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DOA estimation of array signals based on convolutional sparse autoencoder under sparse prior 认领 引用
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作者 REN Jing TAN Xiuhui +4 位作者 BAI Yanping WANG Peng CHENG Rong ZHANG Feng XU Ting 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2026年第2期254-266,共13页
The application of deep learning to direction of arrival(DOA)estimation is of great significance in the field of array signal processing.The use of deep learning for DOA estimation of vector hydrophone array usually d... The application of deep learning to direction of arrival(DOA)estimation is of great significance in the field of array signal processing.The use of deep learning for DOA estimation of vector hydrophone array usually directly inputs the covariance matrix of the signal as the signal feature into the network,but this method has limitations such as high data requirements and high computational complexity.This paper proposes a DOA estimation method for vector hydrophone array based on a convolutional sparse autoencoder under sparse prior conditions.This method adds an L1 norm regularization term to the convolutional layer of a convolutional autoencoder to achieve sparsity constraints,and establishes a convolutional sparse autoencoder.At the same time,a residual compensation mechanism is introduced to avoid overfitting and loss of details during the training process.Subsequently,the columns of the signal covariance matrix of the vector hydrophone array are treated as under-sampled noisy linear measurements of the spatial spectrum,and are input into a convolutional sparse autoencoder for feature extraction and reconstruction.Finally,the obtained features are used as inputs for training a convolutional neural network to achieve multi-source DOA estimation.Furthermore,to address the shortcomings of classification methods in off-grid situations,we propose a DOA regression estimation method based on the convolutional sparse autoencoder.The simulation results show that under complex conditions such as low signal-to-noise ratio and a small number of snapshots,the classification method proposed in this paper outperforms various deep learning algorithms and traditional algorithms mentioned in the literature in terms of estimation performance.In addition,the proposed regression method can further improve the DOA estimation performance in off-grid scenarios. 展开更多
关键词 vector hydrophone array direction of arrival estimation sparse representation convolutional sparse autoencoder L1-norm regularization regression
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Structural Feature Selection in Common Spatial Patterns Using Adaptive Sparse Group Lasso 认领 引用
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作者 Yadi Wang Jiahao Zhang +2 位作者 Tengfei Zhou Bingbing Jiang Jiejiang Chen 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第2期367-384,共18页
With the advancement of brain–computer interfaces(BCI),motor imagery(MI)electroencephalogram(EEG)decoding can greatly benefit from spatial filtering features derived from common spatial patterns(CSP).However,CSP-base... With the advancement of brain–computer interfaces(BCI),motor imagery(MI)electroencephalogram(EEG)decoding can greatly benefit from spatial filtering features derived from common spatial patterns(CSP).However,CSP-based features often exhibit high redundancy and intersubject variability.These limitations make the feature selection methods based on sparse learning difficult to effectively balance the heterogeneous contributions of different temporal and spatial components.Moreover,these models tend to prioritise features with larger coefficients,potentially overlooking intrinsic feature importance and compromising the quality of the selected feature subset.To address these issues,we propose an Adaptive Sparse Group Lasso(ASGL)method for structured feature selection,designed to enhance discriminative CSP features whilst suppressing irrelevant components.The proposed method partitions EEG signals into consecutive segments using a sliding window,treating each as a separate feature group.Benefiting from this,the importance of features at both the group level and the within-group level can be effectively quantified through mutual information and copula mutual information,thereby assigning adaptive weights for selective penalisation within the model.This weight construction strategy preserves important features from relevant time intervals and frequency bands.The resulting optimization problem is solved efficiently via the alternating direction method of multipliers(ADMM).Evaluations on simulated and real-world datasets demonstrate that the proposed ASGL outperforms existing methods. 展开更多
关键词 adaptive sparse group lasso brain-computer interfaces common spatial patterns motor imagery EEG structural feature selection
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WaSA-Net:Wavelet-Guided Tokenization and Dynamic Sparse Attention for Histopathology Image Classification 认领 引用
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作者 Muhammad Zaheer Sajid Muhammad Fareed Hamid +1 位作者 Nauman Ali Khan Imran Qureshi 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第7期1259-1290,共32页
Digital pathology is rapidly transforming histopathological diagnosis,yet many existing deep learning models treat all spatial regions uniformly and do not exploit the multi-frequency structure of tissue,which limits ... Digital pathology is rapidly transforming histopathological diagnosis,yet many existing deep learning models treat all spatial regions uniformly and do not exploit the multi-frequency structure of tissue,which limits both diagnostic accuracy and computational efficiency.This paper proposes WaSA-Net,an end-to-end architecture that integrates three complementary modules for histopathological image analysis.First,the Wavelet-Guided Tokenization(WGT)module decomposes input images into frequency-aware representations using learnable wavelet-like filters,so that both global tissue structures and fine-grained cellular patterns are exposed to attention from the first layer.Second,the Dynamic Sparse Attention with Pathology Priors(DSA-PP)module adaptively selects diagnostically informative tokens through a lightweight gating mechanism and incorporates learnable pathology prior tokens that embed domain-specific inductive biases,reducing attention complexity while preserving critical contextual information.Third,the Cross-Frequency Feature Pyramid Fusion(CFFPF)module performs bidirectional cross-attention across frequency bands and applies adaptive per-sample frequency weighting to identify the most discriminative frequency components for each tissue type.The proposed architecture is evaluated on three widely used histopathology benchmarks:PatchCamelyon for metastasis detection,PathMNIST for multi-class colorectal tissue classification,and BreakHis for breast cancer diagnosis.WaSA-Net achieves strong performance with only 4.8 M parameters,reaching 95.91%accuracy(AUC 0.9981)on PathMNIST,93.47%accuracy(AUC 0.9812)on PatchCamelyon,and 96.72%accuracy(AUC 0.9923)on BreakHis.Despite its compact design,WaSA-Net matches or surpasses larger models while requiring no external pretraining data.These results indicate that frequency-aware representations and dynamic sparse attention can improve both efficiency and diagnostic performance in digital pathology. 展开更多
关键词 Digital pathology wavelet transform sparse attention frequency-guided tokenization telepathology computational pathology deep learning
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Enhanced sparse RCNN for transmission line bolt defect detection via text-to-image data augmentation and quality filtering 认领 引用
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作者 Chen Zhenyu Yan Huaguang +2 位作者 Du Jianguang Xue Meng Zhao Shuai 《High Technology Letters》 EI CAS 2026年第1期11-20,共10页
To address the issue of inconsistent image quality and data scarcity in bolt defect detection for transmission lines,this paper proposes an improved sparse region-based convolutional neural network(RCNN) based detecti... To address the issue of inconsistent image quality and data scarcity in bolt defect detection for transmission lines,this paper proposes an improved sparse region-based convolutional neural network(RCNN) based detection framework integrating image quality evaluation and text-to-image data augmentation.First,a HyperNetwork-based image quality assessment module is introduced to filter low-quality inspection images in terms of clarity and structural integrity,resulting in a high-quality training dataset.Second,a text-to-image diffusion model is utilized for sample augmentation.By designing text prompts that describe various bolt defect types under diverse lighting and viewing conditions,the model automatically generates realistic synthetic samples.The generated images are further filtered using a combination of quality and perceptual similarity metrics to ensure consistency with the real data distribution.Building upon the sparse RCNN baseline,a dynamic label assignment mechanism and a random decision path detection head are incorporated to enhance bounding box matching and prediction accuracy.Experimental results demonstrate that the proposed method significantly improves detection accuracy(mAP@0.5) over the original sparse RCNN while maintaining low computational cost,enabling more efficient and intelligent inspection of transmission line components. 展开更多
关键词 sparse region-based convolutional neural network HyperNetwork image quality assessment text-to-image generation data augmentation bolt defect detection transmission line inspection
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THRESHOLD ANALYSIS OF IMPULSIVE CONTROL IN A MOSQUITO POPULATION SUPPRESSION MODEL WITH SPARSE STATE FEEDBACK 认领 引用
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作者 Shouzong LIU Yang XU Mingzhan HUANG 《Acta Mathematica Scientia》 SCIE CSCD 2026年第2期897-919,共23页
In this study,a mosquito population suppression model that integrates stage structure is introduced,which serves as the foundation for exploring various strategies for the periodic impulsive release of sterile mosquit... In this study,a mosquito population suppression model that integrates stage structure is introduced,which serves as the foundation for exploring various strategies for the periodic impulsive release of sterile mosquitoes,including those that either incorporate or disregard population state feedback,as well as a composite control approach.We identify release thresholds under different strategies that ensure the complete eradication of the wild mosquito population.Numerical analyses are conducted to evaluate the performance of these release strategies.Our findings reveal that integrating state feedback mechanisms can effectively prevent the blindness of release behaviors.Key factors such as the release interval,frequency of population assessments,and control intensity significantly influence the reduction of the cumulative release quantity of sterile mosquitoes,the shortening of control duration,and the decrease in effective release events.The influence of these factors on control outcomes across different strategies and scenarios is also examined. 展开更多
关键词 mosquito population suppression model periodic impulsive release sparse state feedback extinction equilibrium
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RSG-Conformer:ReLU-Based Sparse and Grouped Conformer for Audio-Visual Speech Recognition 认领 引用
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作者 Yewei Xiao Xin Du Wei Zeng 《Computers, Materials & Continua》 SCIE EI 2026年第3期1325-1348,共24页
Audio-visual speech recognition(AVSR),which integrates audio and visual modalities to improve recognition performance and robustness in noisy or adverse acoustic conditions,has attracted significant research interest.... Audio-visual speech recognition(AVSR),which integrates audio and visual modalities to improve recognition performance and robustness in noisy or adverse acoustic conditions,has attracted significant research interest.However,Conformer-based architectures remain computational expensive due to the quadratic increase in the spatial and temporal complexity of their softmax-based attention mechanisms with sequence length.In addition,Conformerbased architectures may not provide sufficient flexibility for modeling local dependencies at different granularities.To mitigate these limitations,this study introduces a novel AVSR framework based on a ReLU-based Sparse and Grouped Conformer(RSG-Conformer)architecture.Specifically,we propose a Global-enhanced Sparse Attention(GSA)module incorporating an efficient context restoration block to recover lost contextual cues.Concurrently,a Grouped-scale Convolution(GSC)module replaces the standard Conformer convolution module,providing adaptive local modeling across varying temporal resolutions.Furthermore,we integrate a Refined Intermediate Contextual CTC(RIC-CTC)supervision strategy.This approach applies progressively increasing loss weights combined with convolution-based context aggregation,thereby further relaxing the constraint of conditional independence inherent in standard CTC frameworks.Evaluations on the LRS2 and LRS3 benchmark validate the efficacy of our approach,with word error rates(WERs)reduced to 1.8%and 1.5%,respectively.These results further demonstrate and validate its state-of-the-art performance in AVSR tasks. 展开更多
关键词 Audio-visual speech recognition conformer CTC sparse attention
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Centralized Circumcentered-Reflection Method for Solving the Convex Feasibility Problem in Sparse Signal Recovery 认领 引用
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作者 Chunmei LI Bangjun CHEN Xuefeng DUAN 《Journal of Mathematical Research with Applications》 CSCD 2026年第1期119-133,共15页
Convex feasibility problems are widely used in image reconstruction, sparse signal recovery, and other areas. This paper is devoted to considering a class of convex feasibility problem arising from sparse signal recov... Convex feasibility problems are widely used in image reconstruction, sparse signal recovery, and other areas. This paper is devoted to considering a class of convex feasibility problem arising from sparse signal recovery. We first derive the projection formulas for a vector onto the feasible sets. The centralized circumcentered-reflection method is designed to solve the convex feasibility problem. Some numerical experiments demonstrate the feasibility and effectiveness of the proposed algorithm, showing superior performance compared to conventional alternating projection methods. 展开更多
关键词 convex feasibility problem centralized circumcentered-re ection method sparse signal recovery compressed sensing
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SparseLinTab:Sparse linear self-attention for efficient feature interaction in tabular data 认领 引用
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作者 Nannan Xu Shuaifei Lin +4 位作者 Zhaoxia Li Haifeng Lv Ningning Wang Chao Liu Zhongwen Guo 《High-Confidence Computing》 EI CSCD 2026年第2期105-117,共13页
Tabular data supports intelligent decision-making in key areas such as finance and healthcare,but its heterogeneous feature interaction modeling and large-scale computing efficiency issues have long restricted the app... Tabular data supports intelligent decision-making in key areas such as finance and healthcare,but its heterogeneous feature interaction modeling and large-scale computing efficiency issues have long restricted the application of deep learning technology.This paper proposes SparseLinTab,an efficient tabular data modeling framework based on bidirectional sparse linear self-attention.By decoupling the interactions between rows(sample level)and columns(feature level),SparseLinTab reduces the quadratic computational complexity of traditional Transformer self-attention O(N2M+NM2)to linear O(NM),while retaining the ability to model global dependencies.SparseLinTab outperforms all traditional gradient boosting models and deep learning models in terms of average performance across 7 public datasets.Specifically,the core metrics are calculated separately for each dataset(accuracy is used for classification tasks,and MAE for regression tasks),and then the simple arithmetic mean of the metric scores from all datasets is computed to balance the impact of different scenario characteristics on the overall performance evaluation.In addition,ablation experiments and attention visualization show that the sparse mechanism significantly enhances the robustness and generalization of the model by filtering noise interactions and focusing on key feature combinations. 展开更多
关键词 Tabular data Sparse self-attention Bidirectional sparse self-attention mechanism Linear complexity
Periodical sparse-assisted decoupling method for local fault detection of spiral bevel gears 认领 引用
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作者 Keyuan LI Yanan WANG +2 位作者 Baijie QIAO Zhibin ZHAO Xuefeng CHEN 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第1期349-369,共21页
Early fault detection for spiral bevel gears is crucial to ensure normal operation and prevent accidents.The harmonic components,excited by the time-varying mesh stiffness,always appear in measured vibration signal.Ho... Early fault detection for spiral bevel gears is crucial to ensure normal operation and prevent accidents.The harmonic components,excited by the time-varying mesh stiffness,always appear in measured vibration signal.How to extract the periodical impulses that indicate gear localized fault buried in the intensive noise and interfered by harmonics is a challenging task.In this paper,a novel Periodical Sparse-Assisted Decoupling(PSAD)method is proposed as an optimization problem to extract fault feature from noisy vibration signal.The PSAD method decouples the impulsive fault feature and harmonic components based on the sparse representation method.The sparsity within and across groups property and the periodicity of the fault feature are incorporated into the regularizer as the prior information.The nonconvex penalty is employed to highlight the sparsity of fault features.Meanwhile,the weight factor based on2norm of each group is constructed to strengthen the amplitude of fault feature.An iterative algorithm with Majorization-Minimization(MM)is derived to solve the optimization problem.Simulation study and experimental analysis confirm the performance of the proposed PSAD method in extracting and enhancing defect impulses from noisy signal.The suggested method surpasses other comparative methods in extracting and enhancing fault features. 展开更多
关键词 Fault detection Nonconvex optimization Sparse decoupling Sparsity within and across groups Spiral bevel gear
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SparseMoE-MFN:A Sparse Attention and Mixture-of-Experts Framework for Multimodal Fake News Detection on Social Media 认领 引用
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作者 Yuechuan Zhang Mingshu Zhang +2 位作者 Bin Wei Hongyu Jin Yaxuan Wang 《Computers, Materials & Continua》 SCIE EI 2026年第5期1646-1669,共24页
Detecting fake news in multimodal and multilingual social media environments is challenging due to inherent noise,inter-modal imbalance,computational bottlenecks,and semantic ambiguity.To address these issues,we propo... Detecting fake news in multimodal and multilingual social media environments is challenging due to inherent noise,inter-modal imbalance,computational bottlenecks,and semantic ambiguity.To address these issues,we propose SparseMoE-MFN,a novel unified framework that integrates sparse attention with a sparse-activated Mixture of-Experts(MoE)architecture.This framework aims to enhance the efficiency,inferential depth,and interpretability of multimodal fake news detection.Sparse MoE-MFN leverages LLaVA-v1.6-Mistral-7B-HF for efficient visual encoding and Qwen/Qwen2-7B for text processing.The sparse attention module adaptively filters irrelevant tokens and focuses on key regions,reducing computational costs and noise.The sparse MoE module dynamically routes inputs to specialized experts(visual,language,cross-modal alignment)based on content heterogeneity.This expert specialization design boosts computational efficiency and semantic adaptability,enabling precise processing of complex content and improving performance on ambiguous categories.Evaluated on the large-scale,multilingualMR2 dataset,SparseMoEMFN achieves state-of-the-art performance.It obtains an accuracy of 86.7%and a macro-averaged F1 score of 0.859,outperforming strong baselines like MiniGPT-4 by 3.4%and 3.2%,respectively.Notably,it shows significant advantages in the“unverified”category.Furthermore,SparseMoE-MFN demonstrates superior computational efficiency,with an average inference latency of 89.1 ms and 95.4 GFLOPs,substantially lower than existing models.Ablation studies and visualization analyses confirm the effectiveness of both sparse attention and sparse MoE components in improving accuracy,generalization,and efficiency. 展开更多
关键词 Fake news detection multimodal sparse attention mixture-of-experts interpretability computational efficiency
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Dual adeno-associated virus system for selective and sparse labeling of astrocytes 认领 引用
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作者 Mei Li Zhuang Liu +5 位作者 Ruixi Chen Ziyue Zhao Qingqing Zhou Ning Zheng Jie Wang Hanbing Wang 《Neural Regeneration Research》 SCIE CAS CSCD 2026年第7期3083-3091,共9页
Astrocytes are the most abundant glial cells in the central nervous system.They perform a diverse array of functions,with a critical role in structural integrity,synapse formation,and neurotransmission.These cells exh... Astrocytes are the most abundant glial cells in the central nervous system.They perform a diverse array of functions,with a critical role in structural integrity,synapse formation,and neurotransmission.These cells exhibit substantial regional heterogeneity and display variable responses to different neurological diseases.Such diversity in astrocyte morphology and function is essential for understanding both normal brain function and the underlying mechanisms of neurological disorders.To investigate this heterogeneity,we developed a novel method for the selective and sparse labeling of astrocytes in various brain regions.This technique utilizes a dual adeno-associated virus system that allows for the expression of Cre recombinase and enhanced green fluorescent protein under the control of the glial fibrillary acidic protein(GfaABC1D)promoter.The system was tested in C57BL/6J mice and successfully labeled astrocytes across multiple brain regions.The method enabled the detailed visualization of individual astrocytes-including their intricate peripheral processes-through three-dimensional reconstructions from confocal microscopy images.Furthermore,the labeling efficiency of this dual adeno-associated virus technology was validated by examining astrocyte function in a spared nerve injury model and through chemogenetic modulation.This innovative approach holds great promise for future research because it enables a more comprehensive understanding of astrocyte variation not only in spared nerve injury but also in a broad spectrum of neurological diseases.The ability to selectively label and study astrocytes in different brain regions provides a powerful tool for exploring the complexities of these essential cells and their roles in physiological and pathological conditions. 展开更多
关键词 astrocytes chemogenetic modulation dual-adeno-associated virus system glial fibrillary acidic protein(GfaABC1D)promoter hierarchical clustering approach morphological parameter analysis PHP.eB Sholl analysis spared nerve injury sparse labeling
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An improved conditional denoising diffusion GAN for Mach number field reconstruction in a multi-tunnel combined inlet based on sparse parameter information 认领 引用
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作者 Ke MIN Fan LEI +2 位作者 Jiale ZHANG Chengxiang ZHU Yancheng YOU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第1期169-190,共22页
The internal flow fields within a three-dimensional inward-tunning combined inlet are extremely complex,especially during the engine mode transition,where the tunnel changes may impact the flow fields significantly.To... The internal flow fields within a three-dimensional inward-tunning combined inlet are extremely complex,especially during the engine mode transition,where the tunnel changes may impact the flow fields significantly.To develop an efficient flow field reconstruction model for this,we present an Improved Conditional Denoising Diffusion Generative Adversarial Network(ICDDGAN),which integrates Conditional Denoising Diffusion Probabilistic Models(CDDPMs)with Style GAN,and introduce a reconstruction discrimination mechanism and dynamic loss weight learning strategy.We establish the Mach number flow field dataset by numerical simulation at various backpressures for the mode transition process from turbine mode to ejector ramjet mode at Mach number 2.5.The proposed ICDDGAN model,given only sparse parameter information,can rapidly generate high-quality Mach number flow fields without a large number of samples for training.The results show that ICDDGAN is superior to CDDGAN in terms of training convergence and stability.Moreover,the interpolation and extrapolation test results during backpressure conditions show that ICDDGAN can accurately and quickly reconstruct Mach number fields at various tunnel slice shapes,with a Structural Similarity Index Measure(SSIM)of over 0.96 and a Mean-Square Error(MSE)of 0.035%to actual flow fields,reducing time costs by 7-8 orders of magnitude compared to Computational Fluid Dynamics(CFD)calculations.This can provide an efficient means for rapid computation of complex flow fields. 展开更多
关键词 Flow field reconstruction Improved Conditional Denoising Diffusion Generative Adversarial Network(ICDDGAN) Mode transition Sparse parameter information Three-dimensional inward-tunning combined inlet
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Conformal Prediction for Reliable Hyperparameter Selection in Sparse FIR Filter Design 认领 引用
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作者 Mohammed Hassan Alnemari Abdelrahman Osman Elfaki +1 位作者 Anas Bushnag Mohamed Hussien Mohamed Nerma 《Computers, Materials & Continua》 SCIE EI 2026年第9期1358-1382,共25页
Sparse finite impulse response(FIR)filters reduce computational cost on resource-constrained devices,but selecting the sparsification thresholdλis typically left to grid search or hand tuning.We propose a two-stage m... Sparse finite impulse response(FIR)filters reduce computational cost on resource-constrained devices,but selecting the sparsification thresholdλis typically left to grid search or hand tuning.We propose a two-stage method:a 67,331-parameter surrogate network predicts(Ap,As,S)(passband ripple in dB,stopband attenuation in dB,sparsity in%)from a filter specification and a candidate λ,and split conformal prediction(CP)calibrates±intervals around each prediction.We then select λ by minimizing a worst-case penalty computed on the conservative ends of the intervals(the upper bound on Ap and the lower bound on As).On 10,000 test specifications the method reaches 76.5%specification satisfaction,near-parity with grid search(78.4%)with a 1.9× speedup,while point-prediction surrogates reach only 39.4%.On feasible specifications(where any grid λ satisfies both constraints),the method reaches 97.6%.Stratified(Mondrian)conformal prediction lifts standard CP coverage from 67%-75%to 95.5%,and adaptive recalibration brings passband coverage to 91.3%.The procedure transfers without modification to iteratively reweighted least squares(IRLS)sparsification(76.6%)and to highpass(79.2%)and bandpass(52.4%)filters.The implementation runs on a central processing unit(CPU)and is suitable for edge deployment;code and data are public. 展开更多
关键词 Conformal prediction sparse FIR filter edge IoT surrogate modeling uncertainty quantification hyperparameter optimization
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Study on Flow and Heat Characteristics of Compressible Gas in a Supersonic Nozzle Based on PINNs with Sparse Data 认领 引用
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作者 Yida Shen Bin Dong +6 位作者 Quan Ma Chao Dang Congjian Li Guojian Ren Shaozhan Wang Xiaozhe Sun Yong Ding 《Frontiers in Heat and Mass Transfer》 EI CAS 2026年第2期134-156,共23页
This article explores the application of Physics-Informed Neural Networks(PINNs)in solving supersonic flow problems within a Laval nozzle,proposing innovative methods by integrating physical constraints and neural net... This article explores the application of Physics-Informed Neural Networks(PINNs)in solving supersonic flow problems within a Laval nozzle,proposing innovative methods by integrating physical constraints and neural network optimization techniques.The main innovations of this study include the construction of a novel neural network architecture with shortcut connections to enhance the prediction of overall flow trends and local fluctuations,thereby improving convergence speed,reducing computational costs,and increasing the accuracy of flow field reconstruction.Additionally,this study designs a PINNs framework that incorporates specific physical knowledge(SPK)to improve model stability,generalization,and accuracy,even with sparse training data.A dynamic loss weighting strategy is employed to optimize training convergence,and velocity components are reformulated as magnitude and angle to simplify boundary conditions and reduce the dimensionality of the solution space.The results demonstrate that the proposed methods achieve satisfactory accuracy and robustness in solving supersonic problems,highlighting their potential application value. 展开更多
关键词 Physics-informed neural networks sparse data supersonic flow specific physical knowledge shortcut connections
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Adaptive structure-guided impedance inversion using implicit sparse constraint 认领 引用
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作者 Hao Li Yi-An Cui +2 位作者 Pu Wang Jing-Ye Li Jian-Xin Liu 《Petroleum Science》 SCIE EI CAS CSCD 2026年第6期3228-3247,共20页
Structure-guided inversion has been increasingly adopted to enhance structural features and improve the interpretability of seismic impedance imaging.However,the limited bandwidth of seismic wavelets leads to low-reso... Structure-guided inversion has been increasingly adopted to enhance structural features and improve the interpretability of seismic impedance imaging.However,the limited bandwidth of seismic wavelets leads to low-resolution poststack records,which inevitably compromises the accuracy of structural estimates in discontinuous regions.Consequently,the success of this approach critically depends on the appropriate use of structural information as geometric constraints.To address this limitation,we propose an adaptive structure-guided approach that maintains large-scale structural continuity while preserving fine-scale variability.The average structural similarity(ASSIM)index and structural constraint sensitivity(SCS)index are introduced to adjust the structural penalty intensity across adjacent traces.The former metric determines the upper bound of penalty intensity for uniform structural constraints,while the latter one characterizes regions prone to distortion where a local reduction in lateral smoothing is required.In addition,the sparse constraint is separated from the objective function and implicitly applied via an iteratively refined reference model,which is generated by an adaptive edge-preserving filter(AEPF)followed by L2-TV denoising.This combined strategy promotes blocky impedance reconstruction and effectively preserves thin-layer details.Numerical and field examples demonstrate the superiority of the proposed approach.These improvements establish a more rational and geologically consistent framework for incorporating prior structural information,thereby enhancing the practicality and imaging performance of the structure-guided inversion in complex scenarios. 展开更多
关键词 Structure-guided inversion Adaptive structural constraint Implicit sparse constraint
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Sparse DOA Estimation Method for Unknown Terrestrial Signals of Opportunity 认领 引用
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作者 TANG Zewen LANG Rongling XU Hao 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第4期858-868,I0004,共11页
Signal of opportunity(SOP)has become an attractive source of navigation in the absence of global navigation satellite system(GNSS).However,in some typical GNSS-limited environments,such as deep urban canyons,the SOP p... Signal of opportunity(SOP)has become an attractive source of navigation in the absence of global navigation satellite system(GNSS).However,in some typical GNSS-limited environments,such as deep urban canyons,the SOP positioning is challenged by low signal-to-noise ratio(SNR),rapidly time-varying channels,and gain/phase uncertainties.To overcome these challenges,we propose a sparse direction of arrival(DOA)estimation method specifically designed for SOP positioning.Under the conditions of low SNR and limited number of snapshots,we conduct detailed theoretical derivations and simulation experiments to analyze the negative impact of gain and phase uncertainties on sparse DOA estimation.The analysis indicates that these uncertainties can lead to an increase in the number or height of spurious peaks in the DOA spatial spectrum,thereby significantly reducing the accuracy of DOA estimation.To address this issue,we propose a non-iterative sparse DOA estimation method that combines blind source separation(BSS)and singular value decomposition(SVD)techniques.The BSS algorithm accurately determines the number of SOPs using a single sensor,effectively eliminating the impact of gain and phase uncertainties between sensors.Once the number of SOPs is obtained,we can introduce the SVD algorithm to further enhance the DOA estimation performance under low SNR conditions.Simulation results validate the effectiveness of the proposed method in DOA estimation,showcasing its excellent robustness and self-calibration characteristics while maintaining reasonable computational costs.The introduction of this method provides a new solution to the navigation and positioning problem in GNSS-denied environments. 展开更多
关键词 signal of opportunity(SOP) direction of arrival estimation(DOA) gain and phase uncertainties sparse reconstruction self-calibration
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Low-subrate sparse reconstruction with threshold-adaptive denoising and basis learning for infrared aerial imagery 认领 引用
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作者 Maoji QIU Hao LIU 《Optoelectronics Letters》 EI 2026年第2期98-104,共7页
Due to the property of infrared aerial imagery, the local prior is sufficient especially for low-subrate block compressive sensing(BCS) reconstruction of infrared aerial images, while its complexity is much lower than... Due to the property of infrared aerial imagery, the local prior is sufficient especially for low-subrate block compressive sensing(BCS) reconstruction of infrared aerial images, while its complexity is much lower than nonlocal prior. The typical low-subrates can effectively improve the BCS transmission efficiency and reduce the burden of transmitter hardware. Therefore, this paper proposes a low-subrate sparse reconstruction algorithm with threshold-adaptive denoising and basis learning(TDBL), which adopts both split Bregman iteration(SBI) and adaptive threshold to implement the model-based BCS reconstruction for infrared aerial imagery. The experimental results show that as compared with the state-of-the-art algorithms, the proposed algorithm can obtain better recovery quality and less runtime on both HIT-UAV and M200-XT2 DroneVehicle datasets. the transmission efficiency and reduce the burden of transmitter hardware. The current NSS-guided reconstruction algorithms are trained and tested on natural image datasets by using relatively high subrates(more than 0.1). Due to significant difference in image contrast and pixel distribution between UAV infrared images and natural images, the performance of these algorithms on UAV infrared image datasets may be difficult to meet expectations. In recent years, the improvement of BCS recovery quality is not obvious with very high complexity, where the core step is to build a suitable dictionary, and then solve the associated sparsity of the dictionary. Previous BCS algorithms usually utilize the special iterative shrinkagehresholding(IST)[11] method to solve the l0 minimization problem. For BCS recovery quality and runtime of UAV infrared imagery, split Bregman iteration(SBI)[12] is a competitive mechanism, so we propose the low-subrate sparse reconstruction with threshold-adaptive denoising and basis learning(TDBL) algorithm under various low-subrate cases. By analyzing the UAV infrared imagery, it is concluded that infrared aerial images are usually characterized by large number of pixels on some gray levels with double or triple peaks on the histogram, and contain more low-frequency components on the Fourier magnitude spectrum. By jointly considering both recovery quality and runtime, we solve the above l0 minimization problem of BCS reconstruction by the SBI method, instead of IST. To obtain gains during different reconstruction phases, we design an adaptive threshold ρ which is related to model-based methods, such as K-singular value decomposition(SVD) sparse coding[13] and orthogonal matching pursuit(OMP) noise constraint[14]. According to the characteristics of UAV infrared images, an updating expression of ρ is designed by combining image variance and mean value. 展开更多
关键词 model based compressive sensing split bregman iteration transmitter hardware infrared aerial imagery basis learning infrared aerial images threshold adaptive denoising low subrate sparse reconstruction
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A Multi-Scale Group Sparse Representation Model with Weighted Log-Sum Penalty for Image Denoising 认领 引用
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作者 JIANG Zhenyi ZHANG Tao 《Wuhan University Journal of Natural Sciences》 CAS CSCD 2026年第3期205-216,共12页
In the field of image denoising,the non-local self-similarity(NSS)prior has been widely validated.By exploiting the similar structural details within groups of similar patches to effectively extract redundant informat... In the field of image denoising,the non-local self-similarity(NSS)prior has been widely validated.By exploiting the similar structural details within groups of similar patches to effectively extract redundant information,it significantly improves the accuracy and quality of image restoration.This method can well preserve textures and edges while removing noise.However,NSS-based methods have certain limitations.They usually process patch groups as a whole and neglect the differences among patches within a group.In addition,constructing similar patch groups using NSS is challenging for highly noisy images,suffering from problems such as scarce similar patches and low similarity.To address these issues,this paper proposes an image denoising model with multi-scale group sparse representation based on weighted log-sum penalty.Based on the original-scale image,a multi-scale image sequence is constructed via upsampling and downsampling operations,which not only generates a large number of similar image patches at each individual scale to expand the overall pool of patch candidates,but also excavates structurally correlated similar patches across different scales through a cross-scale patch matching strategy,thus effectively enhancing the inter-patch similarity.Especially for images with high noise intensity,original image patches are severely disturbed by noise,making it difficult to find a sufficient number of highly similar matching patches at a single scale.In contrast,multi-scale construction can weaken the concentrated impact of noise at a single scale,highlight the inherent structural features of the image at other scales,help select more representative similar patch groups,and alleviate the core problems of scarce similar patches and insufficient similarity in high-noise scenarios.Experimental results demonstrate that the proposed MSGSR-Log model outperforms the state-of-the-art methods. 展开更多
关键词 image denoising group sparse representation non-convex regularization multi-scale images weighted Log-sum penalty
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Off-grid DOA estimation based on coherent accumulation and weighted block sparse Bayesian 认领 引用 被引量：1
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作者 REN Ankang WU Qi +1 位作者 LIANG Pingye XU Yuanyuan 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第2期327-336,共10页
To deal with the problem that the block sparse Bayesian algorithm exists in grid estimation,an off-grid weighted block sparse Bayesian algorithm is proposed based on coherent accumulation.The algorithm first uses the ... To deal with the problem that the block sparse Bayesian algorithm exists in grid estimation,an off-grid weighted block sparse Bayesian algorithm is proposed based on coherent accumulation.The algorithm first uses the signal characteristics to coherently accumulate the polarization-sensitive array received data to enhance the signal-to-noise ratio(SNR);then the first-order Taylor expansion of the steering vector is performed,and an off-grid real-valued model is introduced by improving the block structure;then the weighting vectors are introduced to accelerate the iteration of the algorithm and reduce the number of iterations;and finally,the solution of the off-grid parameters is achieved by iterative optimization of the parameters.Compared with the traditional block sparse Bayesian learning(BSBL)algorithm,the method iterates faster and achieves efficient joint off-grid polarization-DOA estimation.Simulation results show the effectiveness of the algorithm. 展开更多
关键词 direction of arrival estimation weighted block sparse Bayesian coherent accumulation off-grid
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Sparse optimization and multi-performance enhancement of a high frequency to very high frequency wideband radio array using a genetic algorithm 认领 引用
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作者 Yifeng Qin Jiarui Di +8 位作者 Liang Dong Zhang Ning Yan Liu Lesheng He Abidin Zamri Zainal Huanhuan Xie Latef Abdul Tarik Wei He Othman Mohamadariff 《Astronomical Techniques and Instruments》 CSCD 2026年第3期235-249,共15页
The High Frequency(HF, 3–30 MHz) to Very High Frequency(VHF, 30–300 MHz) band is a critical observational window in radio astronomy, playing a key role in the study of early-universe reionization, space weather moni... The High Frequency(HF, 3–30 MHz) to Very High Frequency(VHF, 30–300 MHz) band is a critical observational window in radio astronomy, playing a key role in the study of early-universe reionization, space weather monitoring, and solar physics. We determine whether a genetic algorithm-optimized sparse configuration of a 64-element planar radio antenna array can minimize the peak sidelobe level and enhance performance within the 10–90 MHz frequency range, compared with a regular configuration. The sparse-optimized array achieves a 1.04 dB reduction in peak sidelobe level across the frequency band compared with the regular array. Sensitivity improves significantly at all frequency points, with increases of up to 56% at 10 MHz and 45% at 50 MHz. At 90 MHz, the sensitivity matches that of the regular array. At three representative frequencies(50 MHz, 60 MHz, and 70 MHz), grating lobe suppression tests at different scan angles show that the regular array shows prominent grating lobes at specific scan angles(θ =53° at 50 MHz, θ = 30° at 60 MHz, and θ = 15° at 70 MHz). By contrast, the sparse array shows no observable grating lobes, confirming its superior suppression capability. At wide bandwidths, a sparse array optimized with a genetic algorithm outperforms a regular array in peak sidelobe level, sensitivity, and scanning range, supporting its use as a better technical solution for radio astronomical observations. 展开更多
关键词 HF-VHF band Sparse array Genetic algorithm Peak side lobe level Sensitivity
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