The influences of biological,chemical,and flow processes on soil structure through microbially induced carbonate precipitation(MICP)are not yet fully understood.In this study,we use a multi-level thresholding segmenta...The influences of biological,chemical,and flow processes on soil structure through microbially induced carbonate precipitation(MICP)are not yet fully understood.In this study,we use a multi-level thresholding segmentation algorithm,genetic algorithm(GA)enhanced Kapur entropy(KE)(GAE-KE),to accomplish quantitative characterization of sandy soil structure altered by MICP cementation.A sandy soil sample was treated using MICP method and scanned by the synchrotron radiation(SR)micro-CT with a resolution of 6.5 mm.After validation,tri-level thresholding segmentation using GAE-KE successfully separated the precipitated calcium carbonate crystals from sand particles and pores.The spatial distributions of porosity,pore structure parameters,and flow characteristics were calculated for quantitative characterization.The results offer pore-scale insights into the MICP treatment effect,and the quantitative understanding confirms the feasibility of the GAE-KE multi-level thresholding segmentation algorithm.展开更多
In this paper,we design an efficient,multi-stage image segmentation framework that incorporates a weighted difference of anisotropic and isotropic total variation(AITV).The segmentation framework generally consists of...In this paper,we design an efficient,multi-stage image segmentation framework that incorporates a weighted difference of anisotropic and isotropic total variation(AITV).The segmentation framework generally consists of two stages:smoothing and thresholding,thus referred to as smoothing-and-thresholding(SaT).In the first stage,a smoothed image is obtained by an AITV-regularized Mumford-Shah(MS)model,which can be solved efficiently by the alternating direction method of multipliers(ADMMs)with a closed-form solution of a proximal operator of the l_(1)-αl_(2) regularizer.The convergence of the ADMM algorithm is analyzed.In the second stage,we threshold the smoothed image by K-means clustering to obtain the final segmentation result.Numerical experiments demonstrate that the proposed segmentation framework is versatile for both grayscale and color images,effcient in producing high-quality segmentation results within a few seconds,and robust to input images that are corrupted with noise,blur,or both.We compare the AITV method with its original convex TV and nonconvex TVP(O<p<1)counterparts,showcasing the qualitative and quantitative advantages of our proposed method.展开更多
Memristors have a synapse-like two-terminal structure and electrical properties,which are widely used in the construc-tion of artificial synapses.However,compared to inorganic materials,organic materials are rarely us...Memristors have a synapse-like two-terminal structure and electrical properties,which are widely used in the construc-tion of artificial synapses.However,compared to inorganic materials,organic materials are rarely used for artificial spiking synapses due to their relatively poor memrisitve performance.Here,for the first time,we present an organic memristor based on an electropolymerized dopamine-based memristive layer.This polydopamine-based memristor demonstrates the improve-ments in key performance,including a low threshold voltage of 0.3 V,a thin thickness of 16 nm,and a high parasitic capaci-tance of about 1μF·mm^(-2).By leveraging these properties in combination with its stable threshold switching behavior,we con-struct a capacitor-free and low-power artificial spiking neuron capable of outputting the oscillation voltage,whose spiking fre-quency increases with the increase of current stimulation analogous to a biological neuron.The experimental results indicate that our artificial spiking neuron holds potential for applications in neuromorphic computing and systems.展开更多
Existing semi-supervisedmedical image segmentation algorithms use copy-paste data augmentation to correct the labeled-unlabeled data distribution mismatch.However,current copy-paste methods have three limitations:(1)t...Existing semi-supervisedmedical image segmentation algorithms use copy-paste data augmentation to correct the labeled-unlabeled data distribution mismatch.However,current copy-paste methods have three limitations:(1)training the model solely with copy-paste mixed pictures from labeled and unlabeled input loses a lot of labeled information;(2)low-quality pseudo-labels can cause confirmation bias in pseudo-supervised learning on unlabeled data;(3)the segmentation performance in low-contrast and local regions is less than optimal.We design a Stochastic Augmentation-Based Dual-Teaching Auxiliary Training Strategy(SADT),which enhances feature diversity and learns high-quality features to overcome these problems.To be more precise,SADT trains the Student Network by using pseudo-label-based training from Teacher Network 1 and supervised learning with labeled data,which prevents the loss of rare labeled data.We introduce a bi-directional copy-pastemask with progressive high-entropy filtering to reduce data distribution disparities and mitigate confirmation bias in pseudo-supervision.For the mixed images,Deep-Shallow Spatial Contrastive Learning(DSSCL)is proposed in the feature spaces of Teacher Network 2 and the Student Network to improve the segmentation capabilities in low-contrast and local areas.In this procedure,the features retrieved by the Student Network are subjected to a random feature perturbation technique.On two openly available datasets,extensive trials show that our proposed SADT performs much better than the state-ofthe-art semi-supervised medical segmentation techniques.Using only 10%of the labeled data for training,SADT was able to acquire a Dice score of 90.10%on the ACDC(Automatic Cardiac Diagnosis Challenge)dataset.展开更多
Objective: To study the relationship between cortical auditory evoked potential (CAEP) thresholds and behavioral thresholds in pediatric populations with sensorineural hearing loss (SNHL). Methods: Fifteen children (m...Objective: To study the relationship between cortical auditory evoked potential (CAEP) thresholds and behavioral thresholds in pediatric populations with sensorineural hearing loss (SNHL). Methods: Fifteen children (mean age 6.8 years) with bilateral SNHL underwent behavioral pure-tone audiometry and CAEP testing at 0.5, 1, 2, and 4 kHz. CAEP thresholds were determined using tone bursts, and correlations between CAEP and pure-tone thresholds were analyzed using Pearson correlation and t-tests. Results: A strong positive correlation was observed between P1 thresholds and behavioral thresholds across all test frequencies: 0.5 kHz (r = 0.765, p Conclusion: The strong correlation between P1 and behavioral thresholds demonstrates the reliability of CAEP testing for estimating auditory thresholds in children. These findings support the use of CAEP testing as a reliable objective tool for threshold estimation, particularly in cases where behavioral responses cannot be reliably obtained. When adjusted with frequency-specific correction values, CAEP testing provides a reliable method for assessing hearing thresholds in pediatric populations.展开更多
Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people everywhere.Due to its ability to produce a detailed view of the soft tissues,including the s...Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people everywhere.Due to its ability to produce a detailed view of the soft tissues,including the spinal cord,nerves,intervertebral discs,and vertebrae,Magnetic Resonance Imaging is thought to be the most effective method for imaging the spine.The semantic segmentation of vertebrae plays a major role in the diagnostic process of lumbar diseases.It is difficult to semantically partition the vertebrae in Magnetic Resonance Images from the surrounding variety of tissues,including muscles,ligaments,and intervertebral discs.U-Net is a powerful deep-learning architecture to handle the challenges of medical image analysis tasks and achieves high segmentation accuracy.This work proposes a modified U-Net architecture namely MU-Net,consisting of the Meijering convolutional layer that incorporates the Meijering filter to perform the semantic segmentation of lumbar vertebrae L1 to L5 and sacral vertebra S1.Pseudo-colour mask images were generated and used as ground truth for training the model.The work has been carried out on 1312 images expanded from T1-weighted mid-sagittal MRI images of 515 patients in the Lumbar Spine MRI Dataset publicly available from Mendeley Data.The proposed MU-Net model for the semantic segmentation of the lumbar vertebrae gives better performance with 98.79%of pixel accuracy(PA),98.66%of dice similarity coefficient(DSC),97.36%of Jaccard coefficient,and 92.55%mean Intersection over Union(mean IoU)metrics using the mentioned dataset.展开更多
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
Medical image segmentation has become a cornerstone for many healthcare applications,allowing for the automated extraction of critical information from images such as Computed Tomography(CT)scans,Magnetic Resonance Im...Medical image segmentation has become a cornerstone for many healthcare applications,allowing for the automated extraction of critical information from images such as Computed Tomography(CT)scans,Magnetic Resonance Imaging(MRIs),and X-rays.The introduction of U-Net in 2015 has significantly advanced segmentation capabilities,especially for small datasets commonly found in medical imaging.Since then,various modifications to the original U-Net architecture have been proposed to enhance segmentation accuracy and tackle challenges like class imbalance,data scarcity,and multi-modal image processing.This paper provides a detailed review and comparison of several U-Net-based architectures,focusing on their effectiveness in medical image segmentation tasks.We evaluate performance metrics such as Dice Similarity Coefficient(DSC)and Intersection over Union(IoU)across different U-Net variants including HmsU-Net,CrossU-Net,mResU-Net,and others.Our results indicate that architectural enhancements such as transformers,attention mechanisms,and residual connections improve segmentation performance across diverse medical imaging applications,including tumor detection,organ segmentation,and lesion identification.The study also identifies current challenges in the field,including data variability,limited dataset sizes,and issues with class imbalance.Based on these findings,the paper suggests potential future directions for improving the robustness and clinical applicability of U-Net-based models in medical image segmentation.展开更多
The Internet of Vehicles (IoV) has become an important direction in the field of intelligent transportation, in which vehicle positioning is a crucial part. SLAM (Simultaneous Localization and Mapping) technology play...The Internet of Vehicles (IoV) has become an important direction in the field of intelligent transportation, in which vehicle positioning is a crucial part. SLAM (Simultaneous Localization and Mapping) technology plays a crucial role in vehicle localization and navigation. Traditional Simultaneous Localization and Mapping (SLAM) systems are designed for use in static environments, and they can result in poor performance in terms of accuracy and robustness when used in dynamic environments where objects are in constant movement. To address this issue, a new real-time visual SLAM system called MG-SLAM has been developed. Based on ORB-SLAM2, MG-SLAM incorporates a dynamic target detection process that enables the detection of both known and unknown moving objects. In this process, a separate semantic segmentation thread is required to segment dynamic target instances, and the Mask R-CNN algorithm is applied on the Graphics Processing Unit (GPU) to accelerate segmentation. To reduce computational cost, only key frames are segmented to identify known dynamic objects. Additionally, a multi-view geometry method is adopted to detect unknown moving objects. The results demonstrate that MG-SLAM achieves higher precision, with an improvement from 0.2730 m to 0.0135 m in precision. Moreover, the processing time required by MG-SLAM is significantly reduced compared to other dynamic scene SLAM algorithms, which illustrates its efficacy in locating objects in dynamic scenes.展开更多
The key to the success of few-shot semantic segmentation(FSS)depends on the efficient use of limited annotated support set to accurately segment novel classes in the query set.Due to the few samples in the support set...The key to the success of few-shot semantic segmentation(FSS)depends on the efficient use of limited annotated support set to accurately segment novel classes in the query set.Due to the few samples in the support set,FSS faces challenges such as intra-class differences,background(BG)mismatches between query and support sets,and ambiguous segmentation between the foreground(FG)and BG in the query set.To address these issues,The paper propose a multi-module network called CAMSNet,which includes four modules:the General Information Module(GIM),the Class Activation Map Aggregation(CAMA)module,the Self-Cross Attention(SCA)Block,and the Feature Fusion Module(FFM).In CAMSNet,The GIM employs an improved triplet loss,which concatenates word embedding vectors and support prototypes as anchors,and uses local support features of FG and BG as positive and negative samples to help solve the problem of intra-class differences.Then for the first time,the Class Activation Map(CAM)from the Weakly Supervised Semantic Segmentation(WSSS)is applied to FSS within the CAMA module.This method replaces the traditional use of cosine similarity to locate query information.Subsequently,the SCA Block processes the support and query features aggregated by the CAMA module,significantly enhancing the understanding of input information,leading to more accurate predictions and effectively addressing BG mismatch and ambiguous FG-BG segmentation.Finally,The FFM combines general class information with the enhanced query information to achieve accurate segmentation of the query image.Extensive Experiments on PASCAL and COCO demonstrate that-5i-20ithe CAMSNet yields superior performance and set a state-of-the-art.展开更多
Neuronal soma segmentation plays a crucial role in neuroscience applications.However,the fine structure,such as boundaries,small-volume neuronal somata and fibers,are commonly present in cell images,which pose a chall...Neuronal soma segmentation plays a crucial role in neuroscience applications.However,the fine structure,such as boundaries,small-volume neuronal somata and fibers,are commonly present in cell images,which pose a challenge for accurate segmentation.In this paper,we propose a 3D semantic segmentation network for neuronal soma segmentation to address this issue.Using an encoding-decoding structure,we introduce a Multi-Scale feature extraction and Adaptive Weighting fusion module(MSAW)after each encoding block.The MSAW module can not only emphasize the fine structures via an upsampling strategy,but also provide pixel-wise weights to measure the importance of the multi-scale features.Additionally,a dynamic convolution instead of normal convolution is employed to better adapt the network to input data with different distributions.The proposed MSAW-based semantic segmentation network(MSAW-Net)was evaluated on three neuronal soma images from mouse brain and one neuronal soma image from macaque brain,demonstrating the efficiency of the proposed method.It achieved an F1 score of 91.8%on Fezf2-2A-CreER dataset,97.1%on LSL-H2B-GFP dataset,82.8%on Thy1-EGFP-Mline dataset,and 86.9%on macaque dataset,achieving improvements over the 3D U-Net model by 3.1%,3.3%,3.9%,and 2.3%,respectively.展开更多
Brain tumor segmentation is critical in clinical diagnosis and treatment planning.Existing methods for brain tumor segmentation with missing modalities often struggle when dealing with multiple missing modalities,a co...Brain tumor segmentation is critical in clinical diagnosis and treatment planning.Existing methods for brain tumor segmentation with missing modalities often struggle when dealing with multiple missing modalities,a common scenario in real-world clinical settings.These methods primarily focus on handling a single missing modality at a time,making them insufficiently robust for the additional complexity encountered with incomplete data containing various missing modality combinations.Additionally,most existing methods rely on single models,which may limit their performance and increase the risk of overfitting the training data.This work proposes a novel method called the ensemble adversarial co-training neural network(EACNet)for accurate brain tumor segmentation from multi-modal magnetic resonance imaging(MRI)scans with multiple missing modalities.The proposed method consists of three key modules:the ensemble of pre-trained models,which captures diverse feature representations from the MRI data by employing an ensemble of pre-trained models;adversarial learning,which leverages a competitive training approach involving two models;a generator model,which creates realistic missing data,while sub-networks acting as discriminators learn to distinguish real data from the generated“fake”data.Co-training framework utilizes the information extracted by the multimodal path(trained on complete scans)to guide the learning process in the path handling missing modalities.The model potentially compensates for missing information through co-training interactions by exploiting the relationships between available modalities and the tumor segmentation task.EACNet was evaluated on the BraTS2018 and BraTS2020 challenge datasets and achieved state-of-the-art and competitive performance respectively.Notably,the segmentation results for the whole tumor(WT)dice similarity coefficient(DSC)reached 89.27%,surpassing the performance of existing methods.The analysis suggests that the ensemble approach offers potential benefits,and the adversarial co-training contributes to the increased robustness and accuracy of EACNet for brain tumor segmentation of MRI scans with missing modalities.The experimental results show that EACNet has promising results for the task of brain tumor segmentation of MRI scans with missing modalities and is a better candidate for real-world clinical applications.展开更多
Automatic pancreas segmentation plays a pivotal role in assisting physicians with diagnosing pancreatic diseases,facilitating treatment evaluations,and designing surgical plans.Due to the pancreas’s tiny size,signifi...Automatic pancreas segmentation plays a pivotal role in assisting physicians with diagnosing pancreatic diseases,facilitating treatment evaluations,and designing surgical plans.Due to the pancreas’s tiny size,significant variability in shape and location,and low contrast with surrounding tissues,achieving high segmentation accuracy remains challenging.To improve segmentation precision,we propose a novel network utilizing EfficientNetV2 and multi-branch structures for automatically segmenting the pancreas fromCT images.Firstly,an EfficientNetV2 encoder is employed to extract complex and multi-level features,enhancing the model’s ability to capture the pancreas’s intricate morphology.Then,a residual multi-branch dilated attention(RMDA)module is designed to suppress irrelevant background noise and highlight useful pancreatic features.And re-parameterization Visual Geometry Group(RepVGG)blocks with amulti-branch structure are introduced in the decoder to effectively integrate deep features and low-level details,improving segmentation accuracy.Furthermore,we apply re-parameterization to the model,reducing computations and parameters while accelerating inference and reducing memory usage.Our approach achieves average dice similarity coefficient(DSC)of 85.59%,intersection over union(IoU)of 75.03%,precision of 85.09%,and recall of 86.57%on the NIH pancreas dataset.Compared with other methods,our model has fewer parameters and faster inference speed,demonstrating its enormous potential in practical applications of pancreatic segmentation.展开更多
Segmenting a breast ultrasound image is still challenging due to the presence of speckle noise,dependency on the operator,and the variation of image quality.This paper presents the UltraSegNet architecture that addres...Segmenting a breast ultrasound image is still challenging due to the presence of speckle noise,dependency on the operator,and the variation of image quality.This paper presents the UltraSegNet architecture that addresses these challenges through three key technical innovations:This work adds three things:(1)a changed ResNet-50 backbone with sequential 3×3 convolutions to keep fine anatomical details that are needed for finding lesion boundaries;(2)a computationally efficient regional attention mechanism that works on high-resolution features without using a transformer’s extra memory;and(3)an adaptive feature fusion strategy that changes local and global featuresbasedonhowthe image isbeing used.Extensive evaluation on two distinct datasets demonstrates UltraSegNet’s superior performance:On the BUSI dataset,it obtains a precision of 0.915,a recall of 0.908,and an F1 score of 0.911.In the UDAIT dataset,it achieves robust performance across the board,with a precision of 0.901 and recall of 0.894.Importantly,these improvements are achieved at clinically feasible computation times,taking 235 ms per image on standard GPU hardware.Notably,UltraSegNet does amazingly well on difficult small lesions(less than 10 mm),achieving a detection accuracy of 0.891.This is a huge improvement over traditional methods that have a hard time with small-scale features,as standard models can only achieve 0.63–0.71 accuracy.This improvement in small lesion detection is particularly crucial for early-stage breast cancer identification.Results from this work demonstrate that UltraSegNet can be practically deployable in clinical workflows to improve breast cancer screening accuracy.展开更多
Aiming at the problems of information loss and the relationship between features and target tasks in multimodal medical image segmentation, a multimodal medical image segmentation algorithm based on feature decoupling...Aiming at the problems of information loss and the relationship between features and target tasks in multimodal medical image segmentation, a multimodal medical image segmentation algorithm based on feature decoupling and information bottleneck theory is proposed in this paper. Based on the reversible network, the bottom-up learning method for different modal information is constructed, which enhances the features’ expression ability and the network’s learning ability. The feature fusion module is designed to balance multi-directional information flow. To retain the information relevant to the target task to the maximum extent and suppress the information irrelevant to the target task, the feature decoupling module is designed to ensure a strong correlation between the feature and the target task. A loss function based on information bottleneck theory was intended to improve information quality and remove redundant information. Based on BraTs2021, BraTs2023-MET and ANNLIB datasets, the proposed algorithm is analyzed qualitatively and quantitatively in this paper. In the quantitative experiment, the Dice coefficient of the proposed algorithm was increased by 0.110 on average compared with other methods, and the HD95 was decreased by 28.568 on average compared with other methods. In qualitative analysis, the proposed algorithm can effectively segment the incoherent region between the lesion and the lesion boundary and achieve accurate segmentation of the lesion.展开更多
With the increasing popularity of blockchain applications, the security of data sources on the blockchain is gradually receiving attention. Providing reliable data for the blockchain safely and efficiently has become ...With the increasing popularity of blockchain applications, the security of data sources on the blockchain is gradually receiving attention. Providing reliable data for the blockchain safely and efficiently has become a research hotspot, and the security of the oracle responsible for providing reliable data has attracted much attention. The most widely used centralized oracles in blockchain, such as Provable and Town Crier, all rely on a single oracle to obtain data, which suffers from a single point of failure and limits the large-scale development of blockchain. To this end, the distributed oracle scheme is put forward, but the existing distributed oracle schemes such as Chainlink and Augur generally have low execution efficiency and high communication overhead, which leads to their poor applicability. To solve the above problems, this paper proposes a trusted distributed oracle scheme based on a share recovery threshold signature. First, a data verification method of distributed oracles is designed based on threshold signature. By aggregating the signatures of oracles, data from different data sources can be mutually verified, leading to a more efficient data verification and aggregation process. Then, a credibility-based cluster head election algorithm is designed, which reduces the communication overhead by clarifying the function distribution and building a hierarchical structure. Considering the good performance of the BLS threshold signature in large-scale applications, this paper combines it with distributed oracle technology and proposes a BLS threshold signature algorithm that supports share recovery in distributed oracles. The share recovery mechanism enables the proposed scheme to solve the key loss issue, and the setting of the threshold value enables the proposed scheme to complete signature aggregation with only a threshold number of oracles, making the scheme more robust. Finally, experimental results indicate that, by using the threshold signature technology and the cluster head election algorithm, our scheme effectively improves the execution efficiency of oracles and solves the problem of a single point of failure, leading to higher scalability and robustness.展开更多
Real-time semantic segmentation tasks place stringent demands on network inference speed,often requiring a reduction in network depth to decrease computational load.However,shallow networks tend to exhibit degradation...Real-time semantic segmentation tasks place stringent demands on network inference speed,often requiring a reduction in network depth to decrease computational load.However,shallow networks tend to exhibit degradation in feature extraction completeness and inference accuracy.Therefore,balancing high performance with real-time requirements has become a critical issue in the study of real-time semantic segmentation.To address these challenges,this paper proposes a lightweight bilateral dual-residual network.By introducing a novel residual structure combined with feature extraction and fusion modules,the proposed network significantly enhances representational capacity while reducing computational costs.Specifically,an improved compound residual structure is designed to optimize the efficiency of information propagation and feature extraction.Furthermore,the proposed feature extraction and fusion module enables the network to better capture multi-scale information in images,improving the ability to detect both detailed and global semantic features.Experimental results on the publicly available Cityscapes dataset demonstrate that the proposed lightweight dual-branch network achieves outstanding performance while maintaining low computational complexity.In particular,the network achieved a mean Intersection over Union(mIoU)of 78.4%on the Cityscapes validation set,surpassing many existing semantic segmentation models.Additionally,in terms of inference speed,the network reached 74.5 frames per second when tested on an NVIDIA GeForce RTX 3090 GPU,significantly improving real-time performance.展开更多
Semantic segmentation of novel object categories with limited labeled data remains a challenging problem in computer vision.Few-shot segmentation methods aim to address this problem by recognizing objects from specifi...Semantic segmentation of novel object categories with limited labeled data remains a challenging problem in computer vision.Few-shot segmentation methods aim to address this problem by recognizing objects from specific target classes with a few provided examples.Previous approaches for few-shot semantic segmentation typically represent target classes using class prototypes.These prototypes are matched with the features of the query set to get segmentation results.However,class prototypes are usually obtained by applying global average pooling on masked support images.Global pooling discards much structural information,which may reduce the accuracy of model predictions.To address this issue,we propose a Category-Guided Frequency Modulation(CGFM)method.CGFM is designed to learn category-specific information in the frequency space and leverage it to provide a twostage guidance for the segmentation process.First,to self-adaptively activate class-relevant frequency bands while suppressing irrelevant ones,we leverage the Dual-Perception Gaussian Band Pre-activation(DPGBP)module to generate Gaussian filters using class embedding vectors.Second,to further enhance category-relevant frequency components in activated bands,we design a Support-Guided Category Response Enhancement(SGCRE)module to effectively introduce support frequency components into the modulation of query frequency features.Experiments on the PASCAL-5^(i) and COCO-20^(i) datasets demonstrate the promising performance of our model.展开更多
Instance segmentation is crucial in various domains,such as autonomous driving and robotics.However,there is scope for improvement in the detection speed of instance-segmentation algorithms for edge devices.Therefore,...Instance segmentation is crucial in various domains,such as autonomous driving and robotics.However,there is scope for improvement in the detection speed of instance-segmentation algorithms for edge devices.Therefore,it is essential to enhance detection speed while maintaining high accuracy.In this study,we propose you only look once-layer fusion(YOLO-LF),a lightweight instance segmentation method specifically designed to optimize the speed of instance segmentation for autonomous driving applications.Based on the You Only Look Once version 8 nano(YOLOv8n)framework,we introduce a lightweight convolutional module and design a lightweight layer aggrega-tion module called Reparameterization convolution and Partial convolution Efficient Layer Aggregation Networks(RPELAN).This module effectively reduces the impact of redundant information generated by traditional convolutional stacking on the network size and detection speed while enhancing the capability to process feature information.We experimentally verified that our generalized one-stage detection network lightweight method based on Grouped Spatial Convolution(GSconv)enhances the detection speed while maintaining accuracy across various state-of-the-art(SOTA)networks.Our experiments conducted on the publicly available Cityscapes dataset demonstrated that YOLO-LF maintained the same accuracy as yolov8n(mAP@0.537.9%),the model volume decreased by 14.3%from 3.259 to=2.804 M,and the Frames Per Second(FPS)increased by 14.48%from 57.47 to 65.79 compared with YOLOv8n,thereby demonstrating its potential for real-time instance segmentation on edge devices.展开更多
The oilseed crop Camelina sativa exhibits salinity tolerance,but the effects on early growth stages across a range of different salts and in combination with salicylic acid(SA)have not been thoroughly evaluated.In thi...The oilseed crop Camelina sativa exhibits salinity tolerance,but the effects on early growth stages across a range of different salts and in combination with salicylic acid(SA)have not been thoroughly evaluated.In this study,seeds were germinated in varying concentrations of six salts(NaCl,CaCl_(2),ZnCl_(2),KCl,MgSO_(4),and Na2SO_(4))with or without 0.5 mM SA.Using the halotime model,we estimated salt thresholds for germination and parameters of seedling growth.Germination and seedling growth parameters of camelina significantly decreased with increasing salt concentration across all salt types.Salts containing Zn and SO_(4) were most detrimental to germination and seedling growth.Except for KCl,0.5 mM SA generally reduced the salinity tolerance threshold(Saltb(50))of camelina.Specifically,Saltb(50)was 21.5%higher for KCl and 16.1%,25.0%,54.9%,21.0%,and 5.6%lower for CaCl_(2),NaCl,MgSO_(4),Na2SO_(4),and ZnCl_(2),respectively,when 0.5 mM SA was compared to 0 mM SA.Furthermore,camelina seedling growth was consistently more sensitive than germination across all salt types.SA did not significantly enhance germination or seedling growth and was harmful when combined with certain salts or at the germination stage.It can be concluded that both the type of salt and the concentration of SA are as critical as the salt concentration in saline irrigation water.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.42077232 and 42077235)the Key Research and Development Plan of Jiangsu Province(Grant No.BE2022156).
文摘The influences of biological,chemical,and flow processes on soil structure through microbially induced carbonate precipitation(MICP)are not yet fully understood.In this study,we use a multi-level thresholding segmentation algorithm,genetic algorithm(GA)enhanced Kapur entropy(KE)(GAE-KE),to accomplish quantitative characterization of sandy soil structure altered by MICP cementation.A sandy soil sample was treated using MICP method and scanned by the synchrotron radiation(SR)micro-CT with a resolution of 6.5 mm.After validation,tri-level thresholding segmentation using GAE-KE successfully separated the precipitated calcium carbonate crystals from sand particles and pores.The spatial distributions of porosity,pore structure parameters,and flow characteristics were calculated for quantitative characterization.The results offer pore-scale insights into the MICP treatment effect,and the quantitative understanding confirms the feasibility of the GAE-KE multi-level thresholding segmentation algorithm.
基金partially supported by the NSF grants DMS-1854434,DMS-1952644,DMS-2151235,DMS-2219904,and CAREER 1846690。
文摘In this paper,we design an efficient,multi-stage image segmentation framework that incorporates a weighted difference of anisotropic and isotropic total variation(AITV).The segmentation framework generally consists of two stages:smoothing and thresholding,thus referred to as smoothing-and-thresholding(SaT).In the first stage,a smoothed image is obtained by an AITV-regularized Mumford-Shah(MS)model,which can be solved efficiently by the alternating direction method of multipliers(ADMMs)with a closed-form solution of a proximal operator of the l_(1)-αl_(2) regularizer.The convergence of the ADMM algorithm is analyzed.In the second stage,we threshold the smoothed image by K-means clustering to obtain the final segmentation result.Numerical experiments demonstrate that the proposed segmentation framework is versatile for both grayscale and color images,effcient in producing high-quality segmentation results within a few seconds,and robust to input images that are corrupted with noise,blur,or both.We compare the AITV method with its original convex TV and nonconvex TVP(O<p<1)counterparts,showcasing the qualitative and quantitative advantages of our proposed method.
基金support from the Beijing Natural Science Foundation-Xiaomi Innovation Joint Fund(No.L233009)National Natural Science Foundation of China(NSFC Nos.62422409,62174152,and 62374159)from the Youth Innovation Promotion Association of Chinese Academy of Sciences(No.2020115).
文摘Memristors have a synapse-like two-terminal structure and electrical properties,which are widely used in the construc-tion of artificial synapses.However,compared to inorganic materials,organic materials are rarely used for artificial spiking synapses due to their relatively poor memrisitve performance.Here,for the first time,we present an organic memristor based on an electropolymerized dopamine-based memristive layer.This polydopamine-based memristor demonstrates the improve-ments in key performance,including a low threshold voltage of 0.3 V,a thin thickness of 16 nm,and a high parasitic capaci-tance of about 1μF·mm^(-2).By leveraging these properties in combination with its stable threshold switching behavior,we con-struct a capacitor-free and low-power artificial spiking neuron capable of outputting the oscillation voltage,whose spiking fre-quency increases with the increase of current stimulation analogous to a biological neuron.The experimental results indicate that our artificial spiking neuron holds potential for applications in neuromorphic computing and systems.
基金supported by the Natural Science Foundation of China(No.41804112,author:Chengyun Song).
文摘Existing semi-supervisedmedical image segmentation algorithms use copy-paste data augmentation to correct the labeled-unlabeled data distribution mismatch.However,current copy-paste methods have three limitations:(1)training the model solely with copy-paste mixed pictures from labeled and unlabeled input loses a lot of labeled information;(2)low-quality pseudo-labels can cause confirmation bias in pseudo-supervised learning on unlabeled data;(3)the segmentation performance in low-contrast and local regions is less than optimal.We design a Stochastic Augmentation-Based Dual-Teaching Auxiliary Training Strategy(SADT),which enhances feature diversity and learns high-quality features to overcome these problems.To be more precise,SADT trains the Student Network by using pseudo-label-based training from Teacher Network 1 and supervised learning with labeled data,which prevents the loss of rare labeled data.We introduce a bi-directional copy-pastemask with progressive high-entropy filtering to reduce data distribution disparities and mitigate confirmation bias in pseudo-supervision.For the mixed images,Deep-Shallow Spatial Contrastive Learning(DSSCL)is proposed in the feature spaces of Teacher Network 2 and the Student Network to improve the segmentation capabilities in low-contrast and local areas.In this procedure,the features retrieved by the Student Network are subjected to a random feature perturbation technique.On two openly available datasets,extensive trials show that our proposed SADT performs much better than the state-ofthe-art semi-supervised medical segmentation techniques.Using only 10%of the labeled data for training,SADT was able to acquire a Dice score of 90.10%on the ACDC(Automatic Cardiac Diagnosis Challenge)dataset.
文摘Objective: To study the relationship between cortical auditory evoked potential (CAEP) thresholds and behavioral thresholds in pediatric populations with sensorineural hearing loss (SNHL). Methods: Fifteen children (mean age 6.8 years) with bilateral SNHL underwent behavioral pure-tone audiometry and CAEP testing at 0.5, 1, 2, and 4 kHz. CAEP thresholds were determined using tone bursts, and correlations between CAEP and pure-tone thresholds were analyzed using Pearson correlation and t-tests. Results: A strong positive correlation was observed between P1 thresholds and behavioral thresholds across all test frequencies: 0.5 kHz (r = 0.765, p Conclusion: The strong correlation between P1 and behavioral thresholds demonstrates the reliability of CAEP testing for estimating auditory thresholds in children. These findings support the use of CAEP testing as a reliable objective tool for threshold estimation, particularly in cases where behavioral responses cannot be reliably obtained. When adjusted with frequency-specific correction values, CAEP testing provides a reliable method for assessing hearing thresholds in pediatric populations.
文摘Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people everywhere.Due to its ability to produce a detailed view of the soft tissues,including the spinal cord,nerves,intervertebral discs,and vertebrae,Magnetic Resonance Imaging is thought to be the most effective method for imaging the spine.The semantic segmentation of vertebrae plays a major role in the diagnostic process of lumbar diseases.It is difficult to semantically partition the vertebrae in Magnetic Resonance Images from the surrounding variety of tissues,including muscles,ligaments,and intervertebral discs.U-Net is a powerful deep-learning architecture to handle the challenges of medical image analysis tasks and achieves high segmentation accuracy.This work proposes a modified U-Net architecture namely MU-Net,consisting of the Meijering convolutional layer that incorporates the Meijering filter to perform the semantic segmentation of lumbar vertebrae L1 to L5 and sacral vertebra S1.Pseudo-colour mask images were generated and used as ground truth for training the model.The work has been carried out on 1312 images expanded from T1-weighted mid-sagittal MRI images of 515 patients in the Lumbar Spine MRI Dataset publicly available from Mendeley Data.The proposed MU-Net model for the semantic segmentation of the lumbar vertebrae gives better performance with 98.79%of pixel accuracy(PA),98.66%of dice similarity coefficient(DSC),97.36%of Jaccard coefficient,and 92.55%mean Intersection over Union(mean IoU)metrics using the mentioned dataset.
文摘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.
文摘Medical image segmentation has become a cornerstone for many healthcare applications,allowing for the automated extraction of critical information from images such as Computed Tomography(CT)scans,Magnetic Resonance Imaging(MRIs),and X-rays.The introduction of U-Net in 2015 has significantly advanced segmentation capabilities,especially for small datasets commonly found in medical imaging.Since then,various modifications to the original U-Net architecture have been proposed to enhance segmentation accuracy and tackle challenges like class imbalance,data scarcity,and multi-modal image processing.This paper provides a detailed review and comparison of several U-Net-based architectures,focusing on their effectiveness in medical image segmentation tasks.We evaluate performance metrics such as Dice Similarity Coefficient(DSC)and Intersection over Union(IoU)across different U-Net variants including HmsU-Net,CrossU-Net,mResU-Net,and others.Our results indicate that architectural enhancements such as transformers,attention mechanisms,and residual connections improve segmentation performance across diverse medical imaging applications,including tumor detection,organ segmentation,and lesion identification.The study also identifies current challenges in the field,including data variability,limited dataset sizes,and issues with class imbalance.Based on these findings,the paper suggests potential future directions for improving the robustness and clinical applicability of U-Net-based models in medical image segmentation.
基金funded by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(grant number 22KJD440001)Changzhou Science&Technology Program(grant number CJ20220232).
文摘The Internet of Vehicles (IoV) has become an important direction in the field of intelligent transportation, in which vehicle positioning is a crucial part. SLAM (Simultaneous Localization and Mapping) technology plays a crucial role in vehicle localization and navigation. Traditional Simultaneous Localization and Mapping (SLAM) systems are designed for use in static environments, and they can result in poor performance in terms of accuracy and robustness when used in dynamic environments where objects are in constant movement. To address this issue, a new real-time visual SLAM system called MG-SLAM has been developed. Based on ORB-SLAM2, MG-SLAM incorporates a dynamic target detection process that enables the detection of both known and unknown moving objects. In this process, a separate semantic segmentation thread is required to segment dynamic target instances, and the Mask R-CNN algorithm is applied on the Graphics Processing Unit (GPU) to accelerate segmentation. To reduce computational cost, only key frames are segmented to identify known dynamic objects. Additionally, a multi-view geometry method is adopted to detect unknown moving objects. The results demonstrate that MG-SLAM achieves higher precision, with an improvement from 0.2730 m to 0.0135 m in precision. Moreover, the processing time required by MG-SLAM is significantly reduced compared to other dynamic scene SLAM algorithms, which illustrates its efficacy in locating objects in dynamic scenes.
基金supported by funding from the following sources:National Natural Science Foundation of China(U1904119)Research Programs of Henan Science and Technology Department(232102210033,232102210054)+3 种基金Chongqing Natural Science Foundation(CSTB2023NSCQ-MSX0070)Henan Province Key Research and Development Project(231111212000)Aviation Science Foundation(20230001055002)supported by Henan Center for Outstanding Overseas Scientists(GZS2022011).
文摘The key to the success of few-shot semantic segmentation(FSS)depends on the efficient use of limited annotated support set to accurately segment novel classes in the query set.Due to the few samples in the support set,FSS faces challenges such as intra-class differences,background(BG)mismatches between query and support sets,and ambiguous segmentation between the foreground(FG)and BG in the query set.To address these issues,The paper propose a multi-module network called CAMSNet,which includes four modules:the General Information Module(GIM),the Class Activation Map Aggregation(CAMA)module,the Self-Cross Attention(SCA)Block,and the Feature Fusion Module(FFM).In CAMSNet,The GIM employs an improved triplet loss,which concatenates word embedding vectors and support prototypes as anchors,and uses local support features of FG and BG as positive and negative samples to help solve the problem of intra-class differences.Then for the first time,the Class Activation Map(CAM)from the Weakly Supervised Semantic Segmentation(WSSS)is applied to FSS within the CAMA module.This method replaces the traditional use of cosine similarity to locate query information.Subsequently,the SCA Block processes the support and query features aggregated by the CAMA module,significantly enhancing the understanding of input information,leading to more accurate predictions and effectively addressing BG mismatch and ambiguous FG-BG segmentation.Finally,The FFM combines general class information with the enhanced query information to achieve accurate segmentation of the query image.Extensive Experiments on PASCAL and COCO demonstrate that-5i-20ithe CAMSNet yields superior performance and set a state-of-the-art.
基金supported by the STI2030-Major-Projects(No.2021ZD0200104)the National Natural Science Foundations of China under Grant 61771437.
文摘Neuronal soma segmentation plays a crucial role in neuroscience applications.However,the fine structure,such as boundaries,small-volume neuronal somata and fibers,are commonly present in cell images,which pose a challenge for accurate segmentation.In this paper,we propose a 3D semantic segmentation network for neuronal soma segmentation to address this issue.Using an encoding-decoding structure,we introduce a Multi-Scale feature extraction and Adaptive Weighting fusion module(MSAW)after each encoding block.The MSAW module can not only emphasize the fine structures via an upsampling strategy,but also provide pixel-wise weights to measure the importance of the multi-scale features.Additionally,a dynamic convolution instead of normal convolution is employed to better adapt the network to input data with different distributions.The proposed MSAW-based semantic segmentation network(MSAW-Net)was evaluated on three neuronal soma images from mouse brain and one neuronal soma image from macaque brain,demonstrating the efficiency of the proposed method.It achieved an F1 score of 91.8%on Fezf2-2A-CreER dataset,97.1%on LSL-H2B-GFP dataset,82.8%on Thy1-EGFP-Mline dataset,and 86.9%on macaque dataset,achieving improvements over the 3D U-Net model by 3.1%,3.3%,3.9%,and 2.3%,respectively.
基金supported by Gansu Natural Science Foundation Programme(No.24JRRA231)National Natural Science Foundation of China(No.62061023)Gansu Provincial Education,Science and Technology Innovation and Industry(No.2021CYZC-04)。
文摘Brain tumor segmentation is critical in clinical diagnosis and treatment planning.Existing methods for brain tumor segmentation with missing modalities often struggle when dealing with multiple missing modalities,a common scenario in real-world clinical settings.These methods primarily focus on handling a single missing modality at a time,making them insufficiently robust for the additional complexity encountered with incomplete data containing various missing modality combinations.Additionally,most existing methods rely on single models,which may limit their performance and increase the risk of overfitting the training data.This work proposes a novel method called the ensemble adversarial co-training neural network(EACNet)for accurate brain tumor segmentation from multi-modal magnetic resonance imaging(MRI)scans with multiple missing modalities.The proposed method consists of three key modules:the ensemble of pre-trained models,which captures diverse feature representations from the MRI data by employing an ensemble of pre-trained models;adversarial learning,which leverages a competitive training approach involving two models;a generator model,which creates realistic missing data,while sub-networks acting as discriminators learn to distinguish real data from the generated“fake”data.Co-training framework utilizes the information extracted by the multimodal path(trained on complete scans)to guide the learning process in the path handling missing modalities.The model potentially compensates for missing information through co-training interactions by exploiting the relationships between available modalities and the tumor segmentation task.EACNet was evaluated on the BraTS2018 and BraTS2020 challenge datasets and achieved state-of-the-art and competitive performance respectively.Notably,the segmentation results for the whole tumor(WT)dice similarity coefficient(DSC)reached 89.27%,surpassing the performance of existing methods.The analysis suggests that the ensemble approach offers potential benefits,and the adversarial co-training contributes to the increased robustness and accuracy of EACNet for brain tumor segmentation of MRI scans with missing modalities.The experimental results show that EACNet has promising results for the task of brain tumor segmentation of MRI scans with missing modalities and is a better candidate for real-world clinical applications.
基金supported by the Science and Technology Innovation Programof Hunan Province(Grant No.2022RC1021)the Hunan Provincial Natural Science Foundation Project(Grant No.2023JJ60124)+1 种基金the Changsha Natural Science Foundation Project(Grant No.kq2202265)the key project of the Hunan Provincial of Education(Grant No.22A0255).
文摘Automatic pancreas segmentation plays a pivotal role in assisting physicians with diagnosing pancreatic diseases,facilitating treatment evaluations,and designing surgical plans.Due to the pancreas’s tiny size,significant variability in shape and location,and low contrast with surrounding tissues,achieving high segmentation accuracy remains challenging.To improve segmentation precision,we propose a novel network utilizing EfficientNetV2 and multi-branch structures for automatically segmenting the pancreas fromCT images.Firstly,an EfficientNetV2 encoder is employed to extract complex and multi-level features,enhancing the model’s ability to capture the pancreas’s intricate morphology.Then,a residual multi-branch dilated attention(RMDA)module is designed to suppress irrelevant background noise and highlight useful pancreatic features.And re-parameterization Visual Geometry Group(RepVGG)blocks with amulti-branch structure are introduced in the decoder to effectively integrate deep features and low-level details,improving segmentation accuracy.Furthermore,we apply re-parameterization to the model,reducing computations and parameters while accelerating inference and reducing memory usage.Our approach achieves average dice similarity coefficient(DSC)of 85.59%,intersection over union(IoU)of 75.03%,precision of 85.09%,and recall of 86.57%on the NIH pancreas dataset.Compared with other methods,our model has fewer parameters and faster inference speed,demonstrating its enormous potential in practical applications of pancreatic segmentation.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R435),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Segmenting a breast ultrasound image is still challenging due to the presence of speckle noise,dependency on the operator,and the variation of image quality.This paper presents the UltraSegNet architecture that addresses these challenges through three key technical innovations:This work adds three things:(1)a changed ResNet-50 backbone with sequential 3×3 convolutions to keep fine anatomical details that are needed for finding lesion boundaries;(2)a computationally efficient regional attention mechanism that works on high-resolution features without using a transformer’s extra memory;and(3)an adaptive feature fusion strategy that changes local and global featuresbasedonhowthe image isbeing used.Extensive evaluation on two distinct datasets demonstrates UltraSegNet’s superior performance:On the BUSI dataset,it obtains a precision of 0.915,a recall of 0.908,and an F1 score of 0.911.In the UDAIT dataset,it achieves robust performance across the board,with a precision of 0.901 and recall of 0.894.Importantly,these improvements are achieved at clinically feasible computation times,taking 235 ms per image on standard GPU hardware.Notably,UltraSegNet does amazingly well on difficult small lesions(less than 10 mm),achieving a detection accuracy of 0.891.This is a huge improvement over traditional methods that have a hard time with small-scale features,as standard models can only achieve 0.63–0.71 accuracy.This improvement in small lesion detection is particularly crucial for early-stage breast cancer identification.Results from this work demonstrate that UltraSegNet can be practically deployable in clinical workflows to improve breast cancer screening accuracy.
基金supported by the following grants:Beijing Natural Science Foundation(No.Z210003)National Natural Science Foundation of China(NSFC12026607)+2 种基金NationalNatural Science Foundation of China(NSFC12031016)KeyR&DProgramof the Scientific Research Department(2020YFA0712203)Key R&D Programof the Scientific ResearchDepartment(2020YFA0712201).
文摘Aiming at the problems of information loss and the relationship between features and target tasks in multimodal medical image segmentation, a multimodal medical image segmentation algorithm based on feature decoupling and information bottleneck theory is proposed in this paper. Based on the reversible network, the bottom-up learning method for different modal information is constructed, which enhances the features’ expression ability and the network’s learning ability. The feature fusion module is designed to balance multi-directional information flow. To retain the information relevant to the target task to the maximum extent and suppress the information irrelevant to the target task, the feature decoupling module is designed to ensure a strong correlation between the feature and the target task. A loss function based on information bottleneck theory was intended to improve information quality and remove redundant information. Based on BraTs2021, BraTs2023-MET and ANNLIB datasets, the proposed algorithm is analyzed qualitatively and quantitatively in this paper. In the quantitative experiment, the Dice coefficient of the proposed algorithm was increased by 0.110 on average compared with other methods, and the HD95 was decreased by 28.568 on average compared with other methods. In qualitative analysis, the proposed algorithm can effectively segment the incoherent region between the lesion and the lesion boundary and achieve accurate segmentation of the lesion.
基金supported by the National Natural Science Foundation of China(Grant No.62102449)the Central Plains Talent Program under Grant No.224200510003.
文摘With the increasing popularity of blockchain applications, the security of data sources on the blockchain is gradually receiving attention. Providing reliable data for the blockchain safely and efficiently has become a research hotspot, and the security of the oracle responsible for providing reliable data has attracted much attention. The most widely used centralized oracles in blockchain, such as Provable and Town Crier, all rely on a single oracle to obtain data, which suffers from a single point of failure and limits the large-scale development of blockchain. To this end, the distributed oracle scheme is put forward, but the existing distributed oracle schemes such as Chainlink and Augur generally have low execution efficiency and high communication overhead, which leads to their poor applicability. To solve the above problems, this paper proposes a trusted distributed oracle scheme based on a share recovery threshold signature. First, a data verification method of distributed oracles is designed based on threshold signature. By aggregating the signatures of oracles, data from different data sources can be mutually verified, leading to a more efficient data verification and aggregation process. Then, a credibility-based cluster head election algorithm is designed, which reduces the communication overhead by clarifying the function distribution and building a hierarchical structure. Considering the good performance of the BLS threshold signature in large-scale applications, this paper combines it with distributed oracle technology and proposes a BLS threshold signature algorithm that supports share recovery in distributed oracles. The share recovery mechanism enables the proposed scheme to solve the key loss issue, and the setting of the threshold value enables the proposed scheme to complete signature aggregation with only a threshold number of oracles, making the scheme more robust. Finally, experimental results indicate that, by using the threshold signature technology and the cluster head election algorithm, our scheme effectively improves the execution efficiency of oracles and solves the problem of a single point of failure, leading to higher scalability and robustness.
文摘Real-time semantic segmentation tasks place stringent demands on network inference speed,often requiring a reduction in network depth to decrease computational load.However,shallow networks tend to exhibit degradation in feature extraction completeness and inference accuracy.Therefore,balancing high performance with real-time requirements has become a critical issue in the study of real-time semantic segmentation.To address these challenges,this paper proposes a lightweight bilateral dual-residual network.By introducing a novel residual structure combined with feature extraction and fusion modules,the proposed network significantly enhances representational capacity while reducing computational costs.Specifically,an improved compound residual structure is designed to optimize the efficiency of information propagation and feature extraction.Furthermore,the proposed feature extraction and fusion module enables the network to better capture multi-scale information in images,improving the ability to detect both detailed and global semantic features.Experimental results on the publicly available Cityscapes dataset demonstrate that the proposed lightweight dual-branch network achieves outstanding performance while maintaining low computational complexity.In particular,the network achieved a mean Intersection over Union(mIoU)of 78.4%on the Cityscapes validation set,surpassing many existing semantic segmentation models.Additionally,in terms of inference speed,the network reached 74.5 frames per second when tested on an NVIDIA GeForce RTX 3090 GPU,significantly improving real-time performance.
文摘Semantic segmentation of novel object categories with limited labeled data remains a challenging problem in computer vision.Few-shot segmentation methods aim to address this problem by recognizing objects from specific target classes with a few provided examples.Previous approaches for few-shot semantic segmentation typically represent target classes using class prototypes.These prototypes are matched with the features of the query set to get segmentation results.However,class prototypes are usually obtained by applying global average pooling on masked support images.Global pooling discards much structural information,which may reduce the accuracy of model predictions.To address this issue,we propose a Category-Guided Frequency Modulation(CGFM)method.CGFM is designed to learn category-specific information in the frequency space and leverage it to provide a twostage guidance for the segmentation process.First,to self-adaptively activate class-relevant frequency bands while suppressing irrelevant ones,we leverage the Dual-Perception Gaussian Band Pre-activation(DPGBP)module to generate Gaussian filters using class embedding vectors.Second,to further enhance category-relevant frequency components in activated bands,we design a Support-Guided Category Response Enhancement(SGCRE)module to effectively introduce support frequency components into the modulation of query frequency features.Experiments on the PASCAL-5^(i) and COCO-20^(i) datasets demonstrate the promising performance of our model.
基金supported by Science and Technology Research Youth Project of Chongqing Municipal Education Commission(No.KJQN202301104)Cooperative Project between universities in Chongqing and Affiliated Institutes of Chinese Academy of Sciences(No.HZ2021011)+1 种基金Chongqing Municipal Science and Technology Commission Technology Innovation and Application Development Special Project(No.2022TIAD-KPX0040)Action Plan for Quality Development of Chongqing University of Technology Graduate Education(Grant No.gzlcx20242014).
文摘Instance segmentation is crucial in various domains,such as autonomous driving and robotics.However,there is scope for improvement in the detection speed of instance-segmentation algorithms for edge devices.Therefore,it is essential to enhance detection speed while maintaining high accuracy.In this study,we propose you only look once-layer fusion(YOLO-LF),a lightweight instance segmentation method specifically designed to optimize the speed of instance segmentation for autonomous driving applications.Based on the You Only Look Once version 8 nano(YOLOv8n)framework,we introduce a lightweight convolutional module and design a lightweight layer aggrega-tion module called Reparameterization convolution and Partial convolution Efficient Layer Aggregation Networks(RPELAN).This module effectively reduces the impact of redundant information generated by traditional convolutional stacking on the network size and detection speed while enhancing the capability to process feature information.We experimentally verified that our generalized one-stage detection network lightweight method based on Grouped Spatial Convolution(GSconv)enhances the detection speed while maintaining accuracy across various state-of-the-art(SOTA)networks.Our experiments conducted on the publicly available Cityscapes dataset demonstrated that YOLO-LF maintained the same accuracy as yolov8n(mAP@0.537.9%),the model volume decreased by 14.3%from 3.259 to=2.804 M,and the Frames Per Second(FPS)increased by 14.48%from 57.47 to 65.79 compared with YOLOv8n,thereby demonstrating its potential for real-time instance segmentation on edge devices.
文摘The oilseed crop Camelina sativa exhibits salinity tolerance,but the effects on early growth stages across a range of different salts and in combination with salicylic acid(SA)have not been thoroughly evaluated.In this study,seeds were germinated in varying concentrations of six salts(NaCl,CaCl_(2),ZnCl_(2),KCl,MgSO_(4),and Na2SO_(4))with or without 0.5 mM SA.Using the halotime model,we estimated salt thresholds for germination and parameters of seedling growth.Germination and seedling growth parameters of camelina significantly decreased with increasing salt concentration across all salt types.Salts containing Zn and SO_(4) were most detrimental to germination and seedling growth.Except for KCl,0.5 mM SA generally reduced the salinity tolerance threshold(Saltb(50))of camelina.Specifically,Saltb(50)was 21.5%higher for KCl and 16.1%,25.0%,54.9%,21.0%,and 5.6%lower for CaCl_(2),NaCl,MgSO_(4),Na2SO_(4),and ZnCl_(2),respectively,when 0.5 mM SA was compared to 0 mM SA.Furthermore,camelina seedling growth was consistently more sensitive than germination across all salt types.SA did not significantly enhance germination or seedling growth and was harmful when combined with certain salts or at the germination stage.It can be concluded that both the type of salt and the concentration of SA are as critical as the salt concentration in saline irrigation water.
