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High-Quality Single-Pixel Imaging Based on Large-Kernel Convolution under Low-Sampling Conditions 认领 引用
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作者 Chenyu Yuan Yuanhao Su Chunfang Wang 《Chinese Physics Letters》 SCIE EI CAS CSCD 2025年第4期55-61,共7页
In recent years,deep learning has been introduced into the field of Single-pixel imaging(SPI),garnering significant attention.However,conventional networks still exhibit limitations in preserving image details.To addr... In recent years,deep learning has been introduced into the field of Single-pixel imaging(SPI),garnering significant attention.However,conventional networks still exhibit limitations in preserving image details.To address this issue,we integrate Large Kernel Convolution(LKconv)into the U-Net framework,proposing an enhanced network structure named U-LKconv network,which significantly enhances the capability to recover image details even under low sampling conditions. 展开更多
关键词 large kernel convolution lkconv recover image details U lkconv network high quality single pixel imaging U Net low sampling conditions enhanced network structure large kernel convolution
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A Kernel-Based Convolution Method to Calculate Sparse Aerial Image Intensity for Lithography Simulation 认领 引用 被引量：3
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作者 史峥 王国雄 +2 位作者 严晓浪 陈志锦 高根生 《Journal of Semiconductors》 CAS 北大核心 2003年第4期357-361,共5页
Optical proximity correction (OPC) systems require an accurate and fast way to predict how patterns will be transferred to the wafer.Based on Gabor's 'reduction to principal waves',a partially coherent ima... Optical proximity correction (OPC) systems require an accurate and fast way to predict how patterns will be transferred to the wafer.Based on Gabor's 'reduction to principal waves',a partially coherent imaging system can be represented as a superposition of coherent imaging systems,so an accurate and fast sparse aerial image intensity calculation algorithm for lithography simulation is presented based on convolution kernels,which also include simulating the lateral diffusion and some mask processing effects via Gaussian filter.The simplicity of this model leads to substantial computational and analytical benefits.Efficiency of this method is also shown through simulation results. 展开更多
关键词 lithography simulation optical proximity correction convolution kernels
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An efficient projection defocus algorithm based on multi-scale convolution kernel templates 认领 引用 被引量：1
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作者 Bo ZHU Li-jun XIE +1 位作者 Guang-hua SONG Yao ZHENG 《Journal of Zhejiang University-Science C(Computers and Electronics)》 2013年第12期930-940,共11页
The focal problems of projection include out-of-focus projection images from the projector caused by incomplete mechanical focus and screen-door effects produced by projection pixilation. To eliminate these defects an... The focal problems of projection include out-of-focus projection images from the projector caused by incomplete mechanical focus and screen-door effects produced by projection pixilation. To eliminate these defects and enhance the imaging quality and clarity of projectors, a novel adaptive projection defocus algorithm is proposed based on multi-scale convolution kernel templates. This algorithm applies the improved Sobel-Tenengrad focus evaluation function to calculate the sharpness degree of intensity equalization and then constructs multi-scale defocus convolution kernels to remap and render the defocus projection image. The resulting projection defocus corrected images can eliminate out-of-focus effects and improve the sharpness of uncorrected images. Experiments show that the algorithm works quickly and robustly and that it not only effectively eliminates visual artifacts and can run on a self-designed smart projection system in real time but also significantly improves the resolution and clarity of the observer's visual perception. 展开更多
关键词 Projection focal Sobel-Tenengrad evaluation function Projector defocus Multi-scale convolution kernels
Convolution Kernels Implementation of Cardinalized Probability Hypothesis Density Filter 认领 引用
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作者 Yue MA Jian-zhang ZHU +1 位作者 Qian-qing QIN Yi-jun HU 《Acta Mathematicae Applicatae Sinica》 SCIE CSCD 2013年第4期739-748,共10页
The probability hypothesis density （PHD） propagates the posterior intensity in place of the poste- rior probability density of the multi-target state. The cardinalized PHD （CPHD） recursion is a generalization of P... The probability hypothesis density （PHD） propagates the posterior intensity in place of the poste- rior probability density of the multi-target state. The cardinalized PHD （CPHD） recursion is a generalization of PHD recursion, which jointly propagates the posterior intensity function and posterior cardinality distribution. A number of sequential Monte Carlo （SMC） implementations of PHD and CPHD filters （also known as SMC- PHD and SMC-CPHD filters, respectively） for general non-linear non-Gaussian models have been proposed. However, these approaches encounter the limitations when the observation variable is analytically unknown or the observation noise is null or too small. In this paper, we propose a convolution kernel approach in the SMC-CPHD filter. The simuIation results show the performance of the proposed filter on several simulated case studies when compared to the SMC-CPHD filter. 展开更多
关键词 random finite set （RFS） probability hypothesis density （PHD） filter cardinalized probability hypothesis density （CPHD） filter convolution kernel
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A Generalization of Haussler's Convolution Kernel——Mapping Kernel and Its Application to Tree Kernels 认领 引用
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作者 Kilho Shin Tetsuji Kuboyama 《Journal of Computer Science & Technology》 SCIE EI 2010年第5期1040-1054,共15页
Haussler＇s convolution kernel provides an effective framework for engineering positive semidefinite kernels, and has a wide range of applications.On the other hand,the mapping kernel that we introduce in this paper i... Haussler＇s convolution kernel provides an effective framework for engineering positive semidefinite kernels, and has a wide range of applications.On the other hand,the mapping kernel that we introduce in this paper is its natural generalization,and will enlarge the range of application significantly.Our main theorem with respect to positive semidefiniteness of the mapping kernel（1） implies Haussler＇s theorem as a corollary,（2） exhibits an easy-to-check necessary and sufficient condition for mapping kernels to be positive semidefinite,and（3） formalizes the mapping kernel so that significant flexibility is provided in engineering new kernels.As an evidence of the effectiveness of our results,we present a framework to engineer tree kernels.The tree is a data structure widely used in many applications,and tree kernels provide an effective method to analyze tree-type data.Thus,not only is the framework important as an example but also as a practical research tool.The description of the framework accompanies a survey of the tree kernels in the literature,where we see that 18 out of the 19 surveyed tree kernels of different types are instances of the mapping kernel,and examples of novel interesting tree kernels. 展开更多
关键词 kernel convolution kernel tree edit distance
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SINGULAR INTEGRAL DIFFERENTIAL EQUATIONS WITH BOTH CAUCHY KERNEL AND CONVOLUTION KERNEL AND RIEMANN BOUNDARY VALUE PROBLEM 认领 引用 被引量：2
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作者 Pingrun Li 《Annals of Differential Equations》 2014年第4期407-415,共9页
In this paper, we set up and discuss a kind of singular integral differential equation with convolution kernel and Canchy kernel. By Fourier transform and some lemmas, we turn this class of equations into Riemann boun... In this paper, we set up and discuss a kind of singular integral differential equation with convolution kernel and Canchy kernel. By Fourier transform and some lemmas, we turn this class of equations into Riemann boundary value problems, and obtain the general solution and the condition of solvability in class {0}. 展开更多
关键词 Cauchy kernel convolution kernel Fourier transform singular integral-differential equations
LKAW: A Robust Watermarking Method Based on Large Kernel Convolution and Adaptive Weight Assignment 认领 引用
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作者 Xiaorui Zhang Rui Jiang +3 位作者 Wei Sun Aiguo Song Xindong Wei Ruohan Meng 《Computers, Materials & Continua》 SCIE EI 2023年第4期1-17,共17页
Robust watermarking requires finding invariant features under multiple attacks to ensure correct extraction.Deep learning has extremely powerful in extracting features,and watermarking algorithms based on deep learnin... Robust watermarking requires finding invariant features under multiple attacks to ensure correct extraction.Deep learning has extremely powerful in extracting features,and watermarking algorithms based on deep learning have attracted widespread attention.Most existing methods use 3×3 small kernel convolution to extract image features and embed the watermarking.However,the effective perception fields for small kernel convolution are extremely confined,so the pixels that each watermarking can affect are restricted,thus limiting the performance of the watermarking.To address these problems,we propose a watermarking network based on large kernel convolution and adaptive weight assignment for loss functions.It uses large-kernel depth-wise convolution to extract features for learning large-scale image information and subsequently projects the watermarking into a highdimensional space by 1×1 convolution to achieve adaptability in the channel dimension.Subsequently,the modification of the embedded watermarking on the cover image is extended to more pixels.Because the magnitude and convergence rates of each loss function are different,an adaptive loss weight assignment strategy is proposed to make theweights participate in the network training together and adjust theweight dynamically.Further,a high-frequency wavelet loss is proposed,by which the watermarking is restricted to only the low-frequency wavelet sub-bands,thereby enhancing the robustness of watermarking against image compression.The experimental results show that the peak signal-to-noise ratio(PSNR)of the encoded image reaches 40.12,the structural similarity(SSIM)reaches 0.9721,and the watermarking has good robustness against various types of noise. 展开更多
关键词 Robust watermarking large kernel convolution adaptive loss weights high-frequency wavelet loss deep learning
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A Low-Power 12-Bit SAR ADC for Analog Convolutional Kernel of Mixed-Signal CNN Accelerator 认领 引用
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作者 Jungyeon Lee Malik Summair Asghar HyungWon Kim 《Computers, Materials & Continua》 SCIE EI 2023年第5期4357-4375,共19页
As deep learning techniques such as Convolutional Neural Networks(CNNs)are widely adopted,the complexity of CNNs is rapidly increasing due to the growing demand for CNN accelerator system-on-chip(SoC).Although convent... As deep learning techniques such as Convolutional Neural Networks(CNNs)are widely adopted,the complexity of CNNs is rapidly increasing due to the growing demand for CNN accelerator system-on-chip(SoC).Although conventional CNN accelerators can reduce the computational time of learning and inference tasks,they tend to occupy large chip areas due to many multiply-and-accumulate(MAC)operators when implemented in complex digital circuits,incurring excessive power consumption.To overcome these drawbacks,this work implements an analog convolutional filter consisting of an analog multiply-and-accumulate arithmetic circuit along with an analog-to-digital converter(ADC).This paper introduces the architecture of an analog convolutional kernel comprised of low-power ultra-small circuits for neural network accelerator chips.ADC is an essential component of the analog convolutional kernel used to convert the analog convolutional result to digital values to be stored in memory.This work presents the implementation of a highly low-power and area-efficient 12-bit Successive Approximation Register(SAR)ADC.Unlink most other SAR-ADCs with differential structure;the proposed ADC employs a single-ended capacitor array to support the preceding single-ended max-pooling circuit along with minimal power consumption.The SARADCimplementation also introduces a unique circuit that reduces kick-back noise to increase performance.It was implemented in a test chip using a 55 nm CMOS process.It demonstrates that the proposed ADC reduces Kick-back noise by 40%and consequently improves the ADC’s resolution by about 10%while providing a near rail-to-rail dynamic rangewith significantly lower power consumption than conventional ADCs.The ADC test chip shows a chip size of 4600μm2with a power consumption of 6.6μW while providing an signal-to-noise-and-distortion ratio(SNDR)of 68.45 dB,corresponding to an effective number of bits(ENOB)of 11.07 bits. 展开更多
关键词 Convolution neural networks split-capacitor-based digital-toanalog converter(DAC) SAR analog-to-digital converter artificial intelligence system-on-chip analog convolutional kernel
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An improved YOLOv8 apple leaf disease detection algorithm 认领 引用
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作者 Xinyu PEI Wei YUAN +1 位作者 Yuexiu ZHANG Lianjun SONG 《Optoelectronics Letters》 EI 2026年第5期314-320,共7页
This paper suggests an improved you only look once version 8n(YOLOv8n)algorithm for apple leaf disease detection,abbreviated as ALWB-YOLOv8n.The model is comprised of four essential components.Initially,arbitrary kern... This paper suggests an improved you only look once version 8n(YOLOv8n)algorithm for apple leaf disease detection,abbreviated as ALWB-YOLOv8n.The model is comprised of four essential components.Initially,arbitrary kernel convolution(AKConv)replaces the convolution module,which significantly decreases both the model’s parameter count and its overall size.Secondly,the large selective kernel network(LSKNet)attention mechanism is added in the Backbone,which can dynamically adjust the spatial sensory domain,and experiments have proved that this method is extremely advantageous for small target detection.Third,a weighted bi-directional feature pyramid network is introduced,which enables the model to achieve multi-scale feature fusion and is more concise and faster.Finally,wise intersection over union(WIoU)is used to replace complete intersection over union(CIoU)in YOLOv8,and the idea of focal loss is introduced,which effectively solves the detection problems in cases such as apple leaves occluding each other and blurred boundaries of diseased leaves.The improved algorithm exhibits superior performance compared to other common object detection algorithms.Compared with YOLOv8n,the improved algorithm achieves 2.3%improvement in precision,3.8%improvement in recall,and 2.5%and 2.7%improvement in mAP0.5 and mAP0.5:0.95,respectively.Compared with YOLOv8n,the improved model reduces the number of parameters and size of the model and realizes real-time monitoring with a frames per second(FPS)of 50.5. 展开更多
关键词 large selective kernel network arbitrary kernel convolution large selective kernel network lsknet attention kernel convolution akconv replaces weighted bi directional feature pyramid network adjust spatial sensory domainand improved YOLOv n algorithm apple leaf disease detection
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Fast Image Segmentation Algorithm Based on Salient Features Model and Spatial-frequency Domain Adaptive Kernel 认领 引用 被引量：4
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作者 WU Fupei LIANG Jiaye LI Shengping 《Instrumentation》 2022年第2期33-46,共14页
A fast image segmentation algorithm based on salient features model and spatial-frequency domain adaptive kernel is proposed to solve the accurate discriminate objects problem of online visual detection in such scenes... A fast image segmentation algorithm based on salient features model and spatial-frequency domain adaptive kernel is proposed to solve the accurate discriminate objects problem of online visual detection in such scenes of variable sample morphological characteristics,low contrast and complex background texture.Firstly,by analyzing the spectral component distribution and spatial contour feature of the image,a salient feature model is established in spatial-frequency domain.Then,the salient object detection method based on Gaussian band-pass filter and the design criterion of adaptive convolution kernel are proposed to extract the salient contour feature of the target in spatial and frequency domain.Finally,the selection and growth rules of seed points are improved by integrating the gray level and contour features of the target,and the target is segmented by seeded region growing.Experiments have been performed on Berkeley Segmentation Data Set,as well as sample images of online detection,to verify the effectiveness of the algorithm.The experimental results show that the Jaccard Similarity Coefficient of the segmentation is more than 90%,which indicates that the proposed algorithm can availably extract the target feature information,suppress the background texture and resist noise interference.Besides,the Hausdorff Distance of the segmentation is less than 10,which infers that the proposed algorithm obtains a high evaluation on the target contour preservation.The experimental results also show that the proposed algorithm significantly improves the operation efficiency while obtaining comparable segmentation performance over other algorithms. 展开更多
关键词 Image Segmentation Spatial-frequency Domain Adaptive Convolution Kernel Online Visual Detection
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A multi-scale convolutional auto-encoder and its application in fault diagnosis of rolling bearings 认领 引用 被引量：12
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作者 Ding Yunhao Jia Minping 《Journal of Southeast University(English Edition)》 EI CAS 2019年第4期417-423,共7页
Aiming at the difficulty of fault identification caused by manual extraction of fault features of rotating machinery,a one-dimensional multi-scale convolutional auto-encoder fault diagnosis model is proposed,based on ... Aiming at the difficulty of fault identification caused by manual extraction of fault features of rotating machinery,a one-dimensional multi-scale convolutional auto-encoder fault diagnosis model is proposed,based on the standard convolutional auto-encoder.In this model,the parallel convolutional and deconvolutional kernels of different scales are used to extract the features from the input signal and reconstruct the input signal;then the feature map extracted by multi-scale convolutional kernels is used as the input of the classifier;and finally the parameters of the whole model are fine-tuned using labeled data.Experiments on one set of simulation fault data and two sets of rolling bearing fault data are conducted to validate the proposed method.The results show that the model can achieve 99.75%,99.3%and 100%diagnostic accuracy,respectively.In addition,the diagnostic accuracy and reconstruction error of the one-dimensional multi-scale convolutional auto-encoder are compared with traditional machine learning,convolutional neural networks and a traditional convolutional auto-encoder.The final results show that the proposed model has a better recognition effect for rolling bearing fault data. 展开更多
关键词 fault diagnosis deep learning convolutional auto-encoder multi-scale convolutional kernel feature extraction
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A tool wear monitoring method based on improved DenseNet and GRU 认领 引用
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作者 WANG Yue MA Yajie +1 位作者 ZHOU Jiangnan WU Yanxia 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2025年第6期1562-1578,共17页
The precision and quality of machining in computer numerical control(CNC)machines are significantly impacted by the state of the tool.Therefore,it is essential and crucial to monitor the tool’s condition in real time... The precision and quality of machining in computer numerical control(CNC)machines are significantly impacted by the state of the tool.Therefore,it is essential and crucial to monitor the tool’s condition in real time during operation.To improve the monitoring accuracy of tool wear values,a tool wear monitoring approach is developed in this work,which is based on an improved integrated model of densely connected convolutional network(DenseNet)and gated recurrent unit(GRU),which incorporates data preprocessing via wavelet packet transform(WPT).Firstly,wavelet packet decomposition(WPD)is used to extract time-frequency domain features from the original timeseries monitoring signals of the tool.Secondly,the multidimensional deep features are extracted from DenseNet containing asymmetric convolution kernels,and feature fusion is performed.A dilation scheme is employed to acquire more historical data by utilizing dilated convolutional kernels with different dilation rates.Finally,the GRU is utilized to extract temporal features from the extracted deep-level signal features,and the feature mapping of these temporal features is then carried out by a fully connected neural network,which ultimately achieves the monitoring of tool wear values.Comprehensive experiments conducted on reference datasets show that the proposed model performs better in terms of accuracy and generalization than other cutting-edge tool wear monitoring algorithms. 展开更多
关键词 tool wear monitoring densely connected convolutional network(DenseNet) asymmetric convolutional kernel dilated convolutional kernel gated recurrent unit(GRU)
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Gabor-CNN for object detection based on small samples 认领 引用 被引量：7
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作者 Xiao-dong Hu Xin-qing Wang +5 位作者 Fan-jie Meng Xia Hua Yu-ji Yan Yu-yang Li Jing Huang Xun-lin Jiang 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2020年第6期1116-1129,共14页
Object detection models based on convolutional neural networks(CNN)have achieved state-of-the-art performance by heavily rely on large-scale training samples.They are insufficient when used in specific applications,su... Object detection models based on convolutional neural networks(CNN)have achieved state-of-the-art performance by heavily rely on large-scale training samples.They are insufficient when used in specific applications,such as the detection of military objects,as in these instances,a large number of samples is hard to obtain.In order to solve this problem,this paper proposes the use of Gabor-CNN for object detection based on a small number of samples.First of all,a feature extraction convolution kernel library composed of multi-shape Gabor and color Gabor is constructed,and the optimal Gabor convolution kernel group is obtained by means of training and screening,which is convolved with the input image to obtain feature information of objects with strong auxiliary function.Then,the k-means clustering algorithm is adopted to construct several different sizes of anchor boxes,which improves the quality of the regional proposals.We call this regional proposal process the Gabor-assisted Region Proposal Network(Gabor-assisted RPN).Finally,the Deeply-Utilized Feature Pyramid Network(DU-FPN)method is proposed to strengthen the feature expression of objects in the image.A bottom-up and a topdown feature pyramid is constructed in ResNet-50 and feature information of objects is deeply utilized through the transverse connection and integration of features at various scales.Experimental results show that the method proposed in this paper achieves better results than the state-of-art contrast models on data sets with small samples in terms of accuracy and recall rate,and thus has a strong application prospect. 展开更多
关键词 Deep learning Convolutional neural network Small samples Gabor convolution kernel Feature pyramid
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GA-1DLCNN method and its application in bearing fault diagnosis 认领 引用 被引量：10
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作者 Yang Zhenbo Jia Minping 《Journal of Southeast University(English Edition)》 EI CAS 2019年第1期36-42,共7页
Due to the fact that the vibration signal of the rotating machine is one-dimensional and the large-scale convolution kernel can obtain a better perception field, on the basis of the classical convolution neural networ... Due to the fact that the vibration signal of the rotating machine is one-dimensional and the large-scale convolution kernel can obtain a better perception field, on the basis of the classical convolution neural network model(LetNet-5), one-dimensional large-kernel convolution neural network(1 DLCNN) is designed. Since the hyper-parameters of 1 DLCNN have a greater impact on network performance, the genetic algorithm(GA) is used to optimize the hyper-parameters, and the method of optimizing the parameters of 1 DLCNN by the genetic algorithm is named GA-1 DLCNN. The experimental results show that the optimal network model based on the GA-1 DLCNN method can achieve 99.9% fault diagnosis accuracy, which is much higher than those of other traditional fault diagnosis methods. In addition, the 1 DLCNN is compared with one-dimencional small-kernel convolution neural network(1 DSCNN) and the classical two-dimensional convolution neural network model. The input sample lengths are set to be 128, 256, 512, 1 024, and 2 048, respectively, and the final diagnostic accuracy results and the visual scatter plot show that the effect of 1 DLCNN is optimal. 展开更多
关键词 one-dimensional convolution neural network large-size convolution kernel hyper-parameter optimization genetic algorithm
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A Multi-Category Brain Tumor Classification Method Bases on Improved ResNet50 认领 引用 被引量：2
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作者 Linguo Li Shujing Li Jian Su 《Computers, Materials & Continua》 SCIE EI 2021年第11期2355-2366,共12页
Brain tumor is one of the most common tumors with high mortality.Early detection is of great significance for the treatment and rehabilitation of patients.The single channel convolution layer and pool layer of traditi... Brain tumor is one of the most common tumors with high mortality.Early detection is of great significance for the treatment and rehabilitation of patients.The single channel convolution layer and pool layer of traditional convolutional neural network(CNN)structure can only accept limited local context information.And most of the current methods only focus on the classification of benign and malignant brain tumors,multi classification of brain tumors is not common.In response to these shortcomings,considering that convolution kernels of different sizes can extract more comprehensive features,we put forward the multi-size convolutional kernel module.And considering that the combination of average-pooling with max-pooling can realize the complementary of the high-dimensional information extracted by the two structures,we proposed the dual-channel pooling layer.Combining the two structures with ResNet50,we proposed an improved ResNet50 CNN for the applications in multi-category brain tumor classification.We used data enhancement before training to avoid model over fitting and used five-fold cross-validation in experiments.Finally,the experimental results show that the network proposed in this paper can effectively classify healthy brain,meningioma,diffuse astrocytoma,anaplastic oligodendroglioma and glioblastoma. 展开更多
关键词 Brain tumor convolutional neural network multi-size convolutional kernel module dual-channel pooling layer ResNet50
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A Lightweight Network with Dual Encoder and Cross Feature Fusion for Cement Pavement Crack Detection 认领 引用 被引量：3
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作者 Zhong Qu Guoqing Mu Bin Yuan 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第7期255-273,共19页
Automatic crack detection of cement pavement chiefly benefits from the rapid development of deep learning,with convolutional neural networks(CNN)playing an important role in this field.However,as the performance of cr... Automatic crack detection of cement pavement chiefly benefits from the rapid development of deep learning,with convolutional neural networks(CNN)playing an important role in this field.However,as the performance of crack detection in cement pavement improves,the depth and width of the network structure are significantly increased,which necessitates more computing power and storage space.This limitation hampers the practical implementation of crack detection models on various platforms,particularly portable devices like small mobile devices.To solve these problems,we propose a dual-encoder-based network architecture that focuses on extracting more comprehensive fracture feature information and combines cross-fusion modules and coordinated attention mechanisms formore efficient feature fusion.Firstly,we use small channel convolution to construct shallow feature extractionmodule(SFEM)to extract low-level feature information of cracks in cement pavement images,in order to obtainmore information about cracks in the shallowfeatures of images.In addition,we construct large kernel atrous convolution(LKAC)to enhance crack information,which incorporates coordination attention mechanism for non-crack information filtering,and large kernel atrous convolution with different cores,using different receptive fields to extract more detailed edge and context information.Finally,the three-stage feature map outputs from the shallow feature extraction module is cross-fused with the two-stage feature map outputs from the large kernel atrous convolution module,and the shallow feature and detailed edge feature are fully fused to obtain the final crack prediction map.We evaluate our method on three public crack datasets:DeepCrack,CFD,and Crack500.Experimental results on theDeepCrack dataset demonstrate the effectiveness of our proposed method compared to state-of-the-art crack detection methods,which achieves Precision(P)87.2%,Recall(R)87.7%,and F-score(F1)87.4%.Thanks to our lightweight crack detectionmodel,the parameter count of the model in real-world detection scenarios has been significantly reduced to less than 2M.This advancement also facilitates technical support for portable scene detection. 展开更多
关键词 Shallow feature extraction module large kernel atrous convolution dual encoder lightweight network crack detection
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Positive definiteness of real quadratic forms resulting from the variable-step L1-type approximations of convolution operators 认领 引用 被引量：1
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作者 Hong-Lin Liao Tao Tang Tao Zhou 《Science China Mathematics》 SCIE CSCD 2024年第2期237-252,共16页
The positive definiteness of real quadratic forms with convolution structures plays an important rolein stability analysis for time-stepping schemes for nonlocal operators. In this work, we present a novel analysistoo... The positive definiteness of real quadratic forms with convolution structures plays an important rolein stability analysis for time-stepping schemes for nonlocal operators. In this work, we present a novel analysistool to handle discrete convolution kernels resulting from variable-step approximations for convolution operators.More precisely, for a class of discrete convolution kernels relevant to variable-step L1-type time discretizations, weshow that the associated quadratic form is positive definite under some easy-to-check algebraic conditions. Ourproof is based on an elementary constructing strategy by using the properties of discrete orthogonal convolutionkernels and discrete complementary convolution kernels. To our knowledge, this is the first general result onsimple algebraic conditions for the positive definiteness of variable-step discrete convolution kernels. Using theunified theory, we obtain the stability for some simple nonuniform time-stepping schemes straightforwardly. 展开更多
关键词 discrete convolution kernels positive definiteness variable time-stepping orthogonal convolution kernels complementary convolution kernels
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Super-Resolution Using Enhanced U-Net for Brain MRI Images 认领 引用
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作者 Dalei Jiang Zifei Han +2 位作者 Xiaohan Zhu Yang Zhou Hang Yang 《Journal of Computer and Communications》 2022年第11期154-170,共17页
Super-resolution is an important technique in image processing. It overcomes some hardware limitations failing to get high-resolution image. After machine learning gets involved, the super-resolution technique gets mo... Super-resolution is an important technique in image processing. It overcomes some hardware limitations failing to get high-resolution image. After machine learning gets involved, the super-resolution technique gets more efficient in improving the image quality. In this work, we applied super-resolution to the brain MRI images by proposing an enhanced U-Net. Firstly, we used U-Net to realize super-resolution on brain Magnetic Resonance Images (MRI). Secondly, we expanded the functionality of U-Net to the MRI with different contrasts by edge-to-edge training. Finally, we adopted transfer learning and employed convolutional kernel loss function to improve the performance of the U-Net. Experimental results have shown the superiority of the proposed method, e.g., the resolution on rate was boosted from 81.49% by U-Net to 94.22% by our edge-to-edge training. 展开更多
关键词 Image Super-Resolution Machine Learning Transfer Learning Convolutional Kernel
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Model for the online frontal image selection of silkworm pupae using machine vision 认领 引用
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作者 Feng Guo Jing Li +2 位作者 Wei Qin Chunjiang Zhao Guanglin Li 《International Journal of Agricultural and Biological Engineering》 SCIE 2026年第1期163-169,共7页
The sorting of male and female silkworm pupae is an essential process of silkworm breeding,with its accuracy directly affecting the quality of hybrid silkworm eggs and silk.Gonadal characteristics serve as a reliable ... The sorting of male and female silkworm pupae is an essential process of silkworm breeding,with its accuracy directly affecting the quality of hybrid silkworm eggs and silk.Gonadal characteristics serve as a reliable basis for sex identification in silkworm pupae;however,the gonads only exist on the positive side of the tail.Due to the unique geometry of silkworm pupae,online sex recognition based on machine vision requires flipping and taking many photos of the same silkworm pupae.Thus,accurately selecting the frontal image from multiple images of the same silkworm pupae in different poses is a prerequisite for subsequent sex identification.To address this challenge,we proposed SPNet-GS(Silkworm Pupae Network for Gonad Selection),a lightweight model for online selection of frontal silkworm pupae images.The model first employed a large kernel convolution to enhance the receptive field and capture the relevant information between adjacent pixels.Then the correlation between long-distance pixels under multi-scale information can be obtained by dilated convolutions.Finally,the correlation information between near and far pixels was fused to enhance feature extraction.Experimental results demonstrated that our method outperforms other models with an average accuracy of 98.41%and an average F1 score of 99.02%.The average inference time of each image was 0.03 s,which can fully meet the requirements of online selection of male and female silkworm pupae.Moreover,the gender identification accuracy rates using the selected frontal image and gonad region image reached 84.68%and 94.58%,respectively.These results were 10%and 19.90%higher than using multi-pose images for sex identification,demonstrating the effectiveness of the frontal image selection strategy.The findings of this investigation may provide a valuable reference for the machine vision-based intelligent online sorting of silkworm pupae by gender. 展开更多
关键词 frontal image selection gonad image large kernel convolution sex identification of silkworm pupae
E-SegNet:E-Shaped Structure Networks for Accurate 2D and 3D Medical Image Segmentation 认领 引用
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作者 Wei Wu Xin Yang +3 位作者 Chenggui Yao Ou Liu Qi Zhao Jianwei Shuai 《Research》 SCIE EI CSCD 2026年第3期858-873,共16页
U-structure has become a foundational approach in medical image segmentation,consistently demonstrating strong performance across various segmentation tasks.Most current models are based on this framework,customizing ... U-structure has become a foundational approach in medical image segmentation,consistently demonstrating strong performance across various segmentation tasks.Most current models are based on this framework,customizing encoder-decoder components to achieve higher accuracy across various segmentation challenges.However,this often comes at the cost of increased parameter counts,which inevitably limit their practicality in real-world applications.In this study,we provide an E-shaped segmentation framework that discards the traditional step-by-step resolution recovery decoding process,instead directly aggregating multi-scale features extracted by the encoder at each stage for deep crosslevel integration.Additionally,we propose an innovative multi-scale large-kernel convolution(MLKConv)module,designed to enhance high-level feature representation by effectively capturing both local and global contextual information.Compared to U-structure,the proposed E-structured approach substantially reduces parameters while delivering superior performance,especially in complex segmentation tasks.Based on this structure,we develop 2 segmentation networks specifically for 2-dimensional(2D)and 3D medical images.2D E-SegNet is evaluated on four 2D segmentation benchmark datasets(Synapse multiorgan,ACDC,Kvasir-Seg,and BUSI),while 3D E-SegNet is assessed on four 3D segmentation benchmark datasets(Synapse,ACDC,NIH Pancreas,and Lung).Experimental results demonstrate that our approach outperforms the current leading U-shaped models across multiple datasets,achieving new state-of-the-art(SOTA)performance with fewer parameters.In summary,our research introduces a novel approach to medical image segmentation,offering potential improvements and contributing to ongoing advancements in the field.Our code is publicly available on http://gffzz188fe103f8f1460asbcx5ubwqp60f6uvb.ffgz.tsg.suse.edu.cn/zhaoqi106/E-SegNet. 展开更多
关键词 medical image segmentationconsistently multi scale feature aggregation D segmentation e shaped structure medical image segmentation multi scale large kernel convolution U structure parameter reduction
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