The task of food image recognition,a nuanced subset of fine-grained image recognition,grapples with substantial intra-class variation and minimal inter-class differences.These challenges are compounded by the irregula...The task of food image recognition,a nuanced subset of fine-grained image recognition,grapples with substantial intra-class variation and minimal inter-class differences.These challenges are compounded by the irregular and multi-scale nature of food images.Addressing these complexities,our study introduces an advanced model that leverages multiple attention mechanisms and multi-stage local fusion,grounded in the ConvNeXt architecture.Our model employs hybrid attention(HA)mechanisms to pinpoint critical discriminative regions within images,substantially mitigating the influence of background noise.Furthermore,it introduces a multi-stage local fusion(MSLF)module,fostering long-distance dependencies between feature maps at varying stages.This approach facilitates the assimilation of complementary features across scales,significantly bolstering the model’s capacity for feature extraction.Furthermore,we constructed a dataset named Roushi60,which consists of 60 different categories of common meat dishes.Empirical evaluation of the ETH Food-101,ChineseFoodNet,and Roushi60 datasets reveals that our model achieves recognition accuracies of 91.12%,82.86%,and 92.50%,respectively.These figures not only mark an improvement of 1.04%,3.42%,and 1.36%over the foundational ConvNeXt network but also surpass the performance of most contemporary food image recognition methods.Such advancements underscore the efficacy of our proposed model in navigating the intricate landscape of food image recognition,setting a new benchmark for the field.展开更多
To overcome the shortcomings of 1 D and 2 D Otsu’s thresholding techniques, the 3 D Otsu method has been developed.Among all Otsu’s methods, 3 D Otsu technique provides the best threshold values for the multi-level ...To overcome the shortcomings of 1 D and 2 D Otsu’s thresholding techniques, the 3 D Otsu method has been developed.Among all Otsu’s methods, 3 D Otsu technique provides the best threshold values for the multi-level thresholding processes. In this paper, to improve the quality of segmented images, a simple and effective multilevel thresholding method is introduced. The proposed approach focuses on preserving edge detail by computing the 3 D Otsu along the fusion phenomena. The advantages of the presented scheme include higher quality outcomes, better preservation of tiny details and boundaries and reduced execution time with rising threshold levels. The fusion approach depends upon the differences between pixel intensity values within a small local space of an image;it aims to improve localized information after the thresholding process. The fusion of images based on local contrast can improve image segmentation performance by minimizing the loss of local contrast, loss of details and gray-level distributions. Results show that the proposed method yields more promising segmentation results when compared to conventional1 D Otsu, 2 D Otsu and 3 D Otsu methods, as evident from the objective and subjective evaluations.展开更多
The localization of the blanket jamming is studied and a new method of solving the localization ambiguity is proposed. Radars only can acquire angle information without range information when encountering the blanket ...The localization of the blanket jamming is studied and a new method of solving the localization ambiguity is proposed. Radars only can acquire angle information without range information when encountering the blanket jamming. Netted radars could get position information of the blanket jamming by make use of radars' relative position and the angle information, when there is one blanket jamming. In the presence of error, the localization method and the accuracy analysis of one blanket jamming are given. However, if there are more than one blanket jamming, and the two blanket jamming and two radars are coplanar, the localization of jamming could be error due to localization ambiguity. To solve this confusion, the Kalman filter model is established for all intersections, and through the initiation and association algorithm of multi-target, the false intersection can be eliminated. Simulations show that the presented method is valid.展开更多
For existing indoor localization algorithm has low accuracy, high cost in deployment and maintenance, lack of robustness, and low sensor utilization, this paper proposes a particle filter algorithm based on multi-sens...For existing indoor localization algorithm has low accuracy, high cost in deployment and maintenance, lack of robustness, and low sensor utilization, this paper proposes a particle filter algorithm based on multi-sensor fusion. The pedestrian’s localization in indoor environment is described as dynamic system state estimation problem. The algorithm combines the smart mobile terminal with indoor localization, and filters the result of localization with the particle filter. In this paper, a dynamic interval particle filter algorithm based on pedestrian dead reckoning (PDR) information and RSSI localization information have been used to improve the filtering precision and the stability. Moreover, the localization results will be uploaded to the server in time, and the location fingerprint database will be built incrementally, which can adapt the dynamic changes of the indoor environment. Experimental results show that the algorithm based on multi-sensor improves the localization accuracy and robustness compared with the location algorithm based on Wi-Fi.展开更多
s-Lap is a new gene sequence from pig retinal pigment epithelial(RPE) cells, which was found and cloned in the early period of apoptosis of RPE cells damaged with visible light. We cloned the coding area sequence of t...s-Lap is a new gene sequence from pig retinal pigment epithelial(RPE) cells, which was found and cloned in the early period of apoptosis of RPE cells damaged with visible light. We cloned the coding area sequence of the novel gene of s-Lap and constructed its recombinant eukaryotic plasmid pcDNA3.1-GFP/s-lap with the recombinant DNA technique. The expression and localization of s-lap/GFP fusion protein in CHO and B_~16 cell lines were studied with the instantaneously transfected pcDNA3.1-GFP/s-lap recombinant plasmid. ~s-Lap/GFP fusion protein can be expressed in CHO and B_~16 cells with a high rate expression in the nuclei.展开更多
针对当前电力设备红外图像分辨率低和温度分布模糊问题,提出一种基于局部和全局信息注意力生成对抗网络(local and global information attention generative adversarial network,LGIA-GAN)的超分辨率重建方法。首先,使用门控权重单元...针对当前电力设备红外图像分辨率低和温度分布模糊问题,提出一种基于局部和全局信息注意力生成对抗网络(local and global information attention generative adversarial network,LGIA-GAN)的超分辨率重建方法。首先,使用门控权重单元融合多种卷积输出构建细节增强融合卷积,增加重要信息在输出特征图的占比;其次,搭建双注意力模块,对图像长距离像素依赖关系建模并捕获空间和通道维度信息;然后,构造生成对抗网络,使网络关注电力设备红外图像局部纹理细节和全局轮廓信息;最后,通过实验证明,LGIA-GAN在数据集上的峰值信噪比和结构相似度分别为30.266dB和0.9197,重建时间为0.120s,明显优于其他几种GAN算法,并在主观视觉上重建效果更好。所提方法能够有效提升电力设备热成像分辨率,对电力设备故障诊断具有支撑作用。展开更多
针对现代化鹅养殖场景中饲料投喂移动小车受动态鹅群干扰,致使同时定位与地图构建(Simultaneous Localization And Mapping,SLAM)算法的定位精度、建图质量下降的问题,提出基于多传感融合目标检测的动态SLAM算法。该算法以LIO-SAM框架...针对现代化鹅养殖场景中饲料投喂移动小车受动态鹅群干扰,致使同时定位与地图构建(Simultaneous Localization And Mapping,SLAM)算法的定位精度、建图质量下降的问题,提出基于多传感融合目标检测的动态SLAM算法。该算法以LIO-SAM框架为基础,融合激光雷达与惯性测量单元搭建SLAM系统,采用前后端架构优化定位与建图性能;运用匈牙利算法实时追踪鹅群运动状态,结合多传感融合目标检测算法,精准识别并剔除动态鹅群产生的特征点,有效降低定位与建图误差。经KITTI、UrbanNav等公共数据集与实际养殖场景数据测试,在KITTI07序列中,较LeGO-LOAM、LIO-SAM和LVI-SAM等经典算法,均方根误差(RMSE)降低33.18%;在实际鹅养殖环境中,可以快速滤除动态鹅群干扰,提升建图质量与导航可靠性。本研究为智能化鹅养殖饲料投喂提供了新的技术方案,推动了畜牧业自动化发展。展开更多
为支撑新型电力系统本地侧业务灵活敏捷接入,电力无线通信网络融合化部署成为迫切需要和发展趋势。然而,异构终端密集接入导致了信道拥塞与能耗过高问题。为此,本文以支持多种通信技术的多制式融合通信设备为核心,设计电力本地无线融合...为支撑新型电力系统本地侧业务灵活敏捷接入,电力无线通信网络融合化部署成为迫切需要和发展趋势。然而,异构终端密集接入导致了信道拥塞与能耗过高问题。为此,本文以支持多种通信技术的多制式融合通信设备为核心,设计电力本地无线融合通信架构,并针对终端映射和接入点(access point, AP)启用选择问题,提出基于贪心策略的融合通信接入能耗优化调度算法(greedy-strategy-based fusion communication access energy optimization scheduling algorithm, GFCES)。仿真实验表明,本方法能够在融合通信场景中保障业务接入质量的同时有效降低整体接入能耗。展开更多
基金The support of this research was by Hubei Provincial Natural Science Foundation(2022CFB449)Science Research Foundation of Education Department of Hubei Province(B2020061),are gratefully acknowledged.
文摘The task of food image recognition,a nuanced subset of fine-grained image recognition,grapples with substantial intra-class variation and minimal inter-class differences.These challenges are compounded by the irregular and multi-scale nature of food images.Addressing these complexities,our study introduces an advanced model that leverages multiple attention mechanisms and multi-stage local fusion,grounded in the ConvNeXt architecture.Our model employs hybrid attention(HA)mechanisms to pinpoint critical discriminative regions within images,substantially mitigating the influence of background noise.Furthermore,it introduces a multi-stage local fusion(MSLF)module,fostering long-distance dependencies between feature maps at varying stages.This approach facilitates the assimilation of complementary features across scales,significantly bolstering the model’s capacity for feature extraction.Furthermore,we constructed a dataset named Roushi60,which consists of 60 different categories of common meat dishes.Empirical evaluation of the ETH Food-101,ChineseFoodNet,and Roushi60 datasets reveals that our model achieves recognition accuracies of 91.12%,82.86%,and 92.50%,respectively.These figures not only mark an improvement of 1.04%,3.42%,and 1.36%over the foundational ConvNeXt network but also surpass the performance of most contemporary food image recognition methods.Such advancements underscore the efficacy of our proposed model in navigating the intricate landscape of food image recognition,setting a new benchmark for the field.
文摘To overcome the shortcomings of 1 D and 2 D Otsu’s thresholding techniques, the 3 D Otsu method has been developed.Among all Otsu’s methods, 3 D Otsu technique provides the best threshold values for the multi-level thresholding processes. In this paper, to improve the quality of segmented images, a simple and effective multilevel thresholding method is introduced. The proposed approach focuses on preserving edge detail by computing the 3 D Otsu along the fusion phenomena. The advantages of the presented scheme include higher quality outcomes, better preservation of tiny details and boundaries and reduced execution time with rising threshold levels. The fusion approach depends upon the differences between pixel intensity values within a small local space of an image;it aims to improve localized information after the thresholding process. The fusion of images based on local contrast can improve image segmentation performance by minimizing the loss of local contrast, loss of details and gray-level distributions. Results show that the proposed method yields more promising segmentation results when compared to conventional1 D Otsu, 2 D Otsu and 3 D Otsu methods, as evident from the objective and subjective evaluations.
文摘The localization of the blanket jamming is studied and a new method of solving the localization ambiguity is proposed. Radars only can acquire angle information without range information when encountering the blanket jamming. Netted radars could get position information of the blanket jamming by make use of radars' relative position and the angle information, when there is one blanket jamming. In the presence of error, the localization method and the accuracy analysis of one blanket jamming are given. However, if there are more than one blanket jamming, and the two blanket jamming and two radars are coplanar, the localization of jamming could be error due to localization ambiguity. To solve this confusion, the Kalman filter model is established for all intersections, and through the initiation and association algorithm of multi-target, the false intersection can be eliminated. Simulations show that the presented method is valid.
文摘For existing indoor localization algorithm has low accuracy, high cost in deployment and maintenance, lack of robustness, and low sensor utilization, this paper proposes a particle filter algorithm based on multi-sensor fusion. The pedestrian’s localization in indoor environment is described as dynamic system state estimation problem. The algorithm combines the smart mobile terminal with indoor localization, and filters the result of localization with the particle filter. In this paper, a dynamic interval particle filter algorithm based on pedestrian dead reckoning (PDR) information and RSSI localization information have been used to improve the filtering precision and the stability. Moreover, the localization results will be uploaded to the server in time, and the location fingerprint database will be built incrementally, which can adapt the dynamic changes of the indoor environment. Experimental results show that the algorithm based on multi-sensor improves the localization accuracy and robustness compared with the location algorithm based on Wi-Fi.
文摘s-Lap is a new gene sequence from pig retinal pigment epithelial(RPE) cells, which was found and cloned in the early period of apoptosis of RPE cells damaged with visible light. We cloned the coding area sequence of the novel gene of s-Lap and constructed its recombinant eukaryotic plasmid pcDNA3.1-GFP/s-lap with the recombinant DNA technique. The expression and localization of s-lap/GFP fusion protein in CHO and B_~16 cell lines were studied with the instantaneously transfected pcDNA3.1-GFP/s-lap recombinant plasmid. ~s-Lap/GFP fusion protein can be expressed in CHO and B_~16 cells with a high rate expression in the nuclei.
文摘现有的基于深度学习的医学图像分割方法,大多是利用大量的训练数据拟合检测网络,以获得优异的检测性能。这些方法往往需要较大的模型参数,导致检测实时性较差。为此,提出了基于局部上下文引导特征深度融合轻量级医学分割网络(local context guided feature deep fusion lightweight medical segmentation network,LCGML-net)。LCGML-net通过精确的特征选择与特征融合来减少模型拟合所需的参数数量,从而在保证检测精度的同时实现更小的模型参数。在特征提取阶段和映射阶段,分别通过提取和融合目标的多层次多尺度局部上下文特征来丰富特征表达和精准分割。在STARE、CHASEDB1和KITS19等多个基准数据集上开展的实验证明,与其他方法相比,所提出的LCGML-net具有最佳的检测性能和最小的模型参数。
文摘针对当前电力设备红外图像分辨率低和温度分布模糊问题,提出一种基于局部和全局信息注意力生成对抗网络(local and global information attention generative adversarial network,LGIA-GAN)的超分辨率重建方法。首先,使用门控权重单元融合多种卷积输出构建细节增强融合卷积,增加重要信息在输出特征图的占比;其次,搭建双注意力模块,对图像长距离像素依赖关系建模并捕获空间和通道维度信息;然后,构造生成对抗网络,使网络关注电力设备红外图像局部纹理细节和全局轮廓信息;最后,通过实验证明,LGIA-GAN在数据集上的峰值信噪比和结构相似度分别为30.266dB和0.9197,重建时间为0.120s,明显优于其他几种GAN算法,并在主观视觉上重建效果更好。所提方法能够有效提升电力设备热成像分辨率,对电力设备故障诊断具有支撑作用。
文摘针对现代化鹅养殖场景中饲料投喂移动小车受动态鹅群干扰,致使同时定位与地图构建(Simultaneous Localization And Mapping,SLAM)算法的定位精度、建图质量下降的问题,提出基于多传感融合目标检测的动态SLAM算法。该算法以LIO-SAM框架为基础,融合激光雷达与惯性测量单元搭建SLAM系统,采用前后端架构优化定位与建图性能;运用匈牙利算法实时追踪鹅群运动状态,结合多传感融合目标检测算法,精准识别并剔除动态鹅群产生的特征点,有效降低定位与建图误差。经KITTI、UrbanNav等公共数据集与实际养殖场景数据测试,在KITTI07序列中,较LeGO-LOAM、LIO-SAM和LVI-SAM等经典算法,均方根误差(RMSE)降低33.18%;在实际鹅养殖环境中,可以快速滤除动态鹅群干扰,提升建图质量与导航可靠性。本研究为智能化鹅养殖饲料投喂提供了新的技术方案,推动了畜牧业自动化发展。
文摘为支撑新型电力系统本地侧业务灵活敏捷接入,电力无线通信网络融合化部署成为迫切需要和发展趋势。然而,异构终端密集接入导致了信道拥塞与能耗过高问题。为此,本文以支持多种通信技术的多制式融合通信设备为核心,设计电力本地无线融合通信架构,并针对终端映射和接入点(access point, AP)启用选择问题,提出基于贪心策略的融合通信接入能耗优化调度算法(greedy-strategy-based fusion communication access energy optimization scheduling algorithm, GFCES)。仿真实验表明,本方法能够在融合通信场景中保障业务接入质量的同时有效降低整体接入能耗。
