,has long been implicated in complex visual proces... Understanding how birds perceive and recognize visual objects remains a fundamental question in neuroscience.The entopallium,a key node in the avian tectofugal pathway,has long been implicated in complex visual processing,yet its internal functional architecture remains incompletely understood.In this study,neuronal activity in the pigeon entopallium was systematically mapped using controlled visual stimuli that independently varied in color,shape,and motion.Recordings revealed marked hue selectivity that remained invariant across luminance levels,pronounced orientation tuning in response to shape stimuli,and robust direction selectivity for moving stimuli.Spatial mapping further revealed distinct functional segregation,with color-selective neurons localized anteroventrally,shape-selective neurons dorsally,and motion-selective neurons posteriorly.At the same time,partial overlap among these response classes was observed,with a subset of neurons exhibiting joint tuning across stimulus dimensions,suggesting an organizational scheme characterized by regional specialization and partial cross-feature integration.Notably,entopallium neurons exhibited a moderate level of visual feature integration and shared important functional properties with early to intermediate stages of mammalian visual processing.Together,these findings establish the entopallium as a major site for multidimensional visual analysis in birds and provide evidence for convergent principles underlying the evolution of complex visual systems across vertebrates. 展开更多
关键词 Entopallium Tectofugal pathway Feature encoding Functional organization Object recognition
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FlexCENT:A frequency-flexible CEST imaging network combining frequency offset encoding and three-dimensional U-Net 认领 引用
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作者 Jingyi Yu Mengying Zhu +2 位作者 Yonggui Yang Congbo Cai Shuhui Cai 《Magnetic Resonance Letters》 EI CAS 2026年第2期40-57,共18页
This study proposes a deep learning-based method termed frequency-flexible chemical exchange saturation transfer(CEST)imaging network(FlexCENT),which enables robust CEST quantification across variable frequency offset... This study proposes a deep learning-based method termed frequency-flexible chemical exchange saturation transfer(CEST)imaging network(FlexCENT),which enables robust CEST quantification across variable frequency offset schemes without requiring retraining.FlexCENT integrates frequency offset encoding with a three-dimensional(3D)U-Net to process CEST images and frequency offsets as inputs and predict Lorentzian parameters of the 4-pool model(water,MT,APT,rNOE),including B0 inhomogeneity.By transforming frequency offsets into a continuous spectral feature representation,the frequency offset encoding allows FlexCENT to generalize to unseen frequency offset schemes.Trained on synthetic data generated from the 4-pool Lorentzian model,FlexCENT was validated through numerical simulations,tumor-bearing mouse experiments,and a human brain experiment,alongside comparisons with 4-pool Lorentzian fitting,DeepCEST,and LKAN networks.The results demonstrate that FlexCENT successfully quantified CEST parameters across all experiments,maintaining consistent performance under varying frequency offset conditions without retraining.It exhibited superior noise robustness in numerical simulations and enhanced anatomical delineation in vivo parametric mapping compared to other methods.In conclusion,by combining spectral information with spatial information,FlexCENT provides an efficient,flexible,and robust quantitative approach for CEST imaging.It significantly enhance the quantification capability and clinical potential of CEST imaging. 展开更多
关键词 Chemical exchange saturation transfer Deep learning Three-dimensional U-Net Frequency offset encoding
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Mesolimbic Dopaminergic Encoding of Decision Value:Linking Phenotype-Specific Signals to Strategic Adaptation 认领 引用
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作者 Zhengyi Xu Dadao An +2 位作者 Jingjia Liang Lingyan Zheng Zhong Chen 《Neuroscience Bulletin》 SCIE CAS CSCD 2026年第4期937-940,共4页
Numerous neuropsychiatric disorders are characterized by significant impairments in decision-making function.These include impulsive decision-making in attention-deficit hyperactivity disorder(ADHD)[1],excessive risk-... Numerous neuropsychiatric disorders are characterized by significant impairments in decision-making function.These include impulsive decision-making in attention-deficit hyperactivity disorder(ADHD)[1],excessive risk-taking during manic episodes in bipolar disorder,and the distorted prioritization observed in substance use disorders.Decisionmaking involves reflecting on the outcomes of past actions and weighing the potential consequences of future actions.In this complex balancing process,mesolimbic dopamine influences reward value assessment,the strength of motivation,and the initiation of action[2]. 展开更多
关键词 phenotype specific signals bipolar disorderand strategic adaptation decision making reflecting outcomes past actions mesolimbic dopaminergic encoding distorted prioritization balancing processmesolimbic dopamine
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Thermally Driven Soliton Tuning and State Transition in Bi2TeSe2-Based Ultrafast Fiber Lasers for Encoding Applications 认领 引用
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作者 Bin Shen Rui Diao +4 位作者 Chong-Zhou Zhao Xin Guo Xiao-Bo Ma Chao-Qing Dai Yue-Yue Wang 《Chinese Physics Letters》 SCIE EI CAS CSCD 2026年第2期99-110,共12页
We demonstrate a thermally tunable mode-locked fiber laser integrating a polarization-sensitive SMF-PMFSMF modulator and a Bi2TeSe2 saturable absorber(SMF:single-mode fiber;PMF:polarization-maintaining fiber).By... We demonstrate a thermally tunable mode-locked fiber laser integrating a polarization-sensitive SMF-PMFSMF modulator and a Bi2TeSe2 saturable absorber(SMF:single-mode fiber;PMF:polarization-maintaining fiber).By controlling the PMF temperature,reversible switching among conventional,dissipative,and boundstate solitons is achieved.The wavelength tuning ranges are about 5 nm and 2.8 nm for conventional and dissipative solitons,respectively,with a tuning efficiency of 0.35 nm/℃.Numerical simulations based on temperatureinduced birefringence variation reproduce the observed dynamics.Furthermore,a wavelength-encoding scheme utilizing thermally driven soliton shifts is proposed,providing a feasible approach for soliton-state-controlled optical communication. 展开更多
关键词 polarization sensitive smf pmfsmf modulator wavelength tuning bi tese based ultrafast fiber lasers soliton shifts birefringence variation thermally driven soliton tuning state transition encoding applications
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Super-field-of-view non-line-of-sight imaging via spatial encoding of a translated point spread function 认领 引用
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作者 TONGYAO LI YINGJIE SHI +5 位作者 JINYE MIAO YI WEI LINGFENG LIU LIANFA BAI ENLAI GUO JING HAN 《Photonics Research》 SCIE EI CAS CSCD 2026年第5期1959-1972,共14页
Non-line-of-sight(NLOS)imaging aims to reconstruct objects beyond line-of-sight view,offering potential applications in various fields.However,conventional transient NLOS methods are constrained by the detection regio... Non-line-of-sight(NLOS)imaging aims to reconstruct objects beyond line-of-sight view,offering potential applications in various fields.However,conventional transient NLOS methods are constrained by the detection region,preventing the reconstruction of targets outside its normal space and thereby limiting practical applicability.In this paper,a computational imaging method for super-field-of-view(Super-FoV)reconstruction based on spatial encoding of a translated point spread function(PSF)is proposed. 展开更多
关键词 computational imaging method reconstruct objects translated point spread function psf non line sight imaging spatial encoding super field view computational imaging translated point spread function
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Mid-infrared temporal ghost imaging via two-photon structured encoding 认领 引用
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作者 ZIYU HE KUN HUANG +3 位作者 HUIJIE MA WEN ZHANG JIANAN FANG HEPING ZENG 《Photonics Research》 SCIE EI CAS CSCD 2026年第5期1919-1927,共9页
Temporal ghost imaging(TGI)enables ultrafast signal reconstruction beyond electronic bandwidth limits.Extending this concept to the mid-infrared(MIR)regime through nonlinear frequency conversion offers new opportuniti... Temporal ghost imaging(TGI)enables ultrafast signal reconstruction beyond electronic bandwidth limits.Extending this concept to the mid-infrared(MIR)regime through nonlinear frequency conversion offers new opportunities for high-fidelity temporal detection,but it remains constrained by the stringent phase-matching condition,limited spectral coverage,and intricate optical alignment.Here,we propose and demonstrate a broad-band MIR TGI system based on non-degenerate two-photon absorption. 展开更多
关键词 electronic bandwidth limitsextending mid infrared two photon structured encoding nonlinear frequency conversion phase matching condition temporal ghost imaging tgi enables signal reconstruction temporal ghost imaging
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Autonomous inverse encoding guides 4D nanoprinting for highly programmable shape morphing 认领 引用 被引量：3
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作者 Shuaiqi Ren Zhiang Zhang +6 位作者 Ruokun He Jiahao Fan Guangming Wang Hesheng Wang Bing Han Yong-Lai Zhang Zhuo-Chen Ma 《International Journal of Extreme Manufacturing》 SCIE EI CAS CSCD 2025年第3期467-482,共16页
Highly programmable shape morphing of 4D-printed microanostructures is urgently desired for applications in robotics and intelligent systems.However,due to the lack of autonomous holistic strategies throughout the tar... Highly programmable shape morphing of 4D-printed microanostructures is urgently desired for applications in robotics and intelligent systems.However,due to the lack of autonomous holistic strategies throughout the target shape input,optimal material distribution generation,and fabrication program output,4D nanoprinting that permits arbitrary shape morphing remains a challenging task for manual design.In this study,we report an autonomous inverse encoding strategy to decipher the genetic code for material property distributions that can guide the encoded modeling toward arbitrarily pre-programmed 4D shape morphing.By tuning the laser power of each voxel at the nanoscale,the genetic code can be spatially programmed and controllable shape morphing can be realized through the inverse encoding process.Using this strategy,the 4D-printed structures can be designed and accurately shift to the target morphing of arbitrarily hand-drawn lines under stimulation.Furthermore,as a proof-of-concept,a flexible fiber micromanipulator that can approach the target region through pre-programmed shape morphing is autonomously inversely encoded according to the localized spatial environment.This strategy may contribute to the modeling and arbitrary shape morphing of microanostructures fabricated via 4D nanoprinting,leading to cutting-edge applications in microfluidics,micro-robotics,minimally invasive robotic surgery,and tissue engineering. 展开更多
关键词 femtosecond laser fabrication 4D printing two-photon polymerization autonomous inverse encoding stimuli-responsive materials
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Enhancing the genomic prediction accuracy of swine agricultural economic traits using an expanded one-hot encoding in CNN models 认领 引用 被引量：3
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作者 Zishuai Wang Wangchang Li Zhonglin Tang 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2025年第9期3574-3582,共9页
Deep learning(DL)methods like multilayer perceptrons(MLPs)and convolutional neural networks(CNNs)have been applied to predict the complex traits in animal and plant breeding.However,improving the genomic prediction ac... Deep learning(DL)methods like multilayer perceptrons(MLPs)and convolutional neural networks(CNNs)have been applied to predict the complex traits in animal and plant breeding.However,improving the genomic prediction accuracy still presents signifcant challenges.In this study,we applied CNNs to predict swine traits using previously published data.Specifcally,we extensively evaluated the CNN model's performance by employing various sets of single nucleotide polymorphisms(SNPs)and concluded that the CNN model achieved optimal performance when utilizing SNP sets comprising 1,000 SNPs.Furthermore,we adopted a novel approach using the one-hot encoding method that transforms the 16 different genotypes into sets of eight binary variables.This innovative encoding method signifcantly enhanced the CNN's prediction accuracy for swine traits,outperforming the traditional one-hot encoding techniques.Our fndings suggest that the expanded one-hot encoding method can improve the accuracy of DL methods in the genomic prediction of swine agricultural economic traits.This discovery has significant implications for swine breeding programs,where genomic prediction is pivotal in improving breeding strategies.Furthermore,future research endeavors can explore additional enhancements to DL methods by incorporating advanced data pre-processing techniques. 展开更多
关键词 swine agricultural economic traits genomic prediction deep learning one-hot encoding convolutional neural networks(CNNs)
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Enhanced Multimodal Sentiment Analysis via Integrated Spatial Position Encoding and Fusion Embedding 认领 引用 被引量：1
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作者 Chenquan Gan Xu Liu +3 位作者 Yu Tang Xianrong Yu Qingyi Zhu Deepak Kumar Jain 《Computers, Materials & Continua》 SCIE EI 2025年第12期5399-5421,共23页
Multimodal sentiment analysis aims to understand emotions from text,speech,and video data.However,current methods often overlook the dominant role of text and suffer from feature loss during integration.Given the vary... Multimodal sentiment analysis aims to understand emotions from text,speech,and video data.However,current methods often overlook the dominant role of text and suffer from feature loss during integration.Given the varying importance of each modality across different contexts,a central and pressing challenge in multimodal sentiment analysis lies in maximizing the use of rich intra-modal features while minimizing information loss during the fusion process.In response to these critical limitations,we propose a novel framework that integrates spatial position encoding and fusion embedding modules to address these issues.In our model,text is treated as the core modality,while speech and video features are selectively incorporated through a unique position-aware fusion process.The spatial position encoding strategy preserves the internal structural information of speech and visual modalities,enabling the model to capture localized intra-modal dependencies that are often overlooked.This design enhances the richness and discriminative power of the fused representation,enabling more accurate and context-aware sentiment prediction.Finally,we conduct comprehensive evaluations on two widely recognized standard datasets in the field—CMU-MOSI and CMU-MOSEI to validate the performance of the proposed model.The experimental results demonstrate that our model exhibits good performance and effectiveness for sentiment analysis tasks. 展开更多
关键词 Multimodal sentiment analysis spatial position encoding fusion embedding feature loss reduction
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基于CNN-Transformer encoder-BiLSTM模型的轴承剩余寿命预测 认领 引用 被引量：7
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作者 张代林 孔康 +1 位作者 朱晨曦 杨奕婷 《华中科技大学学报（自然科学版）》 EI CAS CSCD 北大核心 2026年第5期1-8,共8页
针对复杂工况下轴承退化过程非线性强且长期依赖关系难以有效建模的问题,提出一种基于改进的CNNTransformer encoder-BiLSTM模型的剩余寿命预测方法.在该方法中,卷积神经网络(CNN)关注局部信息以更好地提取特征;改进的Transformer enco... 针对复杂工况下轴承退化过程非线性强且长期依赖关系难以有效建模的问题,提出一种基于改进的CNNTransformer encoder-BiLSTM模型的剩余寿命预测方法.在该方法中,卷积神经网络(CNN)关注局部信息以更好地提取特征;改进的Transformer encoder引入三种不同的注意力掩码机制,计算过程仅关注长期信息中重要的部分;使用双向长短期记忆网络(BiLSTM)关注所有信息的长期依赖关系.在C-MAPSS和XJTU-SY数据集上验证了模型的精度,实验结果表明:在加入高斯噪声后,该模型的估计效果优于其他方法,具有更好的稳定性. 展开更多
关键词 剩余寿命预测 注意力掩码机制 卷积神经网络 Transformer encoder 双向长短期记忆网络
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Encoding converters for quantum communication networks 认领 引用
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作者 Hua-Xing Xu Shao-Hua Wang +2 位作者 Ya-Qi Song Ping Zhang Chang-Lei Wang 《Chinese Physics B》 SCIE EI CAS CSCD 2025年第5期64-69,共6页
Quantum communication networks,such as quantum key distribution(QKD)networks,typically employ the measurement-resend mechanism between two users using quantum communication devices based on different quantum encoding ... Quantum communication networks,such as quantum key distribution(QKD)networks,typically employ the measurement-resend mechanism between two users using quantum communication devices based on different quantum encoding types.To achieve direct communication between the devices with different quantum encoding types,in this paper,we propose encoding conversion schemes between the polarization bases(rectilinear,diagonal and circular bases)and the time-bin phase bases(two phase bases and time-bin basis)and design the quantum encoding converters.The theoretical analysis of the encoding conversion schemes is given in detail,and the basis correspondence of encoding conversion and the property of bit flip are revealed.The conversion relationship between polarization bases and time-bin phase bases can be easily selected by controlling a phase shifter.Since no optical switches are used in our scheme,the converter can be operated with high speed.The converters can also be modularized,which may be utilized to realize miniaturization in the future. 展开更多
关键词 quantum communication networks encoding conversion polarization encoding time-bin phase encoding
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Joint Feature Encoding and Task Alignment Mechanism for Emotion-Cause Pair Extraction 认领 引用
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作者 Shi Li Didi Sun 《Computers, Materials & Continua》 SCIE EI 2025年第1期1069-1086,共18页
With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions... With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying reasons.This comprehension is crucial for making informed strategic decisions in various business and societal contexts.However,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause extraction.To address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment mechanisms.The model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause pairs.Second,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among tasks.The proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 score.Experimental results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its effectiveness.This research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-cause pair extraction.However,the study’s limitation lies in the data sources,potentially restricting the generalizability of the findings. 展开更多
关键词 Emotion-cause pair extraction interactive information enhancement joint feature encoding label consistency task alignment mechanisms
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Validity of the Gaussian phase distribution approximation for analysis of isotropic diffusion encoding applied to restricted diffusion in a cylinder 认领 引用
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作者 Daniel Topgaard 《Magnetic Resonance Letters》 EI CAS 2025年第4期20-27,共8页
The Gaussian phase distribution approximation enables analysis of restricted diffusion encoded by general gradient waveforms but fails to account for the diffraction-like features that may occur for simple pore geomet... The Gaussian phase distribution approximation enables analysis of restricted diffusion encoded by general gradient waveforms but fails to account for the diffraction-like features that may occur for simple pore geometries.We investigate the range of validity of the approximation by random walk simulations of restricted diffusion in a cylinder using isotropic diffusion encoding sequences as well as conventional single gradient pulse pairs and oscillating gradient waveforms.The results show that clear deviations from the approximation may be observed at relative signal attenuations below 0.1 for onedimensional sequences with few oscillation periods.Increasing the encoding dimensionality and/or number of oscillations while extending the total duration of the waveform diminishes the non-Gaussian effects while preserving the low apparent diffusivities characteristic of restriction. 展开更多
关键词 NMR Diffusion Porous media Pulsed gradient spin echo Tensor-valued encoding
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Improved Sensitivity Encoding Parallel Magnetic Resonance Imaging Reconstruction Algorithm Based on Efficient Sum of Outer Products Dictionary Learning 认领 引用
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作者 DUAN Jizhong SU Yan 《Journal of Shanghai Jiaotong university(Science)》 EI 2025年第3期561-571,共11页
Sensitivity encoding(SENSE)is a parallel magnetic resonance imaging(MRI)reconstruction model by utilizing the sensitivity information of receiver coils to achieve image reconstruction.The existing SENSE-based reconstr... Sensitivity encoding(SENSE)is a parallel magnetic resonance imaging(MRI)reconstruction model by utilizing the sensitivity information of receiver coils to achieve image reconstruction.The existing SENSE-based reconstruction algorithms usually used nonadaptive sparsifying transforms,resulting in a limited reconstruction accuracy.Therefore,we proposed a new model for accurate parallel MRI reconstruction by combining the L0 norm regularization term based on the efficient sum of outer products dictionary learning(SOUPDIL)with the SENSE model,called SOUPDIL-SENSE.The SOUPDIL-SENSE model is mainly solved by utilizing the variable splitting and alternating direction method of multipliers techniques.The experimental results on four human datasets show that the proposed algorithm effectively promotes the image sparsity,eliminates the noise and artifacts of the reconstructed images,and improves the reconstruction accuracy. 展开更多
关键词 parallel magnetic resonance imaging(MRI) sensitivity encoding(SENSE) efficient sum of outer products dictionary learning(SOUPDIL) alternating direction method of multipliers
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A Blockchain-Based Covert Communication Model Based on Dynamic Base-K Encoding 认领 引用
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作者 Wang Zhujun Zhang Lejun +7 位作者 Li Xueqing Tian Zhihong Su Shen Qiu Jing Chen Huiling Qiu Tie Sergey Gataullin Guo Ran 《China Communications》 SCIE EI CSCD 2025年第6期319-333,共15页
Blockchain,as a distributed ledger,inherently possesses tamper-resistant capabilities,creating a natural channel for covert communication.However,the immutable nature of data storage might introduce challenges to comm... Blockchain,as a distributed ledger,inherently possesses tamper-resistant capabilities,creating a natural channel for covert communication.However,the immutable nature of data storage might introduce challenges to communication security.This study introduces a blockchain-based covert communication model utilizing dynamic Base-K encoding.The proposed encoding scheme utilizes the input address sequence to determine K to encode the secret message and determines the order of transactions based on K,thus ensuring effective concealment of the message.The dynamic encoding parameters enhance flexibility and address issues related to identical transaction amounts for the same secret message.Experimental results demonstrate that the proposed method maintains smooth communication and low susceptibility to tampering,achieving commendable concealment and embedding rates. 展开更多
关键词 base-K encoding blockchain concealment covert communication
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Image encoding-based bearing fault diagnosis:Review and challenges for high-speed trains 认领 引用
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作者 Huimin Li Lingfeng Li +1 位作者 Bin Liu Ge Xin 《High-Speed Railway》 CSCD 2025年第3期251-259,共9页
High-Speed Trains (HSTs) have emerged as a mainstream mode of transportation in China, owing to their exceptional safety and efficiency. Ensuring the reliable operation of HSTs is of paramount economic and societal im... High-Speed Trains (HSTs) have emerged as a mainstream mode of transportation in China, owing to their exceptional safety and efficiency. Ensuring the reliable operation of HSTs is of paramount economic and societal importance. As critical rotating mechanical components of the transmission system, bearings make their fault diagnosis a topic of extensive attention. This paper provides a systematic review of image encoding-based bearing fault diagnosis methods tailored to the condition monitoring of HSTs. First, it categorizes the image encoding techniques applied in the field of bearing fault diagnosis. Then, a review of state-of-the-art studies has been presented, encompassing both monomodal image conversion and multimodal image fusion approaches. Finally, it highlights current challenges and proposes future research directions to advance intelligent fault diagnosis in HSTs, aiming to provide a valuable reference for researchers and engineers in the field of intelligent operation and maintenance. 展开更多
关键词 High-speed trains Image encoding Fault diagnosis Rotating machinery Condition monitoring
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Dual encoding feature filtering generalized attention UNET for retinal vessel segmentation 认领 引用
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作者 ISLAM Md Tauhidul WU Da-Wen +6 位作者 TANG Qing-Qing ZHAO Kai-Yang YIN Teng LI Yan-Fei SHANG Wen-Yi LIU Jing-Yu ZHANG Hai-Xian 《四川大学学报（自然科学版）》 CAS CSCD 北大核心 2025年第1期79-95,共17页
Retinal blood vessel segmentation is crucial for diagnosing ocular and cardiovascular diseases.Although the introduction of U-Net in 2015 by Olaf Ronneberger significantly advanced this field,yet issues like limited t... Retinal blood vessel segmentation is crucial for diagnosing ocular and cardiovascular diseases.Although the introduction of U-Net in 2015 by Olaf Ronneberger significantly advanced this field,yet issues like limited training data,imbalance data distribution,and inadequate feature extraction persist,hindering both the segmentation performance and optimal model generalization.Addressing these critical issues,the DEFFA-Unet is proposed featuring an additional encoder to process domain-invariant pre-processed inputs,thereby improving both richer feature encoding and enhanced model generalization.A feature filtering fusion module is developed to ensure the precise feature filtering and robust hybrid feature fusion.In response to the task-specific need for higher precision where false positives are very costly,traditional skip connections are replaced with the attention-guided feature reconstructing fusion module.Additionally,innovative data augmentation and balancing methods are proposed to counter data scarcity and distribution imbalance,further boosting the robustness and generalization of the model.With a comprehensive suite of evaluation metrics,extensive validations on four benchmark datasets(DRIVE,CHASEDB1,STARE,and HRF)and an SLO dataset(IOSTAR),demonstrate the proposed method’s superiority over both baseline and state-of-the-art models.Particularly the proposed method significantly outperforms the compared methods in cross-validation model generalization. 展开更多
关键词 Vessel segmentation Data balancing Data augmentation Dual encoder Attention Mechanism Model generalization
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