Brain tumors pose significant diagnostic challenges due to their diverse types and complex anatomical locations.Due to the increase in precision image-based diagnostic tools,driven by advancements in artificial intell...Brain tumors pose significant diagnostic challenges due to their diverse types and complex anatomical locations.Due to the increase in precision image-based diagnostic tools,driven by advancements in artificial intelligence(AI)and deep learning,there has been potential to improve diagnostic accuracy,especially with Magnetic Resonance Imaging(MRI).However,traditional state-of-the-art models lack the sensitivity essential for reliable tumor identification and segmentation.Thus,our research aims to enhance brain tumor diagnosis in MRI by proposing an advanced model.The proposed model incorporates dilated convolutions to optimize the brain tumor segmentation and classification.The proposed model is first trained and later evaluated using the BraTS 2020 dataset.In our proposed model preprocessing consists of normalization,noise reduction,and data augmentation to improve model robustness.The attention mechanism and dilated convolutions were introduced to increase the model’s focus on critical regions and capture finer spatial details without compromising image resolution.We have performed experimentation to measure efficiency.For this,we have used various metrics including accuracy,sensitivity,and curve(AUC-ROC).The proposed model achieved a high accuracy of 94%,a sensitivity of 93%,a specificity of 92%,and an AUC-ROC of 0.98,outperforming traditional diagnostic models in brain tumor detection.The proposed model accurately identifies tumor regions,while dilated convolutions enhanced the segmentation accuracy,especially for complex tumor structures.The proposed model demonstrates significant potential for clinical application,providing reliable and precise brain tumor detection in MRI.展开更多
The Bernoulli convolution ν λ measure is shown to be absolutely continuous with L 2 density for almost all 12<λ<1,and singular if λ -1 is a Pisot number. It is an open question whether the Pisot typ...The Bernoulli convolution ν λ measure is shown to be absolutely continuous with L 2 density for almost all 12<λ<1,and singular if λ -1 is a Pisot number. It is an open question whether the Pisot type Bernoulli convolutions are the only singular ones. In this paper,we construct a family of non-Pisot type Bernoulli convolutions ν λ such that their density functions,if they exist,are not L 2. We also construct other Bernolulli convolutions whose density functions,if they exist,behave rather badly.展开更多
Louis Pierre Gratiolet (1815-1865) was one of the first modern anatomists to pay attention to cerebral convolutions. Born in Sainte-Foy-la-Grande (Gironde), he moved to Paris in 1834 to study medicine, as well as comp...Louis Pierre Gratiolet (1815-1865) was one of the first modern anatomists to pay attention to cerebral convolutions. Born in Sainte-Foy-la-Grande (Gironde), he moved to Paris in 1834 to study medicine, as well as comparative anatomy under Henri de Blainville (1777-1850). In 1842, he accepted de Blainville’s offer to become his assistant at the Muséum d’histoire naturelle and progressively abandoned medicine for comparative anatomy. He undertook a detailed study of brains of human and nonhuman primates and soon realized that the organizational pattern of cerebral convolutions was so predictable that it could serve as a criterion to classify primate groups. He noted that only the deepest sulci exist in lower primate forms, while the complexity of cortical folding increases markedly in great apes and humans. Gratiolet provided the first cogent description of the lobular organization of primate cerebral hemispheres. He saw the insula as a central lobe around which revolved the frontal, parietal, temporal (temporo-sphenoidal) and occipital lobes. He correctly identified most gyri and sulci on all brain surfaces, introduced the term “plis de passage” for some interconnecting gyri, and provided the first description of the optic radiations. In the early 1860s, Gratiolet fought a highly publicized battle against Paul Broca (1824-1880) on the relationship between brain and intelligence. Gratiolet agreed that the brain was most likely the seat of intelligence, but he considered human cognition far too subtle to have any direct relationship with brain size. He argued that a detailed study of the human brain architecture would be more profitable than Broca’s vain speculations on the relationship between brain weight and intelligence, which he considered a monolithic entity. Despite remarkable scientific achievements and a unique teaching capacity, Gratiolet was unable to secure any academic position until three years before his sudden death in Paris at age 49.展开更多
Pointwise convolution is usually utilized to expand or squeeze features in modern lightweight deep models.However,it takes up most of the overall computational cost(usually more than 90%).This paper proposes a novel P...Pointwise convolution is usually utilized to expand or squeeze features in modern lightweight deep models.However,it takes up most of the overall computational cost(usually more than 90%).This paper proposes a novel Poker module to expand features by taking advantage of cheap depthwise convolution.As a result,the Poker module can greatly reduce the computational cost,and meanwhile generate a large number of effective features to guarantee the performance.The proposed module is standardized and can be employed wherever the feature expansion is needed.By varying the stride and the number of channels,different kinds of bottlenecks are designed to plug the proposed Poker module into the network.Thus,a lightweight model can be easily assembled.Experiments conducted on benchmarks reveal the effectiveness of our proposed Poker module.And our Poker Net models can reduce the computational cost by 7.1%-15.6%.Poker Net models achieve comparable or even higher recognition accuracy than previous state-of-the-art(SOTA)models on the Image Net ILSVRC2012 classification dataset.Code is available at https://github.com/diaomin/pokernet.展开更多
Text format information is full of most of the resources of Internet,which puts forward higher and higher requirements for the accuracy of text classification.Therefore,in this manuscript,firstly,we design a hybrid mo...Text format information is full of most of the resources of Internet,which puts forward higher and higher requirements for the accuracy of text classification.Therefore,in this manuscript,firstly,we design a hybrid model of bidirectional encoder representation from transformers-hierarchical attention networks-dilated convolutions networks(BERT_HAN_DCN)which based on BERT pre-trained model with superior ability of extracting characteristic.The advantages of HAN model and DCN model are taken into account which can help gain abundant semantic information,fusing context semantic features and hierarchical characteristics.Secondly,the traditional softmax algorithm increases the learning difficulty of the same kind of samples,making it more difficult to distinguish similar features.Based on this,AM-softmax is introduced to replace the traditional softmax.Finally,the fused model is validated,which shows superior performance in the accuracy rate and F1-score of this hybrid model on two datasets and the experimental analysis shows the general single models such as HAN,DCN,based on BERT pre-trained model.Besides,the improved AM-softmax network model is superior to the general softmax network model.展开更多
A discrete algorithm suitable for the computation of complex frequency-domain convolution on computers was derived. The Durbin's numerical inversion of Laplace transforms can be used to figure out the time-domain ...A discrete algorithm suitable for the computation of complex frequency-domain convolution on computers was derived. The Durbin's numerical inversion of Laplace transforms can be used to figure out the time-domain digital solution of the result of complex frequency-domain convolutions. Compared with the digital solutions and corresponding analytical solutions, it is shown that the digital solutions have high precision.展开更多
Here concerned and further investigated is a certain operator method for the computation of convolutions of polynomials.We provide a general formulation of the method with a refinement for certain old results,and also...Here concerned and further investigated is a certain operator method for the computation of convolutions of polynomials.We provide a general formulation of the method with a refinement for certain old results,and also give some new applications to convolved sums involving several noted special polynomials.The advantage of the method using operators is illustrated with concrete examples.Finally,also presented is a brief investigation on convolution polynomials having two types of summations.展开更多
Based on quantum mechanical representation and operator theory,this paper restates the two new convolutions of fractional Fourier transform(FrFT)by making full use of the conversion relationship between two mutual con...Based on quantum mechanical representation and operator theory,this paper restates the two new convolutions of fractional Fourier transform(FrFT)by making full use of the conversion relationship between two mutual conjugates:coordinate representation and momentum representation.This paper gives full play to the efficiency of Dirac notation and proves the convolutions of fractional Fourier transform from the perspective of quantum optics,a field that has been developing rapidly.These two new convolution methods have potential value in signal processing.展开更多
For a locally compact group G, L 1(G) is its group algebra and L ∞(G) is the dual of L 1(G). Lau has studied the bounded linear operators T : L ∞(G) → L ∞(G) which commute with convolutions and translations. For a...For a locally compact group G, L 1(G) is its group algebra and L ∞(G) is the dual of L 1(G). Lau has studied the bounded linear operators T : L ∞(G) → L ∞(G) which commute with convolutions and translations. For a subspace H of L ∞(G), we know that M(L ∞(G),H), the Banach algebra of all bounded linear operators on L ∞(G) into H which commute with convolutions, has been studied by Pym and Lau. In this paper, we generalize these problems to L(K)*, the dual of a hypergroup algebra L(K) in a very general setting, i. e. we do not assume that K admits a Haar measure. It should be noted that these algebras include not only the group algebra L 1(G) but also most of the semigroup algebras. Compact hypergroups have a Haar measure, however, in general it is not known that every hypergroup has a Haar measure. The lack of the Haar measure and involution presents many difficulties; however, we succeed in getting some interesting results.展开更多
Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating In...Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating Interactive Dynamic Graph Convolution Network(IDGCN)with Temporal Multi-Head Trend-Aware Attention.Its core innovation lies in IDGCN,which uniquely splits sequences into symmetric intervals for interactive feature sharing via dynamic graphs,and a novel attention mechanism incorporating convolutional operations to capture essential local traffic trends—addressing a critical gap in standard attention for continuous data.For 15-and 60-min forecasting on METR-LA,AIDGCN achieves MAEs of 0.75%and 0.39%,and RMSEs of 1.32%and 0.14%,respectively.In the 60-min long-term forecasting of the PEMS-BAY dataset,the AIDGCN out-performs the MRA-BGCN method by 6.28%,4.93%,and 7.17%in terms of MAE,RMSE,and MAPE,respectively.Experimental results demonstrate the superiority of our pro-posed model over state-of-the-art methods.展开更多
Advances in optical coherence tomography(OCT)technology allow a clear view of the vitreoretinal interface(VRI).The abnormality of the VRI is one of the common symptoms of high myopia,mainly including posterior vitreou...Advances in optical coherence tomography(OCT)technology allow a clear view of the vitreoretinal interface(VRI).The abnormality of the VRI is one of the common symptoms of high myopia,mainly including posterior vitreous detachment(PVD)and epiretinal membrane(ERM).They can cause severe damage to the structure and function of the retina,leading to permanent vision loss.Therefore,fully automated detection of abnormalities at the VRI is crucial for the management of high myopia.This paper presents a DS-YOLOv7 network aimed at accurately identifying abnormalities,including partial PVD,complete PVD,and ERM from retinal OCT images.Built upon the YOLOv7 network,the proposed model integrates the advanced dynamic snake convolution(DSConv)module to capture the curvilinear characteristics of lesions,and the mixture of attention and convolution(ACMix)module to improve the precision and robustness of feature extraction through effective fusion of self-attention mechanisms and convolution.Moreover,the introduction of the efficient complete intersection-over-union(ECIoU)loss function further enhances the coordinate regression capability of the model.Threefold cross-validation on a dataset with 1973 OCT B-scans from 46 patients shows that the DS-YOLOv7 achieved superior performance in vitreoretinal interface abnormality detection,with mAP@0.5 of 0.714,mAP@0.75 of 0.438,and mAP@0.5:0.95 of 0.424.The proposed model can provide an accurate and efficient diagnostic tool for patients with high myopia.展开更多
Malware poses a significant threat to the Internet of Things(IoT).It enables unauthorized access to devices in the IoT environment.The lack of unique architectural standards causes challenges in developing robust malw...Malware poses a significant threat to the Internet of Things(IoT).It enables unauthorized access to devices in the IoT environment.The lack of unique architectural standards causes challenges in developing robust malware detection(MD)models.The existing models demand substantial computational resources.This study intends to build a lightweight MD model to detect anomalies in IoT networks.The authors develop a transformation technique,converting the malware binaries into images.MobileNet V2 is fine-tuned using improved grey wolf optimization(IGWO)to extract crucial features of malicious and benign samples.The ResNeXt model is combined with the Linformer’s attention mechanism to identify Malware features.A fully connected layer is integrated with gradientweighted class activation mapping(Grad-CAM)in order to facilitate an interpretable classification model.The proposed model is evaluated using the IoT malware and the IoT-23 datasets.The model performs well on the two datasets with an accuracy of 98.94%,precision of 98.46%,recall of 98.11%,and F1-score of 98.28%on the IoT malware dataset,and an accuracy of 98.23%,precision of 96.80%,recall of 96.64%,and F1-score of 96.71%on the IoT-23 dataset,respectively.The findings indicate that the model has a high standard of classification.The lightweight architecture enables efficient deployment with an inference time of 1.42 s.Inference time has no direct impact on accuracy,precision,recall,or F1-score.However,the inference speed would warrant timely detection in latency-sensitive IoT applications.By achieving a remarkable result,the proposed study offers a comprehensive solution:a scalable,interpretable,and computationally efficient MD model for the evolving IoT landscape.展开更多
In daily life,keyword spotting plays an important role in human-computer interaction.However,noise often interferes with the extraction of time-frequency information,and achieving both computational efficiency and rec...In daily life,keyword spotting plays an important role in human-computer interaction.However,noise often interferes with the extraction of time-frequency information,and achieving both computational efficiency and recognition accuracy on resource-constrained devices such as mobile terminals remains a major challenge.To address this,we propose a novel time-frequency dual-branch parallel residual network,which integrates a Dual-Branch Broadcast Residual module and a Time-Frequency Coordinate Attention module.The time-domain and frequency-domain branches are designed in parallel to independently extract temporal and spectral features,effectively avoiding the potential information loss caused by serial stacking,while enhancing information flow and multi-scale feature fusion.In terms of training strategy,a curriculum learning approach is introduced to progressively improve model robustness fromeasy to difficult tasks.Experimental results demonstrate that the proposed method consistently outperforms existing lightweight models under various signal-to-noise ratio(SNR)conditions,achieving superior far-field recognition performance on the Google Speech Commands V2 dataset.Notably,the model maintains stable performance even in low-SNR environments such as–10 dB,and generalizes well to unseen SNR conditions during training,validating its robustness to novel noise scenarios.Furthermore,the proposed model exhibits significantly fewer parameters,making it highly suitable for deployment on resource-limited devices.Overall,the model achieves a favorable balance between performance and parameter efficiency,demonstrating strong potential for practical applications.展开更多
Red chilli powder(RCP)is a versatile spice accepted globally in diverse culinary products due to its distinct pungent characteristics and red colour.The higher market demand makes the spice vulnerable to unethical mix...Red chilli powder(RCP)is a versatile spice accepted globally in diverse culinary products due to its distinct pungent characteristics and red colour.The higher market demand makes the spice vulnerable to unethical mixing,so its quality assessment is crucial.The non-destructive application of computer vision for measuring food adulteration has always attracted researchers and industry due to its robustness and feasibility.Following the current era of Food Quality 4.0 and artificial intelligence,this study follows an approach based on 1D-convolutional neural networks(CNN)and 2D-CNN models for detecting RCP adulteration.The performance evaluation metrics are used to analyse the efficiency of these models.The histogram features from the Lab colour space trained on the 1D-CNN model(BS-40 and Epoch 100)show an accuracy of 84.56%.On the other hand,the 2D-CNN model DenseNet-121(AdamW and BS-30)also shows a test accuracy of 84.62%.From the observations of this study,it is concluded that CNN models can be a promising tool for solving the adulteration detection problem in food quality evaluation.Further,internet of things-based systems can be developed to aid the industry and government agencies in monitoring the quality of RCP to harness the unethical practices of food adulteration.展开更多
Stereo matching is a pivotal task in computer vision,enabling precise depth estimation from stereo image pairs,yet it encounters challenges in regions with reflections,repetitive textures,or fine structures.In this pa...Stereo matching is a pivotal task in computer vision,enabling precise depth estimation from stereo image pairs,yet it encounters challenges in regions with reflections,repetitive textures,or fine structures.In this paper,we propose a Semantic-Guided Parallax Attention Stereo Matching Network(SGPASMnet)that can be trained in unsupervised manner,building upon the Parallax Attention Stereo Matching Network(PASMnet).Our approach leverages unsupervised learning to address the scarcity of ground truth disparity in stereo matching datasets,facilitating robust training across diverse scene-specific datasets and enhancing generalization.SGPASMnet incorporates two novel components:a Cross-Scale Feature Interaction(CSFI)block and semantic feature augmentation using a pre-trained semantic segmentation model,SegFormer,seamlessly embedded into the parallax attention mechanism.The CSFI block enables effective fusion ofmulti-scale features,integrating coarse and fine details to enhance disparity estimation accuracy.Semantic features,extracted by SegFormer,enrich the parallax attention mechanism by providing high-level scene context,significantly improving performance in ambiguous regions.Our model unifies these enhancements within a cohesive architecture,comprising semantic feature extraction,an hourglass network,a semantic-guided cascaded parallax attentionmodule,outputmodule,and a disparity refinement network.Evaluations on the KITTI2015 dataset demonstrate that our unsupervised method achieves a lower error rate compared to the original PASMnet,highlighting the effectiveness of our enhancements in handling complex scenes.By harnessing unsupervised learning without ground truth disparity needed,SGPASMnet offers a scalable and robust solution for accurate stereo matching,with superior generalization across varied real-world applications.展开更多
基金supported by the European University of Atlantic.
文摘Brain tumors pose significant diagnostic challenges due to their diverse types and complex anatomical locations.Due to the increase in precision image-based diagnostic tools,driven by advancements in artificial intelligence(AI)and deep learning,there has been potential to improve diagnostic accuracy,especially with Magnetic Resonance Imaging(MRI).However,traditional state-of-the-art models lack the sensitivity essential for reliable tumor identification and segmentation.Thus,our research aims to enhance brain tumor diagnosis in MRI by proposing an advanced model.The proposed model incorporates dilated convolutions to optimize the brain tumor segmentation and classification.The proposed model is first trained and later evaluated using the BraTS 2020 dataset.In our proposed model preprocessing consists of normalization,noise reduction,and data augmentation to improve model robustness.The attention mechanism and dilated convolutions were introduced to increase the model’s focus on critical regions and capture finer spatial details without compromising image resolution.We have performed experimentation to measure efficiency.For this,we have used various metrics including accuracy,sensitivity,and curve(AUC-ROC).The proposed model achieved a high accuracy of 94%,a sensitivity of 93%,a specificity of 92%,and an AUC-ROC of 0.98,outperforming traditional diagnostic models in brain tumor detection.The proposed model accurately identifies tumor regions,while dilated convolutions enhanced the segmentation accuracy,especially for complex tumor structures.The proposed model demonstrates significant potential for clinical application,providing reliable and precise brain tumor detection in MRI.
文摘The Bernoulli convolution ν λ measure is shown to be absolutely continuous with L 2 density for almost all 12<λ<1,and singular if λ -1 is a Pisot number. It is an open question whether the Pisot type Bernoulli convolutions are the only singular ones. In this paper,we construct a family of non-Pisot type Bernoulli convolutions ν λ such that their density functions,if they exist,are not L 2. We also construct other Bernolulli convolutions whose density functions,if they exist,behave rather badly.
文摘Louis Pierre Gratiolet (1815-1865) was one of the first modern anatomists to pay attention to cerebral convolutions. Born in Sainte-Foy-la-Grande (Gironde), he moved to Paris in 1834 to study medicine, as well as comparative anatomy under Henri de Blainville (1777-1850). In 1842, he accepted de Blainville’s offer to become his assistant at the Muséum d’histoire naturelle and progressively abandoned medicine for comparative anatomy. He undertook a detailed study of brains of human and nonhuman primates and soon realized that the organizational pattern of cerebral convolutions was so predictable that it could serve as a criterion to classify primate groups. He noted that only the deepest sulci exist in lower primate forms, while the complexity of cortical folding increases markedly in great apes and humans. Gratiolet provided the first cogent description of the lobular organization of primate cerebral hemispheres. He saw the insula as a central lobe around which revolved the frontal, parietal, temporal (temporo-sphenoidal) and occipital lobes. He correctly identified most gyri and sulci on all brain surfaces, introduced the term “plis de passage” for some interconnecting gyri, and provided the first description of the optic radiations. In the early 1860s, Gratiolet fought a highly publicized battle against Paul Broca (1824-1880) on the relationship between brain and intelligence. Gratiolet agreed that the brain was most likely the seat of intelligence, but he considered human cognition far too subtle to have any direct relationship with brain size. He argued that a detailed study of the human brain architecture would be more profitable than Broca’s vain speculations on the relationship between brain weight and intelligence, which he considered a monolithic entity. Despite remarkable scientific achievements and a unique teaching capacity, Gratiolet was unable to secure any academic position until three years before his sudden death in Paris at age 49.
基金supported by National Natural Science Foundation of China(Nos.61525306,61633021,61721004,61806194,U1803261 and 61976132)Major Project for New Generation of AI(No.2018AAA0100400)+2 种基金Beijing Nova Program(No.Z201100006820079)Shandong Provincial Key Research and Development Program(No.2019JZZY010119)CAS-AIR。
文摘Pointwise convolution is usually utilized to expand or squeeze features in modern lightweight deep models.However,it takes up most of the overall computational cost(usually more than 90%).This paper proposes a novel Poker module to expand features by taking advantage of cheap depthwise convolution.As a result,the Poker module can greatly reduce the computational cost,and meanwhile generate a large number of effective features to guarantee the performance.The proposed module is standardized and can be employed wherever the feature expansion is needed.By varying the stride and the number of channels,different kinds of bottlenecks are designed to plug the proposed Poker module into the network.Thus,a lightweight model can be easily assembled.Experiments conducted on benchmarks reveal the effectiveness of our proposed Poker module.And our Poker Net models can reduce the computational cost by 7.1%-15.6%.Poker Net models achieve comparable or even higher recognition accuracy than previous state-of-the-art(SOTA)models on the Image Net ILSVRC2012 classification dataset.Code is available at https://github.com/diaomin/pokernet.
基金Fundamental Research Funds for the Central University,China(No.2232018D3-17)。
文摘Text format information is full of most of the resources of Internet,which puts forward higher and higher requirements for the accuracy of text classification.Therefore,in this manuscript,firstly,we design a hybrid model of bidirectional encoder representation from transformers-hierarchical attention networks-dilated convolutions networks(BERT_HAN_DCN)which based on BERT pre-trained model with superior ability of extracting characteristic.The advantages of HAN model and DCN model are taken into account which can help gain abundant semantic information,fusing context semantic features and hierarchical characteristics.Secondly,the traditional softmax algorithm increases the learning difficulty of the same kind of samples,making it more difficult to distinguish similar features.Based on this,AM-softmax is introduced to replace the traditional softmax.Finally,the fused model is validated,which shows superior performance in the accuracy rate and F1-score of this hybrid model on two datasets and the experimental analysis shows the general single models such as HAN,DCN,based on BERT pre-trained model.Besides,the improved AM-softmax network model is superior to the general softmax network model.
文摘A discrete algorithm suitable for the computation of complex frequency-domain convolution on computers was derived. The Durbin's numerical inversion of Laplace transforms can be used to figure out the time-domain digital solution of the result of complex frequency-domain convolutions. Compared with the digital solutions and corresponding analytical solutions, it is shown that the digital solutions have high precision.
文摘Here concerned and further investigated is a certain operator method for the computation of convolutions of polynomials.We provide a general formulation of the method with a refinement for certain old results,and also give some new applications to convolved sums involving several noted special polynomials.The advantage of the method using operators is illustrated with concrete examples.Finally,also presented is a brief investigation on convolution polynomials having two types of summations.
基金National Natural Science Foundation of China(Grant Number:11304126)College Students' Innovation Training Program(Grant Number:202110299696X)。
文摘Based on quantum mechanical representation and operator theory,this paper restates the two new convolutions of fractional Fourier transform(FrFT)by making full use of the conversion relationship between two mutual conjugates:coordinate representation and momentum representation.This paper gives full play to the efficiency of Dirac notation and proves the convolutions of fractional Fourier transform from the perspective of quantum optics,a field that has been developing rapidly.These two new convolution methods have potential value in signal processing.
文摘For a locally compact group G, L 1(G) is its group algebra and L ∞(G) is the dual of L 1(G). Lau has studied the bounded linear operators T : L ∞(G) → L ∞(G) which commute with convolutions and translations. For a subspace H of L ∞(G), we know that M(L ∞(G),H), the Banach algebra of all bounded linear operators on L ∞(G) into H which commute with convolutions, has been studied by Pym and Lau. In this paper, we generalize these problems to L(K)*, the dual of a hypergroup algebra L(K) in a very general setting, i. e. we do not assume that K admits a Haar measure. It should be noted that these algebras include not only the group algebra L 1(G) but also most of the semigroup algebras. Compact hypergroups have a Haar measure, however, in general it is not known that every hypergroup has a Haar measure. The lack of the Haar measure and involution presents many difficulties; however, we succeed in getting some interesting results.
文摘针对果实分拣中存在识别精度低、耗时长等问题,设计实现了一种基于深度学习的智能水果分拣系统.首先,该系统采用残差网络(Residual network,ResNet)模型,通过引入动态残差门控机制优化梯度传播有效解决了深层网络训练中的梯度消失和爆炸问题,使得网络能够通过跳跃连接学习到更有效的特征表示;其次,对ResNet-18模型进行了轻量化设计,利用交叉熵损失函数(CrossEntropy loss,CELoss)和Adam优化器(Adaptive moment estimation,Adam)来进行模型的训练;最后,对数据集peach-split进行实验分析,结果表明构建的智能分拣系统对提高水果分拣精度研究具有一定的实用价值.
文摘Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating Interactive Dynamic Graph Convolution Network(IDGCN)with Temporal Multi-Head Trend-Aware Attention.Its core innovation lies in IDGCN,which uniquely splits sequences into symmetric intervals for interactive feature sharing via dynamic graphs,and a novel attention mechanism incorporating convolutional operations to capture essential local traffic trends—addressing a critical gap in standard attention for continuous data.For 15-and 60-min forecasting on METR-LA,AIDGCN achieves MAEs of 0.75%and 0.39%,and RMSEs of 1.32%and 0.14%,respectively.In the 60-min long-term forecasting of the PEMS-BAY dataset,the AIDGCN out-performs the MRA-BGCN method by 6.28%,4.93%,and 7.17%in terms of MAE,RMSE,and MAPE,respectively.Experimental results demonstrate the superiority of our pro-posed model over state-of-the-art methods.
基金supported by the National Natural Science Foundation of China(62271337,62371326,and 62371328)the National Key Research and Development Program of China(2019FYC1710204)+1 种基金the National Clinical Key Specialty Construction Project(10000015Z155080000004)the Natural Science Foundation of Jiangsu Province(BK20231310).
文摘Advances in optical coherence tomography(OCT)technology allow a clear view of the vitreoretinal interface(VRI).The abnormality of the VRI is one of the common symptoms of high myopia,mainly including posterior vitreous detachment(PVD)and epiretinal membrane(ERM).They can cause severe damage to the structure and function of the retina,leading to permanent vision loss.Therefore,fully automated detection of abnormalities at the VRI is crucial for the management of high myopia.This paper presents a DS-YOLOv7 network aimed at accurately identifying abnormalities,including partial PVD,complete PVD,and ERM from retinal OCT images.Built upon the YOLOv7 network,the proposed model integrates the advanced dynamic snake convolution(DSConv)module to capture the curvilinear characteristics of lesions,and the mixture of attention and convolution(ACMix)module to improve the precision and robustness of feature extraction through effective fusion of self-attention mechanisms and convolution.Moreover,the introduction of the efficient complete intersection-over-union(ECIoU)loss function further enhances the coordinate regression capability of the model.Threefold cross-validation on a dataset with 1973 OCT B-scans from 46 patients shows that the DS-YOLOv7 achieved superior performance in vitreoretinal interface abnormality detection,with mAP@0.5 of 0.714,mAP@0.75 of 0.438,and mAP@0.5:0.95 of 0.424.The proposed model can provide an accurate and efficient diagnostic tool for patients with high myopia.
基金supported by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia[Grant No.KFU253774].
文摘Malware poses a significant threat to the Internet of Things(IoT).It enables unauthorized access to devices in the IoT environment.The lack of unique architectural standards causes challenges in developing robust malware detection(MD)models.The existing models demand substantial computational resources.This study intends to build a lightweight MD model to detect anomalies in IoT networks.The authors develop a transformation technique,converting the malware binaries into images.MobileNet V2 is fine-tuned using improved grey wolf optimization(IGWO)to extract crucial features of malicious and benign samples.The ResNeXt model is combined with the Linformer’s attention mechanism to identify Malware features.A fully connected layer is integrated with gradientweighted class activation mapping(Grad-CAM)in order to facilitate an interpretable classification model.The proposed model is evaluated using the IoT malware and the IoT-23 datasets.The model performs well on the two datasets with an accuracy of 98.94%,precision of 98.46%,recall of 98.11%,and F1-score of 98.28%on the IoT malware dataset,and an accuracy of 98.23%,precision of 96.80%,recall of 96.64%,and F1-score of 96.71%on the IoT-23 dataset,respectively.The findings indicate that the model has a high standard of classification.The lightweight architecture enables efficient deployment with an inference time of 1.42 s.Inference time has no direct impact on accuracy,precision,recall,or F1-score.However,the inference speed would warrant timely detection in latency-sensitive IoT applications.By achieving a remarkable result,the proposed study offers a comprehensive solution:a scalable,interpretable,and computationally efficient MD model for the evolving IoT landscape.
文摘In daily life,keyword spotting plays an important role in human-computer interaction.However,noise often interferes with the extraction of time-frequency information,and achieving both computational efficiency and recognition accuracy on resource-constrained devices such as mobile terminals remains a major challenge.To address this,we propose a novel time-frequency dual-branch parallel residual network,which integrates a Dual-Branch Broadcast Residual module and a Time-Frequency Coordinate Attention module.The time-domain and frequency-domain branches are designed in parallel to independently extract temporal and spectral features,effectively avoiding the potential information loss caused by serial stacking,while enhancing information flow and multi-scale feature fusion.In terms of training strategy,a curriculum learning approach is introduced to progressively improve model robustness fromeasy to difficult tasks.Experimental results demonstrate that the proposed method consistently outperforms existing lightweight models under various signal-to-noise ratio(SNR)conditions,achieving superior far-field recognition performance on the Google Speech Commands V2 dataset.Notably,the model maintains stable performance even in low-SNR environments such as–10 dB,and generalizes well to unseen SNR conditions during training,validating its robustness to novel noise scenarios.Furthermore,the proposed model exhibits significantly fewer parameters,making it highly suitable for deployment on resource-limited devices.Overall,the model achieves a favorable balance between performance and parameter efficiency,demonstrating strong potential for practical applications.
文摘Red chilli powder(RCP)is a versatile spice accepted globally in diverse culinary products due to its distinct pungent characteristics and red colour.The higher market demand makes the spice vulnerable to unethical mixing,so its quality assessment is crucial.The non-destructive application of computer vision for measuring food adulteration has always attracted researchers and industry due to its robustness and feasibility.Following the current era of Food Quality 4.0 and artificial intelligence,this study follows an approach based on 1D-convolutional neural networks(CNN)and 2D-CNN models for detecting RCP adulteration.The performance evaluation metrics are used to analyse the efficiency of these models.The histogram features from the Lab colour space trained on the 1D-CNN model(BS-40 and Epoch 100)show an accuracy of 84.56%.On the other hand,the 2D-CNN model DenseNet-121(AdamW and BS-30)also shows a test accuracy of 84.62%.From the observations of this study,it is concluded that CNN models can be a promising tool for solving the adulteration detection problem in food quality evaluation.Further,internet of things-based systems can be developed to aid the industry and government agencies in monitoring the quality of RCP to harness the unethical practices of food adulteration.
基金supported by the National Natural Science Foundation of China,No.62301497the Science and Technology Research Program of Henan,No.252102211024the Key Research and Development Program of Henan,No.231111212000.
文摘Stereo matching is a pivotal task in computer vision,enabling precise depth estimation from stereo image pairs,yet it encounters challenges in regions with reflections,repetitive textures,or fine structures.In this paper,we propose a Semantic-Guided Parallax Attention Stereo Matching Network(SGPASMnet)that can be trained in unsupervised manner,building upon the Parallax Attention Stereo Matching Network(PASMnet).Our approach leverages unsupervised learning to address the scarcity of ground truth disparity in stereo matching datasets,facilitating robust training across diverse scene-specific datasets and enhancing generalization.SGPASMnet incorporates two novel components:a Cross-Scale Feature Interaction(CSFI)block and semantic feature augmentation using a pre-trained semantic segmentation model,SegFormer,seamlessly embedded into the parallax attention mechanism.The CSFI block enables effective fusion ofmulti-scale features,integrating coarse and fine details to enhance disparity estimation accuracy.Semantic features,extracted by SegFormer,enrich the parallax attention mechanism by providing high-level scene context,significantly improving performance in ambiguous regions.Our model unifies these enhancements within a cohesive architecture,comprising semantic feature extraction,an hourglass network,a semantic-guided cascaded parallax attentionmodule,outputmodule,and a disparity refinement network.Evaluations on the KITTI2015 dataset demonstrate that our unsupervised method achieves a lower error rate compared to the original PASMnet,highlighting the effectiveness of our enhancements in handling complex scenes.By harnessing unsupervised learning without ground truth disparity needed,SGPASMnet offers a scalable and robust solution for accurate stereo matching,with superior generalization across varied real-world applications.
